Original: T RANSFORMER M EETS B OUNDARY V ALUE I NVERSE P ROBLEMS
Revised: T RANSFORMER M EETS B OUNDARY V ALUE I NVERSE P ROBLEMS

Original: A BSTRACT
Revised: A BSTRACT

Original: A Transformer-based deep direct sampling method is proposed for electrical impedance tomography, a well-known severely ill-posed nonlinear boundary value inverse problem.
Revised: A Transformer-based deep direct sampling method is proposed for electrical impedance tomography, a well-known severely ill-posed nonlinear boundary value inverse problem.

Original: A real-time reconstruction is achieved by evaluating the learned inverse operator between carefully designed data and the reconstructed images.
Revised: A real-time reconstruction is achieved by evaluating the learned inverse operator between carefully designed data and the reconstructed images.

Original: An effort is made to give a specific example to a fundamental question: whether and how one can benefit from the theoretical structure of a mathematical problem to develop task-oriented and structure-conforming deep neural networks?
Revised: An effort is made to give a specific example to a fundamental question: whether and how one can benefit from the theoretical structure of a mathematical problem to develop task-oriented and structure-conforming deep neural networks?

Original: Specifically, inspired by direct sampling methods for inverse problems, the 1D boundary data in different frequencies are preprocessed by a partial differential equation-based feature map to yield 2D harmonic extensions as different input channels.
Revised: Specifically, inspired by direct sampling methods for inverse problems, the 1D boundary data in different frequencies are preprocessed by a partial differential equation-based feature map to yield 2D harmonic extensions as different input channels.

Original: Then, by introducing learnable non-local kernels, the direct sampling is recast to a modified attention mechanism.
Revised: Then, by introducing learnable non-local kernels, the direct sampling is recast to a modified attention mechanism.

Original: The new method achieves superior accuracy over its predecessors and contemporary operator learners and shows robustness to noises in benchmarks.
Revised: The new method achieves superior accuracy over its predecessors and contemporary operator learners and shows robustness to noises in benchmarks.

Original: This research shall strengthen the insights that, despite being invented for natural language processing tasks, the attention mechanism offers great flexibility to be modified in conformity with the a priori mathematical knowledge, which ultimately leads to the design of more physics-compatible neural architectures.
Revised: This research shall strengthen the insights that, despite being invented for natural language processing tasks, the attention mechanism offers great flexibility to be modified in conformity with the a priori mathematical knowledge, which ultimately leads to the design of more physics-compatible neural architectures.

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: Boundary value inverse problems aim to recover the internal structure or distribution of multiple media inside an object (2D reconstruction) based on only the data available on the boundary (1D signal input), which arise from many imaging techniques, e g., electrical impedance tomography (EIT) (Holder, 2004), diffuse optical tomography (DOT)
Revised: Boundary value inverse problems aim to recover the internal structure or distribution of multiple media inside an object (2D reconstruction) based on only the data available on the boundary (1D signal input), which arise from many imaging techniques, e g., electrical impedance tomography (EIT) (Holder, 2004), diffuse optical tomography (DOT)

Original: (Culver et al., 2003), magnetic induction tomography (MIT)
Revised: (Culver et al., 2003), magnetic induction tomography (MIT)

Original: (Griffiths et al., 1999).
Revised: (Griffiths et al., 1999).

Original: Not needing any internal data renders these techniques generally non-invasive, safe, cheap, and thus quite suitable for monitoring applications.
Revised: Not needing any internal data renders these techniques generally non-invasive, safe, cheap, and thus quite suitable for monitoring applications.

Original: In this work we shall take EIT as an example to illustrate how a more structure-conforming neural network architecture leads to better results in certain physics-based tasks.
Revised: In this work we shall take EIT as an example to illustrate how a more structure-conforming neural network architecture leads to better results in certain physics-based tasks.

Original: Given a 2D bounded domain Ω and an inclusion D , the forward model is the following partial differential equation (PDE)
Revised: Given a 2D bounded domain Ω and an inclusion D , the forward model is the following partial differential equation (PDE)

Original: [Equation]
Revised: [Equation]

Original: where σ = σ 1 in D, and σ = σ 0 in Ω \ D
Revised: where σ = σ 1 in D, and σ = σ 0 in Ω \ D

Original: [Equation]
Revised: [Equation]

Original: where σ is a piecewise constant function defined on Ω with known function values σ 0 and σ 1 , but the shape of the inclusion D buried in Ω is unknown.
Revised: where σ is a piecewise constant function defined on Ω with known function values σ 0 and σ 1 , but the shape of the inclusion D buried in Ω is unknown.

Original: The goal is to recover the shape of D using only the boundary data on ∂ Ω (Figure 1).
Revised: The goal is to recover the shape of D using only the boundary data on ∂ Ω (Figure 1).

Original: Specifically, by exerting a current g on the boundary, one solves (1) with the Neumann boundary condition σ ∇ u · n | ∂ Ω = g , where n is the outwards unit normal direction of ∂ Ω , to get a unique u on the whole domain Ω .
Revised: Specifically, by exerting a current g on the boundary, one solves (1) with the Neumann boundary condition σ ∇ u · n | ∂ Ω = g , where n is the outwards unit normal direction of ∂ Ω , to get a unique u on the whole domain Ω .

Original: In practice, only the Dirichlet boundary value representing the voltages f = u | ∂ Ω on the boundary can be measured.
Revised: In practice, only the Dirichlet boundary value representing the voltages f = u | ∂ Ω on the boundary can be measured.

Original: This procedure is called
Revised: This procedure is called

Original: Neumann-to-Dirichlet (NtD) mapping:
Revised: Neumann-to-Dirichlet (NtD) mapping:

Original: [Equation]
Revised: [Equation]

Original: [Figure]
Revised: [Figure]

Original: For various notation and the Sobolev space formalism, we refer readers to Appendix A; for a brief review of the theoretical background of EIT we refer readers to Appendix B.
Revised: For various notation and the Sobolev space formalism, we refer readers to Appendix A; for a brief review of the theoretical background of EIT we refer readers to Appendix B.

Original: The NtD map above in (2) can be expressed as
Revised: The NtD map above in (2) can be expressed as

Original: [Equation]
Revised: [Equation]

Original: where g and f are (infinite-dimensional) vector representations of functions g and f relative to a chosen basis, and A σ is the matrix representation of Λ σ (see Appendix B for an example).
Revised: where g and f are (infinite-dimensional) vector representations of functions g and f relative to a chosen basis, and A σ is the matrix representation of Λ σ (see Appendix B for an example).

Original: The original mathematical setup of EIT is to use the NtD map Λ σ in (2) to recover σ , referred to as the case of full measurement (Calderón, 2006).
Revised: The original mathematical setup of EIT is to use the NtD map Λ σ in (2) to recover σ , referred to as the case of full measurement (Calderón, 2006).

Original: In this case, the forward and inverse operators associated with EIT can be formulated as
Revised: In this case, the forward and inverse operators associated with EIT can be formulated as

Original: [Equation]
Revised: [Equation]

Original: Fix a set of basis { g l } ∞ l =1 of the corresponding Hilbert space containing all admissible currents.
Revised: Fix a set of basis { g l } ∞ l =1 of the corresponding Hilbert space containing all admissible currents.

Original: Then, mathematically speaking, “knowing the operator Λ σ ” means that one can measure all the current-to-voltage pairs { g l , f l := Λ σ g l } ∞ l =1 and construct the infinite-dimensional matrix A σ .
Revised: Then, mathematically speaking, “knowing the operator Λ σ ” means that one can measure all the current-to-voltage pairs { g l , f l := Λ σ g l } ∞ l =1 and construct the infinite-dimensional matrix A σ .

Original: However, as infinitely many boundary data pairs are not attainable in practice, the problem of more practical interest is to use only a few data pairs { ( g l , f l ) } Ll =1 for reconstruction.
Revised: However, as infinitely many boundary data pairs are not attainable in practice, the problem of more practical interest is to use only a few data pairs { ( g l , f l ) } Ll =1 for reconstruction.

Original: In this case, the forward and inverse problems can be formulated as
Revised: In this case, the forward and inverse problems can be formulated as

Original: [Equation]
Revised: [Equation]

Original: and
Revised: and

Original: [Equation]
Revised: [Equation]

Original: For limited data pairs, the inverse operator F − 1 L is extremely ill-posed or even not well-defined (Isakov & Powell, 1990; Barceló et al., 1994; Kang & Seo, 2001; Lionheart, 2004); namely, the same boundary measurements may correspond to different σ .
Revised: For limited data pairs, the inverse operator F − 1 L is extremely ill-posed or even not well-defined (Isakov & Powell, 1990; Barceló et al., 1994; Kang & Seo, 2001; Lionheart, 2004); namely, the same boundary measurements may correspond to different σ .

Original: In view of the matrix representation A σ , for g l = e l , l = 1 , ..., L , with e l being unit vectors of a chosen basis, ( f 1 , ..., f L ) only gives the first L columns of A σ .
Revised: In view of the matrix representation A σ , for g l = e l , l = 1 , ..., L , with e l being unit vectors of a chosen basis, ( f 1 , ..., f L ) only gives the first L columns of A σ .

Original: It is possible that two matrices
Revised: It is possible that two matrices

Original: A σ and A ˜ σ have similar first L columns but ∥ σ − ˜ σ ∥ is large.
Revised: A σ and A ˜ σ have similar first L columns but ∥ σ − ˜ σ ∥ is large.

Original: How to deal with this “ill-posedness” is a central theme in boundary value inverse problem theories.
Revised: How to deal with this “ill-posedness” is a central theme in boundary value inverse problem theories.

Original: The operator learning approach has the potential to tame the ill-posedness by restricting F − 1 L at a set of sampled data D := { σ ( k ) } Nk =1 , with different shapes and locations following certain distribution.
Revised: The operator learning approach has the potential to tame the ill-posedness by restricting F − 1 L at a set of sampled data D := { σ ( k ) } Nk =1 , with different shapes and locations following certain distribution.

Original: Then the problem becomes to approximate
Revised: Then the problem becomes to approximate

Original: [Equation]
Revised: [Equation]

Original: The fundamental assumption here is that this map is “well-defined” enough to be regarded as a high-dimensional interpolation (learning) problem on a compact data submanifold (Seo et al., 2019;
Revised: The fundamental assumption here is that this map is “well-defined” enough to be regarded as a high-dimensional interpolation (learning) problem on a compact data submanifold (Seo et al., 2019;

Original: Ghattas & Willcox, 2021), and the learned approximate mapping can be evaluated at newly incoming σ ’s.
Revised: Ghattas & Willcox, 2021), and the learned approximate mapping can be evaluated at newly incoming σ ’s.

Original: The incomplete information of Λ σ due to a small L for one single σ is compensated by a large
Revised: The incomplete information of Λ σ due to a small L for one single σ is compensated by a large

Original: N ≫ 1 sampling of different σ ’s.
Revised: N ≫ 1 sampling of different σ ’s.

Original:  B ACKGROUND , RELATED WORK , AND CONTRIBUTIONS
Revised:  B ACKGROUND , RELATED WORK , AND CONTRIBUTIONS

Original: Classical iterative methods.
Revised: Classical iterative methods.

Original: There are in general two types of methodology to solve inverse problems.
Revised: There are in general two types of methodology to solve inverse problems.

Original: The first one is a large family of iterative or optimization-based methods (Dobson &
Revised: The first one is a large family of iterative or optimization-based methods (Dobson &

Original: Santosa, 1994; Martin & Idier, 1997; Chan & Tai, 2003; Vauhkonen et al., 1999; Guo et al., 2019;
Revised: Santosa, 1994; Martin & Idier, 1997; Chan & Tai, 2003; Vauhkonen et al., 1999; Guo et al., 2019;

Original: Rondi & Santosa, 2001; Chen et al., 2020; Bao et al., 2020; Gu et al., 2021).
Revised: Rondi & Santosa, 2001; Chen et al., 2020; Bao et al., 2020; Gu et al., 2021).

Original: One usually looks for an approximated σ by solving a minimization problem with a regularization R ( σ ) to alleviate the ill-posedness, say
Revised: One usually looks for an approximated σ by solving a minimization problem with a regularization R ( σ ) to alleviate the ill-posedness, say

Original: [Equation]
Revised: [Equation]

Original: The design of regularization R ( σ ) plays a critical role in a successful reconstruction (Tarvainen et al., 2008; Tehrani et al., 2012; Wang et al., 2012).
Revised: The design of regularization R ( σ ) plays a critical role in a successful reconstruction (Tarvainen et al., 2008; Tehrani et al., 2012; Wang et al., 2012).

Original: Due to the ill-posedness, the computation for almost all iterative methods usually takes numerous iterations to converge, and the reconstruction is highly sensitive to noise.
Revised: Due to the ill-posedness, the computation for almost all iterative methods usually takes numerous iterations to converge, and the reconstruction is highly sensitive to noise.

Original: Besides, the forward operator F ( · ) needs to be evaluated at each iteration, which is itself expensive as it requires solving forward PDE models.
Revised: Besides, the forward operator F ( · ) needs to be evaluated at each iteration, which is itself expensive as it requires solving forward PDE models.

Original: Classical direct methods.
Revised: Classical direct methods.

Original: The second methodology is to develop a well-defined mapping G θ parametrized by θ , empirically constructed to approximate the inverse map itself, say G θ ≈ F − 1 .
Revised: The second methodology is to develop a well-defined mapping G θ parametrized by θ , empirically constructed to approximate the inverse map itself, say G θ ≈ F − 1 .

Original: These methods are referred to as non-iterative or direct methods in the literature.
Revised: These methods are referred to as non-iterative or direct methods in the literature.

Original: Distinguished from iterative approaches, direct methods are typically highly problem-specific, as they are designed based on specific mathematical structures of their respective inverse operators.
Revised: Distinguished from iterative approaches, direct methods are typically highly problem-specific, as they are designed based on specific mathematical structures of their respective inverse operators.

Original: For instance, methods in
Revised: For instance, methods in

Original: EIT and DOT include factorization methods (Kirsch & Grinberg, 2007; Azzouz et al., 2007; Brühl, 2001; Hanke & Brühl, 2003), MUSIC-type algorithms (Cheney, 2001; Ammari & Kang, 2004; 2007; Lee et al., 2011), and the D-bar methods (Knudsen et al., 2007; 2009) based on a Fredholm integral equation (Nachman, 1996), among which are the direct sampling methods (DSM) being our focus in this work (Chow et al., 2014; 2015; Kang et al., 2018;
Revised: EIT and DOT include factorization methods (Kirsch & Grinberg, 2007; Azzouz et al., 2007; Brühl, 2001; Hanke & Brühl, 2003), MUSIC-type algorithms (Cheney, 2001; Ammari & Kang, 2004; 2007; Lee et al., 2011), and the D-bar methods (Knudsen et al., 2007; 2009) based on a Fredholm integral equation (Nachman, 1996), among which are the direct sampling methods (DSM) being our focus in this work (Chow et al., 2014; 2015; Kang et al., 2018;

Original: Ito et al., 2013; Ji et al., 2019; 
Revised: Ito et al., 2013; Ji et al., 2019; Harris &

Original: Harris & Kleefeld, 2019; Ahn et al., 2020).
Revised: Kleefeld, 2019; Ahn et al., 2020; Chow et al., 2021; Harris et al., 2022).

Original: These methods generally have a closed-form G θ for approximation, and the parameters θ represent model-specific mathematical objects.
Revised: These methods generally have a closed-form G θ for approximation, and the parameters θ represent model-specific mathematical objects.

Original: For each fixed θ , this procedure is usually much more stable than iterative approaches with respect to the input data.
Revised: For each fixed θ , this procedure is usually much more stable than iterative approaches with respect to the input data.

Original: Furthermore, the evaluation for each boundary data pair is distinctly fast, as no optimization is needed.
Revised: Furthermore, the evaluation for each boundary data pair is distinctly fast, as no optimization is needed.

Original: However, a simple closed-form G θ admitting efficient execution may not be available in practice since some mathematical assumptions and derivation may not hold.
Revised: However, a simple closed-form G θ admitting efficient execution may not be available in practice since some mathematical assumptions and derivation may not hold.

