wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The main contribution of this work is a {\em trie}-based data structure that is space efficient and supports 8-approximate nearest bottleneck queries in O(-\lg(d_B( D ,Q)) \min(m, 10%DIFDELCMD < }%%% ^n) n\lg^3 n ) time, where d_B( D ,Q) is the minimum bottleneck distance from Q to any point set in D. A direct consequence, of independent interest, is a simple O(-\lg(d_B(P,Q)) n \lg^3 n) time algorithm to 2 \sqrt{2 .
Revised:  The main contribution of this work is a {\em trie}-based data structure that is space efficient and supports 8-approximate nearest bottleneck queries in O(-\lg(d_B( D ,Q)) \min(m, 10%DIFDELCMD < }%%% n ) time, where d_B( D ,Q) is the minimum bottleneck distance from Q to any point set in D. A direct consequence, of independent interest, is a simple O(-\lg(d_B(P,Q)) n \lg^3 n) time algorithm to 2 \sqrt{2 .
Edit intent: clarity
Original:  The main contribution of this work is a {\em trie}-based data structure that is space efficient and supports 8-approximate nearest bottleneck queries in O(-\lg(d_B( D ,Q)) \min(m, 10%DIFDELCMD < }%%% ^n) n\lg^3 n ) time, where d_B( D ,Q) is the minimum bottleneck distance from Q to any point set in D. A direct consequence, of independent interest, is a simple O(-\lg(d_B(P,Q)) n \lg^3 n) time algorithm to 2 \sqrt{2 .
Revised:  The main contribution of this work is a {\em trie}-based data structure that is space efficient and supports 8-approximate nearest bottleneck queries in O(-\lg(d_B( D ,Q)) \min(m, 10%DIFDELCMD < }%%% ^n) n\lg^3 n ) time, where d_B( \mathbb{D ,Q) is the minimum bottleneck distance from Q to any point set in D. A direct consequence, of independent interest, is a simple O(-\lg(d_B(P,Q)) n \lg^3 n) time algorithm to 2 \sqrt{2 .
Edit intent: meaning-changed
Original:  The main contribution of this work is a {\em trie}-based data structure that is space efficient and supports 8-approximate nearest bottleneck queries in O(-\lg(d_B( D ,Q)) \min(m, 10%DIFDELCMD < }%%% ^n) n\lg^3 n ) time, where d_B( D ,Q) is the minimum bottleneck distance from Q to any point set in D. A direct consequence, of independent interest, is a simple O(-\lg(d_B(P,Q)) n \lg^3 n) time algorithm to 2 \sqrt{2 .
Revised:  The main contribution of this work is a {\em trie}-based data structure that is space efficient and supports 8-approximate nearest bottleneck queries in O(-\lg(d_B( D ,Q)) \min(m, 10%DIFDELCMD < }%%% ^n) n\lg^3 n ) time, where d_B( D ,Q) is the minimum bottleneck distance from Q to any point set in \mathbb{D .
Edit intent: meaning-changed
Original:  It extends previous studies that have explored the case of a (linear) Hawkes network driven by deterministic rate functions to the case of a stimulation by external inputs (rate functions or spike trains) with arbitrary correlation structure.
Revised:  It extends previous studies that have explored the case of a (linear) Hawkes network driven by deterministic intensity functions to the case of a stimulation by external inputs (rate functions or spike trains) with arbitrary correlation structure.
Edit intent: meaning-changed
Original:  This algebraic viewpoint provides intuition about how the network ingredients shape the input-output mapping for moments, as well as cumulants  .
Revised:  This algebraic viewpoint provides intuition about how the network ingredients shape the input-output mapping for moments, as well as cumulants . We also show using numerical simulation that our results hold for neurons with refractoriness implemented by self-inhibition, provided the corresponding negative feedback for each neuron only mildly alters its mean firing probability .
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Original:  On the other hand, we provide a general analysis of multiple curve markets under minimal assumptions in an extended HJM framework . In this setting, we characterize absence of arbitrage by means of NAFLVR and provide a fundamental theorem of asset pricing for multiple curve markets .
Revised:  On the other hand, we provide a general analysis of multiple curve markets under minimal assumptions in an extended HJM framework and provide a fundamental theorem of asset pricing for multiple curve markets .
