wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We give a simple data structure that generalizes and outperforms previous algorithms such as MinHash [J. Discrete Algorithms 1998], SimHash [STOC 2002], Spherical LSF [SODA  2017] and Chosen Path [STOC 2017] ; and we show matching lower bounds using hypercontractive inequalities for a wide range of parameters and space/time trade-offs. This answers the main open question in Christiani and Pagh STOC 2017%DIFDELCMD < ] %%% on unifying the landscape of Locality Sensitive (non-data-dependent) set similarity search in both time and space;
Revised:  We give a simple data structure that generalizes and outperforms previous algorithms such as MinHash [J. Discrete Algorithms 1998], SimHash [STOC 2002], Spherical LSF [SODA 2017] and Chosen Path [STOC 2017] STOC 2017%DIFDELCMD < ] %%% on unifying the landscape of Locality Sensitive (non-data-dependent) set similarity search in both time and space;
Edit intent: coherence
Original:  This answers the main open question in Christiani and Pagh STOC 2017%DIFDELCMD < ] %%% on unifying the landscape of Locality Sensitive (non-data-dependent) set similarity search in both time and space;
Revised:  This answers the main open question in Christiani and Pagh %DIFDELCMD < ] %%% on unifying the landscape of Locality Sensitive (non-data-dependent) set similarity search in both time and space;
Edit intent: clarity
Original:  This answers the main open question in Christiani and Pagh STOC 2017%DIFDELCMD < ] %%% on unifying the landscape of Locality Sensitive (non-data-dependent) set similarity search in both time and space;
Revised:  This answers the main open question in Christiani and Pagh STOC 2017%DIFDELCMD < ] %%% by as much as a factor n^{0.14 in both time and space;
Edit intent: meaning-changed
Original: Empirical economists are often deterred from the application of binary choice models with fixed effects mainly for two reasons: the incidental parameter bias and the computational challenge in (moderately ) large data sets. We show how both issues can be alleviated in the context of binary choice models with individual and time fixed effects .
Revised: Empirical economists are often deterred from the application of binary choice models with fixed effects mainly for two reasons: the incidental parameter bias and the computational challenge even in moderately large panels. Using the example of binary choice models with individual and time fixed effects .
Edit intent: clarity
Original:  We show how both issues can be alleviated in the context of binary choice models with individual and time fixed effects . Thanks to several bias-corrections proposed by Fernandez-Val and Weidner (2016) , the incidental parameter bias can be reduced substantially.
Revised:  We show how both issues can be alleviated in the context of binary choice models with individual and time fixed effects , we show how both issues can be alleviated by combining the asymptotic bias corrections of Fern\'andez-Val and Weidner (2016) , the incidental parameter bias can be reduced substantially.
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Original:  Thanks to several bias-corrections proposed by Fernandez-Val and Weidner (2016) , the incidental parameter bias can be reduced substantially. In order to make the estimation feasible even in panels with many fixed effects, we develop an efficient software routine, embedded in the R -package alpaca, that combines these corrections with an approach called method of alternating projections. Further, we contribute to the existing literature by conducting extensive simulation experiments  in large and even unbalanced panel settings.
Revised:  Thanks to several bias-corrections proposed by Fernandez-Val and Weidner (2016) with an estimation algorithm proposed by Stammann (2018). We conduct extensive simulation experiments in large and even unbalanced panel settings.
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Original:  Further, we contribute to the existing literature by conducting extensive simulation experiments  in large and even unbalanced panel settings. Finally, we estimate a dynamic probit model, to study the inter-temporal labor force participation of women in Germany .
Revised:  Further, we contribute to the existing literature by conducting extensive simulation experiments in large and even unbalanced panels. Our simulation results provide new insights that are relevant for the application of bias corrections in empirical work. Overall, we find that analytically bias corrected estimators are clearly preferable, especially for unbalanced panels .
Edit intent: meaning-changed
Original:  Using the example of binary choice models with individual and time fixed effects, we show how both issues can be alleviated by combining the asymptotic bias corrections of Fern\'andez-Val and Weidner (2016) with an estimation algorithm proposed by Stammann (2018).
Revised:  Using the example of binary choice models with individual and time fixed effects, we show how both issues can be alleviated by combining asymptotic bias corrections of Fern\'andez-Val and Weidner (2016) with an estimation algorithm proposed by Stammann (2018).
Edit intent: fluency
Original:  Using the example of binary choice models with individual and time fixed effects, we show how both issues can be alleviated by combining the asymptotic bias corrections of Fern\'andez-Val and Weidner (2016) with an estimation algorithm proposed by Stammann (2018). We conduct extensive simulation experiments to analyze the statistical properties of various ( bias-corrected) estimatorsin large and even unbalanced panels.