Original: For instance, MUSIC-type and D-bar methods generally require an accurate approximation to Λ σ , while DSM poses restrictions on the boundary data, domain geometry, etc., see Appendix D for details.
Revised: For instance, MUSIC-type and D-bar methods generally require an accurate approximation to Λ σ , while DSM poses restrictions on the boundary data, domain geometry, etc., see Appendix D for details.

Original: Boundary value inverse problems.
Revised: Boundary value inverse problems.

Original: For most cases of boundary value inverse problems in 2D, the major difference, e.g., with an inverse problem in computer vision (Marroquin et al., 1987), is that data are only available on 1D manifolds, which are used to reconstruct 2D targets.
Revised: For most cases of boundary value inverse problems in 2D, the major difference, e.g., with an inverse problem in computer vision (Marroquin et al., 1987), is that data are only available on 1D manifolds, which are used to reconstruct 2D targets.

Original: When comparing (4) with a linear inverse problem in signal processing y =
Revised: When comparing (4) with a linear inverse problem in signal processing y =

Original: A x + ϵ , to recover a signal x from measurement y with noise ϵ , the difference is more fundamental in that F ( · ) itself is highly nonlinear and involves boundary value PDEs.
Revised: A x + ϵ , to recover a signal x from measurement y with noise ϵ , the difference is more fundamental in that F ( · ) itself is highly nonlinear and involves boundary value PDEs.

Original: Moreover, the boundary data themselves generally involve certain input-output structures (NtD maps), which adds more complexity.
Revised: Moreover, the boundary data themselves generally involve certain input-output structures (NtD maps), which adds more complexity.

Original: In Adler & Guardo (1994); Fernández-Fuentes et al.
Revised: In Adler & Guardo (1994); Fernández-Fuentes et al.

Original: ; Feng et al.
Revised: ; Feng et al.

Original: (2018), boundary measurements are collected and directly input into feedforward fully connected networks.
Revised: (2018), boundary measurements are collected and directly input into feedforward fully connected networks.

Original: As the data reside on different manifolds, special treatments are made to the input data, such as employing pre-reconstruction stages to generate rough 2D input to CNNs (Ben Yedder et al., 2018;
Revised: As the data reside on different manifolds, special treatments are made to the input data, such as employing pre-reconstruction stages to generate rough 2D input to CNNs (Ben Yedder et al., 2018;

Original: Ren et al., 2020; Pakravan et al., 2021).
Revised: Ren et al., 2020; Pakravan et al., 2021).

Original: Deep neural network and inverse problems.
Revised: Deep neural network and inverse problems.

Original: Solving an inverse problem is essentially to give a satisfactory approximation to F − 1 but based on finitely many measurements.
Revised: Solving an inverse problem is essentially to give a satisfactory approximation to F − 1 but based on finitely many measurements.

Original: The emerging deep learning (DL) based on Deep Neural Networks (DNN) to directly emulate operators significantly resembles those classical direct methods mentioned above.
Revised: The emerging deep learning (DL) based on Deep Neural Networks (DNN) to directly emulate operators significantly resembles those classical direct methods mentioned above.

Original: However, operator learners by DNNs are commonly considered black boxes.
Revised: However, operator learners by DNNs are commonly considered black boxes.

Original: A natural question is how the a priori mathematical knowledge can be exploited to design more physics-compatible DNN architectures.
Revised: A natural question is how the a priori mathematical knowledge can be exploited to design more physics-compatible DNN architectures.

Original: In pursuing the answer to this question, we aim to provide a supportive example that bridges deep learning techniques and classical direct methods, which improves the reconstruction of EIT.
Revised: In pursuing the answer to this question, we aim to provide a supportive example that bridges deep learning techniques and classical direct methods, which improves the reconstruction of EIT.

Original: Operator learners.
Revised: Operator learners.

Original: Operator learning has become an active research field for inverse problems in recent years, especially related to image reconstruction where CNN plays a central role, see, e.g.,
Revised: Operator learning has become an active research field for inverse problems in recent years, especially related to image reconstruction where CNN plays a central role, see, e.g.,

Original: Kłosowski & Rymarczyk (2017); Nguyen et al.
Revised: Kłosowski & Rymarczyk (2017); Nguyen et al.

Original: Tan et al.
Revised: Tan et al.

Original: ; Jin et al.
Revised: ; Jin et al.

Original: ; Kang et al.
Revised: ; Kang et al.

Original: (2017); Barbastathis et al.
Revised: (2017); Barbastathis et al.

Original: ; Latif et al.
Revised: ; Latif et al.

Original: Zhu et al.
Revised: Zhu et al.

Original: ; Chen et al.
Revised: ; Chen et al.

Original: ; Coxson et al.
Revised: ; Coxson et al.

Original: ; Zhu et al.
Revised: ; Zhu et al.

Original: (2023b).
Revised: (2023b).

Original: Notable examples of efforts to couple classical reconstruction methods and CNN include Hamilton et al.
Revised: Notable examples of efforts to couple classical reconstruction methods and CNN include Hamilton et al.

Original: ; Hamilton & Hauptmann (2018), where a CNN post-processes images obtained by the classical D-bar methods, and Fan et al.
Revised: ; Hamilton & Hauptmann (2018), where a CNN post-processes images obtained by the classical D-bar methods, and Fan et al.

Original: ; Fan & Ying (2020) where BCR-Net is developed to mimic pseudo-differential operators appearing in many inverse problems.
Revised: ; Fan & Ying (2020) where BCR-Net is developed to mimic pseudo-differential operators appearing in many inverse problems.

Original: A deep direct sampling method is proposed in Guo & Jiang (2020); Guo et al.
Revised: A deep direct sampling method is proposed in Guo & Jiang (2020); Guo et al.

Original: (2021) that learns local convolutional kernels mimicking the gradient operator of DSM.
Revised: (2021) that learns local convolutional kernels mimicking the gradient operator of DSM.

Original: Another example is radial basis function neural networks seen in Hrabuska et al.
Revised: Another example is radial basis function neural networks seen in Hrabuska et al.

Original: ; Michalikova et al.
Revised: ; Michalikova et al.

Original:  Wang et al.
Revised: ; Wang et al.

Original: (2021a).
Revised: (2021a).

Original: Nevertheless, convolutions in CNNs use kernels whose receptive fields involve only a small neighborhood of a pixel.
Revised: Nevertheless, convolutions in CNNs use kernels whose receptive fields involve only a small neighborhood of a pixel.

Original: Thus, layer-wise, CNN does not align well with the non-local nature of inverse problems.
Revised: Thus, layer-wise, CNN does not align well with the non-local nature of inverse problems.

Original: More recently, the learning of PDE-related forward problems using global kernel has gained attraction, most notably the Fourier Neural Operator (FNO) (Li et al., 2021a;
Revised: More recently, the learning of PDE-related forward problems using global kernel has gained attraction, most notably the Fourier Neural Operator (FNO) (Nelsen & Stuart, 2021; Li et al., 2021a;

Original: Kovachki et al., 2021; Guibas et al., 2022; Zhao et al., 2022; Wen et al., 2022; Li et al., 2022b).
Revised: Kovachki et al., 2021; Guibas et al., 2022; Zhao et al., 2022; Wen et al., 2022; Li et al., 2022b).

Original: FNO takes advantage of the low-rank nature of certain problems and proposes to train a local kernel in the frequency domain yet global in the spatial-temporal domain, mimicking the solution’s kernel integral form.
Revised: FNO takes advantage of the low-rank nature of certain problems and learns a local kernel in the frequency domain yet global in the spatial-temporal domain, mimicking the solution’s kernel integral form.

Original: Concurrent studies include DeepONets (Lu et al., 2021;
Revised: Concurrent studies include DeepONets (Lu et al., 2021;

Original: Wang et al., 2021b; Jin et al., 2022b), Transformers (Cao, 2021; Kissas et al., 2022; Li et al., 2022a; Liu et al., 2022; Fonseca et al., 2023), Integral Autoencoder (Ong et al., 2022), Multiwavelet Neural Operators (Gupta et al., 2021; 2022), and others (Lütjens et al., 2022; Hu et al., 2022; Boussif et al., 2022; de Hoop et al., 2022a;b;Ryck &
Revised: Wang et al., 2021b; Jin et al., 2022b), Transformers (Cao, 2021; Kissas et al., 2022; Li et al., 2022a; Liu et al., 2022; Fonseca et al., 2023), Integral Autoencoder (Ong et al., 2022), Multiwavelet Neural Operators (Gupta et al., 2021; 2022), and others (Lütjens et al., 2022; Hu et al., 2022; Boussif et al., 2022; de Hoop et al., 2022a;b;

Original:  Mishra, 2022; Seidman et al., 2022; Zhang et al., 2023; Lee, 2023; Zhu et al., 2023a).
Revised: Ryck & Mishra, 2022; Seidman et al., 2022; Zhang et al., 2023; Lee, 2023; Zhu et al., 2023a).

Original: Related studies on Transformers.
Revised: Related studies on Transformers.

Original: The attention mechanism-based models have become state of the art in many areas since Vaswani et al.
Revised: The attention mechanism-based models have become state of the art in many areas since Vaswani et al.

Original: One of the most important and attractive aspects of the attention mechanism is its unparalleled capability to efficiently model non-local long-range interactions (Katharopoulos et al., 2020; Choromanski et al., 2021; Nguyen et al., 2021).
Revised: One of the most important and attractive aspects of the attention mechanism is its unparalleled capability to efficiently model non-local long-range interactions (Katharopoulos et al., 2020; Choromanski et al., 2021; Nguyen et al., 2021).

Original: The relation of the attention with kernel learning is first studied in Tsai et al.
Revised: The relation of the attention with kernel learning is first studied in Tsai et al.

Original: (2019) and later connected with random feature (Peng et al., 2021).
Revised: (2019) and later connected with random feature (Peng et al., 2021).

Original: Connecting the non-PDE-based integral kernels and the attention mechanism has been seen in Hutchinson et al.
Revised: Connecting the non-PDE-based integral kernels and the attention mechanism has been seen in Hutchinson et al.

Original: ; Guibas et al.
Revised: ; Guibas et al.

Original: ; Nguyen et al.
Revised: ; Nguyen et al.

Original: Han et al.
Revised: Han et al.

Original: Among inverse problems, Transformers have been applied in medical imaging applications, including segmentation (Zhou et al., 2021; Hatamizadeh et al., 2022; Petit et al., 2021),
Revised: Among inverse problems, Transformers have been applied in medical imaging applications, including segmentation (Zhou et al., 2021; Hatamizadeh et al., 2022; Petit et al., 2021),

Original: X-Ray (Tanzi et al., 2022), magnetic resonance imaging (MRI)
Revised: X-Ray (Tanzi et al., 2022), magnetic resonance imaging (MRI)

Original: (He et al., 2022), ultrasound (Perera et al., 2021), optical coherence tomography (OCT) (Song et al., 2021).
Revised: (He et al., 2022), ultrasound (Perera et al., 2021), optical coherence tomography (OCT) (Song et al., 2021).

Original: To our best knowledge, no work in the literature establishes an architectural connection between the attention mechanism in
Revised: To our best knowledge, no work in the literature establishes an architectural connection between the attention mechanism in

Original: Transformer and the mathematical structure of PDE-based inverse problems.
Revised: Transformer and the mathematical structure of PDE-based inverse problems.

Original: 2. C ONTRIBUTIONS
Revised: 2. C ONTRIBUTIONS

Original: • A structure-conforming network architecture.
Revised: • A structure-conforming network architecture.

Original: Inspired by the EIT theory and classic DSM, we decompose the approximation of the inverse operator into a harmonic extension and an integral operator with learnable non-local kernels that has an attention-like structure.
Revised: Inspired by the EIT theory and classic DSM, we decompose the approximation of the inverse operator into a harmonic extension and an integral operator with learnable non-local kernels that has an attention-like structure.

Original: Additionally, the attention architecture is reinterpreted through a Fredholm integral operator to rationalize the application of the Transformer to the boundary value inverse problem.
Revised: Additionally, the attention architecture is reinterpreted through a Fredholm integral operator to rationalize the application of the Transformer to the boundary value inverse problem.

Original: • Theoretical and experimental justification for the advantage of Transformer.
Revised: • Theoretical and experimental justification for the advantage of Transformer.

Original: We have proved that, in Transformers, modified attention can represent target functions exhibiting higher frequency natures from lower frequency input features.
Revised: We have proved that, in Transformers, modified attention can represent target functions exhibiting higher frequency natures from lower frequency input features.

Original: A comparative study in the experiments demonstrates a favorable match between the Transformer and the benchmark problem.
Revised: A comparative study in the experiments demonstrates a favorable match between the Transformer and the benchmark problem.

Original:  I NTERPLAY BETWEEN MATHEMATICS AND NEURAL ARCHITECTURES
Revised:  I NTERPLAY BETWEEN MATHEMATICS AND NEURAL ARCHITECTURES

Original: In this section, we try to articulate that the triple tensor product in the attention mechanism matches exceptionally well with representing a solution in the inverse operator theory of EIT.
Revised: In this section, we try to articulate that the triple tensor product in the attention mechanism matches exceptionally well with representing a solution in the inverse operator theory of EIT.

Original: In pursuing this end goal, this study tries to answer the following motivating questions:
Revised: In pursuing this end goal, this study tries to answer the following motivating questions:

Original: ( Q1 )
Revised: ( Q1 )

Original: What is an appropriate finite-dimensional data format as inputs to the neural network?
Revised: What is an appropriate finite-dimensional data format as inputs to the neural network?

Original: ( Q2 )
Revised: ( Q2 )

Original: Is there a suitable neural network matching the mathematical structure?
Revised: Is there a suitable neural network matching the mathematical structure?

Original: 3. F ROM EIT TO O PERATOR L EARNING
Revised: 3. F ROM EIT TO O PERATOR L EARNING

Original: In the case of full measurement, the operator F − 1 can be well approximated through a large number of ( σ, A σ ) data pairs.
Revised: In the case of full measurement, the operator F − 1 can be well approximated through a large number of ( σ, A σ ) data pairs.

Original: This mechanism essentially results in a tensor2tensor mapping/operator from A σ to the imagery data representing σ .
Revised: This mechanism essentially results in a tensor2tensor mapping/operator from A σ to the imagery data representing σ .

Original: In particular, the BCR-Net (Fan & Ying, 2020) is a
Revised: In particular, the BCR-Net (Fan & Ying, 2020) is a

Original: DNN approximation falling into this category.
Revised: DNN approximation falling into this category.

Original: However, when there are very limited boundary data pairs accessible, the task of learning the full matrix A σ becomes obscure, which complicates the development of a tensor2tensor pipeline for operator learning.
Revised: However, when there are very limited boundary data pairs accessible, the task of learning the full matrix A σ becomes obscure, which complicates the development of a tensor2tensor pipeline for operator learning.

Original: Operator learning problems for EIT.
Revised: Operator learning problems for EIT.

Original: We first introduce several attainable approximations of infinite-dimensional spaces by finite-dimensional counterparts for the proposed method.
Revised: We first introduce several attainable approximations of infinite-dimensional spaces by finite-dimensional counterparts for the proposed method.

Original: (1) Spatial discretization.
Revised: (1) Spatial discretization.

Original: Let Ω h be a mesh of Ω with the mesh spacing h and let { z j } Mj =1 :=
Revised: Let Ω h be a mesh of Ω with the mesh spacing h and let { z j } Mj =1 :=

Original: M be the set of grid points to represent the 2D discretization of continuous signals.
Revised: M be the set of grid points to represent the 2D discretization of continuous signals.

Original: Then a function u defined almost everywhere in Ω can be approximated by a vector u h ∈ R M .
Revised: Then a function u defined almost everywhere in Ω can be approximated by a vector u h ∈ R M .

Original: (2) Sampling of D .
Revised: (2) Sampling of D .

Original: We generate N samples of D with different shapes and locations following certain distributions.
Revised: We generate N samples of D with different shapes and locations following certain distributions.

