wanyu/IteraTeR_v2 huggingface dataset:
Original:  We also show that the rank of M_k over \mathbb Z_k over \mathbb{Z}} _p is \Omega( 1.56 ^k) for any prime p\neq 2. As a consequence, we obtain that Hamiltonian cycles cannot be counted in time O^*((6-\epsilon)^{\mathsf{pw}}) for any \epsilon>0 unless SETH fails , which is tight due to an algorithm by Bodlaender et al. (ICALP 2013).
Revised:  We also show that the rank of \mathbf{M_k over \mathbb{Z}} _p is \Omega( 1.56 ^k) for any prime p\neq 2. As a consequence, we obtain that Hamiltonian cycles cannot be counted in time O^*((6-\epsilon)^{\mathsf{pw}}) for any \epsilon>0 unless SETH fails , which is tight due to an algorithm by Bodlaender et al. (ICALP 2013).
Edit intent: clarity
Original:  We also show that the rank of M_k over \mathbb Z_k over \mathbb{Z}} _p is \Omega( 1.56 ^k) for any prime p\neq 2. As a consequence, we obtain that Hamiltonian cycles cannot be counted in time O^*((6-\epsilon)^{\mathsf{pw}}) for any \epsilon>0 unless SETH fails , which is tight due to an algorithm by Bodlaender et al. (ICALP 2013).
Revised:  We also show that the rank of M_k over \mathbb Z_k over \mathbb{Z}} _p is \Omega( 1.56 ^k) for any prime p\neq 2. As a consequence, we obtain that Hamiltonian cycles cannot be counted in time O^*((6-\epsilon)^{\mathsf{pw}}) for any \epsilon>0 unless SETH fails . This bound is tight due to an algorithm by Bodlaender et al. (ICALP 2013).
Edit intent: clarity
Original:  We also show that the rank of M_k over \mathbb Z_k over \mathbb{Z}} _p is \Omega( 1.56 ^k) for any prime p\neq 2. As a consequence, we obtain that Hamiltonian cycles cannot be counted in time O^*((6-\epsilon)^{\mathsf{pw}}) for any \epsilon>0 unless SETH fails , which is tight due to an algorithm by Bodlaender et al. (ICALP 2013).
Revised:  We also show that the rank of M_k over \mathbb Z_k over \mathbb{Z}} _p is \Omega( 1.56 ^k) for any prime p\neq 2. As a consequence, we obtain that Hamiltonian cycles cannot be counted in time O^*((6-\epsilon)^{\mathsf{pw}}) for any \epsilon>0 unless SETH fails , which is tight due to a O^*(6^{\mathsf{pw algorithm by Bodlaender et al. (ICALP 2013).
Edit intent: clarity
Original:  We also obtain that Hamiltonian cycles cannot be counted modulo primes p\neq 2 in time O^*( 3.56 ^\mathsf{pw}), in contrast to the O^*((2+\sqrt{2)^{\mathsf{pw}})=O^*(3.42^{\mathsf{pw}}) time algorithm for counting mod 2 by Cygan et al. (STOC 2013)}  .
Revised:  We also obtain that Hamiltonian cycles cannot be counted modulo primes p\neq 2 in time O^*( 3.56 ^\mathsf{pw}), )^{\mathsf{pw}})=O^*(3.42^{\mathsf{pw}}) time algorithm for counting mod 2 by Cygan et al. (STOC 2013)} .
Edit intent: coherence
Original: This work combines the free energy principle from cognitive neuroscience and the ensuing active inference dynamics with recent advances in variational inference on deep generative models  and evolution strategies as efficient large-scale black-box optimisation technique, to introduce the "deep active inference" agent.
Revised: This work combines the free energy principle from cognitive neuroscience and the ensuing active inference dynamics with recent advances in variational inference in deep generative models and evolution strategies as efficient large-scale black-box optimisation technique, to introduce the "deep active inference" agent.
Edit intent: fluency
Original: This work combines the free energy principle from cognitive neuroscience and the ensuing active inference dynamics with recent advances in variational inference on deep generative models  and evolution strategies as efficient large-scale black-box optimisation technique, to introduce the "deep active inference" agent.
