Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding Mention Detector-Linker Interaction in Neural Coreference Resolution

Despite significant recent progress in coreference resolution, the quality of current state-of-the-art systems still considerably trails behind human-level performance. Using the CoNLL-2012 and PreCo datasets, we dissect the best instantiation of the mainstream end-to-end coreference resolution model that underlies most current best-performing coreference systems, and empirically analyze the behavior of its two components: mention detector and mention linker. While the detector traditionally focuses heavily on recall as a design decision, we demonstrate the importance of precision, calling for their balance. However, we point out the difficulty in building a precise detector due to its inability to make important anaphoricity decisions. We also highlight the enormous room for improving the linker and show that the rest of its errors mainly involve pronoun resolution. We propose promising next steps and hope our findings will help future research in coreference resolution.
Link Floer homology also detects split links

Inspired by work of Lipshitz–Sarkar, we show that the module structure on link Floer homology detects split links. Using results of Ni, Alishahi–Lipshitz, and Lipshitz–Sarkar, we establish an analogous detection result for sutured Floer homology.
Tradeoffs in Sentence Selection Techniques for Open-Domain Question Answering

Current methods in open-domain question answering (QA) usually employ a pipeline of first retrieving relevant documents, then applying strong reading comprehension (RC) models to that retrieved text. However, modern RC models are complex and expensive to run, so techniques to prune the space of retrieved text are critical to allow this approach to scale. In this paper, we focus on approaches which apply an intermediate sentence selection step to address this issue, and investigate the best practices for this approach. We describe two groups of models for sentence selection: QA-based approaches, which run a full-fledged QA system to identify answer candidates, and retrieval-based models, which find parts of each passage specifically related to each question. We examine trade-offs between processing speed and task performance in these two approaches, and demonstrate an ensemble module that represents a hybrid of the two. From experiments on Open-SQuAD and TriviaQA, we show that very lightweight QA models can do well at this task, but retrieval-based models are faster still. An ensemble module we describe balances between the two and generalizes well cross-domain.
The first shear measurements from precision weak lensing

We present an end-to-end methodology to measure the effects of weak lensing on individual galaxy–galaxy systems exploiting their kinematic information. Using this methodology, we have measured a shear signal from the velocity fields of 18 weakly lensed galaxies. We selected a sample of systems based only on the properties of the sources, requiring them to be bright (apparent i-band magnitude <17.4) and in the nearby Universe ($z$ < 0.15). We have observed the velocity fields of the sources with WiFeS, an optical IFU on a 2.3 m telescope, and fitted them using a simple circular motion model with an external shear. We have measured an average shear of 〈γ〉 = 0.020 ± 0.008 compared to a predicted 〈γpred〉 = 0.005 obtained using median stellar-to-halo relationships from the literature. While still a statistical approach, our results suggest that this new weak lensing methodology can overcome some of the limitations of traditional stacking-based techniques. We describe in detail all the steps of the methodology and make publicly available all the velocity maps for the weakly lensed sources used in this study.
Learn to Exceed: Stereo Inverse Reinforcement Learning with Concurrent Policy Optimization

In this paper, we study the problem of obtaining a control policy that can mimic and then outperform expert demonstrations in Markov decision processes where the reward function is unknown to the learning agent. One main relevant approach is the inverse reinforcement learning (IRL), which mainly focuses on inferring a reward function from expert demonstrations. The obtained control policy by IRL and the associated algorithms, however, can hardly outperform expert demonstrations. To overcome this limitation, we propose a novel method that enables the learning agent to outperform the demonstrator via a new concurrent reward and action policy learning approach. In particular, we first propose a new stereo utility definition that aims to address the bias in the interpretation of expert demonstrations. We then propose a loss function for the learning agent to learn reward and action policies concurrently such that the learning agent can outperform expert demonstrations. The performance of the proposed method is first demonstrated in OpenAI environments. Further efforts are conducted to experimentally validate the proposed method via an indoor drone flight scenario.
Differentiable refraction-tracing for mesh reconstruction of transparent objects