Original: For example, elliptical inclusions with random semi-axes and centers are generated as a benchmark (see Appendix C.1 for details).
Revised: For example, elliptical inclusions with random semi-axes and centers are generated as a benchmark (see Appendix C.1 for details).

Original: With the known σ 0 and σ 1 , set the corresponding data set D = { σ (1) , σ (2) , ..., σ ( N ) } .
Revised: With the known σ 0 and σ 1 , set the corresponding data set D = { σ (1) , σ (2) , ..., σ ( N ) } .

Original: N is usually large enough to represent field applications of interest.
Revised: N is usually large enough to represent field applications of interest.

Original: (3) Sampling of NtD maps.
Revised: (3) Sampling of NtD maps.

Original: For the k -th sample of D , we generate L pairs of boundary data { ( g ( k ) l , f ( k ) l ) } Ll =1 by solving PDE (1), which can be thought of as sampling of columns of the infinite matrix A σ representing the NtD map.
Revised: For the k -th sample of D , we generate L pairs of boundary data { ( g ( k ) l , f ( k ) l ) } Ll =1 by solving PDE (1), which can be thought of as sampling of columns of the infinite matrix A σ representing the NtD map.

Original: By the proposed method, L can be chosen to be very small ( ≤ 3 ) to yield satisfactory results.
Revised: By the proposed method, L can be chosen to be very small ( ≤ 3 ) to yield satisfactory results.

Original: Our task is to find a parameterized mapping G θ to approximate F − 1 L, D (6) by minimizing
Revised: Our task is to find a parameterized mapping G θ to approximate F − 1 L, D (6) by minimizing

Original: [Equation]
Revised: [Equation]

Original: for a suitable norm ∥ · ∥ .
Revised: for a suitable norm ∥ · ∥ .

Original: Hyper-parameters N, h, L will affect the finite-dimensional approximation to the infiniteness in the following way: h determines the resolution to approximate D ; N affects the representativity of the training data set; L decides how much of a finite portion of the infinite spectral information of Λ σ can be accessed.
Revised: Hyper-parameters N, h, L will affect the finite-dimensional approximation to the infiniteness in the following way: h determines the resolution to approximate D ; N affects the representativity of the training data set; L decides how much of a finite portion of the infinite spectral information of Λ σ can be accessed.

Original: 3. F ROM HARMONIC EXTENSION TO TENSOR - TO - TENSOR
Revised: 3. F ROM HARMONIC EXTENSION TO TENSOR - TO - TENSOR

Original: To establish the connection between the problem of interest with the attention used in the Transformers, we first construct higher-dimensional tensors from the 1D boundary data.
Revised: To establish the connection between the problem of interest with the attention used in the Transformers, we first construct higher-dimensional tensors from the 1D boundary data.

Original: The key is a harmonic extension of the boundary data that can be viewed as a PDE-based feature map.
Revised: The key is a harmonic extension of the boundary data that can be viewed as a PDE-based feature map.

Original: We begin with a theorem to motivate it.
Revised: We begin with a theorem to motivate it.

Original: Let I D be the characteristic function of D named index function , i.e., I D ( x )
Revised: Let I D be the characteristic function of D named index function , i.e., I D ( x )

Original: = 1 if x ∈ D
Revised: = 1 if x ∈ D

Original: and
Revised: and

Original: I D ( x ) = 

Revised: I D ( x ) =

Original: if x / ∈ D .
Revised: if x / ∈ D .

Original: Thus, σ can be directly identified by the shape of D through the formula σ = σ 1 I D + σ 0 (1 − I D ) .
Revised: Thus, σ can be directly identified by the shape of D through the formula σ = σ 1 I D + σ 0 (1 − I D ) .

Original: In this setup, reconstructing σ is equivalent to reconstructing I D .
Revised: In this setup, reconstructing σ is equivalent to reconstructing I D .

Original: Without loss of generality, we let σ 1 > σ 0 .
Revised: Without loss of generality, we let σ 1 > σ 0 .

Original: Λ σ 0 is understood as the NtD map with σ = σ 0 on the whole domain, i.e., it is taken as the known background conductivity (no inclusion), and thus Λ σ 0 g can be readily computed.
Revised: Λ σ 0 is understood as the NtD map with σ = σ 0 on the whole domain, i.e., it is taken as the known background conductivity (no inclusion), and thus Λ σ 0 g can be readily computed.

Original: Then f − Λ σ 0 g =
Revised: Then f − Λ σ 0 g =

Original: (Λ σ − Λ σ 0 )
Revised: (Λ σ − Λ σ 0 )

Original: g measures the difference between the NtD mappings and encodes the information of σ .
Revised: g measures the difference between the NtD mappings and encodes the information of σ .

Original: The operator Λ σ − Λ σ 0 is positive definite, and it has eigenvalues { λ l } ∞ l =1 with λ 1 > λ 2 > · · · > 0 (Cheng et al., 1989).
Revised: The operator Λ σ − Λ σ 0 is positive definite, and it has eigenvalues { λ l } ∞ l =1 with λ 1 > λ 2 > · · · > 0 (Cheng et al., 1989).

Original: Theorem 1.
Revised: Theorem 1.

Original: Suppose that the 1D boundary data g l is the eigenfunction of Λ σ − Λ σ 0 corresponding to the l -th eigenvalue λ l , and let the 2D data functions ϕ l be obtained by solving −
Revised: Suppose that the 1D boundary data g l is the eigenfunction of Λ σ − Λ σ 0 corresponding to the l -th eigenvalue λ l , and let the 2D data functions ϕ l be obtained by solving −

Original: [Equation]
Revised: [Equation]

Original: ϕ l = 0 in Ω ,
Revised: ϕ l = 0 in Ω ,

Original: [Equation]
Revised: [Equation]

Original: (9) for l = 1 , 2 , . . . .
Revised: (9) for l = 1 , 2 , . . . .

Original: Define the space (cid:101)
Revised: Define the space (cid:101)

Original: S L = Span { ∂ x 1 ϕ l ∂ x 2 ϕ l : l = 1 , . . .
Revised: S L = Span { ∂ x 1 ϕ l ∂ x 2 ϕ l : l = 1 , . . .

Original: , L } , and the dictionary
Revised: , L } , and the dictionary

Original: S L = { a 1 + a 2 arctan( a 3 v ) : v ∈ (cid:101) S L , a 1 , a 2 , a 3 ∈ R } .
Revised: S L = { a 1 + a 2 arctan( a 3 v ) : v ∈ (cid:101) S L , a 1 , a 2 , a 3 ∈ R } .

Original: Then, for any ϵ > 0 , we con construct an index function
Revised: Then, for any ϵ > 0 , we con construct an index function

Original: [Equation]
Revised: [Equation]

Original: sup
Revised: sup

Original: [Equation]
Revised: [Equation]

Original: ∈ Ω |
Revised: ∈ Ω |

Original: [Equation]
Revised: [Equation]

Original: D
Revised: D

Original: [Equation]
Revised: [Equation]

Original: x ) −
Revised: x ) −

Original: [Equation]
Revised: [Equation]

Original: DL
Revised: DL

Original: [Equation]
Revised: [Equation]

Original: x ) | ≤ ϵ provided L is large enough.
Revised: x ) | ≤ ϵ provided L is large enough.

Original: The full proof of Theorem 1 can be found in Appendix E.
Revised: The full proof of Theorem 1 can be found in Appendix E.

Original: This theorem gives a constructive approach for approximating F − 1 and justifies the practice of approximating I D when L is large enough.
Revised: This theorem gives a constructive approach for approximating F − 1 and justifies the practice of approximating I D when L is large enough.

Original: The function ϕ l is called the harmonic extension of f l − Λ σ 0 g l .
Revised: The function ϕ l is called the harmonic extension of f l − Λ σ 0 g l .

Original: On the other hand, it relies on “knowing” the entire NtD map Λ σ to construct I DL explicitly.
Revised: On the other hand, it relies on “knowing” the entire NtD map Λ σ to construct I DL explicitly.

Original: Namely, the coefficients of ∂ x 1 ϕ l ∂ x
Revised: Namely, the coefficients of ∂ x 1 ϕ l ∂ x

Original: 2 ϕ l depend on a big chunk of spectral information (eigenvalues and eigenfunctions) of Λ σ , which may not be available in practice.
Revised: 2 ϕ l depend on a big chunk of spectral information (eigenvalues and eigenfunctions) of Λ σ , which may not be available in practice.

Original: Thus, the mathematics itself in this theorem does not provide an architectural hint on building a structure-conforming DNN.
Revised: Thus, the mathematics itself in this theorem does not provide an architectural hint on building a structure-conforming DNN.

Original: To further dig out the hidden structure, we focus on the case of a single measurement, i.e., L = 1 .
Revised: To further dig out the hidden structure, we focus on the case of a single measurement, i.e., L = 1 .

Original: With this setting, it is possible to derive an explicit and simple formula to approximate I D which is achieved by the classical direct sampling methods (DSM) (Chow et al., 2014; 2015; Kang et al., 2018;
Revised: With this setting, it is possible to derive an explicit and simple formula to approximate I D which is achieved by the classical direct sampling methods (DSM) (Chow et al., 2014; 2015; Kang et al., 2018;

Original: Ito et al., 2013; Ji et al., 2019; Harris & Kleefeld, 2019; Ahn et al., 2020).
Revised: Ito et al., 2013; Ji et al., 2019; Harris & Kleefeld, 2019; Ahn et al., 2020).

Original: For EIT,
Revised: For EIT,

Original: [Equation]
Revised: [Equation]

Original: is derived in Chow et al.
Revised: is derived in Chow et al.

Original: (2014), where (see a much more detailed formulation in Appendix D)
Revised: (2014), where (see a much more detailed formulation in Appendix D)

Original: • ϕ is the harmonic extension of f − Λ σ 

Revised: • ϕ is the harmonic extension of f − Λ σ

Original: g with certain noise f − Λ σ 0 g + ξ ; • d ( x ) is called a probing direction and can be chosen empirically as d ( x ) =
Revised: g with certain noise f − Λ σ 0 g + ξ ; • d ( x ) is called a probing direction and can be chosen empirically as d ( x ) =

Original: ∇ ϕ
Revised: ∇ ϕ

Original: ( x ) / ∥∇ ϕ
Revised: ( x ) / ∥∇ ϕ

Original: ( x ) ∥ ; • R ( x )
Revised: ( x ) ∥ ; • R ( x )

Original: =
Revised: =

Original: ( ∥ f − Λ σ 0 g
Revised: ( ∥ f − Λ σ 0 g

Original: ∥ ∂
Revised: ∥ ∂

Original: Ω | η x | Y ) − 1 , where η x is a function of d ( x ) and measured in | · | Y semi-norm on boundary ∂ Ω .
Revised: Ω | η x | Y ) − 1 , where η x is a function of d ( x ) and measured in | · | Y semi-norm on boundary ∂ Ω .

Original: Both ϕ and η x can be computed effectively by traditional fast PDE solvers, such as finite difference or finite element methods based on Ω h in Section 3.1.
Revised: Both ϕ and η x can be computed effectively by traditional fast PDE solvers, such as finite difference or finite element methods based on Ω h in Section 3.1.

Original: However, the reconstruction accuracy is much limited by a single measurement, the nonparametric ansatz, and empirical choices of d ( x ) and | · | Y .
Revised: However, the reconstruction accuracy is much limited by a single measurement, the nonparametric ansatz, and empirical choices of d ( x ) and | · | Y .

Original: These restrictions leave room for DL methodology.
Revised: These restrictions leave room for DL methodology.

Original: See Appendix D for a detailed discussion.
Revised: See Appendix D for a detailed discussion.

Original: Constructing harmonic extension (2D features) from boundary data (1D signal input with limited depth) can contribute to the desired high-quality reconstruction.
Revised: Constructing harmonic extension (2D features) from boundary data (1D signal input with limited depth) can contribute to the desired high-quality reconstruction.

Original: First, harmonic functions are highly smooth away from the boundary, of which the solution automatically smooths out the noise on the boundary due to PDE theory (Gilbarg & Trudinger, 2001, Chapter 8), and thus make the reconstruction highly robust with respect to the noise (e.g., see Figure 3 in Appendix C.1).
Revised: First, harmonic functions are highly smooth away from the boundary, of which the solution automatically smooths out the noise on the boundary due to PDE theory (Gilbarg & Trudinger, 2001, Chapter 8), and thus make the reconstruction highly robust with respect to the noise (e.g., see Figure 3 in Appendix C.1).

Original: Second, in terms of using certain backbone networks to generate features for downstream tasks, harmonic extensions can be understood as a problem-specific way to design higher dimensional feature maps (Álvarez et al., 2012), which renders samples more separable in a higher dimensional data manifold than the one with merely boundary data.
Revised: Second, in terms of using certain backbone networks to generate features for downstream tasks, harmonic extensions can be understood as a problem-specific way to design higher dimensional feature maps (Álvarez et al., 2012), which renders samples more separable in a higher dimensional data manifold than the one with merely boundary data.

Original: See Figure 1 to illustrate this procedure.
Revised: See Figure 1 to illustrate this procedure.

Original: The information of σ is deeply hidden in ϕ .
Revised: The information of σ is deeply hidden in ϕ .

Original: As shown in Figure 1 (see also Appendix C), one cannot observe any pattern of σ directly from ϕ .
Revised: As shown in Figure 1 (see also Appendix C), one cannot observe any pattern of σ directly from ϕ .

Original: It is different from and more challenging than the inverse problems studied in (Bhattacharya et al., 2021; Khoo et al., 2021) that aim to reconstruct 2D targets from the much more informative 2D internal data of u .
Revised: It is different from and more challenging than the inverse problems studied in (Bhattacharya et al., 2021; Khoo et al., 2021) that aim to reconstruct 2D targets from the much more informative 2D internal data of u .

Original: In summary, both Theorem 1 and the formula of DSM (10) offer inspiration to give a potential answer to (Q1): the harmonic extension ϕ (2D features) of f − Λ σ 0 g (1D measurements) naturally encodes the information of the true characteristic function I D (2D targets).
Revised: In summary, both Theorem 1 and the formula of DSM (10) offer inspiration to give a potential answer to (Q1): the harmonic extension ϕ (2D features) of f − Λ σ 0 g (1D measurements) naturally encodes the information of the true characteristic function I D (2D targets).

Original: As there is a pointwise correspondence between the harmonic extensions and the targets at 2D grids, a tensor representation of ∇ ϕ l at these grid points can then be used as the input to a tensor2tensor -type DNN to learn I D .
Revised: As there is a pointwise correspondence between the harmonic extensions and the targets at 2D grids, a tensor representation of ∇ ϕ l at these grid points can then be used as the input to a tensor2tensor -type DNN to learn I D .

Original: Naturally, the grids are set as the positional embedding explicitly.
Revised: Naturally, the grids are set as the positional embedding explicitly.

Original: In comparison, the positional information is buried more deeply in 1D measurements.
Revised: In comparison, the positional information is buried more deeply in 1D measurements.

Original: As shown in Figure 1, G θ can be nicely decoupled into a composition of a learnable neural network operator T θ and a non-learnable PDE-based feature map
Revised: As shown in Figure 1, G θ can be nicely decoupled into a composition of a learnable neural network operator T θ and a non-learnable PDE-based feature map

Original: H , i.e., G θ
Revised: H , i.e., G θ

Original: =
Revised: =

Original: T θ ◦ H .
Revised: T θ ◦ H .

Original: The architecture of T θ shall be our interest henceforth.
Revised: The architecture of T θ shall be our interest henceforth.

Original: 3. F ROM CHANNELS IN ATTENTION TO BASIS IN INTEGRAL TRANSFORM
Revised: 3. F ROM CHANNELS IN ATTENTION TO BASIS IN INTEGRAL TRANSFORM

Original: In this subsection, a modified attention mechanism is proposed as the basic block in the tensor2tensortype mapping introduced in the next two subsections.
Revised: In this subsection, a modified attention mechanism is proposed as the basic block in the tensor2tensortype mapping introduced in the next two subsections.

