wanyu/IteraTeR_v2 huggingface dataset:
Original:  We show that the proposed Hybrid-Net achieves over 10\% improvement in classification accuracy over binary networks such as XNOR-Net for ResNet and VGG architectures on CIFAR-100 and ImageNet datasets while still achieving upto 94\% of the energy-efficiency of XNOR-Nets.
Revised:  We show that the proposed Hybrid-Net achieves a more than 10\% improvement in classification accuracy over binary networks such as XNOR-Net for ResNet and VGG architectures on CIFAR-100 and ImageNet datasets while still achieving upto 94\% of the energy-efficiency of XNOR-Nets.
Edit intent: fluency
Original:  We show that the proposed Hybrid-Net achieves over 10\% improvement in classification accuracy over binary networks such as XNOR-Net for ResNet and VGG architectures on CIFAR-100 and ImageNet datasets while still achieving upto 94\% of the energy-efficiency of XNOR-Nets.
Revised:  We show that the proposed Hybrid-Net achieves over 10\% improvement in classification accuracy over binary networks such as XNOR-Net for ResNet and VGG architectures on CIFAR-100 and ImageNet datasets while still achieving up to 94\% of the energy-efficiency of XNOR-Nets.
Edit intent: fluency
Original:  The proposed hybrid networks further the feasibility of using highly compressed neural networks for energy-efficient neural computing in IOT-based edge devices.
Revised:  The proposed hybrid networks further the feasibility of using highly compressed neural networks for energy-efficient neural computing in edge devices.
Edit intent: clarity
Original: Hyperdimensional computing (HDC) is an emerging computing framework that takes inspiration from attributes of neuronal circuits such as hyperdimensionality, fully distributed holographic representation, and (pseudo)randomness.
Revised: Hyperdimensional computing (HDC) is an emerging computational framework that takes inspiration from attributes of neuronal circuits such as hyperdimensionality, fully distributed holographic representation, and (pseudo)randomness.
Edit intent: fluency
Original:  Here, we present a complete in-memory HDC system that achieves a near-optimum trade-off between design complexity and classification accuracy based on three prototypical HDC related learning tasks, namely, language classification, news classification, and hand gesture recognition from electromyography signals.
Revised:  Here, we present a complete in-memory HDC system that achieves a near optimum trade-off between design complexity and classification accuracy based on three prototypical HDC related learning tasks, namely, language classification, news classification, and hand gesture recognition from electromyography signals.
Edit intent: fluency
Original:  We address this question by systematically evaluating neural network architectures featuring combinations of these connections on two synthetic visual tasks, which stress low-level `gestalt' vs. high-level object cues for perceptual grouping.
Revised:  We address this question by systematically evaluating neural network architectures featuring combinations of these connections on two synthetic visual tasks, which stress low-level "Gestalt" vs. high-level object cues for perceptual grouping.
Edit intent: fluency
Original:  We show that increasing the difficulty of either task strains learning for networks that rely solely on bottom-up processing .
Revised:  We show that increasing the difficulty of either task strains learning for networks that rely solely on bottom-up connections .
Edit intent: clarity
Original:  Horizontal connections resolve this limitation on tasks with gestalt cues by supporting incremental spatial propagation of activities , whereas top-down connections rescue learning on tasks featuring object cues by propagating coarse predictions about the position of the target object.
Revised:  Horizontal connections resolve straining on tasks with gestalt cues by supporting incremental spatial propagation of activities , whereas top-down connections rescue learning on tasks featuring object cues by propagating coarse predictions about the position of the target object.
Edit intent: clarity
Original:  Horizontal connections resolve this limitation on tasks with gestalt cues by supporting incremental spatial propagation of activities , whereas top-down connections rescue learning on tasks featuring object cues by propagating coarse predictions about the position of the target object.
Revised:  Horizontal connections resolve this limitation on tasks with Gestalt cues by supporting incremental spatial propagation of activities , whereas top-down connections rescue learning on tasks featuring object cues by propagating coarse predictions about the position of the target object.
Edit intent: fluency
Original:  Horizontal connections resolve this limitation on tasks with gestalt cues by supporting incremental spatial propagation of activities , whereas top-down connections rescue learning on tasks featuring object cues by propagating coarse predictions about the position of the target object.
Revised:  Horizontal connections resolve this limitation on tasks with gestalt cues by supporting incremental grouping , whereas top-down connections rescue learning on tasks featuring object cues by propagating coarse predictions about the position of the target object.