Revised: This work combines the free energy principle from cognitive neuroscience and the ensuing active inference dynamics with recent advances in variational inference on deep generative models , and evolution strategies as efficient large-scale black-box optimisation technique, to introduce the "deep active inference" agent.
Edit intent: fluency
Original: This work combines the free energy principle from cognitive neuroscience and the ensuing active inference dynamics with recent advances in variational inference on deep generative models  and evolution strategies as efficient large-scale black-box optimisation technique, to introduce the "deep active inference" agent.
Revised: This work combines the free energy principle from cognitive neuroscience and the ensuing active inference dynamics with recent advances in variational inference on deep generative models and evolution strategies as efficient large scale, black box optimisation technique, to introduce the "deep active inference" agent.
Edit intent: fluency
Original:  This agent tries to minimize a variational free energy bound on the average surprise of its sensations, which is motivated by a homeostatic argument.
Revised:  This agent minimises a variational free energy bound on the average surprise of its sensations, which is motivated by a homeostatic argument.
Edit intent: clarity
Original:  It does so by changing the parameters of its generative model , together with a variational density approximating the posterior distribution over  latent variables, given its observations, and by acting on its environment to actively sample input that is likely under its generative model.
Revised:  It does so by optimising the parameters of its generative model , together with a variational density approximating the posterior distribution over latent variables, given its observations, and by acting on its environment to actively sample input that is likely under its generative model.
Edit intent: clarity
Original:  It does so by changing the parameters of its generative model , together with a variational density approximating the posterior distribution over  latent variables, given its observations, and by acting on its environment to actively sample input that is likely under its generative model.
Revised:  It does so by changing the parameters of its generative model , together with a variational density approximating the posterior distribution over the latent variables, given its observations, and by acting on its environment to actively sample input that is likely under its generative model.
Edit intent: fluency
Original:  It does so by changing the parameters of its generative model , together with a variational density approximating the posterior distribution over  latent variables, given its observations, and by acting on its environment to actively sample input that is likely under its generative model.
Revised:  It does so by changing the parameters of its generative model , together with a variational density approximating the posterior distribution over latent variables, given its observations, and by acting on its environment to actively sample input that is likely under this generative model.
Edit intent: clarity
Original:  The internal dynamics of the agent are implemented using deep  neural networks, as used in machine learning, and recurrent dynamics, making the deep active inference agent a scalable and very flexible class of active inference agents . Using the mountaincar problem, we show how goal-directed behaviour can be implemented by defining sensible prior expectations on the latent states in the agent's model , that it will try to fulfil .
Revised:  The internal dynamics of the agent are implemented using deep neural networks, as used in machine learning, making the deep active inference agent a scalable and very flexible class of active inference agents . Using the mountaincar problem, we show how goal-directed behaviour can be implemented by defining sensible prior expectations on the latent states in the agent's model , that it will try to fulfil .
Edit intent: coherence
Original:  The internal dynamics of the agent are implemented using deep  neural networks, as used in machine learning, and recurrent dynamics, making the deep active inference agent a scalable and very flexible class of active inference agents . Using the mountaincar problem, we show how goal-directed behaviour can be implemented by defining sensible prior expectations on the latent states in the agent's model , that it will try to fulfil .
Revised:  The internal dynamics of the agent are implemented using deep neural networks, as used in machine learning, and recurrent dynamics, making the deep active inference agent a scalable and very flexible class of active inference agent . Using the mountaincar problem, we show how goal-directed behaviour can be implemented by defining sensible prior expectations on the latent states in the agent's model , that it will try to fulfil .
Edit intent: fluency
Original:  The internal dynamics of the agent are implemented using deep  neural networks, as used in machine learning, and recurrent dynamics, making the deep active inference agent a scalable and very flexible class of active inference agents . Using the mountaincar problem, we show how goal-directed behaviour can be implemented by defining sensible prior expectations on the latent states in the agent's model , that it will try to fulfil .
Revised:  The internal dynamics of the agent are implemented using deep neural networks, as used in machine learning, and recurrent dynamics, making the deep active inference agent a scalable and very flexible class of active inference agents . Using the mountain car problem, we show how goal-directed behaviour can be implemented by defining sensible prior expectations on the latent states in the agent's model , that it will try to fulfil .