Original: Its reformulation conforms with one of the most used tools in applied mathematics: the integral transform.
Revised: Its reformulation conforms with one of the most used tools in applied mathematics: the integral transform.

Original: In many applications such as inverse problems, the interaction (kernel) does not have any explicit form, which meshes well with DL methodology philosophically.
Revised: In many applications such as inverse problems, the interaction (kernel) does not have any explicit form, which meshes well with DL methodology philosophically.

Original: In fact, this is precisely the situation of the EIT problem considered.
Revised: In fact, this is precisely the situation of the EIT problem considered.

Original: Let the input of an encoder attention block be x h ∈ R M × c with c channels, then the query Q , key
Revised: Let the input of an encoder attention block be x h ∈ R M × c with c channels, then the query Q , key

Original: K , value V are generated by three learnable projection matrices θ := { W Q , W K , W V } ⊂ R c × c : ⋄ = x h W ⋄ , ⋄ ∈ { Q, K, V } .
Revised: K , value V are generated by three learnable projection matrices θ := { W Q , W K , W V } ⊂ R c × c : ⋄ = x h W ⋄ , ⋄ ∈ { Q, K, V } .

Original: Here c ≫ L is the number of expanded channels for the latent representations.
Revised: Here c ≫ L is the number of expanded channels for the latent representations.

Original: A modified dot-product attention is proposed as follows:
Revised: A modified dot-product attention is proposed as follows:

Original: [Equation]
Revised: [Equation]

Original: where nl Q ( · ) and nl K ( · ) are two learnable normalizations.
Revised: where nl Q ( · ) and nl K ( · ) are two learnable normalizations.

Original: Different from Nguyen & Salazar (2019)
Revised: Different from Nguyen & Salazar (2019)

Original: ; Xiong et al.
Revised: ; Xiong et al.

Original: (2020), this pre-inner-product normalization is applied right before the matrix multiplication of query and key.
Revised: (2020), this pre-inner-product normalization is applied right before the matrix multiplication of query and key.

Original: This practice takes inspiration from the normalization in the index function kernel integral (10) and (20), see also Boyd (2001) where the normalization for orthogonal bases essentially uses the (pseudo)inverse of the Gram matrices.
Revised: This practice takes inspiration from the normalization in the index function kernel integral (10) and (20), see also Boyd (2001) where the normalization for orthogonal bases essentially uses the (pseudo)inverse of the Gram matrices.

Original: In practice, layer normalization (Ba et al., 2016) or batch normalization (Ioffe & Szegedy, 2015) is used as a cheap alternative.
Revised: In practice, layer normalization (Ba et al., 2016) or batch normalization (Ioffe & Szegedy, 2015) is used as a cheap alternative.

Original: Constant α = h 2 is a mesh-based weight such that the summation becomes an approximation to an integral.
Revised: Constant α = h 2 is a mesh-based weight such that the summation becomes an approximation to an integral.

Original: To elaborate these rationales, the j -th column of the i -th row
Revised: To elaborate these rationales, the j -th column of the i -th row

Original: U i of U is ( U i )
Revised: U i of U is ( U i )

Original: j = α A i • · V j , in which the i -th row A
Revised: j = α A i • · V j , in which the i -th row A

Original: i • =
Revised: i • =

Original: ( (cid:101) Q (cid:101) K ⊤ )
Revised: ( (cid:101) Q (cid:101) K ⊤ )

Original: i • and the j -th column V j :=
Revised: i • and the j -th column V j :=

Original: V • j .
Revised: V • j .

Original: Thus, applying this to every column 1 ≤ j ≤ c , attention (11) becomes a basis expansion representation for the
Revised: Thus, applying this to every column 1 ≤ j ≤ c , attention (11) becomes a basis expansion representation for the

Original: i -th row U
Revised: i -th row U

Original: i
Revised: i

Original: U i =
Revised: U i =

Original: [Equation]
Revised: [Equation]

Original: A
Revised: A

Original: [Equation]
Revised: [Equation]

Original: Here, αA i • contains the coefficients for the linear combination of { V m } Mm =1 .
Revised: Here, αA i • contains the coefficients for the linear combination of { V m } Mm =1 .

Original: This set { V m } Mm =1 forms the V ’s row space, and it further forms each row of the output U by multiplying with A .
Revised: This set { V m } Mm =1 forms the V ’s row space, and it further forms each row of the output U by multiplying with A .

Original: A ( · , · ) in (12) stands for the attention kernel, which aggregates the pixel-wise feature maps to measure how the projected latent representations interact.
Revised: A ( · , · ) in (12) stands for the attention kernel, which aggregates the pixel-wise feature maps to measure how the projected latent representations interact.

Original: Moreover, the latent representation U i in an encoder layer is spanned by the row space of V and is being nonlinearly updated cross-layer-wise.
Revised: Moreover, the latent representation U i in an encoder layer is spanned by the row space of V and is being nonlinearly updated cross-layer-wise.

Original: For x h , U, Q, K, V , a set of feature maps are assumed to exist: for example u ( · ) maps R 2 → R 1 × c , i.e., U i =
Revised: For x h , U, Q, K, V , a set of feature maps are assumed to exist: for example u ( · ) maps R 2 → R 1 × c , i.e., U i =

Original: u ( z i ) =
Revised: u ( z i ) =

Original: [ u 1 ( z i ) , · · · , u c ( z i )] , e.g., see Choromanski et al.
Revised: [ u 1 ( z i ) , · · · , u c ( z i )] , e.g., see Choromanski et al.

Original: (2021), then an instancedependent kernel κ θ ( · , · ) : R 2 × R 2 → R can be defined by
Revised: (2021), then an instancedependent kernel κ θ ( · , · ) : R 2 × R 2 → R can be defined by

Original: [Equation]
Revised: [Equation]

Original: Now the discrete kernel A ( · , · ) with tensorial input is rewritten to this kernel κ θ ( · , · ) , thus the dot-product attention is expressed as a nonlinear integral transform for the l -th channel:
Revised: Now the discrete kernel A ( · , · ) with tensorial input is rewritten to this kernel κ θ ( · , · ) , thus the dot-product attention is expressed as a nonlinear integral transform for the l -th channel:

Original: [Equation]
Revised: [Equation]

Original: Through certain minimization such as (8), the backpropagation updates θ , which further leads a new set of latent representations.
Revised: Through certain minimization such as (8), the backpropagation updates θ , which further leads a new set of latent representations.

Original: This procedure can be viewed as an iterative method to update the basis residing in each channel by solving the Fredholm integral equation of the first kind in (14).
Revised: This procedure can be viewed as an iterative method to update the basis residing in each channel by solving the Fredholm integral equation of the first kind in (14).

Original: To connect attention with inverse problems, the multiplicative structure in a kernel integral form for attention (14) is particularly useful.
Revised: To connect attention with inverse problems, the multiplicative structure in a kernel integral form for attention (14) is particularly useful.

Original: (14) is a type of Pincherle-Goursat (degenerate) kernels (Kress, 1999, Chapter 11) and approximates the full kernel using only a finite number of bases.
Revised: (14) is a type of Pincherle-Goursat (degenerate) kernels (Kress, 1999, Chapter 11) and approximates the full kernel using only a finite number of bases.

Original: The number of learned basis functions in expansion (12) depends on the number of channels n .
Revised: The number of learned basis functions in expansion (12) depends on the number of channels n .

Original: Here we show the following theorem; heuristically, it says that: given enough but finite channels of latent representations, the attention kernel integral can “bootstrap” in the frequency domain, that is, generating an output representation with higher frequencies than the input.
Revised: Here we show the following theorem; heuristically, it says that: given enough but finite channels of latent representations, the attention kernel integral can “bootstrap” in the frequency domain, that is, generating an output representation with higher frequencies than the input.

Original: Similar approximation results are impossible for layer-wise propagation in CNN if one opts for the usual framelet/wavelet interpretation (Ye et al., 2018).
Revised: Similar approximation results are impossible for layer-wise propagation in CNN if one opts for the usual framelet/wavelet interpretation (Ye et al., 2018).

Original: For example, if there are no edge-like local features in the input (see for empirical evidence in Figure 9 and Figure 10), a single layer of CNN filters without nonlinearity cannot learn weights to extract edges.
Revised: For example, if there are no edge-like local features in the input (see for empirical evidence in Figure 9 and Figure 10), a single layer of CNN filters without nonlinearity cannot learn weights to extract edges.

Original: The full proof with a more rigorous setting is in Appendix F.
Revised: The full proof with a more rigorous setting is in Appendix F.

Original: Theorem 2 (Frequency bootstrapping) .
Revised: Theorem 2 (Frequency bootstrapping) .

Original: Suppose there exists a channel l in V such that ( V i ) l = sin( az i ) for some a ∈ Z + , the current finite-channel sum kernel A ( · , · ) approximates a non-separable kernel to an error of O ( ϵ ) under certain norm ∥ · ∥ X .
Revised: Suppose there exists a channel l in V such that ( V i ) l = sin( az i ) for some a ∈ Z + , the current finite-channel sum kernel A ( · , · ) approximates a non-separable kernel to an error of O ( ϵ ) under certain norm ∥ · ∥ X .

Original: Then, there exists a set of weights such that certain channel k ′ in the output of (12) approximates sin( a ′ z ) , Z + ∋ a ′ > a with an error of O ( ϵ ) under the same norm.
Revised: Then, there exists a set of weights such that certain channel k ′ in the output of (12) approximates sin( a ′ z ) , Z + ∋ a ′ > a with an error of O ( ϵ ) under the same norm.

Original: The considered inverse problem is essentially to recover higher-frequency eigenpairs of Λ σ based on lower-frequency data, see, e.g., Figure 1.
Revised: The considered inverse problem is essentially to recover higher-frequency eigenpairs of Λ σ based on lower-frequency data, see, e.g., Figure 1.

Original: Λ σ together with all its spectral information can be determined by the recovered inclusion shape.
Revised: Λ σ together with all its spectral information can be determined by the recovered inclusion shape.

Original: Thus, the existence in Theorem 2 partially justifies the advantages of adopting the attention mechanism for the considered problem.
Revised: Thus, the existence in Theorem 2 partially justifies the advantages of adopting the attention mechanism for the considered problem.

Original: 3. F ROM INDEX FUNCTION INTEGRAL TO T RANSFORMER
Revised: 3. F ROM INDEX FUNCTION INTEGRAL TO T RANSFORMER

Original: In (10), the probing direction d ( x ) , the inner product d ( x ) · ∇ ϕ ( x ) , and the norm | · | Y are used as ingredients to form certain non-local instance-based learnable kernel integration.
Revised: In (10), the probing direction d ( x ) , the inner product d ( x ) · ∇ ϕ ( x ) , and the norm | · | Y are used as ingredients to form certain non-local instance-based learnable kernel integration.

Original: This non-localness is a fundamental trait for many inverse problems, in that I D ( x ) depends on the entire data function.
Revised: This non-localness is a fundamental trait for many inverse problems, in that I D ( x ) depends on the entire data function.

Original: Then, the discretization of the modified index function is shown to match the multiplicative structure of the modified attention mechanism in (11).
Revised: Then, the discretization of the modified index function is shown to match the multiplicative structure of the modified attention mechanism in (11).

Original: In the forthcoming derivations, K ( x, y ) , Q ( x, y ) , and a self-adjoint positive definite linear operator
Revised: In the forthcoming derivations, K ( x, y ) , Q ( x, y ) , and a self-adjoint positive definite linear operator

Original: V : L 2 ( ∂ Ω) →
Revised: V : L 2 ( ∂ Ω) →

Original: L 2 ( ∂ Ω) , are shown to yield the emblematic Q - K - V structure of attention.
Revised: L 2 ( ∂ Ω) , are shown to yield the emblematic Q - K - V structure of attention.

Original: To this end, we make the following modifications and assumptions to the original index function in (10).
Revised: To this end, we make the following modifications and assumptions to the original index function in (10).

Original: • The reformulation of the index function is motivated by the heuristics that the agglomerated global information of ϕ could be used as “keys” to locate a point x .
Revised: • The reformulation of the index function is motivated by the heuristics that the agglomerated global information of ϕ could be used as “keys” to locate a point x .

Original: [Equation]
Revised: [Equation]

Original: If an ansatz K ( x, y )
Revised: If an ansatz K ( x, y )

Original: =
Revised: =

Original: δ x ( y ) is adopted, then (15) reverts to the original one in (10).
Revised: δ x ( y ) is adopted, then (15) reverts to the original one in (10).

Original: • The probing direction d ( x ) as “query” is reasonably assumed to have a global dependence on
Revised: • The probing direction d ( x ) as “query” is reasonably assumed to have a global dependence on

Original: [Equation]
Revised: [Equation]

Original: If Q ( x, y )
Revised: If Q ( x, y )

Original: =
Revised: =

Original: δ x ( y ) / ∥∇ ϕ
Revised: δ x ( y ) / ∥∇ ϕ

Original: ( x ) ∥ , then d ( x ) =
Revised: ( x ) ∥ , then d ( x ) =

Original: ∇ ϕ
Revised: ∇ ϕ

Original: ( x ) / ∥∇ ϕ
Revised: ( x ) / ∥∇ ϕ

Original: ( x ) ∥ which is the choice of the probing direction in (Ikehata, 2000; Ikehata & Siltanen, 2000; Ikehata, 2007).
Revised: ( x ) ∥ which is the choice of the probing direction in (Ikehata, 2000; Ikehata & Siltanen, 2000; Ikehata, 2007).

Original: • In the quantity R ( x ) in (10), the key is | · | Y which is assumed to have the following form:
Revised: • In the quantity R ( x ) in (10), the key is | · | Y which is assumed to have the following form:

Original: [Equation]
Revised: [Equation]

Original: In Chow et al.
Revised: In Chow et al.

Original: (2014), it is shown that if V induces a kernel with sharply peaked Gaussian-like
Revised: (2014), it is shown that if V induces a kernel with sharply peaked Gaussian-like

Original: distribution
Revised: distribution

Original: , the index
Revised: , the index

Original: function in
Revised: function in

Original: can achieve maximum values
Revised: can achieve maximum values

Original: for points inside D .
Revised: for points inside D .

Original: Based on the assumptions from (15) to (17), we derive a matrix representation approximating the new index function on a grid, which accords well with an attention-like architecture.
Revised: Based on the assumptions from (15) to (17), we derive a matrix representation approximating the new index function on a grid, which accords well with an attention-like architecture.

Original: Denote by ϕ n : the vector that interpolates ∂ x n ϕ at the grid points { z j } , n = 1 , 2 .
Revised: Denote by ϕ n : the vector that interpolates ∂ x n ϕ at the grid points { z j } , n = 1 , 2 .

Original: Here, we sketch the outline of the derivation and present the detailed derivation in Appendix D. We shall discretize the variable x by grid points
Revised: Here, we sketch the outline of the derivation and present the detailed derivation in Appendix D. We shall discretize the variable x by grid points

Original: z i in (15) and obtain an approximation to the integral:
Revised: z i in (15) and obtain an approximation to the integral:

Original: [Equation]
Revised: [Equation]

Original: where { ω j } are some integration quadrature weights.
Revised: where { ω j } are some integration quadrature weights.

Original: We then consider (16) and focus on one component d n ( x ) of d ( x ) .
Revised: We then consider (16) and focus on one component d n ( x ) of d ( x ) .

Original: With a suitable approximated integral, it can be rewritten as
Revised: With a suitable approximated integral, it can be rewritten as

Original: [Equation]
Revised: [Equation]

Original: Note that the self-adjoint positive definite operator V in (17) can be parameterized by a symmetric positive definite (SPD) matrix denoted by V .
Revised: Note that the self-adjoint positive definite operator V in (17) can be parameterized by a symmetric positive definite (SPD) matrix denoted by V .

Original: There exist vectors v n,i such that | η z i | 2 Y ≈ (cid:80) n ϕ Tn v n,i v Tn,i ϕ n .
Revised: There exist vectors v n,i such that | η z i | 2 Y ≈ (cid:80) n ϕ Tn v n,i v Tn,i ϕ n .

