Miracle-dz/UniFAR-FTUs huggingface dataset:
Weisfeiler-Leman in the Bamboo: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similarity

Abstract Several metrics have been proposed for assessing the similarity of (abstract) meaning representations (AMRs), but little is known about how they relate to human similarity ratings. Moreover, the current metrics have complementary strengths and weaknesses: Some emphasize speed, while others make the alignment of graph structures explicit, at the price of a costly alignment step. In this work we propose new Weisfeiler-Leman AMR similarity metrics that unify the strengths of previous metrics, while mitigating their weaknesses. Specifically, our new metrics are able to match contextualized substructures and induce n:m alignments between their nodes. Furthermore, we introduce a Benchmark for AMR Metrics based on Overt Objectives (Bamboo), the first benchmark to support empirical assessment of graph-based MR similarity metrics. Bamboo maximizes the interpretability of results by defining multiple overt objectives that range from sentence similarity objectives to stress tests that probe a metric’s robustness against meaning-altering and meaning- preserving graph transformations. We show the benefits of Bamboo by profiling previous metrics and our own metrics. Results indicate that our novel metrics may serve as a strong baseline for future work.
Robust Long-Tailed Learning under Label Noise

Long-tailed learning has attracted much attention recently, with the goal of improving generalisation for tail classes. Most existing works use supervised learning without considering the prevailing noise in the training dataset. To move long-tailed learning towards more realistic scenarios, this work investigates the label noise problem under long-tailed label distribution. We first observe the negative impact of noisy labels on the performance of existing methods, revealing the intrinsic challenges of this problem. As the most commonly used approach to cope with noisy labels in previous literature, we then find that the small-loss trick fails under long-tailed label distribution. The reason is that deep neural networks cannot distinguish correctly-labeled and mislabeled examples on tail classes. To overcome this limitation, we establish a new prototypical noise detection method by designing a distance-based metric that is resistant to label noise. Based on the above findings, we propose a robust framework,~\algo, that realizes noise detection for long-tailed learning, followed by soft pseudo-labeling via both label smoothing and diverse label guessing. Moreover, our framework can naturally leverage semi-supervised learning algorithms to further improve the generalisation. Extensive experiments on benchmark and real-world datasets demonstrate the superiority of our methods over existing baselines. In particular, our method outperforms DivideMix by 3\% in test accuracy. Source code will be released soon.
Theory for doping trends in titanium oxypnictide superconductors

A family of titanium oxypnictide materials BaTi2Pn2O (Pn = pnictogen) becomes superconducting when a charge and/or spin density wave is suppressed. With hole doping, isovalent doping and pressure, a whole range of tuning parameters is available. We investigate how charge doping controls the superconducting transition temperature Tc. To this end, we use experimental crystal structure data to determine the electronic structure and Fermi surface evolution along the doping path. We show that a naive approach to calculating Tc via the density of states at the Fermi level and the McMillan formula systematically fails to yield the observed Tc variation. On the other hand, spin fluctuation theory pairing calculations allow us to consistently explain the Tc increase with doping. All alkali doped materials Ba1−xAxTi2Sb2O (A=Na, K, Rb) are described by a sign-changing swave order parameter. Susceptibilities also reveal that the physics of the materials is controlled by a single Ti 3d orbital.
An efficient unconditionally stable method for Dirichlet partitions in arbitrary domains

A Dirichlet $k$-partition of a domain is a collection of $k$ pairwise disjoint open subsets such that the sum of their first Laplace--Dirichlet eigenvalues is minimal. In this paper, we propose a new relaxation of the problem by introducing auxiliary indicator functions of domains and develop a simple and efficient diffusion generated method to compute Dirichlet $k$-partitions for arbitrary domains. The method only alternates three steps: 1. convolution, 2. thresholding, and 3. projection. The method is simple, easy to implement, insensitive to initial guesses and can be effectively applied to arbitrary domains without any special discretization. At each iteration, the computational complexity is linear in the discretization of the computational domain. Moreover, we theoretically prove the energy decaying property of the method. Experiments are performed to show the accuracy of approximation, efficiency and unconditional stability of the algorithm. We apply the proposed algorithms on both 2- and 3-dimensional flat tori, triangle, square, pentagon, hexagon, disk, three-fold star, five-fold star, cube, ball, and tetrahedron domains to compute Dirichlet $k$-partitions for different $k$ to show the effectiveness of the proposed method. Compared to previous work with reported computational time, the proposed method achieves hundreds of times acceleration.
Circumstellar Medium Constraints on the Environment of Two Nearby Type Ia Supernovae: SN 2017cbv and SN 2020nlb

