wanyu/IteraTeR_full_sent huggingface dataset:
Original:  To address this problem, we present an effective multi-stage pipeline for passage ranking in conversational searchthat integrates a widely-used IR system with a conversational query reformulation module. Along these lines , we propose two simple yet effective query reformulation approaches: historical query expansion (HQE) and neural transfer reformulation (NTR) . Whereas HQE applies query expansion, a traditional IR query reformulation technique, NTR transfers human knowledge of conversational query understanding to a neural query reformulation model.
Revised:  To address this problem, we present an effective multi-stage ad-hoc IR system. Specifically , we propose two simple yet effective query reformulation approaches: historical query expansion (HQE) and neural transfer reformulation (NTR) . Whereas HQE applies query expansion, a traditional IR query reformulation technique, NTR transfers human knowledge of conversational query understanding to a neural query reformulation model.
Edit intent: clarity
Original:  Along these lines , we propose two simple yet effective query reformulation approaches: historical query expansion (HQE) and neural transfer reformulation (NTR) . Whereas HQE applies query expansion, a traditional IR query reformulation technique, NTR transfers human knowledge of conversational query understanding to a neural query reformulation model. The proposed HQE method was the top-performing submission of automatic systems in CAsT Track at TREC 2019. Building on this, our NTR approach improves an additional 18\% over that best entry in terms of NDCG@3. We further analyze the distinct behaviors of the two approaches, and show that fusing their output reduces the performance gap (measured in NDCG@3 ) between the manually-rewritten and automatically-generated queries to 4 from 22 points when compared with the best CAsT submission .
Revised:  Along these lines , we propose two conversational query reformulation (CQR) methods: (1) term importance estimation and (2) neural query rewriting. For the former, we expand conversational queries using important terms extracted from the conversational context with frequency-based signals. For the latter, we reformulate conversational queries into natural, standalone, human-understandable queries with a pretrained sequence-tosequence model. Detailed analyses of the two CQR methods are provided quantitatively and qualitatively, explaining their advantages, disadvantages, and distinct behaviors. Moreover, to leverage the strengths of both CQR methods, we propose combining their output with reciprocal rank fusion, yielding state-of-the-art retrieval effectiveness, 30\% improvement in terms of NDCG@3 ) between the manually-rewritten and automatically-generated queries to 4 from 22 points when compared with the best CAsT submission .
Edit intent: meaning-changed
Original:  We further analyze the distinct behaviors of the two approaches, and show that fusing their output reduces the performance gap (measured in NDCG@3 ) between the manually-rewritten and automatically-generated queries to 4 from 22 points when compared with the best CAsT submission .
Revised:  We further analyze the distinct behaviors of the two approaches, and show that fusing their output reduces the performance gap (measured in NDCG@3 compared to the best submission of TREC CAsT 2019.
Edit intent: clarity
Original: Differential machine learning (ML) extends supervised learning, with models trained on examples of not only inputs and labels , but also differentials of labels to inputs.
Revised: Differential machine learning combines automatic adjoint differentiation (AAD) with modern machine learning (ML) in the context of risk management of financial Derivatives. We introduce novel algorithms for training fast, accurate pricing and risk approximations, online, in real-time, with convergence guarantees. Our machinery is applicable to arbitrary Derivatives instruments or trading books, under arbitrary stochastic models of the underlying market variables. It effectively resolves computational bottlenecks of Derivatives risk reports and capital calculations. Differential ML is a general extension of supervised learning, with models trained on examples of not only inputs and labels , but also differentials of labels to inputs.
Edit intent: meaning-changed
Original: Differential machine learning (ML) extends supervised learning, with models trained on examples of not only inputs and labels , but also differentials of labels to inputs. Differential ML is applicable in all situations where high quality first order derivatives wrt training inputs are available.
Revised: Differential machine learning (ML) extends supervised learning, with models trained on examples of not only inputs and labels , but also differentials of labels wrt inputs. It is also applicable in many situations outside finance, where high quality first order derivatives wrt training inputs are available.