Edit intent: fluency
Original:  The internal dynamics of the agent are implemented using deep  neural networks, as used in machine learning, and recurrent dynamics, making the deep active inference agent a scalable and very flexible class of active inference agents . Using the mountaincar problem, we show how goal-directed behaviour can be implemented by defining sensible prior expectations on the latent states in the agent's model , that it will try to fulfil .
Revised:  The internal dynamics of the agent are implemented using deep neural networks, as used in machine learning, and recurrent dynamics, making the deep active inference agent a scalable and very flexible class of active inference agents . Using the mountaincar problem, we show how goal directed behaviour can be implemented by defining sensible prior expectations on the latent states in the agent's model , that it will try to fulfil .
Edit intent: fluency
Original:  The internal dynamics of the agent are implemented using deep  neural networks, as used in machine learning, and recurrent dynamics, making the deep active inference agent a scalable and very flexible class of active inference agents . Using the mountaincar problem, we show how goal-directed behaviour can be implemented by defining sensible prior expectations on the latent states in the agent's model , that it will try to fulfil .
Revised:  The internal dynamics of the agent are implemented using deep neural networks, as used in machine learning, and recurrent dynamics, making the deep active inference agent a scalable and very flexible class of active inference agents . Using the mountaincar problem, we show how goal-directed behaviour can be implemented by defining appropriate priors on the latent states in the agent's model , that it will try to fulfil .
Edit intent: clarity
Original:  The internal dynamics of the agent are implemented using deep  neural networks, as used in machine learning, and recurrent dynamics, making the deep active inference agent a scalable and very flexible class of active inference agents . Using the mountaincar problem, we show how goal-directed behaviour can be implemented by defining sensible prior expectations on the latent states in the agent's model , that it will try to fulfil .
Revised:  The internal dynamics of the agent are implemented using deep neural networks, as used in machine learning, and recurrent dynamics, making the deep active inference agent a scalable and very flexible class of active inference agents . Using the mountaincar problem, we show how goal-directed behaviour can be implemented by defining sensible prior expectations on the latent states in the agent's model .
Edit intent: coherence
Original:  Furthermore, we show that the deep active inference agent can learn a generative model of the environment, which can be sampled from to understand the agent's beliefs about the environment and its interaction with it .
Revised:  Furthermore, we show that the deep active inference agent can learn a generative model of the environment, which can be sampled from to understand the agent's beliefs about the environment and its interaction therewith .
Edit intent: clarity
Original: This work combines the free energy principle from cognitive neuroscience and the ensuing active inference dynamics with recent advances in variational inference in deep generative models, and evolution strategies as efficient large scale, black box optimisation technique, to introduce the "deep active inference" agent.
Revised: This work combines the free energy principle from cognitive neuroscience and the ensuing active inference dynamics with recent advances in variational inference in deep generative models, and evolution strategies to introduce the "deep active inference" agent.
Edit intent: coherence
Original:  The system has been evaluated through A/B testing with real-world users, where it performed significantly better than  competing systems.
Revised:  The system has been evaluated through A/B testing with real-world users, where it performed significantly better than many competing systems.
Edit intent: clarity
Original: DNA sequencing entails the process of determining the precise physical order of the four bases (Adenine, Guanine, Cytosine, Thymine) in a DNA strand.
Revised: DNA sequencing is the physical or biochemical process of identifying the location of determining the precise physical order of the four bases (Adenine, Guanine, Cytosine, Thymine) in a DNA strand.
Edit intent: clarity
Original: DNA sequencing entails the process of determining the precise physical order of the four bases (Adenine, Guanine, Cytosine, Thymine) in a DNA strand.
Revised: DNA sequencing entails the process of the four bases (Adenine, Guanine, Cytosine, Thymine) in a DNA strand.
Edit intent: clarity
Original:  As semiconductor technology revolutionized computing, DNA sequencing technology , termed often as Next Generation Sequencing (NGS) , revolutionized genomic research.
Revised:  As semiconductor technology revolutionized computing, DNA sequencing technology (termed Next Generation Sequencing (NGS) , revolutionized genomic research.
