wanyu/IteraTeR_v2 huggingface dataset:
Original:  Recent years have seen an emergence of research in  hardware that strives to break the bottleneck of von Neumann architecture and optimise the data flow, namely, to bring memory and computing closer together.
Revised:  Recent years have seen an emergence of research in hardware that strives to bring memory and computing closer together.
Edit intent: coherence
Original:  One of the most often suggested solutions is the physical implementation of artificial neural networks in which their synaptic weights are realised with memristive devices, such as resistive random-access memory .
Revised:  A popular approach is to realise artificial neural networks in which their synaptic weights are realised with memristive devices, such as resistive random-access memory .
Edit intent: clarity
Original:  One of the most often suggested solutions is the physical implementation of artificial neural networks in which their synaptic weights are realised with memristive devices, such as resistive random-access memory .
Revised:  One of the most often suggested solutions is the physical implementation of artificial neural networks in which their synaptic weights using memristive devices .
Edit intent: clarity
Original:  We suggest applying a well-known concept in computer science -- committee machine -- in the context of memristor-based neural networks.
Revised:  We suggest applying a well-known concept in computer science -- committee machines -- in the context of memristor-based neural networks.
Edit intent: fluency
Original:  Importantly, we show that the accuracy can be improved even without increasing the total number of memristors.
Revised:  Importantly, we demonstrate that the accuracy can be improved even without increasing the total number of memristors.
Edit intent: fluency
Original:  This is a useful mathematical model for ``coffee shop'' situations where a group of persistent users stay near an access point for an extended period of time while non-persistent users come and go.
Revised:  This is a useful mathematical model for "coffee shop" situations where a group of persistent users stay near an access point for an extended period of time while non-persistent users come and go.
Edit intent: fluency
Original:  align* \left\|u_{h}\right\|_{L^{\infty}(\varOmega)} \le C\left\|u_{h}\right\|_{L^{\infty}(\partial \varOmega)} , align* with a constant C independent of the mesh size h. By using this result, we show that  Ritz projection operator R_h is stable in L^\infty norm uniformly in h for r\geq 2, i.e. align* \|R_hu\|_{L^{\infty}(\varOmega)} \le C\|u\|_{L^{\infty}(\varOmega)} . align* Thus we remove a logarithmic factor appearing in the previous results for convex polyhedral domains.
Revised:  align* \left\|u_{h}\right\|_{L^{\infty}(\varOmega)} \le C\left\|u_{h}\right\|_{L^{\infty}(\partial \varOmega)} , align* with a constant C independent of the mesh size h. By using this result, we show that the Ritz projection operator R_h is stable in L^\infty norm uniformly in h for r\geq 2, i.e. align* \|R_hu\|_{L^{\infty}(\varOmega)} \le C\|u\|_{L^{\infty}(\varOmega)} . align* Thus we remove a logarithmic factor appearing in the previous results for convex polyhedral domains.
Edit intent: fluency
Original: We model a piece of text of human language telling a story by means of the quantum structure describing a Bose gas in a temperature close to a Bose-Einstein condensate near absolute zero  .
Revised: We model a piece of text of human language telling a story by means of the quantum structure describing a Bose gas in a state close to a Bose-Einstein condensate near absolute zero .
Edit intent: clarity
Original: We model a piece of text of human language telling a story by means of the quantum structure describing a Bose gas in a temperature close to a Bose-Einstein condensate near absolute zero  .
Revised: We model a piece of text of human language telling a story by means of the quantum structure describing a Bose gas in a temperature close to a Bose-Einstein condensate near absolute zero temperature .
Edit intent: clarity
Original:  For this we introduce energy levels for the concepts (words ) used in the story and we also introduce the new notion of 'cogniton' as the quantum of human language.
Revised:  For this we introduce energy levels for the words (concepts ) used in the story and we also introduce the new notion of 'cogniton' as the quantum of human language.
Edit intent: fluency
Original:  We show that Bose-Einstein statistics delivers a very good model for these pieces of texts telling stories, as well for short stories as for long stories of the size of novels.
Revised:  We show that Bose-Einstein statistics delivers a very good model for these pieces of texts telling stories, both for short stories as for long stories of the size of novels.
Edit intent: clarity
Original:  We show that Bose-Einstein statistics delivers a very good model for these pieces of texts telling stories, as well for short stories as for long stories of the size of novels.