Capturing the 3D geometry of transparent objects is a challenging task, ill-suited for general-purpose scanning and reconstruction techniques, since these cannot handle specular light transport phenomena. Existing state-of-the-art methods, designed specifically for this task, either involve a complex setup to reconstruct complete refractive ray paths, or leverage a data-driven approach based on synthetic training data. In either case, the reconstructed 3D models suffer from over-smoothing and loss of fine detail. This paper introduces a novel, high precision, 3D acquisition and reconstruction method for solid transparent objects. Using a static background with a coded pattern, we establish a mapping between the camera view rays and locations on the background. Differentiable tracing of refractive ray paths is then used to directly optimize a 3D mesh approximation of the object, while simultaneously ensuring silhouette consistency and smoothness. Extensive experiments and comparisons demonstrate the superior accuracy of our method.
Co-Evolution of Multi-Robot Controllers and Task Cues for Off-World Open Pit Mining

Robots are ideal for open-pit mining on the Moon as its a dull, dirty, and dangerous task. The challenge is to scale up productivity with an ever-increasing number of robots. This paper presents a novel method for developing scalable controllers for use in multi-robot excavation and site-preparation scenarios. The controller starts with a blank slate and does not require human-authored operations scripts nor detailed modeling of the kinematics and dynamics of the excavator. The 'Artificial Neural Tissue' (ANT) architecture is used as a control system for autonomous robot teams to perform resource gathering. This control architecture combines a variable-topology neural-network structure with a coarse-coding strategy that permits specialized areas to develop in the tissue. Our work in this field shows that fleets of autonomous decentralized robots have an optimal operating density. Too few robots result in insufficient labor, while too many robots cause antagonism, where the robots undo each other's work and are stuck in gridlock. In this paper, we explore the use of templates and task cues to improve group performance further and minimize antagonism. Our results show light beacons and task cues are effective in sparking new and innovative solutions at improving robot performance when placed under stressful situations such as severe time-constraint.
Neutron star parameter constraints for accretion-powered millisecond pulsars from the simulated IXPE data

We have simulated the X-ray polarization data that can be obtained with the Imaging X-ray Polarimetry Explorer, when observing accretion-powered millisecond pulsars. We estimated the necessary exposure times for SAX J1808.4−3658 in order to obtain different accuracy in the measured time-dependent Stokes profiles integrated over all energy channels. We found that the measured relative errors strongly depend on the relative configuration of the observer and the emitting hotspot. The improvement in the minimum relative error in Stokes Q and U parameters as a function of observing time t scales as 1/√t, and it spans the range from 30–90% with a 200 ks exposure time to 20–60% with a 500 ks exposure time (in the case of data binned in 19 phase bins). The simulated data were also used to predict how accurate measurements of the geometrical parameters of the neutron star can be made when modelling only Q and U parameters, but not the flux. We found that the observer inclination and the hotspot co-latitude could be determined with better than 10° accuracy for most of the cases we considered. In addition, we show that the position of a secondary hotspot can also be constrained when the spot is not obscured by an accretion disc. These measurements can be used to further constrain the neutron star mass and radius when combined with modelling of the X-ray pulse profile.
Presenting Simultaneous Translation in Limited Space

Some methods of automatic simultaneous translation of a long-form speech allow revisions of outputs, trading accuracy for low latency. Deploying these systems for users faces the problem of presenting subtitles in a limited space, such as two lines on a television screen. The subtitles must be shown promptly, incrementally, and with adequate time for reading. We provide an algorithm for subtitling. Furthermore, we propose a way how to estimate the overall usability of the combination of automatic translation and subtitling by measuring the quality, latency, and stability on a test set, and propose an improved measure for translation latency.
Generative Imagination Elevates Machine Translation

