wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this paper, we propose a new training objective " Guided Complement Entropy " (GCE) that has dual desirable effects: (a) neutralizing the predicted probabilities of incorrect classes,and (b) maximizing the predicted probability of the ground-truth class , particularly when (a) is achieved. Training with GCE encourages models to learn latent representations where samples of different classes form distinct clusters, which we argue, improves the model robustness against adversarial perturbations. Furthermore, compared with the state-of-the-arts trained with cross-entropy, same models trained with GCE achieve significant improvements on the robustness against white-box adversarial attacks, both with and without adversarial training . When no attack is present, training with GCE also outperforms cross-entropy in terms of model accuracy .
Revised:  In this paper, we propose a new training objective " Guided Complement Entropy " (GCE) that has dual desirable effects: (a) neutralizing the predicted probabilities of incorrect classes,and (b) maximizing the predicted probability of the ground-truth class like cross-entropy, we neutralize its probabilities on the incorrect classes along with a "guided" term to balance between these two terms. We show in the experiments that our method achieves better model robustness with GCE also outperforms cross-entropy in terms of model accuracy .
Edit intent: meaning-changed
Original:  When no attack is present, training with GCE also outperforms cross-entropy in terms of model accuracy .
Revised:  When no attack is present, training with GCE also outperforms cross-entropy training objective. We also show that our method can be used orthogonally to adversarial training across well-known methods with noticeable robustness gain. To the best of our knowledge, our approach is the first one that improves model robustness without compromising performance .
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Original: Summary: Non-canonical nucleic and amino acid monomers are essential to enhance the information capacity, functional capabilities, and stability of DNA, RNA, and protein biopolymers. However, there are few tools for describing the primary structure of biopolymers that include non-canonical monomers.
Revised: Summary: Non-canonical nucleic and amino acid monomers are essential to enhance the information capacity, functional capabilities, and stability of DNA, RNA, proteins, and complexes, we do not fully understand how networks of non-canonical monomers.
Edit intent: meaning-changed
Original:  However, there are few tools for describing the primary structure of biopolymers that include non-canonical monomers. We developed BpForms , the first toolkit for concretely and compactly describing the primary structures of non-canonical 1-dimensional biopolymers. BpForms includes the first alphabets of non-canonical DNA, RNA, and protein monomers; a FASTA-like notation for describing biopolymers; and a website, a command line program, a REST API, and a Python package for calculating properties of biopolymers. We anticipate BpForms will be a valuable tool for communicating data about modified DNA, RNA, and proteins, as well as integrating data about epigenetic, post-transcriptional, and post-translational modification.BpForms will also be valuable for whole-cell modeling and cell engineering. Availability and implementation: BpForms is freely available open-source at URL
Revised:  However, there are few tools for describing the primary structure of biopolymers that include non-canonical macromolecules generate behavior. One barrier is our limited formats, such as IUPAC, for abstractly describing macromolecules. To overcome this barrier, we developed BpForms and BcForms, a toolkit of ontologies, grammars, and software for abstracting the primary structure of polymers and complexes as combinations of residues, caps, crosslinks, and nicks. The toolkit can help quality control, exchange, and integrate information about the primary structure of macromolecules into fine-grained global networks of intracellular biochemistry.
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Original:  The developed model simulates DCV concentrations in boutons, DCV fluxes between the boutons, age density distributions of DCVs, and the mean age of DCVs in various boutons  .
Revised:  The developed model simulates DCV concentrations in boutons, DCV fluxes between the boutons, age density distributions of DCVs, and the mean age of DCVs in various boutons . Unlike published experimental observations, our model predicts DCV circulation in type II terminals after these terminals are filled to saturation. This disagreement is likely because experimentally observed terminals were not at steady-state, but rather were accumulating DCVs for later release. Our estimates show that the number of DCVs in the transiting state is much smaller than that in the resident state. DCVs traveling in the axon, rather than DCVs transiting in the terminal, may provide a reserve of DCVs for replenishing boutons after a release. The techniques for modeling transport of DCVs developed in our paper can be used to model the transport of URLanelles in axons .
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Original: In coastal areas there is a strong interdependence of humans and ecosystems, implying that management approaches need to ensure the provision of services to society. We developed a spatial optimization model that explores how the provision of ecosystem services varies along the coast under different management scenarios.
