wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In recent years, several approaches have been employed for a reliable neuronal cell-type recognition, such as immunohistochemical (IHC) analysis and feature extraction algorithms based on several characteristics of neuronal cells. These methods, however, demand a lot of human intervention and observation, they are time-consuming and regarding the feature extraction algorithms it is not clear or obvious what are the best featuresthat define a neuronal cell class.
Revised:  In recent years, several approaches have been employed for a reliable neuronal cell-type recognition, such as immunohistochemical (IHC) analysis and feature extraction algorithms based on cellular characteristics have recently been proposed. The former rely on molecular markers, which are often expressed in many cell types, while the latter suffer from similar issues: finding features that are distinctive for each class has proven to be equally challenging. Moreover, both approaches are time consuming and demand a lot of human intervention and observation, they are time-consuming and regarding the feature extraction algorithms it is not clear or obvious what are the best featuresthat define a neuronal cell class.
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Original:  These methods, however, demand a lot of human intervention and observation, they are time-consuming and regarding the feature extraction algorithms it is not clear or obvious what are the best featuresthat define a neuronal cell class. In this work we examine three different deep learning models aiming at an automated neuronal cell-type classification and compare their performance.
Revised:  These methods, however, demand a lot of human intervention . In this work we establish the first, automated cell-type classification method that relies on neuronal activity rather than molecular or cellular features. We test our method on a real-world dataset comprising of raw calcium activity signals for four neuronal types. We compare the performance of three different deep learning models aiming at an automated neuronal cell-type classification and compare their performance.
Edit intent: meaning-changed
Original: A basic test of fairness when we divide a manna  of private items  between n agents is the lowest welfare the rule guarantees to each agent, irrespective of others preferences.
Revised: A basic test of fairness when we divide a manna of private items (commodities, workloads, land, time intervals) between n agents is the lowest welfare the rule guarantees to each agent, irrespective of others preferences.
Edit intent: meaning-changed
Original: A basic test of fairness when we divide a manna  of private items  between n agents is the lowest welfare the rule guarantees to each agent, irrespective of others preferences. Two familiar examples are: the Equal Split Guarantee when the manna is divisible and preferences are convex; and 1/n of the utility of a heterogenous non atomic cake, if utilities are additive. The minMax utilityof an agent is that of her best share in the worst possible n-partition of .
Revised: A basic test of fairness when we divide a manna of private items between n agents is the lowest welfare the rule guarantees to each agent, irrespective of others ' preferences. If the manna is non atomic and utilities are continuous (not necessarily monotone or convex), we can guarantee the minMax utility: that of our agent's best share in the worst possible n-partition of .
Edit intent: clarity
Original:  The minMax utilityof an agent is that of her best share in the worst possible n-partition of . It is weakly below her Maxmin utility , that of her worst share in the best possible n-partition.
Revised:  The minMax utilityof an agent is that of her best share in her worst partition of the manna; and implement it by Kuhn's generalisation of Divide and Choose. The larger Maxmin utility -- of her worst share in the best possible n-partition.
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Original:  It is weakly below her Maxmin utility , that of her worst share in the best possible n-partition. The Maxmin guarantee is not feasible, even with two agents, if non convex preferences are allowed. The minMax guarantee is feasible in the very general class of problems where the manna is non atomic and utilities are continuous, but not necessarily additive or monotonic. The proof uses advanced algebraic topology techniques. And the minMax guarantee is implemented by the n-person version of Divide and Choose due to Kuhn (1967). When utilities are co-monotone (a larger part of the manna is weakly better for everyone, or weakly worse for everyone) better guarantees than minMax are feasible. In our Bid & Choose rules , agents bid the smallest size (according to some benchmark measure of the manna) of a share they find acceptable , and the lowest bidder picks such a share.
Revised:  It is weakly below her Maxmin utility , that of her worst share in her best partition -- cannot be guaranteed, even for two agents. If for all agents more manna is better than less (or less is better than more), our Bid & Choose rules , agents bid the smallest size (according to some benchmark measure of the manna) of a share they find acceptable , and the lowest bidder picks such a share.
Edit intent: coherence
Original:  In our Bid & Choose rules , agents bid the smallest size (according to some benchmark measure of the manna) of a share they find acceptable , and the lowest bidder picks such a share.
Revised:  In our Bid & Choose rules implement guarantees between minMax and Maxmin by letting agents bid for the smallest (or largest) size of a share they find acceptable , and the lowest bidder picks such a share.