We present deep Chandra X-ray observations of two nearby Type Ia supernovae, SN 2017cbv and SN 2020nlb, which reveal no X-ray emission down to a luminosity L X ≲ 5.3 × 1037 and ≲ 5.4 × 1037 erg s−1 (0.3–10 keV), respectively, at ∼16–18 days after the explosion. With these limits, we constrain the pre-explosion mass-loss rate of the progenitor system to be Ṁ < 7.2 × 10−9 and < 9.7 × 10−9 M ⊙ yr−1 for each (at a wind velocity v w = 100 km s−1 and a radius of R ≈ 1016 cm), assuming any X-ray emission would originate from inverse Compton emission from optical photons upscattered by the supernova shock. If the supernova environment was a constant-density medium, we would find a number density limit of n CSM < 36 and < 65 cm−3, respectively. These X-ray limits rule out all plausible symbiotic progenitor systems, as well as large swathes of parameter space associated with the single degenerate scenario, such as mass loss at the outer Lagrange point and accretion winds. We also present late-time optical spectroscopy of SN 2020nlb, and set strong limits on any swept up hydrogen (L Hα < 2.7 × 1037 erg s−1) and helium (L He,λ6678 < 2.7 × 1037 erg s−1) from a nondegenerate companion, corresponding to M H ≲ 0.7–2 × 10−3 M ⊙ and M He ≲ 4 × 10−3 M ⊙. Radio observations of SN 2020nlb at 14.6 days after explosion also yield a non-detection, ruling out most plausible symbiotic progenitor systems. While we have doubled the sample of normal Type Ia supernovae with deep X-ray limits, more observations are needed to sample the full range of luminosities and subtypes of these explosions, and set statistical constraints on their circumbinary environments.
Dislocation-CNT Interactions in Aluminium at the Atomic Level

The interaction between edge dislocations and carbon nanotubes (CNTs) embedded into an Al matrix is investigated. Both pristine and Ni-coated CNTs are considered. It is demonstrated that the embedded CNTs lead to a flow stress contribution equivalent to that of an array of non-shearable inclusions that are by-passed via the Orowan mechanism. The strain hardening mechanism associated with multiple dislocation passes shows, however, characteristic differences from the Orowan picture that indicate that the embedded CNTs behave analogous to embedded voids.
A note on the Sundaram--Stanley bijection (or, Viennot for up-down tableaux)

We give a direct proof of a result of Sundaram and Stanley: that the dimension of the space of invariant vectors in a 2k-fold tensor product of the vector representation of sp2n equals the number of (n+1)-avoiding matchings of 2k points. This can be viewed as an extension of Schensted’s theorem on longest decreasing subsequences. Our main tool is an extension of Viennot’s geometric construction to the setting of up-down tableaux.
Anomalous Resistivity at Weak Coupling

Recent cold atom experiments have observed bad and strange metal behaviors in stronglyinteracting Fermi-Hubbard systems. Motivated by these results, we calculate the thermoelectric transport properties of a 2D Fermi-Hubbard system in the weak coupling limit using quantum kinetic theory. We find that many features attributed to strong correlations are also found at weak coupling. In particular, for temperatures T & t the electrical resistivity is nearly linear in temperature despite the fact that the quasiparticle scattering rate is non-linear and changes by nearly an order of magnitude. We argue that this asymptotic behavior is a general feature of systems with a finite spectral width, which implies that there is no MIR bound on the resistivity in single-band models. Due to nesting, the T -linear resistivity persists down to T = 0 at half filling. Our work sheds light on the transport regime in ultracold atom experiments, which can differ substantially from that of condensed matter systems. Disentangling these band-structure effects from the physics of strong correlations is a major challenge for future experiments.
Multiplicative functions with sum zero

Abstract CMO functions are completely multiplicative functions f for which ∞ n=1 f(n) = 0. These functions were first introduced and studied by Kahane and Säıas [5]. The main purpose of this paper is to generalise such functions to multiplicative functions and we shall call them MO functions. More precisely, we define MO functions to be multiplicative functions for which ∞ n=1 f(n) = 0 and ∞ k=0 f(p ) 6= 0 for all p ∈ P. We give some properties and find examples of MO functions, as well as pointing out the connection between these functions and the Riemann hypothesis at the end of the paper.
Fermion zero modes of Denef Bates black holes

We study the fermion zero modes of Denef Bates black holes in N = 2 Supergravity. The goldstino zero modes are constructed explicitly and their boundary conditions, charge, and normalizability are studied. We then study if there can exist fermion zero modes that are normalizable and not gauge equivalent to the goldstino modes. We find that such modes are not possible if we want to preserve the continuity of these solutions. The non-existence of these modes shows that Denef Bates black holes must contribute to the index.
Adjoints and Canonical Forms of Polypols