Revised:  Using the example of binary choice models with individual and time fixed effects, we show how both issues can be alleviated by combining the asymptotic bias corrections with computational advances. Because unbalancedness is often encountered in applied work, we investigate its consequences on the finite sample properties of various ( bias-corrected) estimatorsin large and even unbalanced panels.
Edit intent: clarity
Original:  We conduct extensive simulation experiments to analyze the statistical properties of various ( bias-corrected) estimatorsin large and even unbalanced panels. Our simulation results provide new insights that are relevant for the application of bias corrections in empirical work. Overall, we find that analytically bias corrected estimators are clearly preferable, especially for unbalanced panels.
Revised:  We conduct extensive simulation experiments to analyze the statistical properties of various ( bias corrected) estimators. In simulation experiments we find that analytically bias corrected estimators are clearly preferable, especially for unbalanced panels.
Edit intent: clarity
Original:  Along with this statistical model, it is possible to propose a simplified design for the neuronitself, but based on the idea that it can vary its input and output signals. Some test results also help to support the theory.
Revised:  Along with this statistical model, it is possible to propose a simplified design for a neuron, based on an action potential and variable output signal. A strong comparison with Hebbian theory is then proposed, with some test results to support the theory.
Edit intent: meaning-changed
Original: In vivo, DNA forms loops over a broad range of length scales: from several thousand base pairs (bp) down to about 100 bp. In loops shorter than the DNA persistence length (150 bp), thermal fluctuations can be neglected, and the molecule assumes approximately its minimal-energy shape. It is well-known that for a simple isotropic wormlike chain model, the minimal-energy shape of a loop can be derived exactly and is expressed as a combination of inverse elliptic integrals.
Revised: In vivo, DNA forms loops over a broad range of length scales: from several thousand base pairs (bp) . We show that in these loops the minimal-energy shape of a loop can be derived exactly and is expressed as a combination of inverse elliptic integrals.
Edit intent: coherence
Original:  It is well-known that for a simple isotropic wormlike chain model, the minimal-energy shape of a loop can be derived exactly and is expressed as a combination of inverse elliptic integrals. Here, we construct a simple explicit two-parameter variational solution, referred to as harmonic loop, which reproduces the exact loop energy up to the fourth significant digit .
Revised:  It is well-known that for a simple isotropic wormlike chain model, the excess twist density oscillates as a response to an imposed bending stress, as recently found in DNA minicircles and observed in nucleosomal DNA. These twist oscillations, here referred to as twist waves, are due to the coupling between twist and bending deformations, which in turn originates from the asymmetry between DNA major and minor grooves. We introduce a simple analytical variational shape, that reproduces the exact loop energy up to the fourth significant digit .
Edit intent: meaning-changed
Original:  Here, we construct a simple explicit two-parameter variational solution, referred to as harmonic loop, which reproduces the exact loop energy up to the fourth significant digit . The harmonic-loop solution is easier to handle than the exact one, simplifying the analytical calculation of several quantities. We generalize this solution to a twistable DNA model with anisotropic bending and twist-bend coupling, and show that the loop shape thus derived is in excellent agreement with simulations of two different coarse-grained DNA models.
Revised:  Here, we construct a simple explicit two-parameter variational solution, referred to as harmonic loop, which reproduces the exact loop energy up to the fourth significant digit , and is in very good agreement with shapes obtained from coarse-grained DNA models.
Edit intent: clarity
Original:  We generalize this solution to a twistable DNA model with anisotropic bending and twist-bend coupling, and show that the loop shape thus derived is in excellent agreement with simulations of two different coarse-grained DNA models. As recently found for DNA minicircles and observed in nucleosomal DNA data, twist-bend coupling induces twist oscillations in bent DNA. Here, we show that twist waves in DNA loops have a modulated amplitude, which is maximal in the middle of the loop and decaying at the loop edges, following the curvature modulation.
Revised:  We generalize this solution to a twistable DNA model with anisotropic bending and twist-bend coupling, and show that the loop shape thus derived is in excellent agreement with simulations of two different coarse-grained simulations.
Edit intent: coherence
Original:  The key rationale behind this type of work is that people behaviors can influence the WiFi signal propagation and introduce specific patterns into WiFi signals, called WiFi fingerprints, which further can be explored to identify human activities and locations.
Revised:  The key rationale behind this type of work is that people behaviors can influence the WiFi signal propagation and introduce specific patterns into WiFi signals, called WiFi fingerprints, which can be further explored to identify human activities and locations.