Revised: Ecological connectivity in coastal oceanic waters is mediated by dispersion of the early life stages of URLanisms and conditions the structure of biological communities and the provision of ecosystem services. Integrated management strategies aimed at ensuring long-term service provision to society do not currently consider the importance of dispersal and larval connectivity. A spatial optimization model that explores how the provision of ecosystem services varies along the coast under different management scenarios.
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Original:  We developed a spatial optimization model that explores how the provision of ecosystem services varies along the coast under different management scenarios. Our approach assumes that the baseline structure of biological communities largely relies on large-scale connectivity . Therefore, based on the baseline potential of a site to provide services, conditioned by biophysical connectivity, the model explores the optimal allocation of protected areas. This approach was tested in central Chile, where the biological productivity is tightly linked to the hydrodynamics driven by the Humboldt current, through biophysical connectivity of early-life stages.
Revised:  We developed a spatial optimization model that explores how the provision of ecosystem services in coastal areas by accounting for the role of dispersal and larval connectivity. The approach combines a validated coastal circulation model that reproduces realistic patterns of larval transport along the coast, which ultimately conditions the biological connectivity and productivity of an area, with additional spatial layers describing potential ecosystem services. The spatial optimisation exercise was tested along the coast of Central Chile, a highly productive area dominated by the Humboldt current, through biophysical connectivity of early-life stages.
Edit intent: clarity
Original:  This approach was tested in central Chile, where the biological productivity is tightly linked to the hydrodynamics driven by the Humboldt current, through biophysical connectivity of early-life stages. In this region small-scale fisheries are regulated through management areasthat alternate with open access fisheries. Some management areas have been shown to enhance abundance of target species and richness of associated communities, however, there are evidences for over-exploitation of fishery resources. By using this scenario as case study , we explored how the current coastal ecosystem could be spatially managed to optimize the provision of ecosystem services. The model evidenced that the current positioning of management areas could be maintained, but an additional 10 \% of the coast should be no-take area.
Revised:  This approach was tested in central Chile, where the biological productivity is tightly linked to the hydrodynamics driven by the Humboldt Current. Results show it is unnecessary to relocate existing management areas, as increasing no-take areas by 10 \% of the coast should be no-take area.
Edit intent: coherence
Original:  The model evidenced that the current positioning of management areas could be maintained, but an additional 10 \% of the coast should be no-take area. Importantly, by considering all ecosystem properties prioritized by end-users, the model improved the spatial representativeness. The novelty of this approach is that it develops a spatial optimization model that embeds the dynamism of biophysical connectivity to assess the provision of ecosystem services within a network of protected areas .
Revised:  The model evidenced that the current positioning of management areas could be maintained, but an additional 10 \% could maximise ecosystem service provision, while improving the spatial representativeness of protected areas and minimising social conflicts. The location of protected areas was underrepresented in some sections of the study domain, principally due to the restriction of the model to rocky subtidal habitats. Future model developments should encompass the diversity of coastal ecosystems and human activities to inform integrative spatial management. Nevertheless, the spatial optimization model is innovative not only for its integrated ecosystem perspective, but also because it demonstrates that it is possible to incorporate time-varying biophysical connectivity within the optimization problem, thereby linking the dynamics of exploited populations produced by the spatial management regime .
Edit intent: coherence
Original:  Results show it is unnecessary to relocate existing management areas, as increasing no-take areas by 10\% could maximise ecosystem service provision, while improving the spatial representativeness of protected areas and minimising social conflicts.
Revised:  Results show it is unnecessary to relocate existing management areas, as increasing no-take areas by 10\% could maximise ecosystem service provision, while improving the spatial representativeness of protected areas and minimizing social conflicts.
Edit intent: clarity
Original: Random objects arrive sequentially and must be divided among a fixed set of n agents upon arrival: the distribution \mu of the profile of utilities is IID across periods, but arbitrary across agents. We consider a class of online division rules that take, as an input, only the realized utility profile, and from \mu the individual expected utilities. They have no record from past realized utilities, and do not know either if and how many new objects will appear in the future. We call such rules prior-independent. A rule is fair if each agent , ex ante, expects at least 1/n-th of his utility for the object if it is a good, at most 1/n-th of his disutility for it if it is a bad. Among fair prior-independent rules to divide goods (bads)we uncover those collecting the largest (lowest) total expected (dis)utility . There is exactly one fair rule for bads that is optimal in this sense. However, for goods, the set of optimal fair rules is one dimensional. Both in the worst case and in the asymptotic sense, our optimal rules perform much better than the natural Proportional rule (for goods or for bads), and not much worse than the optimal fair prior-dependent rule that knows the full distribution \mu in addition to realized utilities .