Edit intent: meaning-changed
Original:  In the context of financial Derivatives risk management, pathwise differentials are efficiently computed with automatic adjoint differentiation (AAD). Differential ML , combined with AAD, provides extremely effective pricing and risk approximations.
Revised:  Applications in Physics, for example, may leverage differentials known from first principles to learn function approximations more effectively. In finance, AAD computes pathwise differentials with remarkable efficacy so differential ML algorithms provide extremely effective pricing and risk approximations.
Edit intent: meaning-changed
Original:  We can produce fast pricing analytics in models too complex for closed form solutions, extract the risk factors of complex transactions and trading books, and effectively compute risk management metrics like reports across a large number of scenarios, backtesting and simulation of hedge strategies, or capital regulations .
Revised:  We can produce fast analytics in models too complex for closed form solutions, extract the risk factors of complex transactions and trading books, and effectively compute risk management metrics like reports across a large number of scenarios, backtesting and simulation of hedge strategies, or capital regulations .
Edit intent: clarity
Original:  We can produce fast pricing analytics in models too complex for closed form solutions, extract the risk factors of complex transactions and trading books, and effectively compute risk management metrics like reports across a large number of scenarios, backtesting and simulation of hedge strategies, or capital regulations . The article focuses on differential deep learning (DL), arguably the strongest application. Standard DL trains neural networks (NN) on punctual examples, whereas differential DL teaches them the shape of the target function, resulting in vastly improved performance, illustrated with a number of numerical examples, both idealized and real world. In the online appendices, we apply differential learning to other ML models, like classic regression or principal component analysis (PCA), with equally remarkable results. This paper is meant to be read in conjunction with its companion GitHub repo URL where we posted a TensorFlow implementation , tested on Google Colab, along with examples from the article and additional ones. We also posted appendices covering many practical implementation details not covered in the paper, mathematical proofs, application to ML models besides neural networks and extensions necessary for a reliable implementation in production.
Revised:  We can produce fast pricing analytics in models too complex for closed form solutions, extract the risk factors of complex transactions and trading books, and effectively compute risk management metrics like reports across a large number of scenarios, backtesting and simulation of hedge strategies, or regulations like XVA, CCR, FRTB or SIMM-MVA. TensorFlow implementation is available on URL
Edit intent: meaning-changed
Original: Case isolation and contact tracing is a widely-used intervention method for controlling epidemic outbreaks. Here, we argue that the effectiveness of contact tracing and isolation is likely underestimated by existing studies because they do not take into account the different forms of heterogeneity and sampling biases from the network structure.
Revised: Case isolation and contact tracing is a key tool for case discovery and control. However, although contact tracing has been performed widely, the mathematical understanding of contact tracing and isolation is likely underestimated by existing studies because they do not take into account the different forms of heterogeneity and sampling biases from the network structure.
Edit intent: meaning-changed
Original:  Here, we argue that the effectiveness of contact tracing and isolation is likely underestimated by existing studies because they do not take into account the different forms of heterogeneity and sampling biases from the network structure. Specifically, we show thatcontact tracing can be even more effectivethan acquaintance sampling at locating hubs. Our results call for the need for contact tracing to go both backward and forward, in multiple steps, to leverage all forms of positive biases .  Using simulations on networks with a power-law degree distribution , we show that this deep contact tracing can potentially prevent almost all further transmissions even at a small probability of detecting infected individuals  .
Revised:  Here, we argue that the effectiveness of contact tracing has not been fully established and it has not been clearly understood what determines the efficacy of contact tracing. Here, we reveal that, compared with "forward" tracing---tracing to whom disease spreads, "backward" tracing---tracing from whom disease spreads---is profoundly more effective . Using simulations on networks with a power-law degree distribution , we show that this deep contact tracing can potentially prevent almost all further transmissions even at a small probability of detecting infected individuals .