Edit intent: clarity
Original:  Horizontal connections resolve this limitation on tasks with gestalt cues by supporting incremental spatial propagation of activities , whereas top-down connections rescue learning on tasks featuring object cues by propagating coarse predictions about the position of the target object.
Revised:  Horizontal connections resolve this limitation on tasks with gestalt cues by supporting incremental spatial propagation of activities , whereas top-down connections rescue learning on tasks featuring object cues by modifying coarse predictions about the position of the target object.
Edit intent: clarity
Original:  Our findings disassociate the computational roles of bottom-up, horizontal and top-down connectivity, and demonstrate how a model featuring all of these interactions can more flexibly learn to form perceptual groups.
Revised:  Our findings dissociate the computational roles of bottom-up, horizontal and top-down connectivity, and demonstrate how a model featuring all of these interactions can more flexibly learn to form perceptual groups.
Edit intent: fluency
Original:  This decline in legislative effectiveness has occurred in parallel with rising levels of political polarization, where coordination occurs primarily within  not between groups, which are often defined by political party affiliation.
Revised:  This decline in legislative effectiveness has occurred in parallel with rising levels of political polarization, where coordination occurs primarily within and not between groups, which are often defined by political party affiliation.
Edit intent: clarity
Original:  Substantively, we use the partisanship of such coalitions in the US Congress since 1979 to examine the impact of polarization on rates of bill passage.
Revised:  Substantively, we use the partisanship of such coalitions in the US Congress since 1979 and examine the impact of polarization on rates of bill passage.
Edit intent: coherence
Original:  In particular, our analysis allows for curved element shapes, without the use of (iso-)parametric elemental maps.
Revised:  In particular, our analysis allows for curved element shapes, without the use of non-linear elemental maps.
Edit intent: clarity
Original:  The feasibility of the method relies on the definition of a suitable choice of the discontinuity-penal-ization parameter , which turns out to be essentially independent on the particular element shape  . A priori error bounds for the resulting method are given under very mild structural assumptions restricting the magnitude of the local curvature of element boundaries.
Revised:  The feasibility of the method relies on the definition of a suitable choice of the discontinuity penalization , which turns out to be essentially independent on the particular element shape . A priori error bounds for the resulting method are given under very mild structural assumptions restricting the magnitude of the local curvature of element boundaries.
Edit intent: clarity
Original:  The feasibility of the method relies on the definition of a suitable choice of the discontinuity-penal-ization parameter , which turns out to be essentially independent on the particular element shape  . A priori error bounds for the resulting method are given under very mild structural assumptions restricting the magnitude of the local curvature of element boundaries.
Revised:  The feasibility of the method relies on the definition of a suitable choice of the discontinuity-penal-ization parameter , which turns out to be explicitly dependent on the particular element shape . A priori error bounds for the resulting method are given under very mild structural assumptions restricting the magnitude of the local curvature of element boundaries.
Edit intent: clarity
Original: The essential task of multi-dimensional data analysis focuses on the tensor decomposition and the corresponding notion of rank.
Revised: The essential task of tensor data analysis focuses on the tensor decomposition and the corresponding notion of rank.
Edit intent: clarity
Original:  In this paper, by introducing the notion of tensor singular value decomposition (t-SVD), we establish a regularized tensor nuclear norm minimization (RTNNM) model for low-tubal-rank tensor recovery.
Revised:  In this paper, by introducing the notion of tensor Singular Value Decomposition (t-SVD), we establish a regularized tensor nuclear norm minimization (RTNNM) model for low-tubal-rank tensor recovery.
Edit intent: fluency
Original:  In this paper, by introducing the notion of tensor singular value decomposition (t-SVD), we establish a regularized tensor nuclear norm minimization (RTNNM) model for low-tubal-rank tensor recovery.
Revised:  In this paper, by introducing the notion of tensor singular value decomposition (t-SVD), we establish a Regularized Tensor Nuclear Norm Minimization (RTNNM) model for low-tubal-rank tensor recovery.
Edit intent: fluency
Original:  On the other hand, many variants of the restricted isometry property (RIP) have proven to be crucial frameworks and analysis tools for recovery of sparse vectors and low-rank tensors.
Revised:  As we know that many variants of the restricted isometry property (RIP) have proven to be crucial frameworks and analysis tools for recovery of sparse vectors and low-rank tensors.
Edit intent: coherence
Original:  On the other hand, many variants of the restricted isometry property (RIP) have proven to be crucial frameworks and analysis tools for recovery of sparse vectors and low-rank tensors.