Revised: Ann likes oranges and dislikes apples; Bob likes apples and dislikes oranges. Tomorrow they will receive one fruit that will be an orange or an apple with equal probability 0.5. Giving to each half of that fruit is fair for each realisation of the fruit; but agreeing that whatever fruit appears will go to the agent who likes it more gives a higher expected utility to each agent and is fair in the average sense: in expectation, each agent prefers his allocation to the equal division of the object, he gets a Fair Share. We turn this familiar observation into an economic design problem: upon drawing a random object (the fruit), we learn the realised utility of each agent and can compare it to the mean of his distribution of utilities; no other statistical information about the distribution is available. We fully characterize the division rules that use only this sparse information in the most efficient possible way, while giving everyone a Fair Share. Although the probability distribution of individual utilities is arbitrary and mostly unknown to the designer, these rules perform in the asymptotic sense, our optimal rules perform much better than the natural Proportional rule (for goods or for bads), and not much worse than the optimal fair prior-dependent rule that knows the full distribution \mu in addition to realized utilities .
Edit intent: meaning-changed
Original:  Tomorrow they will receive one fruit that will be an orange or an apple with equal probability 0.5. Giving to each half of that fruit is fair for each realisation of the fruit ; but agreeing that whatever fruit appears will go to the agent who likes it more gives a higher expected utility to each agent and is fair in the average sense: in expectation, each agent prefers his allocation to the equal division of the object, he gets a Fair Share .
Revised:  Tomorrow they will receive one fruit that will be an orange or an apple with equal probability 0.5. Giving to each half of that fruit is fair for each realisation of the fruit . However, agreeing that whatever fruit appears will go to the agent who likes it more gives a higher expected utility to each agent and is fair in the average sense: in expectation, each agent prefers his allocation to the equal division of the object, he gets a Fair Share .
Edit intent: coherence
Original:  but agreeing that whatever fruit appears will go to the agent who likes it more gives a higher expected utility to each agent and is fair in the average sense: in expectation, each agent prefers his allocation to the equal division of the object, he gets a Fair Share .
Revised:  but agreeing that whatever fruit appears will go to the agent who likes it more gives a higher expected utility to each agent and is fair in the average sense: in expectation, each agent prefers his allocation to the equal division of the fruit, i.e., he gets a Fair Share .
Edit intent: clarity
Original:  but agreeing that whatever fruit appears will go to the agent who likes it more gives a higher expected utility to each agent and is fair in the average sense: in expectation, each agent prefers his allocation to the equal division of the object, he gets a Fair Share .
Revised:  but agreeing that whatever fruit appears will go to the agent who likes it more gives a higher expected utility to each agent and is fair in the average sense: in expectation, each agent prefers his allocation to the equal division of the object, he gets a fair share .
Edit intent: fluency
Original:  We also consider a variant of the problem with size and magnitude constraints which we formulate using binary variables. In this case, only numerical solutions are possible .
Revised:  For the general case of an arbitrary joint distribution for the asset returns, under certain conditions, we characterize the optimal portfolio as the optimal Lagrange multiplier associated to an equality-constrained dual problem. Then, we consider the special case of Gaussian returns for which it is possible to identify all cases where an optimal solution exists and to give an explicit formula for the optimal portfolio whenever it exists .
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Original:  Therefore, we propose a method to improve the accuracy and efficiency of inference by iteratively applying perturbations to a network that are advantageous under a Bayesian framework. The spectrum of the empirical Fisher information can be used as a measure for the difficulty of the inference during the training process.
Revised:  Therefore, we propose a method to improve the accuracy and efficiency of inference by iteratively applying perturbations to a network . Formulating active learning in the language of information geometry, our framework quantifies the difficulty of inference as well as the information gain due to perturbations through the curvature of the underlying parameter manifold as measured though the empirical Fisher information can be used as a measure for the difficulty of the inference during the training process.
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Original:  The spectrum of the empirical Fisher information can be used as a measure for the difficulty of the inference during the training process. We significantly improve the accuracy and efficiency of inference in medium-sized networks based on this strategy with a reasonable number of experimental queries .
Revised:  The spectrum of the empirical Fisher information . Perturbations are then chosen that reduce most the variance of the Bayesian posterior. We apply this framework to a specific probabilistic graphical model where the nodes in the network are modeled as binary variables, "spins" with Ising-form pairwise interactions. Based on this strategy, we significantly improve the accuracy and efficiency of inference in medium-sized networks based on this strategy with a reasonable number of experimental queries .