Edit intent: clarity
Original:  Our results call for the need for contact tracing to go both backward and forward, in multiple steps, to leverage all forms of positive biases .  Using simulations on networks with a power-law degree distribution , we show that this deep contact tracing can potentially prevent almost all further transmissions even at a small probability of detecting infected individuals  .
Revised:  Our results call for the need for contact tracing to go both backward and forward, in multiple steps, to leverage all forms of positive biases . The effectiveness of backward tracing is due to simple but overlooked biases arising from the heterogeneity in contacts. Using simulations on networks with a power-law degree distribution , we show that this deep contact tracing can potentially prevent almost all further transmissions even at a small probability of detecting infected individuals .
Edit intent: meaning-changed
Original:  Our results call for the need for contact tracing to go both backward and forward, in multiple steps, to leverage all forms of positive biases .  Using simulations on networks with a power-law degree distribution , we show that this deep contact tracing can potentially prevent almost all further transmissions even at a small probability of detecting infected individuals  .
Revised:  Our results call for the need for contact tracing to go both backward and forward, in multiple steps, to leverage all forms of positive biases . Using simulations on both synthetic and high-resolution empirical contact datasets , we show that this deep contact tracing can potentially prevent almost all further transmissions even at a small probability of detecting infected individuals .
Edit intent: meaning-changed
Original:  Our results call for the need for contact tracing to go both backward and forward, in multiple steps, to leverage all forms of positive biases .  Using simulations on networks with a power-law degree distribution , we show that this deep contact tracing can potentially prevent almost all further transmissions even at a small probability of detecting infected individuals  .
Revised:  Our results call for the need for contact tracing to go both backward and forward, in multiple steps, to leverage all forms of positive biases . Using simulations on networks with a power-law degree distribution , we show that even at a small probability of detecting infected individuals .
Edit intent: clarity
Original:  Our results call for the need for contact tracing to go both backward and forward, in multiple steps, to leverage all forms of positive biases .  Using simulations on networks with a power-law degree distribution , we show that this deep contact tracing can potentially prevent almost all further transmissions even at a small probability of detecting infected individuals  .
Revised:  Our results call for the need for contact tracing to go both backward and forward, in multiple steps, to leverage all forms of positive biases . Using simulations on networks with a power-law degree distribution , we show that this deep contact tracing can potentially prevent almost all further transmissions even at a small probability of detecting infected individuals , strategically executed contact tracing can prevent a significant fraction of further transmissions .
Edit intent: meaning-changed
Original:  We also show that, when the number of traced contacts is small, the number of prevented transmission per traced node is even higher---although most traced individuals are healthy---than that from case isolation without contact tracing .
Revised:  We also show that, when the number of traced contacts is small, the number of prevented transmissions per isolation---case isolation combined with a small amount of contact tracing is more efficient than case isolation alone. By demonstrating that backward contact tracing is highly effective at discovering super-spreading events, we argue that the potential effectiveness of contact tracing has been underestimated. Therefore, there is a critical need for revisiting current contact tracing strategies so that they leverage all forms of biases.
Edit intent: meaning-changed
Original:  Our results also have important consequences for new implementations of digital contact tracing and we argue backward and deep tracing can be incorporated without the important sacrificing privacy-preserving requirements of these new platforms.
Revised:  Our results also have important consequences for new implementations of digital contact tracing because it will be crucial to incorporate the capability for backward and deep tracing can be incorporated without the important sacrificing privacy-preserving requirements of these new platforms.
Edit intent: coherence
Original:  In this research, we are exploring the possibility of using deep learning in graph clustering . Graphs have gained growing traction in different fields, including social networks, information graphs, the recommender system, and also life sciences.
Revised:  In this research, we are exploring the possibility of using deep learning in community detection among the graph datasets . Graphs have gained growing traction in different fields, including social networks, information graphs, the recommender system, and also life sciences.