Edit intent: fluency
Original: Many stochastic networks encountered in practice exhibit some kind of blockingbehaviour, where traffic is lost due to congestion. Examples include call dropping in cellular networks, difficulties with task migration in mobile cloud computing, and depleted stock points in spare parts supply chains.
Revised: This paper introduces a class of stochastic networks with blocking, motivated by applications arising in cellular network planning, mobile cloud computing, and depleted stock points in spare parts supply chains.
Edit intent: clarity
Original:  Blocking can be mitigated by increasing service capacity at congested stations, but purchasing these additional resources may be costly. Thus, finding the right resource allocation requires striking a careful balance between blocking and costs, a problem which is further complicated by the stochastic nature of the network.
Revised:  Blocking results in lost revenue due to customers or jobs being permanently removed from the system. We are interested in striking a balance between mitigating blocking by increasing service capacity, and maintaining low costs for service capacity. This problem is further complicated by the stochastic nature of the network.
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Original:  Thus, finding the right resource allocation requires striking a careful balance between blocking and costs, a problem which is further complicated by the stochastic nature of the network. Although certain classes of queueing networks allow for a closed form optimisation problem to be formulated and solved, such results only exist for highly stylised networks, and in particularly do not allow for blocking. Another class of current solution methods is simulation-based optimisation, where the resource allocation is evaluated and updated using simulation.
Revised:  Thus, finding the right resource allocation requires striking a careful balance between blocking and costs, a problem which is further complicated by the stochastic nature of the system. Owing to the complexity of the system there are no analytical results available that formulate and solve the relevant optimization problem in closed form. Traditional simulation-based optimisation, where the resource allocation is evaluated and updated using simulation.
Edit intent: clarity
Original:  Although this works well for small instances,  the associated computational costs are prohibitive for networks of real-life size. In this paper we propose a hybrid functional-form based approach that combines the strengths of the analytical and simulation-based approaches into a novel iterative algorithm.
Revised:  Although this works well for small instances, the associated computational costs are prohibitive for networks of realistic size. We propose a hybrid functional-form based approach that combines the strengths of the analytical and simulation-based approaches into a novel iterative algorithm.
Edit intent: clarity
Original:  In this paper we propose a hybrid functional-form based approach that combines the strengths of the analytical and simulation-based approaches into a novel iterative algorithm. We do this by locally approximating the objective function through a functional form calibrated using simulation. In each iteration step we choose a new resource allocation based on this local approximation, which in turn gives rise to a new approximation. We implement this algorithm for a range of functional forms, computationally determine which work best, and provide an alternative formulation of the algorithm that does not rely on numerical solvers. Extensive experiments show that our functional-form approach has significantly lower computational costs compared to stochastic approximation.
Revised:  In this paper we propose a hybrid functional-form based approach that combines the strengths of the analytical and simulation-based optimisation. The key insight is to replace the computationally expensive gradient estimation in simulation optimisation with a closed-form analytical approximation that is calibrated using a single simulation run. We develop two implementations of this approach and conduct extensive computational experiments on complex examples to show that it is capable of substantially improving system performance. We also provide evidence that our approach has substantially lower computational costs compared to stochastic approximation.
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Original: The main objective of this paper is to introduce and study a notion of perfectness for discrete gradient vector fields with respect to ( multi-parameter ) persistent homology .
Revised: The combination of persistent homology and discrete Morse theory has proven very effective in visualizing and analyzing big and heterogeneous data. Indeed, topology provides computable and coarse summaries of data independently from specific coordinate systems and does so robustly to noise. Moreover, the geometric content of a discrete gradient vector field is very useful for visualization purposes. The specific case of multivariate data still demands for further investigations, on the one hand, for computational reasons, it is important to reduce the necessary amount of data to be processed. On the other hand, for analysis reasons, the multivariate case requires the detection and interpretation of the possible interdepedance among data components. To this end, in this paper we introduce and study a notion of perfectness for discrete gradient vector fields with respect to ( multi-parameter ) persistent homology .
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Original:  As a natural generalization of usual perfectness in Morse theory for homology, persistence-perfectness entails having the least number of critical cells relevant for persistent homology. The first result about a persistence-perfect gradient vector-field is that the number of its critical cells yield inequalities bounding the Betti tables of persistence modules , as a sort of Morse inequalities for multi-parameter persistence.