Polypols are natural generalizations of polytopes, with boundaries given by nonlinear algebraic hypersurfaces. We describe polypols in the plane and in 3-space that admit a unique adjoint hypersurface and study them from an algebro-geometric perspective. We relate planar polypols to positive geometries introduced originally in particle physics, and identify the adjoint curve of a planar polypol with the numerator of the canonical differential form associated with the positive geometry. We settle several cases of a conjecture by Wachspress claiming that the adjoint curve of a regular planar polypol does not intersect its interior. In particular, we provide a complete characterization of the real topology of the adjoint curve for arbitrary convex polygons. Finally, we determine all types of planar polypols such that the rational map sending a polypol to its adjoint is finite, and explore connections of our topic with algebraic statistics.
Improving HRI through robot architecture transparency

In recent years, an increased effort has been invested to improve the capabilities of robots. Nevertheless, human-robot interaction remains a complex field of application where errors occur frequently. The reasons for these errors can primarily be divided into two classes. Foremost, the recent increase in capabilities also widened possible sources of errors on the robot's side. This entails problems in the perception of the world, but also faulty behavior, based on errors in the system. Apart from that, non-expert users frequently have incorrect assumptions about the functionality and limitations of a robotic system. This leads to incompatibilities between the user's behavior and the functioning of the robot's system, causing problems on the robot's side and in the human-robot interaction. While engineers constantly improve the reliability of robots, the user's understanding about robots and their limitations have to be addressed as well. In this work, we investigate ways to improve the understanding about robots. For this, we employ FAMILIAR - FunctionAl user Mental model by Increased LegIbility ARchitecture, a transparent robot architecture with regard to the robot behavior and decision-making process. We conducted an online simulation user study to evaluate two complementary approaches to convey and increase the knowledge about this architecture to non-expert users: a dynamic visualization of the system's processes as well as a visual programming interface. The results of this study reveal that visual programming improves knowledge about the architecture. Furthermore, we show that with increased knowledge about the control architecture of the robot, users were significantly better in reaching the interaction goal. Furthermore, we showed that anthropomorphism may reduce interaction success.
A Computational Approach to Measure Empathy and Theory-of-Mind from Written Texts

Theory-of-mind (ToM), a human ability to infer the intentions and thoughts of others, is an essential part of empathetic experiences. We provide here the framework for using NLP models to measure ToM expressed in written texts. For this purpose, we introduce ToM-Diary, a crowdsourced 18,238 diaries with 74,014 Korean sentences annotated with different ToM levels. Each diary was annotated with ToM levels by trained psychology students and reviewed by selected psychology experts. The annotators first divided the diaries based on whether they mentioned other people: self-focused and other-focused. Examples of self-focused sentences are "I am feeling good". The other-focused sentences were further classified into different levels. These levels differ by whether the writer 1) mentions the presence of others without inferring their mental state(e.g., I saw a man walking down the street), 2) fails to take the perspective of others (e.g., I don't understand why they refuse to wear masks), or 3) successfully takes the perspective of others (It must have been hard for them to continue working). We tested whether state-of-the-art transformer-based models (e.g., BERT) could predict underlying ToM levels in sentences. We found that BERT more successfully detected self-focused sentences than other-focused ones. Sentences that successfully take the perspective of others (the highest ToM level) were the most difficult to predict. Our study suggests a promising direction for large-scale and computational approaches for identifying the ability of authors to empathize and take the perspective of others. The dataset is at [URL](https://github.com/humanfactorspsych/covid19-tom-empathy-diary)
Computation in a general physical setting

The computational abilities of theories within the generalised probabilistic theory framework has been the subject of much recent study. Such investigations aim to gain an understanding of the possible connections between physical principles and computation. Moreover, comparing and contrasting the computational properties of quantum theory with other operationally-sensible theories could shed light on the strengths and limitations of quantum computation. This paper reviews and extends some of these results, deriving new bounds on the computational ability of theories satisfying n-local tomography, and theories in which states are represented as generalised superpositions. It moreover provides a refined version of the conjecture that a quantum computer can simulate the computation in any theory within a certain sub-class of generalised probabilistic theories with at most polynomial overhead. The paper ends by describing an important relation between this conjecture and delegated computation, similar to the relation between quantum non-locality and device-independent cryptography.
Self-Attention for Audio Super-Resolution

Convolutions operate only locally, thus failing to model global interactions. Self-attention is, however, able to learn representations that capture long-range dependencies in sequences. We propose a network architecture for audio super-resolution that combines convolution and self-attention. Attention-based Feature-Wise Linear Modulation (AFiLM) uses self-attention mechanism instead of recurrent neural networks to modulate the activations of the convolutional model. Extensive experiments show that our model outperforms existing approaches on standard benchmarks. Moreover, it allows for more parallelization resulting in significantly faster training.
Multiple Sclerosis Lesions Identification/Segmentation in Magnetic Resonance Imaging using Ensemble CNN and Uncertainty Classification

