wanyu/IteraTeR_v2 huggingface dataset:
Original:  In recent years, in order to mine the topology information of graph-structured data in wireless network as well as contextual information, graph neural networks  have been introduced and have achieved the state-of-the-art performance of a series of wireless network problems .
Revised:  In recent years, in order to mine the topology information of graph-structured data in wireless network as well as contextual information, graph neural networks have been introduced and have achieved the state-of-the-art performance of a series of optimization problems of wireless networks .
Edit intent: clarity
Original:  In this review , we first  simply introduce the progress of several classical paradigms , such as graph convolutional neural networks, graph attention networks, graph auto-encoder, graph recurrent networks, graph reinforcement learning and spatial-temporal graph neural networks, of graph neural networks comprehensively .
Revised:  In this overview , we first simply introduce the progress of several classical paradigms , such as graph convolutional neural networks, graph attention networks, graph auto-encoder, graph recurrent networks, graph reinforcement learning and spatial-temporal graph neural networks, of graph neural networks comprehensively .
Edit intent: clarity
Original:  In this review , we first  simply introduce the progress of several classical paradigms , such as graph convolutional neural networks, graph attention networks, graph auto-encoder, graph recurrent networks, graph reinforcement learning and spatial-temporal graph neural networks, of graph neural networks comprehensively .
Revised:  In this review , we first simply introduce the progress of several classical paradigms of GNNs .
Edit intent: clarity
Original:  Then, several applications of graph neural networks in wireless networks such as power control, link scheduling, channel control, wireless traffic prediction, vehicular communication, point cloud, etc., are discussed in detail.
Revised:  Then, several applications of GNNs in wireless networks such as power control, link scheduling, channel control, wireless traffic prediction, vehicular communication, point cloud, etc., are discussed in detail.
Edit intent: clarity
Original:  Finally, some research trends about the applications of graph neural networks in wireless networks are discussed.
Revised:  Finally, some research trends about the applications of GNNs in wireless communication systems are discussed.
Edit intent: clarity
Original:  Here, we propose a mechanism of long-range force propagation through the extracellular matrix.
Revised:  Here, we investigate a multi-layer system where a layer of epithelial cells is seeded upon an elastic substrate in contact with a hard surface. Our experiments show that, upon a cell extrusion event in the cellular layer, long-range force propagation through the extracellular matrix.
Edit intent: coherence
Original:  These results infer that the tissue can interact with embedding substrate with weakly adhesive structures to precisely transmit long-range forces for the regulation of a variety of cellular behaviors .
Revised:  These results highlight the importance of interfacial friction between layers in transmitting mechanical cues in tissues in vivo .
Edit intent: clarity
Original: In this paper , we put forward a brand-new idea of masking the algebraic structure of linear codes used in code-based cryptography.
Revised: This paper presents a brand-new idea of masking the algebraic structure of linear codes used in code-based cryptography.
Edit intent: clarity
Original:  Note that F_{q^m} can be viewed as an F_q-linear space of dimension m, a semilinear transformation \varphi is therefore defined to be an F_q-linear transformation over F_{q^m}.
Revised:  Note that F_{q^m} can be viewed as an F_q-linear space of dimension m, a semilinear transformation \varphi is therefore defined to be an F_q-linear automorphism of F_{q^m}.
Edit intent: clarity
Original:  Then we impose this transformation to a linear code C over F_{q^m}.
Revised:  After that, we impose this transformation to a linear code C over F_{q^m}.
Edit intent: coherence
Original: This paper presents a brand-new idea of masking the algebraic structure of linear codes used in code-based cryptography.
Revised: This paper presents a new technique for disturbing the algebraic structure of linear codes used in code-based cryptography.
Edit intent: clarity
Original: This paper presents a brand-new idea of masking the algebraic structure of linear codes used in code-based cryptography.
Revised: This paper presents a brand-new idea of masking the algebraic structure of linear codes in code-based cryptography.
Edit intent: coherence
Original:  Specially , we introduce the so-called semilinear transformations in coding theory , make a thorough study on their algebraic properties and then creatively apply them to the construction of code-based cryptosystems.