Revised:  On the other hand, many variants of the Restricted Isometry Property (RIP) have proven to be crucial frameworks and analysis tools for recovery of sparse vectors and low-rank tensors.
Edit intent: fluency
Original:  On the other hand, many variants of the restricted isometry property (RIP) have proven to be crucial frameworks and analysis tools for recovery of sparse vectors and low-rank tensors.
Revised:  On the other hand, many variants of the restricted isometry property (RIP) have proven to be crucial analysis tools for recovery of sparse vectors and low-rank tensors.
Edit intent: clarity
Original:  So , we initiatively define a novel tensor restricted isometry property (t-RIP)
Revised:  So , we initiatively define a novel tensor Restricted Isometry Property (t-RIP)
Edit intent: fluency
Original:  our theoretical results show that any third-order tensor \mathcal{X} \in\mathbb{R^{n_{1}\times n_{2}\times n_{3}} whose tubal rank is at most r } can stably be recovered from its as few as measurements =%DIFDELCMD < \mathfrak{M}%%% (%DIFDELCMD < \boldsymbol{\mathcal{X}}%%% )+%DIFDELCMD < \boldsymbol{w} %%% with a bounded noise constraint \|%DIFDELCMD < \boldsymbol{w}%%% \|_{2\leq\epsilon via the RTNNM model,
Revised:  our theoretical results show that any third-order tensor \mathcal{X} ^{n_{1}\times n_{2}\times n_{3}} whose tubal rank is at most r } can stably be recovered from its as few as measurements =%DIFDELCMD < \mathfrak{M}%%% (%DIFDELCMD < \boldsymbol{\mathcal{X}}%%% )+%DIFDELCMD < \boldsymbol{w} %%% with a bounded noise constraint \|%DIFDELCMD < \boldsymbol{w}%%% \|_{2\leq\epsilon via the RTNNM model,
Edit intent: clarity
Original:  our theoretical results show that any third-order tensor \mathcal{X} \in\mathbb{R^{n_{1}\times n_{2}\times n_{3}} whose tubal rank is at most r } can stably be recovered from its as few as measurements =%DIFDELCMD < \mathfrak{M}%%% (%DIFDELCMD < \boldsymbol{\mathcal{X}}%%% )+%DIFDELCMD < \boldsymbol{w} %%% with a bounded noise constraint \|%DIFDELCMD < \boldsymbol{w}%%% \|_{2\leq\epsilon via the RTNNM model,
Revised:  our theoretical results show that any third-order tensor \mathcal{X} \in\mathbb{R^{n_{1}\times n_{2}\times n_{3}} whose tubal rank is at most r } can stably be recovered from its as few as measurements %DIFDELCMD < \mathfrak{M}%%% (%DIFDELCMD < \boldsymbol{\mathcal{X}}%%% )+%DIFDELCMD < \boldsymbol{w} %%% with a bounded noise constraint \|%DIFDELCMD < \boldsymbol{w}%%% \|_{2\leq\epsilon via the RTNNM model,
Edit intent: coherence
Original:  our theoretical results show that any third-order tensor \mathcal{X} \in\mathbb{R^{n_{1}\times n_{2}\times n_{3}} whose tubal rank is at most r } can stably be recovered from its as few as measurements =%DIFDELCMD < \mathfrak{M}%%% (%DIFDELCMD < \boldsymbol{\mathcal{X}}%%% )+%DIFDELCMD < \boldsymbol{w} %%% with a bounded noise constraint \|%DIFDELCMD < \boldsymbol{w}%%% \|_{2\leq\epsilon via the RTNNM model,
Revised:  our theoretical results show that any third-order tensor \mathcal{X} \in\mathbb{R^{n_{1}\times n_{2}\times n_{3}} whose tubal rank is at most r } can stably be recovered from its as few as measurements =%DIFDELCMD < \mathfrak{M}%%% %DIFDELCMD < \boldsymbol{\mathcal{X}}%%% )+%DIFDELCMD < \boldsymbol{w} %%% with a bounded noise constraint \|%DIFDELCMD < \boldsymbol{w}%%% \|_{2\leq\epsilon via the RTNNM model,
Edit intent: fluency
Original:  our theoretical results show that any third-order tensor \mathcal{X} \in\mathbb{R^{n_{1}\times n_{2}\times n_{3}} whose tubal rank is at most r } can stably be recovered from its as few as measurements =%DIFDELCMD < \mathfrak{M}%%% (%DIFDELCMD < \boldsymbol{\mathcal{X}}%%% )+%DIFDELCMD < \boldsymbol{w} %%% with a bounded noise constraint \|%DIFDELCMD < \boldsymbol{w}%%% \|_{2\leq\epsilon via the RTNNM model,