Edit intent: meaning-changed
Original:  In our Bid & Choose rules , agents bid the smallest size (according to some benchmark measure of the manna) of a share they find acceptable , and the lowest bidder picks such a share.The resulting guarantee is between the minMax and Maxmin utilities. 1
Revised:  In our Bid & Choose rules , agents bid the smallest size (according to some benchmark measure of the manna) of a share they find acceptable .
Edit intent: coherence
Original:  The observation network generally presents an obstruction to the efficient rate of signal aggregation, as agents compromise between incorporating the signals of the observed neighbors and not over-counting the  signals of the unobserved early movers.
Revised:  The observation network generally presents an obstruction to the efficient rate of signal aggregation, as agents compromise between incorporating the signals of the observed neighbors and not over-counting the confounding signals of the unobserved early movers.
Edit intent: clarity
Original:  The additional information aggregated by each generation is asymptotically equivalent to fewer than two independent signals, even when generations are arbitrarily large  .
Revised:  The additional information aggregated by each generation is asymptotically equivalent to fewer than two independent signals, even when generations are arbitrarily large . When agents observe all predecessors from the previous generation, social learning aggregates no more than three signals per generation starting from the third generation, and the long-run learning rate is slower when generations are larger .
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Original:  The observation network generally presents an obstruction to the efficient rate of signal aggregation , as agents compromise between incorporating the signals of the observed neighbors and not over-counting the confounding signals of the unobserved early movers.
Revised:  Equilibrium learning may be slow when agents do not observe all predecessors , as agents compromise between incorporating the signals of the observed neighbors and not over-counting the confounding signals of the unobserved early movers.
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Original:  Agents learn about a binary state and take turns choosing actions based on own signals and network neighbors' behavior. Equilibrium learning may be slow when agents do not observe all predecessors, as agents compromise between incorporating the signals of the observed neighbors and not over-counting the confounding signals of the unobserved early movers. We show that on any network, equilibrium actions are a log-linear function of observations and each agent's accuracy admits a signal-counting interpretation .
Revised:  Agents learn about a binary state and take turns choosing actions based on own signals and network neighbors' actions, the network structure often obstructs information aggregation. We consider sequential social learning with rational agents and Gaussian signals and ask how the efficiency of signal aggregation changes with the network. Rational actions in our model admit a signal-counting interpretation .
Edit intent: clarity
Original:  We show that on any network, equilibrium actions are a log-linear function of observations and each agent's accuracy admits a signal-counting interpretation . Adding links to the observation network can harm agents even without introducing new confounds. We then consider a network structure where agents move in generations and observe some members of the previous generation.
Revised:  We show that on any network, equilibrium actions are a log-linear function of observations and each agent's accuracy admits a signal-counting interpretation of accuracy, which lets us compare the aggregative efficiency of social learning across networks. Learning is very inefficient in a class of networks where agents move in generations and observe some members of the previous generation.
Edit intent: meaning-changed
Original:  When this observation structure is sufficiently symmetric, the additional information aggregated by each generation is asymptotically equivalent to fewer than two independent signals , even when generations are arbitrarily large . When agents observe all predecessors from the previous generation, social learning aggregates no more than three signals per generation starting from the third generation, and the long-run learning rate is slower when generations are larger .
Revised:  When this observation structure is sufficiently symmetric, the additional information aggregated by each generation is asymptotically equivalent to fewer than two independent signals , even when generations are arbitrarily large .
Edit intent: coherence
Original:  We consider sequential social learning with rational agents and Gaussian signals  and ask how the  efficiency of signal aggregation changes with the network . Rational actions in our model  admit a signal-counting interpretation of accuracy ,
Revised:  We consider sequential social learning with rational agents and Gaussian signals in the canonical sequential social-learning problem and ask how the efficiency of signal aggregation changes with the network . Rational actions in our model admit a signal-counting interpretation of accuracy ,
Edit intent: meaning-changed
Original:  We consider sequential social learning with rational agents and Gaussian signals  and ask how the  efficiency of signal aggregation changes with the network . Rational actions in our model  admit a signal-counting interpretation of accuracy ,
Revised:  We consider sequential social learning with rational agents and Gaussian signals and ask how the network changes the efficiency of signal aggregation changes with the network . Rational actions in our model admit a signal-counting interpretation of accuracy ,
Edit intent: meaning-changed
Original:  We consider sequential social learning with rational agents and Gaussian signals  and ask how the  efficiency of signal aggregation changes with the network . Rational actions in our model  admit a signal-counting interpretation of accuracy ,
Revised:  We consider sequential social learning with rational agents and Gaussian signals and ask how the efficiency of signal aggregation changes with the network . Rational actions in our model are a log-linear function of observations and admit a signal-counting interpretation of accuracy ,
Edit intent: meaning-changed
Original:  We consider sequential social learning with rational agents and Gaussian signals  and ask how the  efficiency of signal aggregation changes with the network . Rational actions in our model  admit a signal-counting interpretation of accuracy , which lets us compare the aggregative efficiency of social learning across networks . Learning is very inefficientin a class of networks where agents move in generations and observe the previous generation  . Generations after the first contribute very little additional information  , even when generations are arbitrarily large.