Revised:  We show that Bose-Einstein statistics delivers a very good model for these pieces of texts telling stories, as well for short stories and for long stories of the size of novels.
Edit intent: clarity
Original: The use of lower precision to perform computations has emerged as a popular technique to enable complex Deep Neural Networks (DNNs) to be realized on energy-constrained platforms .
Revised: The use of lower precision has emerged as a popular technique to enable complex Deep Neural Networks (DNNs) to be realized on energy-constrained platforms .
Edit intent: clarity
Original: The use of lower precision to perform computations has emerged as a popular technique to enable complex Deep Neural Networks (DNNs) to be realized on energy-constrained platforms .
Revised: The use of lower precision to perform computations has emerged as a popular technique to enable complex Deep Neural Networks (DNNs) .
Edit intent: clarity
Original:  In the quest for lower precision, studies to date have shown that ternary DNNs, which represent weights and activations by signed ternary values, represent a promising sweet spot, and achieve accuracy close to full-precision networks on complex tasks such as language modeling and image classification.
Revised:  In the quest for lower precision, recent studies have shown that ternary DNNs, which represent weights and activations by signed ternary values, represent a promising sweet spot, and achieve accuracy close to full-precision networks on complex tasks such as language modeling and image classification.
Edit intent: clarity
Original:  We propose TiM-DNN, a programmable hardware accelerator that is specifically designed to execute state-of-the-art ternary DNNs. TiM-DNN supports various ternary representations including unweighted (-1,0,1), symmetric weighted (-a,0,a), and asymmetric weighted (-a,0,b) ternary systems.
Revised:  We propose TiM-DNN, a programmable hardware accelerator that is specifically designed to execute ternary DNNs. TiM-DNN supports various ternary representations including unweighted (-1,0,1), symmetric weighted (-a,0,a), and asymmetric weighted (-a,0,b) ternary systems.
Edit intent: clarity
Original:  TiM-DNN is an in-memory accelerator designed using TiM tiles - specialized memory arrays that perform massively parallel signed vector-matrix multiplications on ternary values per access.
Revised:  TiM-DNN is designed using TiM tiles - specialized memory arrays that perform massively parallel signed vector-matrix multiplications on ternary values per access.
Edit intent: clarity
Original:  TiM-DNN is an in-memory accelerator designed using TiM tiles - specialized memory arrays that perform massively parallel signed vector-matrix multiplications on ternary values per access.
Revised:  TiM-DNN is an in-memory accelerator designed using TiM tiles -- specialized memory arrays that perform massively parallel signed vector-matrix multiplications on ternary values per access.
Edit intent: fluency
Original:  TiM-DNN is an in-memory accelerator designed using TiM tiles - specialized memory arrays that perform massively parallel signed vector-matrix multiplications on ternary values per access.
Revised:  TiM-DNN is an in-memory accelerator designed using TiM tiles - specialized memory arrays that perform massively parallel signed vector-matrix multiplications on ternary values with a single access.
Edit intent: clarity
Original:  In the quest for lower precision, recent studies have shown that ternary DNNs , which represent weights and activations by signed ternary values , represent a promising sweet spot, and achieve accuracy close to full-precision networks on complex tasks such as language modeling and image classification .
Revised:  In the quest for lower precision, recent studies have shown that ternary DNNs ( which represent weights and activations by signed ternary values , represent a promising sweet spot, and achieve accuracy close to full-precision networks on complex tasks such as language modeling and image classification .
Edit intent: fluency
Original:  In the quest for lower precision, recent studies have shown that ternary DNNs , which represent weights and activations by signed ternary values , represent a promising sweet spot, and achieve accuracy close to full-precision networks on complex tasks such as language modeling and image classification .
Revised:  In the quest for lower precision, recent studies have shown that ternary DNNs , which represent weights and activations by signed ternary values ) represent a promising sweet spot, and achieve accuracy close to full-precision networks on complex tasks such as language modeling and image classification .
Edit intent: fluency
Original:  In the quest for lower precision, recent studies have shown that ternary DNNs , which represent weights and activations by signed ternary values , represent a promising sweet spot, and achieve accuracy close to full-precision networks on complex tasks such as language modeling and image classification .
Revised:  In the quest for lower precision, recent studies have shown that ternary DNNs , which represent weights and activations by signed ternary values , represent a promising sweet spot, achieving accuracy close to full-precision networks on complex tasks such as language modeling and image classification .