Revised:  As a natural generalization of usual perfectness in Morse theory for homology, persistence-perfectness entails having the least number of critical cells relevant for multi-parameter persistence. As a first contribution, we support our definition of relative-perfectness by generalizing Morse inequalities to the filtration structure where homology groups involved are relative with respect to subsequent sublevel sets. In order to allow for an interpretation of critical cells in 2-parameter persistence, our second contribution consists of two inequalities bounding Betti tables of persistence modules , as a sort of Morse inequalities for multi-parameter persistence.
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Original:  The first result about a persistence-perfect gradient vector-field is that the number of its critical cells yield inequalities bounding the Betti tables of persistence modules , as a sort of Morse inequalities for multi-parameter persistence. The second result is that, at least in low dimensions, persistence-perfect gradient vector-fields not only exist but can be constructed by an algorithm based on local homotopy expansions .
Revised:  The first result about a persistence-perfect gradient vector-field is that the number of its critical cells yield inequalities bounding the Betti tables of persistence modules from above and below, via the number of critical cells. Our last result is the proof that existing algorithms based on local homotopy expansions .
Edit intent: clarity
Original:  The second result is that, at least in low dimensions, persistence-perfect gradient vector-fields not only exist but can be constructed by an algorithm based on local homotopy expansions . These results show a link between multi-parameter persistence and discrete Morse theory that can be leveraged for a better understanding of the former.
Revised:  The second result is that, at least in low dimensions, persistence-perfect gradient vector-fields not only exist but can be constructed by an algorithm based on local homotopy expansions allow for efficient computability over simplicial complexes up to dimension 2.
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Original: Recent advances in automatic depression detection mostly derive from modality fusion and deep learning methods. However multi-modal approaches insert significant difficulty in datacollection phase while deep learning methods' opaqueness lowers its credibility. This current work proposes a text-based multi-task BLSTM model with pretrained word embeddings .
Revised: Previous text-based depression detection is commonly based on large user-generated data. Sparse scenarios like clinical conversations are less investigated. This work proposes a text-based multi-task BLSTM model with pretrained word embeddings .
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Original:  This current work proposes a text-based multi-task BLSTM model with pretrained word embeddings . Our method outputs depression presence results as well as predicted severity score, culminating a state-of-the-art F1 score of 0.87, outperforming previous multi-modal studies.
Revised:  This current work proposes a text-based multi-task BLSTM model with pretrained word embeddings to model patients' responses during clinical interviews. Our main approach uses a novel multi-task loss function, aiming at modeling both depression severity and binary health state. We independently investigate word- and sentence-level word-embeddings as well as the use of large-data pretraining for depression detection. To strengthen our findings, we report mean-averaged results for a multitude of independent runs on sparse data. First, we show that pretraining is helpful for word-level text-based depression detection. Second, our results demonstrate that sentence-level word-embeddings should be mostly preferred over word-level ones. While the choice of pooling function is less crucial, mean and attention pooling should be preferred over last-timestep pooling. Our method outputs depression presence results as well as predicted severity score, culminating a state-of-the-art F1 score of 0.87, outperforming previous multi-modal studies.
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Original:  Our method outputs depression presence results as well as predicted severity score, culminating a state-of-the-art F1 score of 0.87, outperforming previous multi-modal studies. We also achieve the lowest RMSE compared with currently available text-based approaches. Further, by utilizing a per time step attention mechanism we analyse the sentences/words that contribute most in predicting the depressed state. Surprisingly, `unmeaningful' words/paralinguistic information such as `um' and `uh' are the indicators to our model when making a depression prediction. It is for the first time revealed that fillers in a conversation trigger a depression alert for a deep learning model .
Revised:  Our method outputs depression presence results as well as predicted severity score, culminating a state-of-the-art F1 score of 0.84 and MAE of 3.48 on the DAIC-WOZ development set .
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Original: An approach to asymptotic finite approximations of geometrical spaces is presented  .
Revised: An approach to asymptotic finite approximations of geometrical spaces is presented . It is based on the well known inverse sequences of finite graphs that determine topological spaces. If a pattern of the construction is sufficiently regular and uniform, then the notions of geodesic and curvature can be defined in the space as the limits of their finite versions in the graphs. This gives rise to consider the graphs as finite approximations of the geometry of the space .
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Original: Given two point sets S and T, we first study the many-to-many matching with demands problem (MMD problem) . In an MMD, each point of one set must be matched to a given number of the points of the other set , and the cost of matching a point to another point is equal to the distance between the two points.
Revised: Given two point sets S and T, we first study the many-to-many matching with demands problem (MMD problem) where each point of one set must be matched to a given number of the points of the other set , and the cost of matching a point to another point is equal to the distance between the two points.
Edit intent: clarity