Revised:  Specifically , we introduce the so-called semilinear transformations in coding theory , make a thorough study on their algebraic properties and then creatively apply them to the construction of code-based cryptosystems.
Edit intent: fluency
Original:  Specially , we introduce the so-called semilinear transformations in coding theory , make a thorough study on their algebraic properties and then creatively apply them to the construction of code-based cryptosystems.
Revised:  Specially , we introduce the so-called semilinear transformations in coding theory and then creatively apply them to the construction of code-based cryptosystems.
Edit intent: coherence
Original:  Note that F_{q^m} can be viewed as an F_q-linear space of dimension m, a semilinear transformation \varphi is therefore defined to be an F_q-linear automorphism of F_{q^m}.
Revised:  Note that F_{q^m} can be viewed as an F_q-linear space of dimension m, a semilinear transformation \varphi is therefore defined as an F_q-linear automorphism of F_{q^m}.
Edit intent: fluency
Original:  After that, we impose this transformation to a linear code C over F_{q^m}.
Revised:  Then we impose this transformation to a linear code C over F_{q^m}.
Edit intent: coherence
Original:  Apparently \varphi(\mathcal{C}) forms an \mathbb{F}_q-linear space, but generally does not preserve the \mathbb{F}_{q^m}-linearity according to our analysis .
Revised:  It is clear that \varphi(\mathcal{C}) forms an \mathbb{F}_q-linear space, but generally does not preserve the \mathbb{F}_{q^m}-linearity according to our analysis .
Edit intent: coherence
Original:  Apparently \varphi(\mathcal{C}) forms an \mathbb{F}_q-linear space, but generally does not preserve the \mathbb{F}_{q^m}-linearity according to our analysis .
Revised:  Apparently \varphi(\mathcal{C}) forms an \mathbb{F}_q-linear space, but generally does not preserve the \mathbb{F}_{q^m}-linearity any longer .
Edit intent: clarity
Original:  Meanwhile, we endow the secret code with the so-called partial cyclic structure to make a reduction in public-key size.
Revised:  Meanwhile, we endow the secret code with the so-called partial cyclic structure to reduce the public-key size.
Edit intent: coherence
Original:  For instance, 1058 bytes are enough to reach the security of 256 bits, almost 1000 times smaller than that of the Classic McEliece entering the third round of the NIST PQC project.
Revised:  For instance, 1058 bytes are enough to achieve the security of 256 bits, almost 1000 times smaller than that of the Classic McEliece entering the third round of the NIST PQC project.
Edit intent: clarity
Original:  For instance, 1058 bytes are enough to reach the security of 256 bits, almost 1000 times smaller than that of the Classic McEliece entering the third round of the NIST PQC project.
Revised:  For instance, 1058 bytes are enough to reach the security of 256 bits, almost 1000 times smaller than that of Classic McEliece entering the third round of the NIST PQC project.
Edit intent: fluency
Original:  However, this is impractical in the context of robotics and building  time-invariant solutions is of crucial importance.
Revised:  However, this is impractical in the context of robotics and building , time-invariant solutions is of crucial importance.
Edit intent: fluency
Original: Open Source Software  plays an important role in the digital economy.
Revised: Open Source Software (OSS) plays an important role in the digital economy.
Edit intent: clarity
Original:  Yet although software production is amenable to remote collaboration and its end products are easily shared across distances, software development seems to cluster geographically in places such as Silicon Valley, London, or Berlin.
Revised:  Yet although software production is amenable to remote collaboration and its outputs are easily shared across distances, software development seems to cluster geographically in places such as Silicon Valley, London, or Berlin.
Edit intent: clarity
Original:  And while recent work indicates that positive effects of open source software production accrue locally through knowledge spillovers and information effects, up-to-date data on the geographic distribution of active open source developers remains limited.
Revised:  And while recent work indicates that OSS activity creates positive externalities which accrue locally through knowledge spillovers and information effects, up-to-date data on the geographic distribution of active open source developers remains limited.