Edit intent: coherence
Original:  We also develop a new tractable class of models, based on affine semimartingales, going beyond the classical requirement of stochastic continuity . Due to the generality of the setting, the existing approaches in the literature can be embedded as special cases .
Revised:  We also develop a new tractable class of models, based on affine semimartingales, going beyond the classical requirement of stochastic continuity .
Edit intent: coherence
Original:  In the neural setting, conditioned on cluster membership and the parameters governing the dynamics, time series within a cluster are assumed independent and generated according to a nonlinear binomial state-space model. We derive a Metropolis-within-Gibbs algorithm for full Bayesian inference that alternates between sampling of cluster membership and sampling of parameters of interest .
Revised:  In the neural setting, conditioned on cluster membership and the parameters governing the dynamics, time series within a cluster are assumed independent and generated according to a nonlinear binomial state-space models, we derive a Metropolis-within-Gibbs algorithm for full Bayesian inference that alternates between sampling of cluster membership and sampling of parameters of interest .
Edit intent: fluency
Original:  Of particular interest is the modular nature of the human brain, where the connectome is divided into subnetworks and subsequently studied to investigate changes with development, aging or disease . Here, we present a new, weighted network measure called the Network Dependency Index (NDI), which can be used to identify an individual region's importance to the global functioning of the network.
Revised:  Of particular interest is the modular nature of the human brain, where the connectome is divided into subnetworks and subsequently studied to investigate changes with development, aging or disease are investigated. We present a weighted network measure called the Network Dependency Index (NDI), which can be used to identify an individual region's importance to the global functioning of the network.
Edit intent: clarity
Original:  After identification of these subnetworks, we analyze their individual topological aspects with respect to age and compare it to the rich-club topology, which we use to define three subnetworks (rich-club, feeder and seeder).
Revised:  We analyze the topological aspects of each subnetwork with respect to age and compare it to the rich-club topology, which we use to define three subnetworks (rich-club, feeder and seeder).
Edit intent: clarity
Original:  After identification of these subnetworks, we analyze their individual topological aspects with respect to age and compare it to the rich-club topology, which we use to define three subnetworks (rich-club, feeder and seeder).
Revised:  After identification of these subnetworks, we analyze their individual topological aspects with respect to age and compare it to rich-club topology, which we use to define three subnetworks (rich-club, feeder and seeder).
Edit intent: fluency
Original:  After identification of these subnetworks, we analyze their individual topological aspects with respect to age and compare it to the rich-club topology, which we use to define three subnetworks (rich-club, feeder and seeder).
Revised:  After identification of these subnetworks, we analyze their individual topological aspects with respect to age and compare it to the rich-club based subnetworks (rich-club, feeder and seeder).
Edit intent: clarity
Original:  Stratifying the connectome accordingly by NDI led to consistent subnetworks across the life-span revealing distinct patterns associated with age where, e.g., the key relay nuclei and cortical regions are contained in a subnetwork with highest NDI.
Revised:  Stratifying the connectome by NDI led to consistent subnetworks across the life-span revealing distinct patterns associated with age where, e.g., the key relay nuclei and cortical regions are contained in a subnetwork with highest NDI.
Edit intent: clarity
Original:  The divisions of the human connectome derived from our data-driven NDI framework have the potential to reveal topological alterations due to disease and/or development , which are not detectable by utilizing the connectome as a whole.
Revised:  The divisions of the human connectome derived from our data-driven NDI framework have the potential to reveal topological alterations due to disease and/or development which are not detectable by utilizing the connectome as a whole.
Edit intent: fluency
Original:  Extensive experiments on two real-world stock data show that our method outperforms the state-of-the-art solution with 3.11\% relative improvements on average w.r.t. accuracy, verifying the usefulness of adversarial training for stock prediction task . Codes will be made available upon acceptance .
Revised:  Extensive experiments on two real-world stock data show that our method outperforms the state-of-the-art solution with 3.11\% relative improvements on average w.r.t. accuracy, validating the usefulness of adversarial training for stock prediction task . Codes will be made available upon acceptance .