Edit intent: coherence
Original:  In the quest for lower precision, recent studies have shown that ternary DNNs , which represent weights and activations by signed ternary values , represent a promising sweet spot, and achieve accuracy close to full-precision networks on complex tasks such as language modeling and image classification .
Revised:  In the quest for lower precision, recent studies have shown that ternary DNNs , which represent weights and activations by signed ternary values , represent a promising sweet spot, and achieve accuracy close to full-precision networks on complex tasks .
Edit intent: clarity
Original:  We propose TiM-DNN, a programmable , in-memory accelerator that is specifically designed to execute ternary DNNs. TiM-DNN supports various ternary representations including unweighted ( -1,0,1 ) , symmetric weighted ( -a,0,a ) , and asymmetric weighted ( -a,0,b ) ternary systems.
Revised:  We propose TiM-DNN, a programmable in-memory accelerator that is specifically designed to execute ternary DNNs. TiM-DNN supports various ternary representations including unweighted ( -1,0,1 ) , symmetric weighted ( -a,0,a ) , and asymmetric weighted ( -a,0,b ) ternary systems.
Edit intent: fluency
Original:  We propose TiM-DNN, a programmable , in-memory accelerator that is specifically designed to execute ternary DNNs. TiM-DNN supports various ternary representations including unweighted ( -1,0,1 ) , symmetric weighted ( -a,0,a ) , and asymmetric weighted ( -a,0,b ) ternary systems.
Revised:  We propose TiM-DNN, a programmable , in-memory accelerator that is specifically designed to execute ternary DNNs. TiM-DNN supports various ternary representations including unweighted -1,0,1 ) , symmetric weighted ( -a,0,a ) , and asymmetric weighted ( -a,0,b ) ternary systems.
Edit intent: fluency
Original:  We propose TiM-DNN, a programmable , in-memory accelerator that is specifically designed to execute ternary DNNs. TiM-DNN supports various ternary representations including unweighted ( -1,0,1 ) , symmetric weighted ( -a,0,a ) , and asymmetric weighted ( -a,0,b ) ternary systems.
Revised:  We propose TiM-DNN, a programmable , in-memory accelerator that is specifically designed to execute ternary DNNs. TiM-DNN supports various ternary representations including unweighted ( -1,0,1 , symmetric weighted ( -a,0,a ) , and asymmetric weighted ( -a,0,b ) ternary systems.
Edit intent: fluency
Original:  We propose TiM-DNN, a programmable , in-memory accelerator that is specifically designed to execute ternary DNNs. TiM-DNN supports various ternary representations including unweighted ( -1,0,1 ) , symmetric weighted ( -a,0,a ) , and asymmetric weighted ( -a,0,b ) ternary systems.
Revised:  We propose TiM-DNN, a programmable , in-memory accelerator that is specifically designed to execute ternary DNNs. TiM-DNN supports various ternary representations including unweighted ( -1,0,1 ) , symmetric weighted -a,0,a ) , and asymmetric weighted ( -a,0,b ) ternary systems.
Edit intent: fluency
Original:  We propose TiM-DNN, a programmable , in-memory accelerator that is specifically designed to execute ternary DNNs. TiM-DNN supports various ternary representations including unweighted ( -1,0,1 ) , symmetric weighted ( -a,0,a ) , and asymmetric weighted ( -a,0,b ) ternary systems.
Revised:  We propose TiM-DNN, a programmable , in-memory accelerator that is specifically designed to execute ternary DNNs. TiM-DNN supports various ternary representations including unweighted ( -1,0,1 ) , symmetric weighted ( -a,0,a , and asymmetric weighted ( -a,0,b ) ternary systems.
Edit intent: fluency
Original:  We propose TiM-DNN, a programmable , in-memory accelerator that is specifically designed to execute ternary DNNs. TiM-DNN supports various ternary representations including unweighted ( -1,0,1 ) , symmetric weighted ( -a,0,a ) , and asymmetric weighted ( -a,0,b ) ternary systems.
Revised:  We propose TiM-DNN, a programmable , in-memory accelerator that is specifically designed to execute ternary DNNs. TiM-DNN supports various ternary representations including unweighted ( -1,0,1 ) , symmetric weighted ( -a,0,a ) , and asymmetric weighted -a,0,b ) ternary systems.
Edit intent: fluency
Original:  We propose TiM-DNN, a programmable , in-memory accelerator that is specifically designed to execute ternary DNNs. TiM-DNN supports various ternary representations including unweighted ( -1,0,1 ) , symmetric weighted ( -a,0,a ) , and asymmetric weighted ( -a,0,b ) ternary systems.