Revised:  We consider sequential social learning with rational agents and Gaussian signals and ask how the efficiency of signal aggregation changes with the network . Rational actions in our model admit a signal-counting interpretation of accuracy . This generates a fine-grained ranking of networks based on their aggregative efficiency index. Networks where agents observe multiple neighbors but not their common predecessors confound information, and we show confounding can make learning very inefficient. In a class of networks where agents move in generations and observe the previous generation . Generations after the first contribute very little additional information , even when generations are arbitrarily large.
Edit intent: clarity
Original:  which lets us compare the aggregative efficiency of social learning across networks . Learning is very inefficientin a class of networks where agents move in generations and observe the previous generation  . Generations after the first contribute very little additional information  , even when generations are arbitrarily large.
Revised:  which lets us compare the aggregative efficiency of social learning across networks . Learning is very inefficientin a class of networks where agents move in generations and observe the previous generation , aggregative efficiency is a simple function of network parameters: increasing in observations and decreasing in confounding . Generations after the first contribute very little additional information , even when generations are arbitrarily large.
Edit intent: meaning-changed
Original:  which lets us compare the aggregative efficiency of social learning across networks . Learning is very inefficientin a class of networks where agents move in generations and observe the previous generation  . Generations after the first contribute very little additional information  , even when generations are arbitrarily large.
Revised:  which lets us compare the aggregative efficiency of social learning across networks . Learning is very inefficientin a class of networks where agents move in generations and observe the previous generation . Generations after the first contribute very little additional information due to confounding , even when generations are arbitrarily large.
Edit intent: meaning-changed
Original:  By following [ 27 ], we define the fundamental value F of a risky asset S as the price of a super-replicating portfolio for a position terminating in one unit of the asset and zero cash.
Revised:  By following [ 28 ], we define the fundamental value F of a risky asset S as the price of a super-replicating portfolio for a position terminating in one unit of the asset and zero cash.
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Original: This paper develops a novel approach to uniform inference for functions that bound the distribution and quantile functions of heterogeneous treatment effects in randomized experiments when only marginal treatment and control distributions are observed and the joint distribution of outcomes is unobserved. These bounds are nonlinear maps of the marginal distribution functions of control and treatment outcomes, and  statistical inference methods for nonlinear maps usually rely on smoothness through a type of differentiability.
Revised: This paper develops a novel approach to uniform inference for the optimal value function, that is, the function that results from optimizing an objective function marginally over one of its arguments. The marginal optimization map is nonlinear and statistical inference methods for nonlinear maps usually rely on smoothness through a type of differentiability.
Edit intent: meaning-changed
Original:  These bounds are nonlinear maps of the marginal distribution functions of control and treatment outcomes, and  statistical inference methods for nonlinear maps usually rely on smoothness through a type of differentiability.
Revised:  These bounds are nonlinear maps of the marginal distribution functions of control and treatment outcomes, and not differentiable, which complicates inference procedures, since statistical inference methods for nonlinear maps usually rely on smoothness through a type of differentiability.
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Original:  We show that the maps from marginal distributions to bound functions are not differentiable, but uniform test statistics applied to the bound functions - such as Kolmogorov-Smirnov or Cram\'er-von Mises  - are directionally differentiable.
Revised:  We show that the map from objective function to uniform test statistics applied to the bound functions - such as Kolmogorov-Smirnov or Cram\'er-von Mises - are directionally differentiable.
Edit intent: meaning-changed
Original:  We show that the maps from marginal distributions to bound functions are not differentiable, but uniform test statistics applied to the bound functions - such as Kolmogorov-Smirnov or Cram\'er-von Mises  - are directionally differentiable.
Revised:  We show that the maps from marginal distributions to bound functions are not differentiable, but uniform test statistics applied to the value functions - such as Kolmogorov-Smirnov or Cram\'er-von Mises - are directionally differentiable.
Edit intent: clarity
Original:  We show that the maps from marginal distributions to bound functions are not differentiable, but uniform test statistics applied to the bound functions - such as Kolmogorov-Smirnov or Cram\'er-von Mises  - are directionally differentiable.