Revised:  our theoretical results show that any third-order tensor \mathcal{X} \in\mathbb{R^{n_{1}\times n_{2}\times n_{3}} whose tubal rank is at most r } can stably be recovered from its as few as measurements =%DIFDELCMD < \mathfrak{M}%%% (%DIFDELCMD < \boldsymbol{\mathcal{X}}%%% %DIFDELCMD < \boldsymbol{w} %%% with a bounded noise constraint \|%DIFDELCMD < \boldsymbol{w}%%% \|_{2\leq\epsilon via the RTNNM model,
Edit intent: coherence
Original:  our theoretical results show that any third-order tensor \mathcal{X} \in\mathbb{R^{n_{1}\times n_{2}\times n_{3}} whose tubal rank is at most r } can stably be recovered from its as few as measurements =%DIFDELCMD < \mathfrak{M}%%% (%DIFDELCMD < \boldsymbol{\mathcal{X}}%%% )+%DIFDELCMD < \boldsymbol{w} %%% with a bounded noise constraint \|%DIFDELCMD < \boldsymbol{w}%%% \|_{2\leq\epsilon via the RTNNM model,
Revised:  our theoretical results show that any third-order tensor \mathcal{X} \in\mathbb{R^{n_{1}\times n_{2}\times n_{3}} whose tubal rank is at most r } can stably be recovered from its as few as measurements =%DIFDELCMD < \mathfrak{M}%%% (%DIFDELCMD < \boldsymbol{\mathcal{X}}%%% )+%DIFDELCMD < \boldsymbol{w} %%% with a bounded noise constraint \|%DIFDELCMD < \boldsymbol{w}%%% \leq\epsilon via the RTNNM model,
Edit intent: clarity
Original:  if the linear map }%DIFDELCMD < \boldsymbol{\mathfrak{M}} %%% obeys t-RIP with%DIFDELCMD < \begin{equation*} %DIFDELCMD < \delta_{tr}^{\boldsymbol{\mathfrak{M}}}<\sqrt{\frac{t-1}{n_{3}^{2}+t-1}} %DIFDELCMD < \end{equation*} %%% for certain fixed t>1.
Revised:  if the linear map }%DIFDELCMD < \boldsymbol{\mathfrak{M}} %%% %DIFDELCMD < \begin{equation*} %DIFDELCMD < \delta_{tr}^{\boldsymbol{\mathfrak{M}}}<\sqrt{\frac{t-1}{n_{3}^{2}+t-1}} %DIFDELCMD < \end{equation*} %%% for certain fixed t>1.
Edit intent: clarity
Original:  Then, we develop a data structure for fair similarity search under inner product , which requires nearly-linear space and exploits locality sensitive filters  .
Revised:  Then, we develop a data structure for fair similarity search under inner product that requires nearly-linear space and exploits locality sensitive filters .
Edit intent: coherence
Original:  Yet, semantic heterogeneity remains a problem when interoperating  data from sources of different scopes and knowledge domains.
Revised:  Yet, semantic heterogeneity remains a problem when interoperating with data from sources of different scopes and knowledge domains.
Edit intent: fluency
Original:  Causes for this challenge are context-specific requirements (i.e. no "one model fits all"), different  modelling decisions, domain purpose , and technical constraints.
Revised:  Causes for this challenge are context-specific requirements (i.e. no "one model fits all"), different data modelling decisions, domain purpose , and technical constraints.
Edit intent: clarity
Original:  Causes for this challenge are context-specific requirements (i.e. no "one model fits all"), different  modelling decisions, domain purpose , and technical constraints.
Revised:  Causes for this challenge are context-specific requirements (i.e. no "one model fits all"), different modelling decisions, domain-specific purposes , and technical constraints.
Edit intent: fluency
Original:  Moreover, even if the problem of semantic heterogeneity among different RDF publishers and knowledge domains is mitigated or solved, querying and accessing the data of multiple distributed RDF datasets on the web is not straightforward.
Revised:  Moreover, even if the problem of semantic heterogeneity among different RDF publishers and knowledge domains is solved, querying and accessing the data of multiple distributed RDF datasets on the web is not straightforward.