Original: Then, the modified indicator function can be written as
Revised: Then, the modified indicator function can be written as

Original: [Equation]
Revised: [Equation]

Original: Now, using the notation from Section 3.3, we denote the learnable kernel matrices and an input vector: for ⋄ ∈ { Q, K, V } , and u ∈ { q , k , v }
Revised: Now, using the notation from Section 3.3, we denote the learnable kernel matrices and an input vector: for ⋄ ∈ { Q, K, V } , and u ∈ { q , k , v }

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: Then, we can rewrite (20) as
Revised: Then, we can rewrite (20) as

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: where C f,g = ∥
Revised: where C f,g = ∥

Original: f − Λ σ 0 g ∥ − 1 ∂ Ω is a normalization weight, and both ∗ and / are element-wise.
Revised: f − Λ σ 0 g ∥ − 1 ∂ Ω is a normalization weight, and both ∗ and / are element-wise.

Original: Here, we may define Q = x h W Q , K = x h W K , and V = x h W V as the query, keys, and values.
Revised: Here, we may define Q = x h W Q , K = x h W K , and V = x h W V as the query, keys, and values.

Original: We can see that the right matrix multiplications (11) in the attention mechanism are low-rank approximations of the ones above.
Revised: We can see that the right matrix multiplications (11) in the attention mechanism are low-rank approximations of the ones above.

Original: Hence, based on (22), essentially we need to find a function I resulting in a vector approximation to the true characteristic function {I D ( z j ) }
Revised: Hence, based on (22), essentially we need to find a function I resulting in a vector approximation to the true characteristic function {I D ( z j ) }

Original: [Equation]
Revised: [Equation]

Original: Moreover, when there are L data pairs, the data functions ϕ l are generated by computing their harmonic extensions as in (9).
Revised: Moreover, when there are L data pairs, the data functions ϕ l are generated by computing their harmonic extensions as in (9).

Original: Then, each ϕ l is then treated as a channel of the input data x h .
Revised: Then, each ϕ l is then treated as a channel of the input data x h .

Original: In summary, the expressions in (22) and (23) reveal that a Transformer may be able to generalize the classical non-parametrized DSM formula further in (10) to non-local learnable kernels.
Revised: In summary, the expressions in (22) and (23) reveal that a Transformer may be able to generalize the classical non-parametrized DSM formula further in (10) to non-local learnable kernels.

Original: Thus, it may have an intrinsic architectural advantage that handles multiple data pairs.
Revised: Thus, it may have an intrinsic architectural advantage that handles multiple data pairs.

Original: In the subsequent EIT benchmarks, we provide a potential answer to the question (Q2); namely, the attention architecture is better suited for the tasks of reconstruction, as it conforms better with the underlying mathematical structure.
Revised: In the subsequent EIT benchmarks, we provide a potential answer to the question (Q2); namely, the attention architecture is better suited for the tasks of reconstruction, as it conforms better with the underlying mathematical structure.

Original: The ability to learn global interactions by attention, supported by a non-local kernel interpretation, matches the long-range dependence nature of inverse problems.
Revised: The ability to learn global interactions by attention, supported by a non-local kernel interpretation, matches the long-range dependence nature of inverse problems.

Original:  E XPERIMENTS
Revised:  E XPERIMENTS

Original: In this section we present some experimental results to show the quality of the reconstruction.
Revised: In this section we present some experimental results to show the quality of the reconstruction.

Original: The benchmark contains sampling of inclusions of random ellipses (targets), and the input data has a single channel ( L = 1 ) of the 2D harmonic extension feature from the 1D boundary measurements.
Revised: The benchmark contains sampling of inclusions of random ellipses (targets), and the input data has a single channel ( L = 1 ) of the 2D harmonic extension feature from the 1D boundary measurements.

Original: The training uses 1cycle and a mini-batch ADAM for 50 epochs.
Revised: The training uses 1cycle and a mini-batch ADAM for 50 epochs.

Original: The evaluated model is taken from the epoch with the best validation metric on a reserved subset.
Revised: The evaluated model is taken from the epoch with the best validation metric on a reserved subset.

Original: There are several baseline models to compare: the CNN-based U-Nets (Ronneberger et al., 2015; Guo & Jiang, 2020); the state-of-the-art operator learner Fourier Neural Operator (FNO) (Li et al., 2021a) and its variant with a token-mixing layer (Guibas et al., 2022); MultiWavelet Neural Operator (MWO) (Gupta et al., 2021).
Revised: There are several baseline models to compare: the CNN-based U-Nets (Ronneberger et al., 2015; Guo & Jiang, 2020); the state-of-the-art operator learner Fourier Neural Operator (FNO) (Li et al., 2021a) and its variant with a token-mixing layer (Guibas et al., 2022); MultiWavelet Neural Operator (MWO) (Gupta et al., 2021).

Original: The Transformer model of interest is a drop-in replacement of the baseline U-Net, and it is named by U-Integral Transformer (UIT).
Revised: The Transformer model of interest is a drop-in replacement of the baseline U-Net, and it is named by U-Integral Transformer (UIT).

Original: UIT uses the kernel integral inspired attention (11), and we also compare UIT with the linear attention-based Hybrid U-Transformer in Gao et al.
Revised: UIT uses the kernel integral inspired attention (11), and we also compare UIT with the linear attention-based Hybrid U-Transformer in Gao et al.

Original: (2021), as well as a Hadamard product-based cross-attention U-Transformer in Wang et al.
Revised: (2021), as well as a Hadamard product-based cross-attention U-Transformer in Wang et al.

Original: An ablation study is also conducted by replacing the convolution layers in the U-Net with attention (11) on the coarsest level.
Revised: An ablation study is also conducted by replacing the convolution layers in the U-Net with attention (11) on the coarsest level.

Original: For more details of the hyperparameters’ setup in the data generation, training, evaluation, network architectures please refer to Section 3.1, Appendix C.1, and Appendix C.2.
Revised: For more details of the hyperparameters’ setup in the data generation, training, evaluation, network architectures please refer to Section 3.1, Appendix C.1, and Appendix C.2.

Original: The comparison result can be found in Table 1.
Revised: The comparison result can be found in Table 1.

Original: Because FNO (AFNO, MWO) keeps only the lower modes in spectra, it performs relatively poor in this EIT benchmark where one needs to recover traits that consist of higher modes (sharp boundary edges of inclusion) from lower modes (smooth harmonic extension).
Revised: Because FNO (AFNO, MWO) keeps only the lower modes in spectra, it performs relatively poor in this EIT benchmark where one needs to recover traits that consist of higher modes (sharp boundary edges of inclusion) from lower modes (smooth harmonic extension).

Original: Attention-based models are capable to recover “high-frequency target from low-frequency data”, and generally outperform the CNN-based U-Nets despite having only 1 / 3 of the parameters.
Revised: Attention-based models are capable to recover “high-frequency target from low-frequency data”, and generally outperform the CNN-based U-Nets despite having only 1 / 3 of the parameters.

Original: Another highlight is that the proposed models are highly robust to noise thanks to the unique PDE-based feature map through harmonic extension.
Revised: Another highlight is that the proposed models are highly robust to noise thanks to the unique PDE-based feature map through harmonic extension.

Original: The proposed models can recover the buried domain under a moderately large noise (5%) and an extreme amount of noise (20%) which can be disastrous for many classical methods.
Revised: The proposed models can recover the buried domain under a moderately large noise (5%) and an extreme amount of noise (20%) which can be disastrous for many classical methods.

Original: [Table]
Revised: [Table]

Original:  C ONCLUSION
Revised:  C ONCLUSION

Original: For a boundary value inverse problem, we propose a novel operator learner based on the mathematical structure of the inverse operator and Transformer.
Revised: For a boundary value inverse problem, we propose a novel operator learner based on the mathematical structure of the inverse operator and Transformer.

Original: The proposed architecture consists of two components: the first one is a harmonic extension of boundary data (a PDE-based feature map), and the second one is a modified attention mechanism derived from the classical DSM by introducing learnable non-local integral kernels.
Revised: The proposed architecture consists of two components: the first one is a harmonic extension of boundary data (a PDE-based feature map), and the second one is a modified attention mechanism derived from the classical DSM by introducing learnable non-local integral kernels.

Original: The evaluation accuracy on the benchmark problems surpasses the current widely-used CNN-based U-Net and the best operator learner FNO.
Revised: The evaluation accuracy on the benchmark problems surpasses the current widely-used CNN-based U-Net and the best operator learner FNO.

Original: This research strengthens the insights that the attention is an adaptable neural architecture that can incorporate a priori mathematical knowledge to design more physics-compatible DNN architectures.
Revised: This research strengthens the insights that the attention is an adaptable neural architecture that can incorporate a priori mathematical knowledge to design more physics-compatible DNN architectures.

Original: However, we acknowledge some limitations: in this study, σ to be recovered relies on a piecewise constant assumption.
Revised: However, we acknowledge some limitations: in this study, σ to be recovered relies on a piecewise constant assumption.

Original: For many EIT applications in medical imaging and industrial monitoring, σ may involve non-sharp transitions or even contain highly anisotropic/
Revised: For many EIT applications in medical imaging and industrial monitoring, σ may involve non-sharp transitions or even contain highly anisotropic/

Original: A CKNOWLEDGMENTS
Revised: A CKNOWLEDGMENTS

Original: L. Chen is supported in part by National Science Foundation grants DMS-1913080 and DMS2012465, and DMS-2132710.
Revised: L. Chen is supported in part by National Science Foundation grants DMS-1913080 and DMS2012465, and DMS-2132710.

Original: S. Cao is supported in part by National Science Foundation grants
Revised: S. Cao is supported in part by National Science Foundation grants

Original: DMS-1913080 and DMS-2136075.
Revised: DMS-1913080 and DMS-2136075.

Original: The hardware to perform the experiments are sponsored by
Revised: The hardware to perform the experiments are sponsored by

Original: NSF grants DMS-2136075, and UMKC School of Science and Engineering computing facilities.
Revised: NSF grants DMS-2136075, and UMKC School of Science and Engineering computing facilities.

Original: No additional revenues are related to this work.
Revised: No additional revenues are related to this work.

Original: The authors would like to thank Ms. Jinrong Wei (University of California Irvine) for the proofreading and various suggestions on the manuscript.
Revised: The authors would like to thank Ms. Jinrong Wei (University of California Irvine) for the proofreading and various suggestions on the manuscript.

Original: The authors would like to thank Dr. Jun Zou (The Chinese University of Hong Kong) and Dr. Bangti
Revised: The authors would like to thank Dr. Jun Zou (The Chinese University of Hong Kong) and Dr. Bangti

Original: Jin (University College London & The Chinese University of Hong Kong) for their comments on inverse problems.
Revised: Jin (University College London & The Chinese University of Hong Kong) for their comments on inverse problems.

Original: The authors also greatly appreciate the valuable suggestions and comments by the anonymous reviewers.
Revised: The authors also greatly appreciate the valuable suggestions and comments by the anonymous reviewers.

Original: R EPRODUCIBILITY S TATEMENT
Revised: R EPRODUCIBILITY S TATEMENT

Original: This paper is reproducible.
Revised: This paper is reproducible.

Original: Experimental details about all empirical results described in this paper are provided in Appendix C. Additionally, we provide the PyTorch (Paszke et al., 2019) code for reproducing our results at https://github.com/scaomath/eit-transformer .
Revised: Experimental details about all empirical results described in this paper are provided in Appendix C. Additionally, we provide the PyTorch (Paszke et al., 2019) code for reproducing our results at https://github.com/scaomath/eit-transformer .

Original: The dataset used in this paper is available at https://www.kaggle.com/datasets/scaomath/ eletrical-impedance-tomography-dataset .
Revised: The dataset used in this paper is available at https://www.kaggle.com/datasets/scaomath/ eletrical-impedance-tomography-dataset .

Original: Formal proofs under a rigorous setting of all our theoretical results are provided in Appendices E-F.
Revised: Formal proofs under a rigorous setting of all our theoretical results are provided in Appendices E-F.

Original: A T ABLE OF N OTATIONS
Revised: A T ABLE OF N OTATIONS

Original: [Table]
Revised: [Table]

Original: normal derivative of u , measures the rate of change along the direction of n Λ σ
Revised: normal derivative of u , measures the rate of change along the direction of n Λ σ

Original: NtD map from Neumann data g := ∇ u · n (how fast the solution changes toward the outward normal direction) to
Revised: NtD map from Neumann data g := ∇ u · n (how fast the solution changes toward the outward normal direction) to

Original: Dirichlet data f := u | ∂ Ω (the solution’s value along the tangential direction)
Revised: Dirichlet data f := u | ∂ Ω (the solution’s value along the tangential direction)

Original: [Table]
Revised: [Table]

Original: the Sobolev space of functions
Revised: the Sobolev space of functions

Original: [Table]
Revised: [Table]

Original: the bounded linear functional defined on H s (Ω)
Revised: the bounded linear functional defined on H s (Ω)

Original: [Table]
Revised: [Table]

Original: all u ∈ H s (Ω) such that u ’s integral on Ω vanishes|
Revised: all u ∈ H s (Ω) such that u ’s integral on Ω vanishes

Original: [Table]
Revised: [Table]

Original: the seminorm defined for functions in Y
Revised: the seminorm defined for functions in Y

Original: B B ACKGROUND OF EIT
Revised: B B ACKGROUND OF EIT

Original: For EIT
Revised: For EIT

Original: [Table]
Revised: [Table]

Original: an immediate question is whether F − 1 and F − 1 L in (4) and (5) are well-defined, namely whether σ can be uniquely determined.
Revised: an immediate question is whether F − 1 and F − 1 L in (4) and (5) are well-defined, namely whether σ can be uniquely determined.

Original: In fact, for the case of full measurements ( L = ∞ ), the uniqueness for F − 1 has been well established, (Brühl, 2001; Hanke & Brühl, 2003; Astala &
Revised: In fact, for the case of full measurements ( L = ∞ ), the uniqueness for F − 1 has been well established, (Brühl, 2001; Hanke & Brühl, 2003; Astala &

Original: Päivärinta, 2006a;b; Nachman, 1996; Kohn & Vogelius, 1984; Sylvester & Uhlmann, 1987).
Revised: Päivärinta, 2006; Nachman, 1996; Kohn & Vogelius, 1984; Sylvester & Uhlmann, 1987).

Original: It is worthwhile to point out that, in this case, σ is not necessarily a piecewise constant function.
Revised: It is worthwhile to point out that, in this case, σ is not necessarily a piecewise constant function.

Original: In (1), we present a simplified case for purposes of illustrating as well as benchmarking.
Revised: In (1), we present a simplified case for purposes of illustrating as well as benchmarking.

Original: In general, with the full spectral information of the NtD map, σ can be uniquely determined as a general positive function.
Revised: In general, with the full spectral information of the NtD map, σ can be uniquely determined as a general positive function.

Original: If infinitely many eigenpairs are known, then the operator itself can be precisely characterized using infinitely many feature channels by Reproducing Kernel Hilbert Space (RKHS) theory, e.g., Mercer (1909); Aronszajn (1950); Minh et al.
Revised: If infinitely many eigenpairs are known, then the operator itself can be precisely characterized using infinitely many feature channels by Reproducing Kernel Hilbert Space (RKHS) theory, e.g., Mercer (1909); Aronszajn (1950); Minh et al.

Original: ; Morris (2015); Kadri et al. (2016); Lu et al. (2022).
Revised: ; Morris (2015); Kadri et al. (2016); Lu et al. (2022).

Original: In the context of EIT, this is known as the “full measurement”.
Revised: In the context of EIT, this is known as the “full measurement”.

Original: A more challenging and practical problem is to recover σ from only finitely many boundary data pairs.
Revised: A more challenging and practical problem is to recover σ from only finitely many boundary data pairs.

Original: A common practice for the theoretical study of reconstruction using finite measurements is the assumption of σ being a piecewise constant function.
Revised: A common practice for the theoretical study of reconstruction using finite measurements is the assumption of σ being a piecewise constant function.