Revised:  We show that the maps from marginal distributions to bound functions are not differentiable, but uniform test statistics applied to the bound functions - such as Kolmogorov-Smirnov or Cram\'er-von Mises statistics - are directionally differentiable.
Edit intent: clarity
Original:  We establish the consistency and weak convergence of nonparametric plug-in estimates of the test statistics and show how they can be used to conduct inference for bounds uniformly over the distribution of treatment effects. We also establish the directional differentiability of minimax operators applied to general - that is , not only convex-concave - functions, which may be of independent interest .
Revised:  We establish the consistency and weak convergence of nonparametric plug-in estimates of the test statistics and show how they can be used to conduct uniform inference. Because the limiting distribution of sample value functions is not generally tractable, to conduct practical inference, we develop detailed resampling techniques that combine a bootstrap procedure with estimates of the directional derivatives .
Edit intent: meaning-changed
Original:  The marginal optimization map is nonlinear and not differentiable, which complicates inference procedures, since statistical inference methods for nonlinear maps usually rely on regularity through a type of differentiability.
Revised:  Marginal optimization is not compactly differentiable as a map between the spaces of objective and value functions, which is problematic because standard inference methods for nonlinear maps usually rely on regularity through a type of differentiability.
Edit intent: meaning-changed
Original:  The marginal optimization map is nonlinear and not differentiable, which complicates inference procedures, since statistical inference methods for nonlinear maps usually rely on regularity through a type of differentiability. We show that the map from objective function to uniform test statistics applied to the value functions - such as Kolmogorov-Smirnov or Cram\'er-von Mises statistics - are directionally differentiable.
Revised:  The marginal optimization map is nonlinear and not differentiable, which complicates inference procedures, since statistical inference methods for nonlinear maps usually rely on compact differentiability. However, we show that the map from objective function to uniform test statistics applied to the value functions - such as Kolmogorov-Smirnov or Cram\'er-von Mises statistics - are directionally differentiable.
Edit intent: clarity
Original:  We show that the map from objective function to uniform test statistics applied to the value functions - such as Kolmogorov-Smirnov or Cram\'er-von Mises statistics - are directionally differentiable.
Revised:  We show that the map from objective function to uniform test statistics applied to the value function -- specifically, Kolmogorov-Smirnov or Cram\'er-von Mises statistics - are directionally differentiable.
Edit intent: clarity
Original:  We show that the map from objective function to uniform test statistics applied to the value functions - such as Kolmogorov-Smirnov or Cram\'er-von Mises statistics - are directionally differentiable.
Revised:  We show that the map from objective function to uniform test statistics applied to the value functions - such as Kolmogorov-Smirnov or Cram\'er-von Mises statistics -- are directionally differentiable.
Edit intent: fluency
Original:  We establish consistency and weak convergence of nonparametric plug-in estimates of the test statistics and show how they can be used to conduct uniform inference. Because the limiting distribution of sample value functions is not generally tractable, to conduct practical inference, we develop detailed resampling techniques that combine a bootstrap procedure with estimates of the directional derivatives.
Revised:  We establish consistency and weak convergence of nonparametric plug-in estimates of the test statistics . For practical inference, we develop detailed resampling techniques that combine a bootstrap procedure with estimates of the directional derivatives.
Edit intent: coherence
Original: Modeling the relationship between chemical structure and molecular activity is a key task in drug development and precision medicine. In this paper, we utilize a novel deep learning architecture to jointly train coordinated embeddings of chemical structures and transcriptional signatures. We do so by training neural networks in a coordinated manner such that learned chemical representations correlate most highly with the encodings of the transcriptional patterns they induce. We then test this approach by using held-out gene expression signatures as queries into embedding space to recover their corresponding compounds. We evaluate these embeddings' utility for small molecule prioritization on this new benchmark task. Our method outperforms a series of baselines, successfully generalizing to unseen transcriptional experiments, but still struggles to generalize to entirely unseen chemical structures .
Revised: Modeling the relationship between chemical structure and molecular activity is a key task in drug development . Many benchmark tasks have been proposed for molecular property prediction, but these tasks are generally aimed at specific, isolated biomedical properties. In this work, we propose a new cross-modal small molecule retrieval task, designed to force a model to learn to associate the structure of a small molecule with the transcriptional change it induces. We develop this task formally as multi-view alignment problem, and present a coordinated deep learning approach that jointly optimizes representations of both chemical structure and perturbational gene expression profiles. We benchmark our results against oracle models and principled baselines, and find that cell line variability markedly influences performance in this domain. Our work establishes the feasibility of this new task, elucidates the limitations of current data and systems, and may serve to catalyze future research in small molecule representation learning .
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