Edit intent: clarity
Original:  Here we analyze the geographic distribution of more than half a million active contributors to GitHub located in early 2021 at various spatial scales.
Revised:  Here we analyze the geographic distribution of more than half a million active contributors to GitHub in early 2021 at various spatial scales.
Edit intent: clarity
Original:  Comparing our data with results from before 2010, we find a significant increase in the relative share of developers based in Asia, Latin America and Eastern Europe, suggesting a more even spread of OSS developers globally.
Revised:  Compared to results from 2010, we find a significant increase in the relative share of developers based in Asia, Latin America and Eastern Europe, suggesting a more even spread of OSS developers globally.
Edit intent: clarity
Original:  Comparing our data with results from before 2010, we find a significant increase in the relative share of developers based in Asia, Latin America and Eastern Europe, suggesting a more even spread of OSS developers globally.
Revised:  Comparing our data with results from before 2010, we find a significant increase in the share of developers based in Asia, Latin America and Eastern Europe, suggesting a more even spread of OSS developers globally.
Edit intent: clarity
Original:  Within countries, however, we find significant concentration in regions, exceeding by some margin the concentration of workers in high-tech fields.
Revised:  Within countries, however, we find significant concentration in regions, exceeding the concentration of workers in high-tech fields.
Edit intent: clarity
Original: The consequences of disruptions in railway traffic are the primary cause of passengers' dissatisfaction.
Revised: As consequences of disruptions in railway traffic are the primary cause of passengers' dissatisfaction.
Edit intent: fluency
Original:  Hence , appropriate dispatching decisions are necessary (e. g., by assigning the order of trains ) , given the numerous restrictions of traffic nature.
Revised:  Hence , appropriate dispatching decisions are necessary . The problem of optimal scheduling the order of trains ) , given the numerous restrictions of traffic nature.
Edit intent: clarity
Original:  This paper outlines QUBO (quadratic unconstrained binary optimization) and HOBO (higher-order binary optimization)  representations for dispatching problems of railway traffic management . Specifically, we consider minimal span between trains, minimal stay on stations, station/ track occupation, and rolling stock circulation.
Revised:  This paper outlines QUBO (quadratic unconstrained binary optimization) or HOBO (higher-order binary optimization) representations for dispatching problems of railway traffic management . Specifically, we consider minimal span between trains, minimal stay on stations, station/ track occupation, and rolling stock circulation.
Edit intent: fluency
Original:  This paper outlines QUBO (quadratic unconstrained binary optimization) and HOBO (higher-order binary optimization)  representations for dispatching problems of railway traffic management . Specifically, we consider minimal span between trains, minimal stay on stations, station/ track occupation, and rolling stock circulation.
Revised:  This paper outlines QUBO (quadratic unconstrained binary optimization) and HOBO (higher-order binary optimization) formulation. This paper introduces QUBO and HOBO representations for dispatching problems of railway traffic management . Specifically, we consider minimal span between trains, minimal stay on stations, station/ track occupation, and rolling stock circulation.
Edit intent: coherence
Original:  This paper outlines QUBO (quadratic unconstrained binary optimization) and HOBO (higher-order binary optimization)  representations for dispatching problems of railway traffic management . Specifically, we consider minimal span between trains, minimal stay on stations, station/ track occupation, and rolling stock circulation.
Revised:  This paper outlines QUBO (quadratic unconstrained binary optimization) and HOBO (higher-order binary optimization) representations for dispatching problems of railway traffic management . Specifically, we consider minimal span between trains, minimal stay on stations, track occupation, and rolling stock circulation.
Edit intent: clarity
Original: In this article, we introduce the novel concept of samplets by transferring the construction of Tausch-White wavelets to the realm of data.
Revised: In this article, we introduce the concept of samplets by transferring the construction of Tausch-White wavelets to the realm of data.
Edit intent: clarity
Original: Electrical energy consumption data accessibility for low voltage (LV) end users is one of the pillars of smart grids.
Revised: Electrical energy consumption data accessibility for low voltage end users is one of the pillars of smart grids.