There are common semantics shared across text and images. Given a sentence in a source language, whether depicting the visual scene helps translation into a target language? Existing multimodal neural machine translation methods (MNMT) require triplets of bilingual sentence - image for training and tuples of source sentence - image for inference. In this paper, we propose ImagiT, a novel machine translation method via visual imagination. ImagiT first learns to generate visual representation from the source sentence, and then utilizes both source sentence and the “imagined representation” to produce a target translation. Unlike previous methods, it only needs the source sentence at the inference time. Experiments demonstrate that ImagiT benefits from visual imagination and significantly outperforms the text-only neural machine translation baselines. Further analysis reveals that the imagination process in ImagiT helps fill in missing information when performing the degradation strategy.
What do Animal Nutritionists Expect from Plant Breeding?

The‘wish list’ from animal nutritionists to plant breeders can be summarized in terms of global nutritional security and resource efficiency, along with the increasing improvement/safety of the quality of feeds of plant origin. Reductions in the content of undesirable substances are more important than increases in the content of value-determining components of the plants. The following are also noteworthy: (a) studies with the target animal species are necessary to evaluate the nutritional value and safety of the changes induced in the plants; (b) cooperation among plant breeders and animal nutritionists should be a necessary element from the early stages of breeding programmes to evaluate the changes induced; and (c) life-cycle assessments (eco-balances, documentation of inputs and outputs) should be conducted to compare and evaluate the consequences of breeding programmes.
Machine learning based forecasting of significant daily returns in foreign exchange markets

Asset value forecasting has always attracted an enormous amount of interest among researchers in quantitative analysis. The advent of modern machine learning models has introduced new tools to tackle this classical problem. In this paper, we apply machine learning algorithms to hitherto unexplored question of forecasting instances of significant fluctuations in currency exchange rates. We perform analysis of nine modern machine learning algorithms using data on four major currency pairs over a 10 year period. A key contribution is the novel use of outlier detection methods for this purpose. Numerical experiments show that outlier detection methods substantially outperform traditional machine learning and finance techniques. In addition, we show that a recently proposed new outlier detection method PKDE produces best overall results. Our findings hold across different currency pairs, significance levels, and time horizons indicating the robustness of the proposed method.
Open-set Short Utterance Forensic Speaker Verification using Teacher-Student Network with Explicit Inductive Bias

In forensic applications, it is very common that only small naturalistic datasets consisting of short utterances in complex or unknown acoustic environments are available. In this study, we propose a pipeline solution to improve speaker verification on a small actual forensic field dataset. By leveraging large-scale out-of-domain datasets, a knowledge distillation based objective function is proposed for teacher-student learning, which is applied for short utterance forensic speaker verification. The objective function collectively considers speaker classification loss, Kullback-Leibler divergence, and similarity of embeddings. In order to advance the trained deep speaker embedding network to be robust for a small target dataset, we introduce a novel strategy to fine-tune the pre-trained student model towards a forensic target domain by utilizing the model as a finetuning start point and a reference in regularization. The proposed approaches are evaluated on the 1st48-UTD forensic corpus, a newly established naturalistic dataset of actual homicide investigations consisting of short utterances recorded in uncontrolled conditions. We show that the proposed objective function can efficiently improve the performance of teacher-student learning on short utterances and that our fine-tuning strategy outperforms the commonly used weight decay method by providing an explicit inductive bias towards the pre-trained model.
Cross-Entropy Method Variants for Optimization

The cross-entropy (CE) method is a popular stochastic method for optimization due to its simplicity and effectiveness. Designed for rare-event simulations where the probability of a target event occurring is relatively small, the CE-method relies on enough objective function calls to accurately estimate the optimal parameters of the underlying distribution. Certain objective functions may be computationally expensive to evaluate, and the CE-method could potentially get stuck in local minima. This is compounded with the need to have an initial covariance wide enough to cover the design space of interest. We introduce novel variants of the CE-method to address these concerns. To mitigate expensive function calls, during optimization we use every sample to build a surrogate model to approximate the objective function. The surrogate model augments the belief of the objective function with less expensive evaluations. We use a Gaussian process for our surrogate model to incorporate uncertainty in the predictions which is especially helpful when dealing with sparse data. To address local minima convergence, we use Gaussian mixture models to encourage exploration of the design space. We experiment with evaluation scheduling techniques to reallocate true objective function calls earlier in the optimization when the covariance is the largest. To test our approach, we created a parameterized test objective function with many local minima and a single global minimum. Our test function can be adjusted to control the spread and distinction of the minima. Experiments were run to stress the cross-entropy method variants and results indicate that the surrogate model-based approach reduces local minima convergence using the same number of function evaluations.
"Click" Is Not Equal to "Like": Counterfactual Recommendation for Mitigating Clickbait Issue