Revised:  We propose TiM-DNN, a programmable , in-memory accelerator that is specifically designed to execute ternary DNNs. TiM-DNN supports various ternary representations including unweighted ( -1,0,1 ) , symmetric weighted ( -a,0,a ) , and asymmetric weighted ( -a,0,b ternary systems.
Edit intent: fluency
Original:   TiM-DNN is designed using TiM tiles -- specialized memory arrays that perform massively parallel signed  vector-matrix multiplications on ternary values with a single access.
Revised:  TiM-DNN are TiM tiles -- specialized memory arrays that perform massively parallel signed vector-matrix multiplications on ternary values with a single access.
Edit intent: clarity
Original:   TiM-DNN is designed using TiM tiles -- specialized memory arrays that perform massively parallel signed  vector-matrix multiplications on ternary values with a single access.
Revised:  TiM-DNN is designed using TiM tiles -- specialized memory arrays that perform massively parallel signed ternary vector-matrix multiplications on ternary values with a single access.
Edit intent: clarity
Original:   TiM-DNN is designed using TiM tiles -- specialized memory arrays that perform massively parallel signed  vector-matrix multiplications on ternary values with a single access.
Revised:  TiM-DNN is designed using TiM tiles -- specialized memory arrays that perform massively parallel signed vector-matrix multiplications with a single access.
Edit intent: clarity
Original:  TiM tiles are in turn composed of Ternary Processing Cells (TPCs), new bit-cells that function as both ternary storage units and signed scalar multiplication units.
Revised:  TiM tiles are in turn composed of Ternary Processing Cells (TPCs), bit-cells that function as both ternary storage units and signed scalar multiplication units.
Edit intent: clarity
Original:  TiM tiles are in turn composed of Ternary Processing Cells (TPCs), new bit-cells that function as both ternary storage units and signed scalar multiplication units.
Revised:  TiM tiles are in turn composed of Ternary Processing Cells (TPCs), new bit-cells that function as both ternary storage units and signed ternary multiplication units.
Edit intent: fluency
Original:   TiM-DNN  achieves a peak performance of 114 TOPs/s, consumes 0.9W power, and occupies 1.96mm2 chip area, representing a 300X and 388X improvement in TOPS/W and TOPS/mm2, respectively, compared to a state-of-the-art NVIDIA Tesla V100 GPU.
Revised:  We evaluate TiM-DNN achieves a peak performance of 114 TOPs/s, consumes 0.9W power, and occupies 1.96mm2 chip area, representing a 300X and 388X improvement in TOPS/W and TOPS/mm2, respectively, compared to a state-of-the-art NVIDIA Tesla V100 GPU.
Edit intent: clarity
Original:   TiM-DNN  achieves a peak performance of 114 TOPs/s, consumes 0.9W power, and occupies 1.96mm2 chip area, representing a 300X and 388X improvement in TOPS/W and TOPS/mm2, respectively, compared to a state-of-the-art NVIDIA Tesla V100 GPU.
Revised:  TiM-DNN achieves a peak performance of 114 TOPs/s, consumes 0.9W power, and occupies 1.96mm2 chip area, representing a 300X and 388X improvement in TOPS/W and TOPS/mm2, respectively, compared to an NVIDIA Tesla V100 GPU.
Edit intent: clarity
Original:  In comparison to popular DNN accelerators, TiM-DNN achieves 55.2X-240X and 160X-291X improvement in TOPS/W and TOPS/mm2, respectively.
Revised:  In comparison to popular DNN accelerators, TiM-DNN achieves 55X-240X and 160X-291X improvement in TOPS/W and TOPS/mm2, respectively.
Edit intent: clarity
Original:  We compare TiM-DNN with a well-optimized near-memory accelerator for ternary DNNs across a suite of state-of-the-art DNN benchmarks including both deep convolutional and recurrent neural networks, demonstrating 3.9x-4.7x improvement in system-level energy and 3.2x-4.2x speedup  .
Revised:  Finally, when compared to a well-optimized near-memory accelerator for ternary DNNs across a suite of state-of-the-art DNN benchmarks including both deep convolutional and recurrent neural networks, demonstrating 3.9x-4.7x improvement in system-level energy and 3.2x-4.2x speedup .