Edit intent: clarity
Original:  In some countries, despite the presence of smart meters (SMs) , a fragmentary data availability  hinders the creation of post-metering value-added services and confines such innovative solutions to the prototypal and experimental level.
Revised:  In some countries, despite the presence of smart meters , a fragmentary data availability hinders the creation of post-metering value-added services and confines such innovative solutions to the prototypal and experimental level.
Edit intent: clarity
Original:  This article focuses on a specific approach adopted in Italy by the national regulatory authority (NRA) and the distribution system operators (DSOs): SMs are enabled to convey data to LV end users through a power line communication (PLC) channel (chain 2) in near real-time.
Revised:  We take inspiration from the technology adopted in Italy by the national regulatory authority (NRA) and the distribution system operators (DSOs): SMs are enabled to convey data to LV end users through a power line communication (PLC) channel (chain 2) in near real-time.
Edit intent: clarity
Original:  This article focuses on a specific approach adopted in Italy by the national regulatory authority (NRA) and the distribution system operators (DSOs): SMs are enabled to convey data to LV end users through a power line communication (PLC) channel (chain 2) in near real-time.
Revised:  This article focuses on a specific approach adopted in Italy , where the national regulatory authority (NRA) and the distribution system operators (DSOs): SMs are enabled to convey data to LV end users through a power line communication (PLC) channel (chain 2) in near real-time.
Edit intent: coherence
Original:  This article focuses on a specific approach adopted in Italy by the national regulatory authority (NRA) and the distribution system operators (DSOs): SMs are enabled to convey data to LV end users through a power line communication (PLC) channel (chain 2) in near real-time.
Revised:  This article focuses on a specific approach adopted in Italy by the national regulatory authority (NRA) and the distribution system operators (DSOs): SMs are enabled to convey data to LV end users through a power line communication channel (CHAIN 2) in near real-time.
Edit intent: clarity
Original:  This paper aims to describe the proof of concept that the channel underwent and its subsequent validation (with performances nearing 99\% success rate) , as well as its deployment and the market of post-metering services enabled by chain 2 (e.g., customer awareness, scheduling and power modulation of smart appliances, participation in the electricity market, etc.).
Revised:  The aim of this paper is twofold. On the one hand, it describes the proof of concept that the channel underwent and its subsequent validation (with performances nearing 99\% success rate) , as well as its deployment and the market of post-metering services enabled by chain 2 (e.g., customer awareness, scheduling and power modulation of smart appliances, participation in the electricity market, etc.).
Edit intent: coherence
Original: In this paper, we present Long Short-term TRansformer (LSTR), a new temporal modeling algorithm for online action detection, by employing a long- and short-term memories mechanism that is able to model prolonged sequence data.
Revised: We present Long Short-term TRansformer (LSTR), a new temporal modeling algorithm for online action detection, by employing a long- and short-term memories mechanism that is able to model prolonged sequence data.
Edit intent: coherence
Original: In this paper, we present Long Short-term TRansformer (LSTR), a new temporal modeling algorithm for online action detection, by employing a long- and short-term memories mechanism that is able to model prolonged sequence data.
Revised: In this paper, we present Long Short-term TRansformer (LSTR), a temporal modeling algorithm for online action detection, by employing a long- and short-term memories mechanism that is able to model prolonged sequence data.
Edit intent: clarity
Original: In this paper, we present Long Short-term TRansformer (LSTR), a new temporal modeling algorithm for online action detection, by employing a long- and short-term memories mechanism that is able to model prolonged sequence data.
Revised: In this paper, we present Long Short-term TRansformer (LSTR), a new temporal modeling algorithm for online action detection, which employs a long- and short-term memories mechanism that is able to model prolonged sequence data.
Edit intent: coherence
Original: In this paper, we present Long Short-term TRansformer (LSTR), a new temporal modeling algorithm for online action detection, by employing a long- and short-term memories mechanism that is able to model prolonged sequence data.
Revised: In this paper, we present Long Short-term TRansformer (LSTR), a new temporal modeling algorithm for online action detection, by employing a long- and short-term memory mechanism to model prolonged sequence data.