Edit intent: clarity
Original:  As semiconductor technology revolutionized computing, DNA sequencing technology , termed often as Next Generation Sequencing (NGS) , revolutionized genomic research.
Revised:  As semiconductor technology revolutionized computing, DNA sequencing technology , termed often as Next Generation Sequencing , NGS) revolutionized genomic research.
Edit intent: fluency
Original:  The resulting short DNA sequences are termed (short) reads.
Revised:  The output short DNA fragments from NGS platforms are termed reads.
Edit intent: clarity
Original:  Due to the large scale of the problem, well-studied pair-wise sequence similarity detection (or sequence alignment ) algorithms fall short of efficiently mapping individual reads to the reference genome . Mapping represents a search-heavy data-intensive operation and barely features any complex floating point arithmetic .
Revised:  Due to the large scale of the problem, well-studied pair-wise sequence similarity detection (or sequence alignment ) algorithms fall short of tackling the growing scale of the problem . Mapping represents a search-heavy data-intensive operation and barely features any complex floating point arithmetic .
Edit intent: clarity
Original:  Due to the large scale of the problem, well-studied pair-wise sequence similarity detection (or sequence alignment ) algorithms fall short of efficiently mapping individual reads to the reference genome . Mapping represents a search-heavy data-intensive operation and barely features any complex floating point arithmetic .
Revised:  Due to the large scale of the problem, well-studied pair-wise sequence similarity detection (or sequence alignment ) algorithms fall short of efficiently mapping individual reads to the reference genome . Mapping represents a search-heavy memory-intensive operation and barely features any complex floating point arithmetic .
Edit intent: clarity
Original:  Due to the large scale of the problem, well-studied pair-wise sequence similarity detection (or sequence alignment ) algorithms fall short of efficiently mapping individual reads to the reference genome . Mapping represents a search-heavy data-intensive operation and barely features any complex floating point arithmetic .
Revised:  Due to the large scale of the problem, well-studied pair-wise sequence similarity detection (or sequence alignment ) algorithms fall short of efficiently mapping individual reads to the reference genome . Mapping represents a search-heavy data-intensive operation and barely requires complex floating point arithmetic .
Edit intent: clarity
Original:  the energy consumption , by 109.0x when compared to a highly-optimized software implementation for modern GPUs.
Revised:  the energy consumption , by 109.0x when compared to a highly-optimized algorithm for modern GPUs.
Edit intent: clarity
Original: DNA sequencing is the physical or biochemical process of identifying the location of the four bases (Adenine, Guanine, Cytosine, Thymine) in a DNA strand.
Revised: DNA sequencing is the physical / biochemical process of identifying the location of the four bases (Adenine, Guanine, Cytosine, Thymine) in a DNA strand.
Edit intent: fluency
Original:  As semiconductor technology revolutionized computing,  DNA sequencing technology (termed Next Generation Sequencing, NGS)revolutionized genomic research.
Revised:  As semiconductor technology revolutionized computing, modern DNA sequencing technology (termed Next Generation Sequencing, NGS)revolutionized genomic research.
Edit intent: clarity
Original:  Modern NGS platforms can sequence hundreds of millions of short DNA fragments in parallel.
Revised:  As a result, modern NGS platforms can sequence hundreds of millions of short DNA fragments in parallel.
Edit intent: coherence
Original:  The output short DNA fragmentsfrom NGS platforms are termed reads.
Revised:  The sequenced DNA fragments, representing the output of NGS platforms, are termed reads.
Edit intent: clarity
Original:  Mapping each output read to a reference genome of the same species ( i.e., read mapping ) is a common critical first step in a rich and diverse set of emerging bioinformatics applications.
Revised:  Mapping each output read to a reference genome of the same species , i.e., read mapping ) is a common critical first step in a rich and diverse set of emerging bioinformatics applications.
Edit intent: fluency
Original:  Mapping each output read to a reference genome of the same species ( i.e., read mapping ) is a common critical first step in a rich and diverse set of emerging bioinformatics applications.