Edit intent: meaning-changed
Original:  We do a series of experiments on our graph clustering algorithm, using various datasets: Zachary karate club, Highland Tribe, Train bombing Italian Gangs, Jazz musicians , American Revolution, Dolphins, Zebra, Windsurfers, Les Mis\'erables, Political books.
Revised:  We do a series of experiments on our graph clustering algorithm, using various datasets: Zachary karate club, Highland Tribe, Train bombing , American Revolution, Dolphins, Zebra, Windsurfers, Les Mis\'erables, Political books.
Edit intent: clarity
Original:  We solve a calibrated model with standard constant relative risk aversion utility and show that asymptotic MPCs can be zero in empirically plausiblesettings, implying an increasing and large saving rate of the rich and high wealth inequality  .
Revised:  We calibrate a model with standard constant relative risk aversion utility and show that asymptotic MPCs can be zero in empirically plausiblesettings, implying an increasing and large saving rate of the rich and high wealth inequality .
Edit intent: clarity
Original:  We solve a calibrated model with standard constant relative risk aversion utility and show that asymptotic MPCs can be zero in empirically plausiblesettings, implying an increasing and large saving rate of the rich and high wealth inequality  .
Revised:  We solve a calibrated model with standard constant relative risk aversion utility and show that zero asymptotic MPCs are empirically plausible, implying that our mechanism has the potential to accommodate a large saving rate of the rich and high wealth inequality .
Edit intent: clarity
Original:  We solve a calibrated model with standard constant relative risk aversion utility and show that asymptotic MPCs can be zero in empirically plausiblesettings, implying an increasing and large saving rate of the rich and high wealth inequality  .
Revised:  We solve a calibrated model with standard constant relative risk aversion utility and show that asymptotic MPCs can be zero in empirically plausiblesettings, implying an increasing and large saving rate of the rich and high wealth inequality (small Pareto exponent) as observed in the data .
Edit intent: meaning-changed
Original:  This should be seen in the context of neural SDEs and (causal) generative adversarial networks: we generate volatility surfaces by specific neural SDEs, whose quality is assessed by quantifying,  in an adversarial manner, distances to market prices.
Revised:  This should be seen in the context of neural SDEs and (causal) generative adversarial networks: we generate volatility surfaces by specific neural SDEs, whose quality is assessed by quantifying, possibly in an adversarial manner, distances to market prices.
Edit intent: clarity
Original:  The minimization of the calibration functional relies strongly on a variance reduction technique based on hedging and deep hedging, which is interesting in its own right: it allows to calculate model prices and model implied volatilities in an accurate way using only small sets of sample paths.
Revised:  The minimization of the calibration functional relies strongly on a variance reduction technique based on hedging and deep hedging, which is interesting in its own right: it allows the calculation of model prices and model implied volatilities in an accurate way using only small sets of sample paths.
Edit intent: fluency
Original:  Using whole-brain imaging data, we demonstrate that the brain fundamentally operates out of equilibrium at large scales. Moreover, we find that the brain produces more entropy -- operating further from equilibrium -- when performing physically and cognitively demanding tasks.
Revised:  Using whole-brain imaging data, we demonstrate that the brain nearly obeys detailed balance when at rest, but strongly breaks detailed balance when performing physically and cognitively demanding tasks.
Edit intent: meaning-changed
Original:  We present a polynomial-time algorithm with an approximation factor of O(k \log \frac{w_{max}{w_{\min}}} ) to the optimal solution, where w_{ max } and w_{ min } are the maximum and minimum weight of the sites respectively.
Revised:  We present a polynomial-time algorithm with an approximation factor of O(k ^2 \log \frac{w_{\max}{w_{\min}}} ) to the optimal solution, where w_{ max } and w_{ min } are the maximum and minimum weight of the sites respectively.
Edit intent: meaning-changed
Original:  We present a polynomial-time algorithm with an approximation factor of O(k \log \frac{w_{max}{w_{\min}}} ) to the optimal solution, where w_{ max } and w_{ min } are the maximum and minimum weight of the sites respectively.