Original: The task is usually set to recover the shape and location of the inclusion D .
Revised: The task is usually set to recover the shape and location of the inclusion D .

Original: Otherwise, the problem is too ill-posed.
Revised: Otherwise, the problem is too ill-posed.

Original: With finite measurements, the uniqueness of the inclusion remains a long-standing theoretical open problem, and it can be only established for several special classes of the inclusion shape, such as the convex cylinders in Isakov & Powell (1990) or convex polyhedrons in Barceló et al.
Revised: With finite measurements, the uniqueness of the inclusion remains a long-standing theoretical open problem, and it can be only established for several special classes of the inclusion shape, such as the convex cylinders in Isakov & Powell (1990) or convex polyhedrons in Barceló et al.

Original: We refer readers to some counter-examples in Kang & Seo (2001) where a two- or three-dimensional ball may not be identified uniquely by one single measurement if the values of σ 0 and σ 1 are unknown.
Revised: We refer readers to some counter-examples in Kang & Seo (2001) where a two- or three-dimensional ball may not be identified uniquely by one single measurement if the values of σ 0 and σ 1 are unknown.

Original: Furthermore, here we provide one example to illustrate the difficulty in the reconstruction procedure (Pidcock et al., 1995).
Revised: Furthermore, here we provide one example to illustrate the difficulty in the reconstruction procedure (Pidcock et al., 1995).

Original: Let Ω be a unit circle, let D be a circle with the radius ρ < 1 , and define
Revised: Let Ω be a unit circle, let D be a circle with the radius ρ < 1 , and define

Original: [Equation]
Revised: [Equation]

Revised: [Equation]

Original: with σ 1 < 1 being an arbitrary constant.
Revised: with σ 1 < 1 being an arbitrary constant.

Original: In this case, the eigenpairs of Λ σ can be explicitly calculated
Revised: In this case, the eigenpairs of Λ σ can be explicitly calculated

Original: [Equation]
Revised: [Equation]

Original: with µ = (1 − √ σ 1 ) / (1 + √ σ 1 ) , which are exactly the Fourier modes in a unit circle.
Revised: with µ = (1 − √ σ 1 ) / (1 + √ σ 1 ) , which are exactly the Fourier modes in a unit circle.

Original: In this case, if the set of basis { g l } ∞ l =1 is just chosen as { cos( θ ) , sin( θ ) , cos(2 θ ) , sin(2 θ ) , ... } , the matrix representation A σ in (3) can be written as an infinite diagonal matrix
Revised: In this case, if the set of basis { g l } ∞ l =1 is just chosen as { cos( θ ) , sin( θ ) , cos(2 θ ) , sin(2 θ ) , ... } , the matrix representation A σ in (3) can be written as an infinite diagonal matrix

Original: [Equation]
Revised: [Equation]

Original: Thanks to this special geometry, eigenvalues in (25) can clearly determine ρ and σ 1 as follows:
Revised: Thanks to this special geometry, eigenvalues in (25) can clearly determine ρ and σ 1 as follows:

Original: [Equation]
Revised: [Equation]

Original: However, in practice, A σ does not have such a simple structure.
Revised: However, in practice, A σ does not have such a simple structure.

Original: Approximating A σ itself requires a large number of data pairs that are not available in the considered case.
Revised: Approximating A σ itself requires a large number of data pairs that are not available in the considered case.

Original: Besides, an accurate approximation of the eigenvalues of A σ is also very expensive.
Revised: Besides, an accurate approximation of the eigenvalues of A σ is also very expensive.

Original: Furthermore, for complex inclusion shapes, two eigenvalues are not sufficient to exactly recover the shape and conductivity values.
Revised: Furthermore, for complex inclusion shapes, two eigenvalues are not sufficient to exactly recover the shape and conductivity values.

Original: C E XPERIMENT S ET - UP C. D ATA GENERATION AND TRAINING
Revised: C E XPERIMENT S ET - UP C. D ATA GENERATION AND TRAINING

Original: In the numerical examples, the data generation mainly follows standard practice in theoretical prototyping for solving EIT problems, see e.g.,Chow et al.
Revised: In the numerical examples, the data generation mainly follows standard practice in theoretical prototyping for solving EIT problems, see e.g.,Chow et al.

Original: ; Michalikova et al.
Revised: ; Michalikova et al.

Original: Hamilton & Hauptmann (2018); Guo & Jiang (2020); Fan & Ying (2020).
Revised: Hamilton & Hauptmann (2018); Guo & Jiang (2020); Fan & Ying (2020).

Original: For examples, please refer to Figure 2.
Revised: For examples, please refer to Figure 2.

Original: The computational domain is set to be Ω :=
Revised: The computational domain is set to be Ω :=

Original: ( − 1 , 1) 2 , and the two media with the different conductivities are with σ 1 = 10 (inclusion) and σ 0 = 1 (background).
Revised: ( − 1 , 1) 2 , and the two media with the different conductivities are with σ 1 = 10 (inclusion) and σ 0 = 1 (background).

Original: The inclusions are four random ellipses.
Revised: The inclusions are four random ellipses.

Original: The lengths of the semi-major axis and semi-minor axis of these ellipses are sampled from U (0 . 1 , 0 . 2) and U (0 . 2 , 0 . 4) , respectively.
Revised: The lengths of the semi-major axis and semi-minor axis of these ellipses are sampled from U (0 . 1 , 0 . 2) and U (0 . 2 , 0 . 4) , respectively.

Original: The rotation angles are sampled from
Revised: The rotation angles are sampled from

Original: U (0 , 2 π ) .
Revised: U (0 , 2 π ) .

Original: There are 10800 samples in the training set, from which 20% are reserved as validation.
Revised: There are 10800 samples in the training set, from which 20% are reserved as validation.

Original: There are 2000 in the testing set for evaluation.
Revised: There are 2000 in the testing set for evaluation.

Original: The noise ξ = ξ ( x ) below (10) is assumed to be
Revised: The noise ξ = ξ ( x ) below (10) is assumed to be

Original: [Equation]
Revised: [Equation]

Original: where τ specifies the relative strength of noise, and G ( x ) is a normal Gaussian distribution independent with respect to x .
Revised: where τ specifies the relative strength of noise, and G ( x ) is a normal Gaussian distribution independent with respect to x .

Original: As ξ ( x ) is merely pointwise imposed, the boundary data can be highly rough, even if the ground truth f ( · ) − Λ σ 0 g ( · ) is chosen to be smooth.
Revised: As ξ ( x ) is merely pointwise imposed, the boundary data can be highly rough, even if the ground truth f ( · ) − Λ σ 0 g ( · ) is chosen to be smooth.

Original: Nevertheless, the harmonic extension makes the noise from boundary data have a minimal impact on the overall reconstruction, thanks to the smoothing property of the inverse of the Laplacian operator ( − ∆) − 1 ; for example, please refer to
Revised: Nevertheless, the harmonic extension makes the noise from boundary data have a minimal impact on the overall reconstruction, thanks to the smoothing property of the inverse of the Laplacian operator ( − ∆) − 1 ; for example, please refer to

Original: Figure3. In data generation, the harmonic extensionfeature map is approximatedby finite element methods incorporating stencil modification near the inclusion interfaces (Guo et al., 2019). Similar augmentation practices internal can be found in Nachman et al. Bal (2013)
Revised: Figure extension map is approximated

Original: Jin et al. (2022a). Thanks to the position-wise binary nature of I D , another choice of the loss function during training can be the binary cross entropy L ( · , · ) , applied for a function in P , to measure the distance between the ground truth and the network’s prediction 
Revised: Thanks to the position-wise binary nature of I D , another choice of the loss function during training can be the binary cross entropy L ( · , · ) , applied for a function in P , to measure the distance between the ground truth and the network’s prediction L

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: −0. −0.
Revised: −0. −0.

Original: −0. −0.
Revised: −0. −0.

Original: Thanks to the Pinsker inequality (e g., see (Cover, 1999, Section 11.6)), L ( p h , u h ) serves as a good upper bound for the square of the total variation, which can be further bounded below by the L 2 -error given the boundedness of the position-wise value.
Revised: Thanks to the Pinsker inequality (e g., see (Cover, 1999, Section 11.6)), L ( p h , u h ) serves as a good upper bound for the square of the total variation, which can be further bounded below by the L 2 -error given the boundedness of the position-wise value.

Original: The training uses 1cycle (Smith & Topin, 2019) learning rate strategy with a warm-up phase.
Revised: The training uses 1cycle (Smith & Topin, 2019) learning rate strategy with a warm-up phase.

Original: A mini-batch ADAM iterations are run for 50 epochs with no extra regularization, such as weight decay.
Revised: A mini-batch ADAM iterations are run for 50 epochs with no extra regularization, such as weight decay.

Original: The evaluated model is taken from the epoch that has the best validation metric.
Revised: The evaluated model is taken from the epoch that has the best validation metric.

Original: The learning rate starts and ends with 10 − 3 · lr max , and reaches the maximum of lr max at the end of the 10 -th epoch.
Revised: The learning rate starts and ends with 10 − 3 · lr max , and reaches the maximum of lr max at the end of the 10 -th epoch.

Original: The lr max = 10 − 3 .
Revised: The lr max = 10 − 3 .

Original: The result demonstrated is obtained from fixing the random number generator seed.
Revised: The result demonstrated is obtained from fixing the random number generator seed.

Original: Figure 6 shows the testing results for a randomly chosen sample.
Revised: Figure 6 shows the testing results for a randomly chosen sample.

Original: All models are trained on an RTX 3090 or an A4000.
Revised: All models are trained on an RTX 3090 or an A4000.

Original: The codes to replicate the experiments are open-source and publicly available.
Revised: The codes to replicate the experiments are open-source and publicly available.

Original: C. N ETWORK ARCHITECTURE
Revised: C. N ETWORK ARCHITECTURE

Original: The difference in architectural hyperparameters, together with training and evaluation costs comparison for all the models compared in the task of EIT reconstruction can be found in Table 3.
Revised: The difference in architectural hyperparameters, together with training and evaluation costs comparison for all the models compared in the task of EIT reconstruction can be found in Table 3.

Original: U-Integral Transformer • Overall architecture.
Revised: • Overall architecture.

Original: The U-Integral-Transformer architecture is a drop-in replacement of the standard CNN-based U-
Revised: The U-Integral-Transformer architecture is a drop-in replacement of the standard CNN-based U-

Original: Net baseline model (7.7m) in Table 1.
Revised: Net baseline model (7.7m) in Table 1.

Revised: The CNN-based U-Net is used in DL-based approaches for boundary value inverse problem in Guo & Jiang (2020);

Revised: Guo et al.

Revised: ; Le et al. (2022).

Original: One of the novelties is that the input is a tensor that concatenates different measurement matrices as different channels, and a similar practice can be found in Brandstetter et al.
Revised: One of the novelties is that the input is a tensor that concatenates different measurement matrices as different channels, and a similar practice can be found in Brandstetter et al.

Original: Same with the baseline U-Net, the UIT has three downsampling layers as the encoder (feature extractor).
Revised: Same with the baseline U-Net, the UIT has three downsampling layers as the encoder (feature extractor).

Original: The downsampling layers map m × m latent representations to m/ 2 × m/ 2 , andexpand the number of channels from C to 2 C .
Revised: The downsampling layers map m × m latent representations to m/ 2 × m/ 2 , and

Original: To leverage the “basis ⇔ channel” interpretation and the basis update nature of the attention

Original: [Figure]
Revised: [Figure]

Original: A model’s input is the left most function in Figure 5 if this model uses a single channel, or all left three in Figure 5 if a model uses 3-channel input
Revised: A model’s input is the left most function in Figure 5 if this model uses a single channel, or all left three in Figure 5 if a model uses 3-channel input

Original: Ground truth inclusion in Figure
Revised: Ground truth inclusion in Figure

Original: U
Revised: U

Original: Net  mechanism in Section 3. the proposed attention block is first added on the coarsest grid, which has the most number of channels.
Revised: Net expand the number of channels from C to 2 C . To leverage the “basis ⇔ channel” interpretation and the basis update nature of the attention mechanism in Section 3.3, the proposed attention block is first added on the coarsest grid, which has the most number of channels.

Original: UIT has three upsampling layers as the decoder (feature selector), which map m/ 2 × m/ 2 latent representations to m × m  and shrink the number of channels from 2 C to C .
Revised: UIT has three upsampling layers as the decoder (feature selector), which map m/ 2 × m/ 2 latent representations to m × m , and shrink the number of channels from 2 C to C .

Original: In these upsampling layers, attention blocks are applied on each cross-layer propagation to compute the interaction between the latent representations on both coarse and fine grids (see below).
Revised: In these upsampling layers, attention blocks are applied on each cross-layer propagation to compute the interaction between the latent representations on both coarse and fine grids (see below).

Original: Please refer to Figure 8 for a high-level encoder-decoder schematic.
Revised: Please refer to Figure 8 for a high-level encoder-decoder schematic.

Original: • Double convolution block.
Revised: • Double convolution block.

Original: The double convolution block is modified from that commonly seen in Computer Vision (CV) models, such as ResNet (He et al., 2016).
Revised: The double convolution block is modified from that commonly seen in Computer Vision (CV) models, such as ResNet (He et al., 2016).

Original: We modify this block such that upon being used in an attention block, the batch normalization (Ioffe & Szegedy, 2015) can be replaced by the layer normalization (Ba et al., 2016), which can be understood as a learnable approximation to the Gram matrices’ inverse by a diagonal matrix.
Revised: We modify this block such that upon being used in an attention block, the batch normalization (Ioffe & Szegedy, 2015) can be replaced by the layer normalization (Ba et al., 2016), which can be understood as a learnable approximation to the Gram matrices’ inverse by a diagonal matrix.

Original: • Positional embedding.
Revised: • Positional embedding.

Original: At each resolution, the 2D Euclidean coordinates of an m × m regular Cartesian grid are the input of a channel expansion through a fixed learnable linear layer and are then added to each latent representation.
Revised: At each resolution, the 2D Euclidean coordinates of an m × m regular Cartesian grid are the input of a channel expansion through a fixed learnable linear layer and are then added to each latent representation.

Original: This choice of positional embedding enables a bilinear interpolation between the coarse and fine grids or vice versa (see below).
Revised: This choice of positional embedding enables a bilinear interpolation between the coarse and fine grids or vice versa (see below).

Original: • Mesh-normalized attention.
Revised: • Mesh-normalized attention.

Original: The scaled dot-product attention in the network is chosen to be the integral kernel attention in (11) with a mesh-based normalization.
Revised: The scaled dot-product attention in the network is chosen to be the integral kernel attention in (11) with a mesh-based normalization.

Original: Please refer to Figure 7 for a diagram in a single attention head.
Revised: Please refer to Figure 7 for a diagram in a single attention head.

Original: [Figure]
Revised: [Figure]

Original: • Interpolation.
Revised: • Interpolation.

Original: Instead of max pooling used in the standard CNN-based U-Net, we opt for a bilinear interpolation on the Cartesian grid to map a latent representation from the fine grid to the coarse grid or vice versa.
Revised: Instead of max pooling used in the standard CNN-based U-Net, we opt for a bilinear interpolation on the Cartesian grid to map a latent representation from the fine grid to the coarse grid or vice versa.

Original: Note that, in the upsampling layers, the interpolation’s outputs are directly inputted into an attention block that computes the interaction of latent representations between coarse and fine grids (see below).
Revised: Note that, in the upsampling layers, the interpolation’s outputs are directly inputted into an attention block that computes the interaction of latent representations between coarse and fine grids (see below).

Original: • Coarse-fine attention in up blocks.
Revised: • Coarse-fine attention in up blocks.

Original: A modified attention in Section 3.3 with a pre-inner-product normalization replaces the convolution layer on the coarsest level.
Revised: A modified attention in Section 3.3 with a pre-inner-product normalization replaces the convolution layer on the coarsest level.

Original: The skip connection from the encoder latent representations to the ones in the decoder are generated using an architecture similar to the cross attention used in Petit et al.
Revised: The skip connection from the encoder latent representations to the ones in the decoder are generated using an architecture similar to the cross attention used in Petit et al.