Edit intent: clarity
Original:  Extensive experiments on two real-world stock data show that our method outperforms the state-of-the-art solution with 3.11\% relative improvements on average w.r.t. accuracy, verifying the usefulness of adversarial training for stock prediction task . Codes will be made available upon acceptance .
Revised:  Extensive experiments on two real-world stock data show that our method outperforms the state-of-the-art solution with 3.11\% relative improvements on average w.r.t. accuracy, verifying the usefulness of adversarial training for stock prediction task .
Edit intent: coherence
Original:  We then recognize a limitation regarding planarity, and help to guide future research by introducing a new classification of 'power-confluent' drawing .
Revised:  We also classify the exact type of confluent drawings that the algorithm can produce as 'power-confluent', and prove that it is a subclass of the previously studied 'strict confluent' drawing. A description and source code of our implementation is also provided, which additionally includes an improved method for power graph construction .
Edit intent: meaning-changed
Original:  The transition from desynchronous spikes to synchronous bursts activities, induced by varying the synaptic coupling, emerges in a hysteretic loop due to a bistable regime where normal ( synchronous ) or abnormal ( desynchronous ) regimes exist.
Revised:  The transition from desynchronous spikes to synchronous bursts activities, induced by varying the synaptic coupling, emerges in a hysteretic loop due to a bistable regime where normal ( desynchronous ) or abnormal ( desynchronous ) regimes exist.
Edit intent: fluency
Original:  The transition from desynchronous spikes to synchronous bursts activities, induced by varying the synaptic coupling, emerges in a hysteretic loop due to a bistable regime where normal ( synchronous ) or abnormal ( desynchronous ) regimes exist.
Revised:  The transition from desynchronous spikes to synchronous bursts activities, induced by varying the synaptic coupling, emerges in a hysteretic loop due to a bistable regime where normal ( synchronous ) or abnormal ( synchronous ) regimes exist.
Edit intent: fluency
Original:  First , we show that for any proper quasiconvex law-invariant functional \rho on \mathcal{X \sigma(X,X_n^\sim) -lower semicontinuity and \sigma(X,L^\infty) -lower semicontinuity of \rho are equivalent, where \mathcal{X}_n^\sim is the order continuous dual of \mathcal{X}.
Revised:  In particular , we show that for any proper quasiconvex law-invariant functional \rho on \mathcal{X \sigma(X,X_n^\sim) -lower semicontinuity and \sigma(X,L^\infty) -lower semicontinuity of \rho are equivalent, where \mathcal{X}_n^\sim is the order continuous dual of \mathcal{X}.
Edit intent: coherence
Original:  First , we show that for any proper quasiconvex law-invariant functional \rho on \mathcal{X \sigma(X,X_n^\sim) -lower semicontinuity and \sigma(X,L^\infty) -lower semicontinuity of \rho are equivalent, where \mathcal{X}_n^\sim is the order continuous dual of \mathcal{X}.
Revised:  First , we show that order closedness, \sigma(X,X_n^\sim) -lower semicontinuity and \sigma(X,L^\infty) -lower semicontinuity of \rho are equivalent, where \mathcal{X}_n^\sim is the order continuous dual of \mathcal{X}.
Edit intent: clarity
Original:  First , we show that for any proper quasiconvex law-invariant functional \rho on \mathcal{X \sigma(X,X_n^\sim) -lower semicontinuity and \sigma(X,L^\infty) -lower semicontinuity of \rho are equivalent, where \mathcal{X}_n^\sim is the order continuous dual of \mathcal{X}.
Revised:  First , we show that for any proper quasiconvex law-invariant functional \rho on \mathcal{X \sigma(X,X_n^\sim) -closedness and \sigma(X,L^\infty) -lower semicontinuity of \rho are equivalent, where \mathcal{X}_n^\sim is the order continuous dual of \mathcal{X}.
Edit intent: meaning-changed
Original:  First , we show that for any proper quasiconvex law-invariant functional \rho on \mathcal{X \sigma(X,X_n^\sim) -lower semicontinuity and \sigma(X,L^\infty) -lower semicontinuity of \rho are equivalent, where \mathcal{X}_n^\sim is the order continuous dual of \mathcal{X}.