To date, several automated strategies for identiﬁcation/segmentation of Multiple Sclerosis (MS) lesions with the use of Magnetic Resonance Imaging (MRI) have been presented but they are either outperformed by human experts or perform diﬀerently from them. This is mainly due to the ambiguity originated by MRI instabilities, peculiar variability of MS and unspeciﬁc nature of MRI with respect to MS. Physicians partially manage the uncertainty generated by ambiguity relying on their personal radiological/clinical/anatomical background and experience. We present an automated framework based on three pivotal concepts to better emulate human reasoning: 1. the modelling of uncertainty; 2. the proposal of two, separately trained, CNN, one optimized with respect to lesions themselves and the other to the environment surrounding lesions, respectively repeated for axial, coronal and sagittal directions; 3. the deﬁnition of an ensemble classiﬁer to merge the information collected by all CNN. The proposed framework is trained, validated and tested on the 2016 MSSEG benchmark public data set from a single imaging modality, the FLuid-Attenuated Inversion Recovery (FLAIR). The comparison, made with the consensus (the ground-truth) between 7 human raters and with each of the 7 human raters, proves that there is no signiﬁcant diﬀerence between the automated and the human raters. The results of our framework concerning the uncertainty are also reported, even if a comparison with the raters is impossible because they don’t recognize this class.
Attention-based Neural Load Forecasting: A Dynamic Feature Selection Approach

Encoder-decoder-based recurrent neural network (RNN) has made significant progress in sequence-to-sequence learning tasks such as machine translation and conversational models. Recent works have shown the advantage of this type of network in dealing with various time series forecasting tasks. The present paper focuses on the problem of multi-horizon short-term load forecasting, which plays a key role in the power system's planning and operation. Leveraging the encoder-decoder RNN, we develop an attention model to select the relevant features and similar temporal information adaptively. First, input features are assigned with different weights by a feature selection attention layer, while the updated historical features are encoded by a bi-directional long short-term memory (BiLSTM) layer. Then, a decoder with hierarchical temporal attention enables a similar day selection, which re-evaluates the importance of historical information at each time step. Numerical results tested on the dataset of the global energy forecasting competition 2014 show that our proposed model significantly outperforms some existing forecasting schemes.
Randomly Distributed Plasmonic Hot Spots for Multilevel Optical Storage

Multilevel optical storage is regarded as one efficient way to achieve higher capacity. In this paper, a kind of multilevel optical storage is presented by encoding the plasmonic hot spots among coupling gold nanorods (GNRs). The hot spots not only lower the recoding energy, but enhance two-photon-induced luminescence (TPL) intensity of the GNRs adjacent to hot spots significantly. From the numerical simulations based on finite-difference time-domain technique, it can be seen that the existence of hot spots expands the range of TPL intensity and makes a steeper function of TPL intensity distribution than isolated GNRs. The multilevel optical storage is performed experimentally in the GNR–poly(vinyl alcohol) (PVA) films with optical density (OD) = 3, 12, and 24. The six-level optical storage with high quality could be fulfilled with ultralow energy of only a few picojoule per pulse in the GNR–PVA films of OD = 12. This work can be the building blocks for the cold data storage in Big Data era.
Effect of Some Process Parameters on the Properties of Edible Film Produced from Lizard Fish (Saurida undosquamis) Muscle

Abstract This study was conducted to investigate the effect of some process parameters on the properties of cast edible protein film produced from lizard fish muscle (Saurida undosquamis) and the effects of pH (2.5, 3.0 and 3.5), heating temperature (50, 60 and 70 o C), and heating time (10, 20 and 30 min) of the film-solution on various film properties. These were determined using the Response Surface Methodology (RSM). For all types of films, tensile strength (TS), percentage of elongation (E), water vapor permeability (WVP) and Hunter colour values (L*, a * and b*) after conditioning at 60% RH and 27 + 2 oC for 72 hrs, and film (FS) and protein solubility (PS) after immersion in water at 25 oC for 24 hrs, were measured. The impact of pH and the heating temperature of the film-solution was more significant, overall, on the film’s properties than the heating time. Contour plots of TS and E were highest at pH of 10.0 at 70 oC (2.75-3.02 MPa) but low in E (6.35-9.16%), while WVP was at its lowest (58.55-65.96 g.mm/m2.d.kPa). There was a direct correlation between the FS on one hand, and heating temperature of film-solution on the other, which reversed with change in the pH of film-solution. Film color was darker and more yellowish with an increase in the pH of film-solution.