Original: Q and K are generated from the latent representation functions on the same coarser grids.
Revised: Q and K are generated from the latent representation functions on the same coarser grids.

Original: As such, the attention kernel to measure the interaction between different channels is built from the coarse grid.
Revised: As such, the attention kernel to measure the interaction between different channels is built from the coarse grid.

Original: V is associated with a finer grid.
Revised: V is associated with a finer grid.

Original: Compared with the one in Petit et al.
Revised: Compared with the one in Petit et al.

Original: (2021), the modified attention in our method is inspired by the kernel integral for a PDE problem.
Revised: (2021), the modified attention in our method is inspired by the kernel integral for a PDE problem.

Original: Thus, it has (1) no softmax normalization or (2) no Hadamard product-type skip connection.
Revised: Thus, it has (1) no softmax normalization or (2) no Hadamard product-type skip connection.

Original: Other operator learners compared • Fourier Neural Operator (FNO) and variants.
Revised: Other operator learners compared • Fourier Neural Operator (FNO) and variants.

Original: Fourier Neural Operator (FNO2d) learns convolutional filters in the frequency domain for some pre-selected modes, efficiently capturing globally-supported spatial interactions for these modes.
Revised: Fourier Neural Operator (FNO2d) learns convolutional filters in the frequency domain for some pre-selected modes, efficiently capturing globally-supported spatial interactions for these modes.

Original: The weight filter in the frequency domain multiplies with the lowest modes in the latent representations (four corners in the FFT).
Revised: The weight filter in the frequency domain multiplies with the lowest modes in the latent representations (four corners in the FFT).

Original: The Adaptive Fourier Neural Operator (AFNO2d) adds a token-mixing layer, as seen in Figure 2 in Guibas et al.
Revised: The Adaptive Fourier Neural Operator (AFNO2d) adds a token-mixing layer, as seen in Figure 2 in Guibas et al.

Original: , appending every spectral convolution layer in the baseline FNO2d model.
Revised: , appending every spectral convolution layer in the baseline FNO2d model.

Original: • Multi-Wavelet Neural Operator (MWO).
Revised: • Multi-Wavelet Neural Operator (MWO).

Original: The MultiWavelet Neural Operator (MWO) is proposed in Gupta et al.
Revised: The MultiWavelet Neural Operator (MWO) is proposed in Gupta et al.

Original: (2021), which introduces a multilevel structure into the FNO architecture.
Revised: (2021), which introduces a multilevel structure into the FNO architecture.

Original: MWO still follows FNO’s practice on each level by pre-selecting the lowest modes.
Revised: MWO still follows FNO’s practice on each level by pre-selecting the lowest modes.

Original: D F ROM DSM TO T RANSFORMER
Revised: D F ROM DSM TO T RANSFORMER

Original: This section gives a more detailed presentation of how the attention-like operator is derived from  (15) with learnable kernels.
Revised: This section gives a more detailed presentation of how the attention-like operator is derived from the DSM ansatz (15) with learnable kernels.

Original: We begin with recalling the original indicator function from (10):
Revised: We begin with recalling the original indicator function from (10):

Original: [Equation]
Revised: [Equation]

Original: where C f,g = ∥
Revised: where C f,g = ∥

Original: f − Λ σ 0 g ∥ − 1 L 2 ( ∂ Ω) is a constant, and the equations of the functions ϕ and η x are : 
Revised: f − Λ σ 0 g ∥ − 1 L 2 ( ∂ Ω) is a constant, and the equations of the functions ϕ and η x are : − ∆ ϕ = 0 in Ω ,

Original: [Equation]
Revised: [Equation]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: in certain Hilbert spaces.
Revised: in certain Hilbert spaces.

Original: The TikZ source code to produce this figure is modified from the examples in Iqbal (2018). 
Revised: The TikZ source code to produce this figure is modified from the examples in Iqbal (2018). [Equation]

Original: Chow et al.
Revised: Chow et al.

Original: (2014) shows that I D ( x ) can be written as a sum of Gaussian-like distributions that attain maximum values for x ∈ D or close to D .
Revised: (2014) shows that I D ( x ) can be written as a sum of Gaussian-like distributions that attain maximum values for x ∈ D or close to D .

Original: However, the accuracy is much limited by empirical choices of quantities in (30), for example, d ( x ) =
Revised: However, the accuracy is much limited by empirical choices of quantities in (30), for example, d ( x ) =

Original: ∇ ϕ
Revised: ∇ ϕ

Original: ( x ) / ∥∇ ϕ
Revised: ( x ) / ∥∇ ϕ

Original: ( x ) ∥ and s = 3 / 2 .
Revised: ( x ) ∥ and s = 3 / 2 .

Original: In addition, the frequency of the boundary data plays an important role in reconstruction; see the derivation in Chow et al.
Revised: In addition, the frequency of the boundary data plays an important role in reconstruction; see the derivation in Chow et al.

Original: (2014, Section 5) for circular Ω .
Revised: (2014, Section 5) for circular Ω .

Original: What is more, such a simple formula can be derived only for a single data pair.
Revised: What is more, such a simple formula can be derived only for a single data pair.

Original: Henceforth, these “empirical chosen quantities” are made to be learnable from data by introducing two undetermined kernels K ( x, y ) , Q ( x, y ) , and a self-adjoint positive definite linear operator V , the modified indicator function is written as
Revised: Henceforth, these “empirical chosen quantities” are made to be learnable from data by introducing two undetermined kernels K ( x, y ) , Q ( x, y ) , and a self-adjoint positive definite linear operator V , the modified indicator function is written as

Original: [Equation]
Revised: [Equation]

Original: 33) with
Revised: with

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: [Equation]
Revised: [Equation]

Original: Applying certain quadrature rule to (33) with the quadrature points z i , i.e., the grid points of Ω h , and weights { ω j } , we obtain an approximation to the integral:
Revised: Applying certain quadrature rule to (33) with the quadrature points z i , i.e., the grid points of Ω h , and weights { ω j } , we obtain an approximation to the integral:

Original: [Equation]
Revised: [Equation]

Original: i.e., q Ti is the vector of [ ω j K ( z i , z j )]
Revised: i.e., q Ti is the vector of [ ω j K ( z i , z j )]

Original: j .
Revised: j .

Original: For (34), we consider one component d n ( x ) of d ( x ) .
Revised: For (34), we consider one component d n ( x ) of d ( x ) .

Original: With the same rule to compute the integral, (16) can be written as
Revised: With the same rule to compute the integral, (16) can be written as

Original: [Equation]
Revised: [Equation]

Original: i.e., q Ti is the vector of [ ω j Q ( z i , z j )] j .
Revised: i.e., q Ti is the vector of [ ω j Q ( z i , z j )] j .

Original: Next, we proceed to express | η
Revised: Next, we proceed to express | η

Original: z i | Y by discretizing the variational form in (32) using a linear finite element method (FEM).
Revised: z i | Y by discretizing the variational form in (32) using a linear finite element method (FEM).

Original: Applying integration by parts, the weak form of (32) at x =
Revised: Applying integration by parts, the weak form of (32) at x =

Original: z i is
Revised: z i is

Original: [Equation]
Revised: [Equation]

Original: for any test function ψ ∈ H 10 (Ω) .
Revised: for any test function ψ ∈ H 10 (Ω) .

Original: Here, we let { ψ ( j ) } Mj =1 be the collection of the finite element basis functions, and for the fixed z i we further let ψ n,i be the vector approximating [ ∂ x n ψ ( j ) ( z i )]
Revised: Here, we let { ψ ( j ) } Mj =1 be the collection of the finite element basis functions, and for the fixed z i we further let ψ n,i be the vector approximating [ ∂ x n ψ ( j ) ( z i )]

Original: Mj =1 .
Revised: Mj =1 .

Original: Denote η i as the vector approximating { η z i (
Revised: Denote η i as the vector approximating { η z i (

Original: z j ) } Mj =1 .
Revised: z j ) } Mj =1 .

Original: Introduce the matrices
Revised: Introduce the matrices

Original: • B : the finite element/finite difference discretization of −
Revised: • B : the finite element/finite difference discretization of −

Original: ∆
Revised: ∆

Original: on
Revised: on

Original: Ω h
Revised: Ω h

Original: (a discrete Laplacian) coupled with
Revised: (a discrete Laplacian) coupled with

Original: the Neumann boundary condition and the zero integral normalization condition in (9)
Revised: the Neumann boundary condition and the zero integral normalization condition in (9)

Original: • R : the matrix that projects a vector defined at interior grids to the one defined on ∂ Ω .
Revised: • R : the matrix that projects a vector defined at interior grids to the one defined on ∂ Ω .

Original: Then, the finite element discretization of (38) yields the linear system:
Revised: Then, the finite element discretization of (38) yields the linear system:

Original: [Equation]
Revised: [Equation]

Original: where we have used (37).
Revised: where we have used (37).

Original: Then, the trace of η z i on ∂ Ω admits the following approximation
Revised: Then, the trace of η z i on ∂ Ω admits the following approximation

Original: [Equation]
Revised: [Equation]

Original: [Equation]
Revised: [Equation]

Original: Now, we can discretize (35).
Revised: Now, we can discretize (35).

Original: Note that the trace of a linear finite element space on ∂ Ω is still a continuous piecewise linear space, defined as S h ( ∂ Ω) .
Revised: Note that the trace of a linear finite element space on ∂ Ω is still a continuous piecewise linear space, defined as S h ( ∂ Ω) .

Original: Then, the self-adjoint positive definite operator
Revised: Then, the self-adjoint positive definite operator

Original: V can be parameterized by a symmetric positive definite (SPD) matrix denoted by V operating on the space S h ( ∂ Ω) .
Revised: V can be parameterized by a symmetric positive definite (SPD) matrix denoted by V operating on the space S h ( ∂ Ω) .

Original: We can approximate | η
Revised: We can approximate | η

Original: z i | 2 Y as
Revised: z i | 2 Y as

Original: [Equation]
Revised: [Equation]

Original: where ψ Tn,i B − 1 R T V RB − 1 ψ n,i ≥ 0 as V is SPD.
Revised: where ψ Tn,i B − 1 R T V RB − 1 ψ n,i ≥ 0 as V is SPD.

Original: Define
Revised: Define

Original: [Equation]
Revised: [Equation]

Original: This can be considered another learnable vector since the coefficient of q i comes from the learnable matrix V .
Revised: This can be considered another learnable vector since the coefficient of q i comes from the learnable matrix V .

Original: Then, (41) reduces to
Revised: Then, (41) reduces to

Original: [Equation]
Revised: [Equation]

Original: [Equation]
Revised: [Equation]

Original: Putting (36), (37) and (43) into (15), we have
Revised: Putting (36), (37) and (43) into (15), we have

Original: [Equation]
Revised: [Equation]

Original: Now, using the notation in (21), we get the desired representation (22).
Revised: Now, using the notation in (21), we get the desired representation (22).

Original: E P ROOF OF T HEOREM
Revised: E P ROOF OF T HEOREM

Original: Lemma 1.
Revised: Lemma 1.

Original: Suppose the boundary data g l is the eigenfunction of Λ σ − Λ σ 0 corresponding to the l -th eigenvalue λ l , and let ϕ l be the data functions generated by harmonic extensions − ∆ ϕ l = 0 in Ω ,
Revised: Suppose the boundary data g l is the eigenfunction of Λ σ − Λ σ 0 corresponding to the l -th eigenvalue λ l , and let ϕ l be the data functions generated by harmonic extensions − ∆ ϕ l = 0 in Ω ,

Original: [Equation]
Revised: [Equation]

Original: where l = 1 , 2 ,
Revised: where l = 1 , 2 ,

Original: Let d be an arbitrary unit vector in R 2 , define a function
Revised: Let d be an arbitrary unit vector in R 2 , define a function

Original: [Equation]
Revised: [Equation]

Original: Then, there holds
Revised: Then, there holds

Original: [Equation]
Revised: [Equation]

Original: Proof.
Revised: Proof.

Original: See Theorem 4.1 in Guo & Jiang (2020), and also see Brühl (2001)
Revised: See Theorem 4.1 in Guo & Jiang (2020), and also see Brühl (2001)

Original: ; Hanke & Brühl (2003).
Revised: ; Hanke & Brühl (2003).

Original: Theorem 1 (A finite-dimensional approximation of the index function) .
Revised: Theorem 1 (A finite-dimensional approximation of the index function) .

Original: Suppose the boundary data g l is the eigenfunction of Λ σ − Λ σ 0 corresponding to the l -th eigenvalue λ l , and let ϕ l be the data functions generated by harmonic extensions given in (9) .
Revised: Suppose the boundary data g l is the eigenfunction of Λ σ − Λ σ 0 corresponding to the l -th eigenvalue λ l , and let ϕ l be the data functions generated by harmonic extensions given in (9) .

Original: Define the space:
Revised: Define the space:

Original: [Equation]
Revised: [Equation]

Original: 48) and the dictionary:
Revised: 48) and the dictionary:

Original: [Equation]
Revised: [Equation]

Original: Then, for any ϵ > 0 , we con construct an index function I DL ∈ S L s.t.
Revised: Then, for any ϵ > 0 , we con construct an index function I DL ∈ S L s.t.

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: provided L is large enough.
Revised: provided L is large enough.

Original: Proof.
Revised: Proof.

Original: Consider the function Θ L ( x ) from Lemma 1.
Revised: Consider the function Θ L ( x ) from Lemma 1.

Original: As Θ L ( x ) > 0 , it is increasing with respect to
Revised: As Θ L ( x ) > 0 , it is increasing with respect to

Original: L .
Revised: L .

Original: Then, there is a constant ρ such that ρ > Θ L ( x ) , ∀ x ∈ D .
Revised: Then, there is a constant ρ such that ρ > Θ L ( x ) , ∀ x ∈ D .

Original: Given any ϵ > 0 , there is an integer L such that Θ L ( x ) > 4 ρϵ − 2 /π 2 , ∀ x / ∈ D .
Revised: Given any ϵ > 0 , there is an integer L such that Θ L ( x ) > 4 ρϵ − 2 /π 2 , ∀ x / ∈ D .

Original: Define
Revised: Define

Original: [Equation]
Revised: [Equation]

Original: Note the fundamental inequality z > arctan( z ) ≥ π 2 − z − 1 , ∀ z > 0 .
Revised: Note the fundamental inequality z > arctan( z ) ≥ π 2 − z − 1 , ∀ z > 0 .

Original: Then, if x ∈ D , there holds
Revised: Then, if x ∈ D , there holds

Original: [Equation]
Revised: [Equation]

Original: if x / ∈ D , there holds
Revised: if x / ∈ D , there holds

Original: [Equation]
Revised: [Equation]

Original: Therefore, the function in (51) fulfills (50).
Revised: Therefore, the function in (51) fulfills (50).

Original: F P ROOF OF T HEOREM
Revised: F P ROOF OF T HEOREM

Original: In presenting Theorem 2 in Section 3.3, we use the term “multiplicative” to describe the fact that two latent representations are multiplied in the attention mechanism.
Revised: In presenting Theorem 2 in Section 3.3, we use the term “multiplicative” to describe the fact that two latent representations are multiplied in the attention mechanism.

Original: In contrast, no such operation exists in, e.g., a pointwise FFN or a convolution layer.
Revised: In contrast, no such operation exists in, e.g., a pointwise FFN or a convolution layer.

Original: Heuristically speaking, the main result in
Revised: Heuristically speaking, the main result in

Original: Theorem 2 states that the output latent representations can be of a higher “frequency” than the input if the neural network architecture has “multiplicative” layers in it.
Revised: Theorem 2 states that the output latent representations can be of a higher “frequency” than the input if the neural network architecture has “multiplicative” layers in it.

Original: The input latent representations are discretizations of certain functions, and they are combined using matrix dot product as the one used in attention.
Revised: The input latent representations are discretizations of certain functions, and they are combined using matrix dot product as the one used in attention.