Revised:  First , we show that for any proper quasiconvex law-invariant functional \rho on \mathcal{X \sigma(X,X_n^\sim) -lower semicontinuity and \sigma(X,L^\infty) -closedness of a law-invariant convex set in \mathcal{X are equivalent, where \mathcal{X}_n^\sim is the order continuous dual of \mathcal{X}.
Edit intent: meaning-changed
Original:  Second, we provide some relations between the Fatou property and some other types of lower semicontinuity, namely norm lower semicontinuity and the strong Fatou property.
Revised:  We also provide some application to proper quasiconvex law-invariant functionals with the Fatou property and some other types of lower semicontinuity, namely norm lower semicontinuity and the strong Fatou property.
Edit intent: meaning-changed
Original:  Second, we provide some relations between the Fatou property and some other types of lower semicontinuity, namely norm lower semicontinuity and the strong Fatou property. In particular, we show that when \mathcal{X\neq L^1, the strong Fatou property and the Fatou property coincide.
Revised:  Second, we provide some relations between the Fatou property \neq L^1, the strong Fatou property and the Fatou property coincide.
Edit intent: coherence
Original:  The classical picture for deterministic cell decision making includes bistability and hysteresis . Despite numerous experimental works supporting evidence of hysteresis in gene regulatory networks , such phenomenon may not be compatible with the stochasticityunderlying gene regulation dynamics.
Revised:  The classical picture for deterministic cell decision making includes bistability and hysteresis , which enables the persistence of the acquired cellular state after withdrawal of the stimulus, ensuring a robust cellular response. However, the stochasticity inherent to gene expression dynamics is not compatible with hysteresis, since the stationary solution of the governing Chemical Master Equation does not depend on the initial conditions. In this work, we provide a quantitative description of a transient hysteresis phenomenon that reconciles experimental evidence of hysteretic behaviour in gene regulatory networks , such phenomenon may not be compatible with the stochasticityunderlying gene regulation dynamics.
Edit intent: meaning-changed
Original:  Despite numerous experimental works supporting evidence of hysteresis in gene regulatory networks , such phenomenon may not be compatible with the stochasticityunderlying gene regulation dynamics. Here we show how under sufficiently slow dynamics, the dependency of the transient solutions on the initial state of the cells can be mistakenly interpreted as hysteresis and, to quantify this phenomenon, we provide an estimate of the convergence rate to the equilibrium.
Revised:  Despite numerous experimental works supporting evidence of hysteresis in gene regulatory networks with their inherent stochasticity. Under sufficiently slow dynamics, the dependency of the transient solutions on the initial state of the cells can be mistakenly interpreted as hysteresis and, to quantify this phenomenon, we provide an estimate of the convergence rate to the equilibrium.
Edit intent: clarity
Original:  Here we show how under sufficiently slow dynamics, the dependency of the transient solutions on the initial state of the cells can be mistakenly interpreted as hysteresis and, to quantify this phenomenon, we provide an estimate of the convergence rate to the equilibrium.
Revised:  Here we show how under sufficiently slow dynamics, the dependency of the non-stationary solutions on the initial state of the cells can be mistakenly interpreted as hysteresis and, to quantify this phenomenon, we provide an estimate of the convergence rate to the equilibrium.
Edit intent: clarity
Original:  Here we show how under sufficiently slow dynamics, the dependency of the transient solutions on the initial state of the cells can be mistakenly interpreted as hysteresis and, to quantify this phenomenon, we provide an estimate of the convergence rate to the equilibrium.
Revised:  Here we show how under sufficiently slow dynamics, the dependency of the transient solutions on the initial state of the cells can lead to what we denote here as transient hysteresis. To quantify this phenomenon, we provide an estimate of the convergence rate to the equilibrium.
Edit intent: clarity
Original:  The intent is to identify the feasibility of a floorplan topology of the given design netlist by estimating routability, routed net length and the number of vias while taking into account global congestion scenario across the layout.
Revised:  We assess feasibility of a floorplan topology of the given design netlist by estimating routability, routed net length and the number of vias while taking into account global congestion scenario across the layout.
Edit intent: clarity
Original:  The intent is to identify the feasibility of a floorplan topology of the given design netlist by estimating routability, routed net length and the number of vias while taking into account global congestion scenario across the layout.