Revised:  We present a polynomial-time algorithm with an approximation factor of O(k \log \frac{w_{max}{w_{\min}}} ) to the optimal solution, where w_{ \max } and w_{ min } are the maximum and minimum weight of the sites respectively.
Edit intent: fluency
Original:  We present a polynomial-time algorithm with an approximation factor of O(k \log \frac{w_{max}{w_{\min}}} ) to the optimal solution, where w_{ max } and w_{ min } are the maximum and minimum weight of the sites respectively.
Revised:  We present a polynomial-time algorithm with an approximation factor of O(k \log \frac{w_{max}{w_{\min}}} ) to the optimal solution, where w_{ max } and w_{ \min } are the maximum and minimum weight of the sites respectively.
Edit intent: fluency
Original:  Our "business as usual" completely unconditional forecast has SIE hitting 0 in September by the 2060s . Impulse response functions reveal that anthropogenic \text{CO emission shocks have a permanent effect on SIE - a property shared by no other shock.
Revised:  Our completely unconditional forecast has SIE hitting 0 in September by the 2060s . Impulse response functions reveal that anthropogenic \text{CO emission shocks have a permanent effect on SIE - a property shared by no other shock.
Edit intent: clarity
Original:  Our "business as usual" completely unconditional forecast has SIE hitting 0 in September by the 2060s . Impulse response functions reveal that anthropogenic \text{CO emission shocks have a permanent effect on SIE - a property shared by no other shock.
Revised:  Our "business as usual" completely unconditional forecast has SIE hitting 0 in September by the 2060's . Impulse response functions reveal that anthropogenic \text{CO emission shocks have a permanent effect on SIE - a property shared by no other shock.
Edit intent: fluency
Original:  Our "business as usual" completely unconditional forecast has SIE hitting 0 in September by the 2060s . Impulse response functions reveal that anthropogenic \text{CO emission shocks have a permanent effect on SIE - a property shared by no other shock.
Revised:  Our "business as usual" completely unconditional forecast has SIE hitting 0 in September by the 2060s . Impulse response functions reveal that anthropogenic CO2 emission shocks have a permanent effect on SIE - a property shared by no other shock.
Edit intent: meaning-changed
Original:  Our "business as usual" completely unconditional forecast has SIE hitting 0 in September by the 2060s . Impulse response functions reveal that anthropogenic \text{CO emission shocks have a permanent effect on SIE - a property shared by no other shock.
Revised:  Our "business as usual" completely unconditional forecast has SIE hitting 0 in September by the 2060s . Impulse response functions reveal that anthropogenic \text{CO emission shocks have an unusually durable effect on SIE - a property shared by no other shock.
Edit intent: clarity
Original:  Our "business as usual" completely unconditional forecast has SIE hitting 0 in September by the 2060s . Impulse response functions reveal that anthropogenic \text{CO emission shocks have a permanent effect on SIE - a property shared by no other shock.
Revised:  Our "business as usual" completely unconditional forecast has SIE hitting 0 in September by the 2060s . Impulse response functions reveal that anthropogenic \text{CO emission shocks have a permanent effect on SIE -- a property shared by no other shock.
Edit intent: fluency
Original:  Conditional forecast analyses reveal that the future path of SIE crucially depends on the evolution of \text{CO emissions, with outcomes ranging from recovering SIE to it reaching 0 in the 2050s . Finally, Albedo and Thickness feedbacks are shown to play an important role in accelerating the speed at which predicted SIE is heading towards 0.
Revised:  Further, conditional forecast analyses reveal that the future path of SIE crucially depends on the evolution of \text{CO emissions, with outcomes ranging from recovering SIE to it reaching 0 in the 2050s . Finally, Albedo and Thickness feedbacks are shown to play an important role in accelerating the speed at which predicted SIE is heading towards 0.
Edit intent: coherence