Edit intent: clarity
Original:  Moreover, even if the problem of semantic heterogeneity among different RDF publishers and knowledge domains is mitigated or solved, querying and accessing the data of multiple distributed RDF datasets on the web is not straightforward.
Revised:  Moreover, even if the problem of semantic heterogeneity among different RDF publishers and knowledge domains is mitigated or solved, querying and accessing the data of distributed RDF datasets on the web is not straightforward.
Edit intent: clarity
Original:  Moreover, even if the problem of semantic heterogeneity among different RDF publishers and knowledge domains is mitigated or solved, querying and accessing the data of multiple distributed RDF datasets on the web is not straightforward.
Revised:  Moreover, even if the problem of semantic heterogeneity among different RDF publishers and knowledge domains is mitigated or solved, querying and accessing the data of multiple distributed RDF datasets on the Web is not straightforward.
Edit intent: fluency
Original:  This is because of the complex and fastidious process needed to understand how the data are structured and how these datasets can be related or linked, and consequently, queried.
Revised:  This is because of the complex and fastidious process needed to understand how these datasets can be related or linked, and consequently, queried.
Edit intent: clarity
Original:  To address these issues , we propose to extend the existing Vocabulary of Interlinked Datasets (VoID) .
Revised:  To address this issue , we propose to extend the existing Vocabulary of Interlinked Datasets (VoID) .
Edit intent: fluency
Original:  Semantic relaxation is also considered when defining commonality between resources.
Edit intent: coherence
Original:  The links are required in order to understand how to semantically relate the datasets.
Revised:  The links are required in order to understand how to semantically relate datasets.
Edit intent: clarity
Original:  In addition, we argue several benefits of using virtual links to improve semantic interoperability between unlike datasets.
Revised:  In addition, we describe several benefits of using virtual links to improve semantic interoperability between unlike datasets.
Edit intent: clarity
Original:  Finally, we applied them to multiple worldwide used RDF datasets in life sciences .
Revised:  Finally, we exemplify and apply our approach to multiple world-wide used RDF datasets in life sciences .
Edit intent: clarity
Original:  Finally, we applied them to multiple worldwide used RDF datasets in life sciences .
Revised:  Finally, we applied them to multiple worldwide used RDF datasets .
Edit intent: clarity
Original:  However, most existing model serving systems approach ML inference as an extension of conventional data serving workloads and miss critical opportunities for performance .
Revised:  However, they typically use techniques designed for conventional data serving workloads and miss critical opportunities for performance .
Edit intent: clarity
Original:  However, most existing model serving systems approach ML inference as an extension of conventional data serving workloads and miss critical opportunities for performance .
Revised:  However, most existing model serving systems approach ML inference as an extension of conventional data serving workloads , missing critical opportunities to leverage the statistical nature of ML inference .
Edit intent: clarity
Original:  In this paper, we present Willump, a statistically-aware optimizer for ML inference that takes advantage of key properties of ML inference not shared by traditional workloads .
Revised:  In this paper, we present Willump, an optimizer for ML inference that takes advantage of key properties of ML inference not shared by traditional workloads .
Edit intent: clarity
Original:  Willump combines these novel optimizations with standard compiler optimizations and a computation graph-aware feature caching scheme to automatically generate fast inference code for ML pipelines .
Revised:  Willump combines these novel optimizations with powerful compiler optimizations to automatically generate fast inference code for ML pipelines .
Edit intent: clarity
Original:  Willump combines these novel optimizations with standard compiler optimizations and a computation graph-aware feature caching scheme to automatically generate fast inference code for ML pipelines .
Revised:  Willump combines these novel optimizations with standard compiler optimizations and a computation graph-aware feature caching scheme to automatically generate fast inference code for ML applications .
Edit intent: clarity
Original: Systems for performing ML inference are widely deployed today .
Revised: Systems for ML inference are widely deployed today .
Edit intent: clarity
Original:  However, they typically use techniques designed for conventional data serving workloads , missing critical opportunities to leverage the statistical nature of ML inference .
Revised:  However, they typically use techniques designed for conventional data serving workloads and miss critical opportunities to leverage the statistical nature of ML inference .
Edit intent: coherence
Original:  However, they typically use techniques designed for conventional data serving workloads , missing critical opportunities to leverage the statistical nature of ML inference .
Revised:  However, they typically use techniques designed for conventional data serving workloads , missing critical opportunities to leverage the statistical nature of ML .
Edit intent: clarity