Revised:  The intent is to identify the feasibility of a floorplan topology of a given design by estimating routability, routed net length and the number of vias while taking into account global congestion scenario across the layout.
Edit intent: fluency
Original:  The intent is to identify the feasibility of a floorplan topology of the given design netlist by estimating routability, routed net length and the number of vias while taking into account global congestion scenario across the layout.
Revised:  The intent is to identify the feasibility of a floorplan topology of the given design netlist by estimating routability, routed wirelength and vias count while addressing the global congestion scenario across the layout.
Edit intent: clarity
Original:  This framework works on both unreserved as well as HV reserved layer model for M(\geq 2) metal layers and accommodates different capacity profiles of the routing resources, due to uniform or different cases of metal pitch variation across the metals layers akin to the latest technologies.
Revised:  Different capacity profiles for the routing regions, such as uniform or non-uniform different cases of metal pitch variation across the metals layers akin to the latest technologies.
Edit intent: clarity
Original:  This framework works on both unreserved as well as HV reserved layer model for M(\geq 2) metal layers and accommodates different capacity profiles of the routing resources, due to uniform or different cases of metal pitch variation across the metals layers akin to the latest technologies. This algorithm takes O(n^2kt) time for a given design with n blocks and k nets having at most t terminals.
Revised:  This framework works on both unreserved as well as HV reserved layer model for M(\geq 2) metal layers and accommodates different capacity profiles of the routing resources, due to uniform or different cases of metal pitch variation across the metals layers ensures adaptability to technology scaling. The proposed algorithm STAIRoute takes O(n^2kt) time for a given design with n blocks and k nets having at most t terminals.
Edit intent: meaning-changed
Original: Despite recent developments, deploying deep neural networks on resource constrained general purpose hardware remains a significant challenge. There hasbeen much work in developing methods for reshaping neural networks, usually with a focus on minimising total parameter count. These methods are typically developed in a hardware-agnostic manner and do not exploit hardware behaviour .
Revised: Despite recent developments, deploying deep neural networks on resource constrained general purpose hardware has, in recent years, prompted the use of channel pruning to reduce network size. However, the efficacy of pruning based approaches has since been called into question .
Edit intent: meaning-changed
Original:  In this paper we propose a new approach, Hardware Aware Knowledge Distillation (HAKD) which uses empirical observations of hardware behaviour to design efficient student networks which are then trained with knowledge distillation.
Revised:  In this paper , we turn to distillation for model compression---specifically, attention transfer---and develop a simple method for discovering performance enhanced student networks. We combine channel saliency metrics with empirical observations of hardware behaviour to design efficient student networks which are then trained with knowledge distillation.
Edit intent: meaning-changed
Original:  In this paper we propose a new approach, Hardware Aware Knowledge Distillation (HAKD) which uses empirical observations of hardware behaviour to design efficient student networks which are then trained with knowledge distillation. This allows the trade-off between accuracy and performance to be managed explicitly. We have applied this approach across three platforms and evaluated it on two networks, MobileNet and DenseNet, on CIFAR-10. We show that HAKD outperforms Deep Compression and Fisher pruning in terms of size, accuracy and performance .
Revised:  In this paper we propose a new approach, Hardware Aware Knowledge Distillation (HAKD) which uses empirical observations of runtime performance to design more accurate networks for a given latency budget. We apply our methodology to residual and densely-connected networks, and show that we are able to find resource-efficient student networks on different hardware platforms while maintaining very high accuracy. These performance-enhanced student networks achieve up to 10\% boosts in top-1 ImageNet accuracy over their channel-pruned counterparts for the same inference time .
Edit intent: meaning-changed
Original:  The activity displayed in such a state is known to be beneficial for cognitive functions, stabilizing memories and improving the performances in several tasks. Despite this , a theoretical and computational approach to achieve the understanding of such mechanism is still lacking.
Revised:  The activity displayed in such a state is known to be beneficial to cognitive and mnemonic tasks, while chronic sleep deprivation is detrimental. Despite the importance of the phenomenon , a theoretical and computational approach to achieve the understanding of such mechanism is still lacking.
Edit intent: meaning-changed