Revised:  Mapping each output read to a reference genome of the same species ( i.e., read mapping , is a common critical first step in a rich and diverse set of emerging bioinformatics applications.
Edit intent: fluency
Original:  Mapping each output read to a reference genome of the same species ( i.e., read mapping ) is a common critical first step in a rich and diverse set of emerging bioinformatics applications.
Revised:  Mapping each output read to a reference genome of the same species ( i.e., read mapping ) is a common critical first step in a diverse set of emerging bioinformatics applications.
Edit intent: clarity
Original:  The importance of read mapping motivated various sequence alignment and mapping algorithms, which start to fall short of tackling the growing scale of the problem.
Edit intent: coherence
Original:  Mapping represents a search-heavy memory-intensive operation and barely requires complex floating point arithmetic , therefore, can greatly benefit from in- or near-memory processing , where non-volatile memory can accommodate the large memory footprint in an area and energy efficient manner .
Revised:  Mapping represents a search-heavy memory-intensive operation and barely requires complex floating point arithmetic , therefore, can greatly benefit from near-memory processing , where non-volatile memory can accommodate the large memory footprint in an area and energy efficient manner .
Edit intent: clarity
Original:  Mapping represents a search-heavy memory-intensive operation and barely requires complex floating point arithmetic , therefore, can greatly benefit from in- or near-memory processing , where non-volatile memory can accommodate the large memory footprint in an area and energy efficient manner .
Revised:  Mapping represents a search-heavy memory-intensive operation and barely requires complex floating point arithmetic , therefore, can greatly benefit from in- or near-memory processing , where non-volatile TCAM can accommodate the large memory footprint in an area and energy efficient manner .
Edit intent: clarity
Original:  Mapping represents a search-heavy memory-intensive operation and barely requires complex floating point arithmetic , therefore, can greatly benefit from in- or near-memory processing , where non-volatile memory can accommodate the large memory footprint in an area and energy efficient manner .
Revised:  Mapping represents a search-heavy memory-intensive operation and barely requires complex floating point arithmetic , therefore, can greatly benefit from in- or near-memory processing , where non-volatile memory can accommodate the large memory footprint in an area-efficient way .
Edit intent: clarity
Original:  This paper introduces  a scalable, energy-efficient  high-throughput near (non-volatile) memory read mapping accelerator: BioMAP . Instead of optimizing an algorithm developed for general-purpose computers or GPUs, BioMAP rethinks the algorithm and  accelerator design together from the ground up.
Revised:  This paper introduces a scalable, energy-efficient and high-throughput near (non-volatile) memory read mapping accelerator: BioMAP . Instead of optimizing an algorithm developed for general-purpose computers or GPUs, BioMAP rethinks the algorithm and accelerator design together from the ground up.
Edit intent: coherence
Original:  This paper introduces  a scalable, energy-efficient  high-throughput near (non-volatile) memory read mapping accelerator: BioMAP . Instead of optimizing an algorithm developed for general-purpose computers or GPUs, BioMAP rethinks the algorithm and  accelerator design together from the ground up.
Revised:  This paper introduces a scalable, energy-efficient high-throughput solution . Instead of optimizing an algorithm developed for general-purpose computers or GPUs, BioMAP rethinks the algorithm and accelerator design together from the ground up.
Edit intent: clarity
Original: The increasing demand for renewable energy is projected to result in a 40-fold increase in offshore wind electricity in the European Union by ~ 2030.
Revised: The increasing demand for renewable energy is projected to result in a 40-fold increase in offshore wind electricity in the European Union by 2030.
Edit intent: fluency
Original:  Despite a great number of local impact studies for selected marine populations, the regional ecosystem impacts of offshore windfarm structures are not yet well investigated nor understood.
Revised:  Despite a great number of local impact studies for selected marine populations, the regional ecosystem impacts of offshore wind farm structures are not yet well investigated nor understood.
Edit intent: fluency
Original:  Despite a great number of local impact studies for selected marine populations, the regional ecosystem impacts of offshore windfarm structures are not yet well investigated nor understood.
Revised:  Despite a great number of local impact studies for selected marine populations, the regional ecosystem impacts of offshore windfarm structures are not yet well assessed nor understood.
Edit intent: clarity