Edit intent: clarity
Original:  We compare TiM-DNN with a well-optimized near-memory accelerator for ternary DNNs across a suite of state-of-the-art DNN benchmarks including both deep convolutional and recurrent neural networks, demonstrating 3.9x-4.7x improvement in system-level energy and 3.2x-4.2x speedup  .
Revised:  We compare TiM-DNN with a well-optimized near-memory accelerator for ternary DNNs , TiM-DNN demonstrates 3.9x-4.7x improvement in system-level energy and 3.2x-4.2x speedup .
Edit intent: clarity
Original:  We compare TiM-DNN with a well-optimized near-memory accelerator for ternary DNNs across a suite of state-of-the-art DNN benchmarks including both deep convolutional and recurrent neural networks, demonstrating 3.9x-4.7x improvement in system-level energy and 3.2x-4.2x speedup  .
Revised:  We compare TiM-DNN with a well-optimized near-memory accelerator for ternary DNNs across a suite of state-of-the-art DNN benchmarks including both deep convolutional and recurrent neural networks, demonstrating 3.9x-4.7x improvement in system-level energy and 3.2x-4.2x speedup , underscoring the potential of in-memory computing for ternary DNNs .
Edit intent: clarity
Original:  As with recent reduction-based approach , our algorithm has the nice feature that the computation of a telescoper is independent of its certificate.
Revised:  As with recent reduction-based approaches , our algorithm has the nice feature that the computation of a telescoper is independent of its certificate.
Edit intent: fluency
Original:  As with recent reduction-based approach , our algorithm has the nice feature that the computation of a telescoper is independent of its certificate.
Revised:  As with recent reduction-based approach , our algorithm has the important feature that the computation of a telescoper is independent of its certificate.
Edit intent: clarity
Original:  Moreover , our algorithm uses a sparse representation of the certificate, which allows it to be easily manipulated and analyzed without knowing the precise expanded form.
Revised:  In addition , our algorithm uses a sparse representation of the certificate, which allows it to be easily manipulated and analyzed without knowing the precise expanded form.
Edit intent: coherence
Original:  Moreover , our algorithm uses a sparse representation of the certificate, which allows it to be easily manipulated and analyzed without knowing the precise expanded form.
Revised:  Moreover , our algorithm uses a compact representation of the certificate, which allows it to be easily manipulated and analyzed without knowing the precise expanded form.
Edit intent: clarity
Original:  A complexity analysis, along with a Maple implementation, suggests that our algorithm has better theoretical and practical performance than the reduction-based approach in the rational case.
Revised:  A complexity analysis, along with a Maple implementation, indicates that our algorithm has better theoretical and practical performance than the reduction-based approach in the rational case.
Edit intent: fluency
Original: Information gathering while interacting with other agents  is critical in many emerging domains , such as self-driving cars , service robots, drone racing, and active surveillance.
Revised: Information gathering while interacting with other agents is critical in domains such as driving , service robots, drone racing, and active surveillance.
Edit intent: clarity
Original:  Since unveiling one's own strategy to adversaries is undesirable, each agent must independently predict the other agents ' future actions without communication .
Revised:  Agents must predict others ' future actions without communication .
Edit intent: clarity
Original:  Agents with game-theoretic belief space planning win 44\% more races compared to a baseline without game theory and 34\% more than without belief space planning.
Revised:  Agents with game-theoretic belief-space planning win 44\% more races compared to a baseline without game theory and 34\% more than without belief space planning.
Edit intent: fluency
Original:  Agents with game-theoretic belief space planning win 44\% more races compared to a baseline without game theory and 34\% more than without belief space planning.
Revised:  Agents with game-theoretic belief space planning win 44\% more races than without game theory and 34\% more than without belief space planning.
Edit intent: clarity
Original:  Agents with game-theoretic belief space planning win 44\% more races compared to a baseline without game theory and 34\% more than without belief space planning.
Revised:  Agents with game-theoretic belief space planning win 44\% more races compared to a baseline without game theory and 34\% more than without belief-space planning.
Edit intent: fluency
Original: This paper presents a data-driven method for producing annual continuous dynamic rating of power transformer for long-term planning purpose.
Revised: This paper presents a data-driven method for producing annual continuous dynamic rating of power transformers to serve the long-term planning purpose.
Edit intent: clarity
Original:  Historically, researches on dynamic rating have been focused on real-time or near-future system operations.
Revised:  Historically, research works on dynamic rating have been focused on real-time or near-future system operations.
Edit intent: clarity