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Original:  We significantly improve the accuracy and efficiency of inference in medium-sized networks based on this strategy with a reasonable number of experimental queries . Our method could be powerful in the analysis of complex networks as well as in the rational design of experiments.
Revised:  We significantly improve the accuracy and efficiency of inference in medium-sized networks based on this strategy with a reasonable number of experimental queries for medium sized networks. Our active learning framework could be powerful in the analysis of complex networks as well as in the rational design of experiments.
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Original:  In the context of the experimental sciences, well-controlled perturbations can be applied to a system, probing the internal structure of the network .
Revised:  In the context of the experimental sciences, well-controlled perturbation experiments are widely used to probe the structure of complex networks. However, our understanding of how and why perturbations aid inference remains heuristic, and we lack automated procedures that determine network structure by combining inference and perturbation .
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Original:  Therefore, we propose a general mathematical framework to study inference with iteratively applied perturbations to a network. Formulating active learning in the language of information geometry, our framework quantifies the difficulty of inference as well as the information gain due to perturbations through the curvature of the underlying parameter manifold as measured though the empirical Fisher information.
Revised:  Therefore, we propose a general mathematical framework to study inference with iteratively applied perturbations . Using the formulation of information geometry, our framework quantifies the difficulty of inference as well as the information gain due to perturbations through the curvature of the underlying parameter manifold as measured though the empirical Fisher information.
Edit intent: clarity
Original:  Formulating active learning in the language of information geometry, our framework quantifies the difficulty of inference as well as the information gain due to perturbations through the curvature of the underlying parameter manifold as measured though the empirical Fisher information.
Revised:  Formulating active learning in the language of information geometry, our framework quantifies the difficulty of inference and the information gain due to perturbations through the curvature of the underlying parameter manifold as measured though the empirical Fisher information.
Edit intent: coherence
Original:  Formulating active learning in the language of information geometry, our framework quantifies the difficulty of inference as well as the information gain due to perturbations through the curvature of the underlying parameter manifold as measured though the empirical Fisher information.
Revised:  Formulating active learning in the language of information geometry, our framework quantifies the difficulty of inference as well as the information gain from perturbations through the curvature of the underlying parameter manifold as measured though the empirical Fisher information.
Edit intent: clarity
Original:  Formulating active learning in the language of information geometry, our framework quantifies the difficulty of inference as well as the information gain due to perturbations through the curvature of the underlying parameter manifold as measured though the empirical Fisher information.
Revised:  Formulating active learning in the language of information geometry, our framework quantifies the difficulty of inference as well as the information gain due to perturbations through the curvature of the underlying parameter manifold , measured by Fisher information.
Edit intent: coherence
Original:  Perturbations are then chosen that reduce most the variance of the Bayesian posterior. We apply this framework to a specific probabilistic graphical model where the nodes in the networkare modeled as binary variables, "spins" with Ising-form pairwise interactions. Based on this strategy, we significantly improve the accuracy and efficiency of inference from a reasonable number of experimental queries for medium sized networks .
Revised:  We apply the framework to the inference of spin network models and find that designed perturbations can reduce the sampling complexity by 10^6-fold across a variety of network architectures. Physically, our framework reveals that perturbations boost inference by causing a network to explore previously inaccessible states. Optimal perturbations break spin-spin correlations within a network, increasing the information available for inference and thus reducing sampling complexity by orders of magnitude .
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Original:  A key finding is that while the test based on the infinity-one\to1 norm requires relatively new tools from the semidefinite programming literature to implement, it can be substantially more powerful than that based on the two-two\to2 norm for the kinds of sparse and degree-heterogeneous networks common in economics.
Revised:  A key finding is that while the test based on the \infty\to1 norm requires relatively new tools from the semidefinite programming literature to implement, it can be substantially more powerful than that based on the two-two\to2 norm for the kinds of sparse and degree-heterogeneous networks common in economics.
Edit intent: clarity
Original:  A key finding is that while the test based on the infinity-one\to1 norm requires relatively new tools from the semidefinite programming literature to implement, it can be substantially more powerful than that based on the two-two\to2 norm for the kinds of sparse and degree-heterogeneous networks common in economics.
Revised:  A key finding is that while the test based on the infinity-one\to1 norm requires relatively new tools from the semidefinite programming literature to implement, it can be substantially more powerful than that based on the 2\to2 norm for the kinds of sparse and degree-heterogeneous networks common in economics.
Edit intent: fluency