Original: Suppose that this discretization can represent functions of such a frequency with a certain approximation error, the resulting matrix/tensor can be an approximation of a function with a higher frequency than the existing latent representation under the same discretization.
Revised: Suppose that this discretization can represent functions of such a frequency with a certain approximation error, the resulting matrix/tensor can be an approximation of a function with a higher frequency than the existing latent representation under the same discretization.

Original: Please see
Revised: Please see

Original: Figure 9 and Figure 10 for empirical evidence of this phenomenon for the latent representations: with completely smooth input (harmonic extensions), e.g., see Figure 5, attention-based learner can generate latent representations with multiple peaks and valleys.
Revised: Figure 9 and Figure 10 for empirical evidence of this phenomenon for the latent representations: with completely smooth input (harmonic extensions), e.g., see Figure 5, attention-based learner can generate latent representations with multiple peaks and valleys.

Original: Theorem 2 (Frequency-bootstrapping for multiplicative neural architectures) .
Revised: Theorem 2 (Frequency-bootstrapping for multiplicative neural architectures) .

Original: Consider Ω = (0 , π ) which has a uniform discretization of { z i } Mi =1 of size h , and v ( x ) = sin( ax ) for some a ∈ Z + .
Revised: Consider Ω = (0 , π ) which has a uniform discretization of { z i } Mi =1 of size h , and v ( x ) = sin( ax ) for some a ∈ Z + .

Original: Let
Revised: Let

Original: N := a − 1 ≥ 1 be the number of channels in the attention layer of interest, assume that (i) the current latent representation p h ∈ R M × N consists of the discretization of the first N Legendre polynomials { p j ( · ) } Nj =1 such that ( p h ) ij =
Revised: N := a − 1 ≥ 1 be the number of channels in the attention layer of interest, assume that (i) the current latent representation p h ∈ R M × N consists of the discretization of the first N Legendre polynomials { p j ( · ) } Nj =1 such that ( p h ) ij =

Original: p j ( z i ) , (ii) p h is normalized and the normalization weights α ≡ 1 in (14) , (iii) the discretization satisfies that | (cid:80) Mi
Revised: p j ( z i ) , (ii) p h is normalized and the normalization weights α ≡ 1 in (14) , (iii) the discretization satisfies that | (cid:80) Mi

Original: =1 hf ( z i ) − (cid:82) Ω f ( x ) d x | ≤ C h .
Revised: =1 hf ( z i ) − (cid:82) Ω f ( x ) d x | ≤ C h .

Original: Then, there exists a set of attention weights { W Q , W K , W V } such that for u ( x ) = sin( a ′ x ) with
Revised: Then, there exists a set of attention weights { W Q , W K , W V } such that for u ( x ) = sin( a ′ x ) with

Original: [Equation]
Revised: [Equation]

Original: where ˜ u and ˜ κ ( · , · ) are defined as the output of and the kernel of the attention formulation in (14) , respectively; ε ( x ) :
Revised: where ˜ u and ˜ κ ( · , · ) are defined as the output of and the kernel of the attention formulation in (14) , respectively; ε ( x ) :

Original: = ∥ κ ( x, · ) − ˜ κ ( x, · )
Revised: = ∥ κ ( x, · ) − ˜ κ ( x, · )

Original: ∥
Revised: ∥

Original: L 2 (Ω) is the error function for the kernel approximation.
Revised: L 2 (Ω) is the error function for the kernel approximation.

Original: Proof.
Revised: Proof.

Original: Without loss of generality, it is assumed that a ′ =
Revised: Without loss of generality, it is assumed that a ′ =

Original: a + 1 .
Revised: a + 1 .

Original: The essential technical tools used suggest the validity for any a ′ > a > 0 ( a ′ , a ∈ Z + ).
Revised: The essential technical tools used suggest the validity for any a ′ > a > 0 ( a ′ , a ∈ Z + ).

Original: Consider a simple non-separable smooth kernel function
Revised: Consider a simple non-separable smooth kernel function

Original: [Equation]
Revised: [Equation]

Original: it is straightforward to verify that for v ( x ) :
Revised: it is straightforward to verify that for v ( x ) :

Original: = sin( ax ) , we have
Revised: = sin( ax ) , we have

Original: [Equation]
Revised: [Equation]

Original: As is shown, it suffices to show that the matrix multiplication (a separable kernel) in the attention mechanism approximates this non-separable kernel with an error related to the number of channels.
Revised: As is shown, it suffices to show that the matrix multiplication (a separable kernel) in the attention mechanism approximates this non-separable kernel with an error related to the number of channels.

Original: To this end, taking the Taylor expansion, centered at a z 0 ∈ Ω of κ ( x, · ) with respect to the second variable at each x ∈ Ω , we have
Revised: To this end, taking the Taylor expansion, centered at a z 0 ∈ Ω of κ ( x, · ) with respect to the second variable at each x ∈ Ω , we have

Original: [Equation]
Revised: [Equation]

Original: [Equation]
Revised: [Equation]

Original: It is straightforward to check that
Revised: It is straightforward to check that

Original: [Equation]
Revised: [Equation]

Original: [Equation]
Revised: [Equation]

Original: By the assumptions on κ ( · , · ) , and a straightforward computation we have
Revised: By the assumptions on κ ( · , · ) , and a straightforward computation we have

Original: [Equation]
Revised: [Equation]

Original: Next, let q l ( z ) :
Revised: Next, let q l ( z ) :

Original: = ( z − z 0 ) l − 1 / ( l − 1)!
Revised: = ( z − z 0 ) l − 1 / ( l − 1)!

Original: and k l ( x ) :
Revised: and k l ( x ) :

Original: = ∂ l − 1 κ/∂z l − 1 ( x, z 0 ) for 1 ≤ l ≤ N , i.e., they form a Pincherle-Goursat (degenerate) kernels (Kress, 1999, Chapter 11)
Revised: = ∂ l − 1 κ/∂z l − 1 ( x, z 0 ) for 1 ≤ l ≤ N , i.e., they form a Pincherle-Goursat (degenerate) kernels (Kress, 1999, Chapter 11)

Original: [Equation]
Revised: [Equation]

Original: By this choice of the latent representation space being the first N Legendre polynomials, q l ∈ Y := span { p j } , thus there exists a set of weights { w Ql ∈ R l } Nl =1 corresponding to each channel, such that
Revised: By this choice of the latent representation space being the first N Legendre polynomials, q l ∈ Y := span { p j } , thus there exists a set of weights { w Ql ∈ R l } Nl =1 corresponding to each channel, such that

Original: [Equation]
Revised: [Equation]

Original: This is to say, Q = p h W Q ∈ R M × N with the l -th column of Q being the discretization of ˜ q l ( · ) .
Revised: This is to say, Q = p h W Q ∈ R M × N with the l -th column of Q being the discretization of ˜ q l ( · ) .

Original: For the key matrix, by standard polynomial approximation since Y ≃ P N (Ω) , there exists a set of weights {
Revised: For the key matrix, by standard polynomial approximation since Y ≃ P N (Ω) , there exists a set of weights {

Original: w Kl ∈ R N } Nl =1 , such that
Revised: w Kl ∈ R N } Nl =1 , such that

Original: [Equation]
Revised: [Equation]

Original: i.e., K = p h W K ∈ R M × N with the l -th column of K being the discretization of ˜ k l ( · ) .
Revised: i.e., K = p h W K ∈ R M × N with the l -th column of K being the discretization of ˜ k l ( · ) .

Original: Moreover, it can approximate k l ( · ) with the following estimate
Revised: Moreover, it can approximate k l ( · ) with the following estimate

Original: [Equation]
Revised: [Equation]

Original: Similarly, without loss of generality, we choose v ( · ) := v 1 ( · ) , which is concatenated to V such that it occupies the first channel of V defined earlier, we have { w Vl ∈ R N } such that
Revised: Similarly, without loss of generality, we choose v ( · ) := v 1 ( · ) , which is concatenated to V such that it occupies the first channel of V defined earlier, we have { w Vl ∈ R N } such that

Original: [Equation]
Revised: [Equation]

Original: and
Revised: and

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: Now, to approximate the frequency bootstrapping in (54), define
Revised: Now, to approximate the frequency bootstrapping in (54), define

Original: [Equation]
Revised: [Equation]

Original: κ N
Revised: κ N

Original: [Equation]
Revised: [Equation]

Original: x, z
Revised: x, z

Original: [Equation]
Revised: [Equation]

Original: x
Revised: x

Original: [Equation]
Revised: [Equation]

Original: x
Revised: x

Original: [Equation]
Revised: [Equation]

Original: with ˜ κ N
Revised: with ˜ κ N

Original: [Equation]
Revised: [Equation]

Original: x, z
Revised: x, z

Original: [Equation]
Revised: [Equation]

Original: Then, we have for any z ∈ Ω
Revised: Then, we have for any z ∈ Ω

Original: [Equation]
Revised: [Equation]

Original: Thus
Revised: Thus

Original: [Equation]
Revised: [Equation]

Original: we have
Revised: we have

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: Now
Revised: Now

Original: [Equation]
Revised: [Equation]

Original: by triangle inequality
Revised: by triangle inequality

Original: [Equation]
Revised: [Equation]

Original: q l = ˜ q l
Revised: q l = ˜ q l

Original: [Equation]
Revised: [Equation]

Original: the definitions above (53) implying | k l ( · ) | H N (Ω) ≤ c 2 N and ∥
Revised: the definitions above (53) implying | k l ( · ) | H N (Ω) ≤ c 2 N and ∥

Original: q l ∥ L 2 (Ω) ≤ cπ N / ( √ NN !) , and the estimate in (61)
Revised: q l ∥ L 2 (Ω) ≤ cπ N / ( √ NN !) , and the estimate in (61)

Original: [Equation]
Revised: [Equation]

Original: Notice this is the same order with the estimate of ∥ κ ( x, · ) − κ N ( x, · )
Revised: Notice this is the same order with the estimate of ∥ κ ( x, · ) − κ N ( x, · )

Original: ∥
Revised: ∥

Original: L 2 (Ω) .
Revised: L 2 (Ω) .

Original: For the term ∥ ˜ κ N ( x, · ) ∥
Revised: For the term ∥ ˜ κ N ( x, · ) ∥

Original: L 2 (Ω) , a simple triangle inequality trick can be used:
Revised: L 2 (Ω) , a simple triangle inequality trick can be used:

Original: [Equation]
Revised: [Equation]

Original: which can be further estimated by reusing the argument in (67).
Revised: which can be further estimated by reusing the argument in (67).

Original: Lastly, using the following argument and the estimate for ∥ u − ˜ u
Revised: Lastly, using the following argument and the estimate for ∥ u − ˜ u

Original: ∥ L ∞ (Ω) yield the desired result:
Revised: ∥ L ∞ (Ω) yield the desired result:

Original: [Equation]
Revised: [Equation]

Original: [Figure] [Figure]
Revised: [Figure] [Figure]

Original: [Figure] [Figure]
Revised: [Figure] [Figure]

Original: [Figure]
Revised: [Figure]

Original: ) (g) (h) (i) (j) (k) (l)
Revised: ) (g) (h) (i) (j) (k) (l)

Original: [Figure]
Revised: [Figure]

Original: [Figure] [Figure]
Revised: [Figure] [Figure]

Original: [Figure] [Figure]
Revised: [Figure] [Figure]

Original: ) (g) (h) (i) (j) (k) (l)
Revised: ) (g) (h) (i) (j) (k) (l)

Original: G L IMITATIONS , E XTENSIONS , AND F UTURE WORK
Revised: G L IMITATIONS , E XTENSIONS , AND F UTURE WORK

Original: In this study, the σ to be recovered relies on a piecewise constant assumption.
Revised: In this study, the σ to be recovered relies on a piecewise constant assumption.

Original: This assumption is commonly seen in the theoretical study of the original DSM.
Revised: This assumption is commonly seen in the theoretical study of the original DSM.

Original: For many EIT applications in medical imaging and industrial monitoring, σ may involve non-sharp transitions or even contain highly anisotropic/multiscale behaviors making it merely an L ∞ function.
Revised: For many EIT applications in medical imaging and industrial monitoring, σ may involve non-sharp transitions or even contain highly anisotropic/multiscale behaviors making it merely an L ∞ function.

Original: If the boundary data pairs are still quite limited, i.e., only a few electric modes are placed on the boundary ∂ Ω , the proposed model alone is not expected to perform as well as in benchmark problems.
Revised: If the boundary data pairs are still quite limited, i.e., only a few electric modes are placed on the boundary ∂ Ω , the proposed model alone is not expected to perform as well as in benchmark problems.

Original: Nevertheless, it can still contribute to achieving reconstruction with satisfactory accuracy, if certain a priori knowledge of the problem is accessible.
Revised: Nevertheless, it can still contribute to achieving reconstruction with satisfactory accuracy, if certain a priori knowledge of the problem is accessible.

Original: End2end-wise, our proposed method has limitations like other operator learners: the data manifold on which the operator is learned is assumed to exhibit low-dimensional/low-rank attributes.
Revised: End2end-wise, our proposed method has limitations like other operator learners: the data manifold on which the operator is learned is assumed to exhibit low-dimensional/low-rank attributes.

Original: The behavior of the operator of interest on a compact subset is assumed to be reasonably well approximated by a finite number of bases.
Revised: The behavior of the operator of interest on a compact subset is assumed to be reasonably well approximated by a finite number of bases.

Original: Therefore, for non-piecewise constant conductivities, the modification can be to employ a suitable data set, in which the sampling of { σ ( k ) } represents the true σ ’s distribution a posteriori to a certain degree.
Revised: Therefore, for non-piecewise constant conductivities, the modification can be to employ a suitable data set, in which the sampling of { σ ( k ) } represents the true σ ’s distribution a posteriori to a certain degree.

Original: However, to reconstruct non-piecewise constant conductivities, more boundary data pairs or even the entire NtD map is demanded from a theoretical perspective (Astala & Päivärinta, 2006a;b; Nachman, 1996; Kohn & Vogelius, 1984; Sylvester & Uhlmann, 1987).
Revised: However, to reconstruct non-piecewise constant conductivities, more boundary data pairs or even the entire NtD map is demanded from a theoretical perspective (Astala & Päivärinta, 2006; Nachman, 1996; Kohn & Vogelius, 1984; Sylvester & Uhlmann, 1987).

Original: For fewer data pairs and more complicated conductivity set-up, there have been efforts in this direction hierarchically using matrix completion (Bui-Thanh et al., 2022) to recover Λ σ .
Revised: For fewer data pairs and more complicated conductivity set-up, there have been efforts in this direction hierarchically using matrix completion (Bui-Thanh et al., 2022) to recover Λ σ .

Original: When Λ σ is indeed available, σ can be described by a Fredholm integral equation, see Nachman (1996, Theorem 4.1), which itself is strongly related to the modified attention mechanism (14) of the proposed Transformer.
Revised: When Λ σ is indeed available, σ can be described by a Fredholm integral equation, see Nachman (1996, Theorem 4.1), which itself is strongly related to the modified attention mechanism (14) of the proposed Transformer.

Original: The architectural resemblance may lead to future explorations in this direction.
Revised: The architectural resemblance may lead to future explorations in this direction.

Original: Optimization with regularization can be applied for the instance of interest (fine-tune) from the perspective of improving the reconstruction for a single instance.
Revised: Optimization with regularization can be applied for the instance of interest (fine-tune) from the perspective of improving the reconstruction for a single instance.

Original: This approach dates back to the classical iterative methods involving adaptivity (Jin & Xu, 2019).
Revised: This approach dates back to the classical iterative methods involving adaptivity (Jin & Xu, 2019).

Original: Recent novel DL-inspired adaptions (Li et al., 2021b; Benitez et al., 2023) re-introduce this type of method.
Revised: Recent novel DL-inspired adaptions (Li et al., 2021b; Benitez et al., 2023) re-introduce this type of method.

Original: In fine-tuning, the initial guess is the reconstruction by the operator learner trained in the end2end pipeline (pre-train).
Revised: In fine-tuning, the initial guess is the reconstruction by the operator learner trained in the end2end pipeline (pre-train).