Edit intent: coherence
Original:  It consists of an LSTR encoder that is capable of dynamically exploiting coarse-scale historical information from an extensively long time window (e.g., 2048 long-range frames of up to 8 minutes), together with an LSTR decoder that focuses on a short time window (e.g., 32 short-range frames of 8 seconds)
Revised:  It consists of an LSTR encoder that dynamically leverages coarse-scale historical information from an extensively long time window (e.g., 2048 long-range frames of up to 8 minutes), together with an LSTR decoder that focuses on a short time window (e.g., 32 short-range frames of 8 seconds)
Edit intent: clarity
Original:  It consists of an LSTR encoder that is capable of dynamically exploiting coarse-scale historical information from an extensively long time window (e.g., 2048 long-range frames of up to 8 minutes), together with an LSTR decoder that focuses on a short time window (e.g., 32 short-range frames of 8 seconds)
Revised:  It consists of an LSTR encoder that is capable of dynamically exploiting coarse-scale historical information from an extended temporal window (e.g., 2048 long-range frames of up to 8 minutes), together with an LSTR decoder that focuses on a short time window (e.g., 32 short-range frames of 8 seconds)
Edit intent: clarity
Original:  It consists of an LSTR encoder that is capable of dynamically exploiting coarse-scale historical information from an extensively long time window (e.g., 2048 long-range frames of up to 8 minutes), together with an LSTR decoder that focuses on a short time window (e.g., 32 short-range frames of 8 seconds)
Revised:  It consists of an LSTR encoder that is capable of dynamically exploiting coarse-scale historical information from an extensively long time window (e.g., 2048 frames spanning of up to 8 minutes), together with an LSTR decoder that focuses on a short time window (e.g., 32 short-range frames of 8 seconds)
Edit intent: clarity
Original:  It consists of an LSTR encoder that is capable of dynamically exploiting coarse-scale historical information from an extensively long time window (e.g., 2048 long-range frames of up to 8 minutes), together with an LSTR decoder that focuses on a short time window (e.g., 32 short-range frames of 8 seconds)
Revised:  It consists of an LSTR encoder that is capable of dynamically exploiting coarse-scale historical information from an extensively long time window (e.g., 2048 long-range frames of up to 8 minutes), together with an LSTR decoder that focuses on a short time window (e.g., 32 frames spanning 8 seconds)
Edit intent: clarity
Original:  to model the fine-scale characterization of the ongoing event . Compared to prior work, LSTR provides an effective and efficient method to model long videos with less heuristic algorithm design . LSTR achieves significantly improved results on standard online action detection benchmarks, THUMOS'14, TVSeries, and HACS Segment , over the existing state-of-the-art approaches.
Revised:  to model the fine-scale characteristics of the data . Compared to prior work, LSTR provides an effective and efficient method to model long videos with less heuristic algorithm design . LSTR achieves significantly improved results on standard online action detection benchmarks, THUMOS'14, TVSeries, and HACS Segment , over the existing state-of-the-art approaches.
Edit intent: clarity
Original:  to model the fine-scale characterization of the ongoing event . Compared to prior work, LSTR provides an effective and efficient method to model long videos with less heuristic algorithm design . LSTR achieves significantly improved results on standard online action detection benchmarks, THUMOS'14, TVSeries, and HACS Segment , over the existing state-of-the-art approaches.
Revised:  to model the fine-scale characterization of the ongoing event . Compared to prior work, LSTR provides an effective and efficient method to model long videos with less heuristic algorithm design . LSTR achieves state-of-the-art performance on three standard online action detection benchmarks, THUMOS'14, TVSeries, and HACS Segment , over the existing state-of-the-art approaches.
Edit intent: clarity
Original:  The Hausdorff Distance and Adjsted Random Index were the metrics with the highest correlation and the best results were obtained from the UNet architecture with a Weighted Cross-Entropy Loss.
Revised:  The Hausdorff Distance and Adjusted Random Index were the metrics with the highest correlation and the best results were obtained from the UNet architecture with a Weighted Cross-Entropy Loss.
Edit intent: fluency