Recommendation is a prevalent and critical service in information systems. To provide personalized suggestions to users, industry players embrace machine learning, more specifically, building predictive models based on the click behavior data. This is known as the Click-Through Rate (CTR) prediction, which has become the gold standard for building personalized recommendation service. However, we argue that there is a significant gap between clicks and user satisfaction -- it is common that a user is "cheated" to click an item by the attractive title/cover of the item. This will severely hurt user's trust on the system if the user finds the actual content of the clicked item disappointing. What's even worse, optimizing CTR models on such flawed data will result in the Matthew Effect, making the seemingly attractive but actually low-quality items be more frequently recommended. In this paper, we formulate the recommendation process as a causal graph that reflects the cause-effect factors in recommendation, and address the clickbait issue by performing counterfactual inference on the causal graph. We imagine a counterfactual world where each item has only exposure features (i.e., the features that the user can see before making a click decision). By estimating the click likelihood of a user in the counterfactual world, we are able to remove the effect of exposure features and eliminate the clickbait issue. Experiments on real-world datasets demonstrate that our method significantly improves the post-click satisfaction of CTR models.
Lagrangian reduction of nonholonomic discrete mechanical systems by stages

In this work we introduce a category $LDP_d$ of discrete-time dynamical systems, that we call discrete Lagrange--D'Alembert--Poincare systems, and study some of its elementary properties. Examples of objects of $LDP_d$ are nonholonomic discrete mechanical systems as well as their lagrangian reductions and, also, discrete Lagrange-Poincare systems. We also introduce a notion of symmetry group for objects of $LDP_d$ and a process of reduction when symmetries are present. This reduction process extends the reduction process of discrete Lagrange--Poincare systems as well as the one defined for nonholonomic discrete mechanical systems. In addition, we prove that, under some conditions, the two-stage reduction process (first by a closed and normal subgroup of the symmetry group and, then, by the residual symmetry group) produces a system that is isomorphic in $LDP_d$ to the system obtained by a one-stage reduction by the full symmetry group.
Composed Variational Natural Language Generation for Few-shot Intents

In this paper, we focus on generating training examples for few-shot intents in the realistic imbalanced scenario. To build connections between existing many-shot intents and few-shot intents, we consider an intent as a combination of a domain and an action, and propose a composed variational natural language generator (CLANG), a transformer-based conditional variational autoencoder. CLANG utilizes two latent variables to represent the utterances corresponding to two different independent parts (domain and action) in the intent, and the latent variables are composed together to generate natural examples. Additionally, to improve the generator learning, we adopt the contrastive regularization loss that contrasts the in-class with the out-of-class utterance generation given the intent. To evaluate the quality of the generated utterances, experiments are conducted on the generalized few-shot intent detection task. Empirical results show that our proposed model achieves state-of-the-art performances on two real-world intent detection datasets.
Machine Guides, Human Supervises: Interactive Learning with Global Explanations

We introduce explanatory guided learning (XGL), a novel interactive learning strategy in which a machine guides a human supervisor toward selecting informative examples for a classifier. The guidance is provided by means of global explanations, which summarize the classifier's behavior on different regions of the instance space and expose its flaws. Compared to other explanatory interactive learning strategies, which are machine-initiated and rely on local explanations, XGL is designed to be robust against cases in which the explanations supplied by the machine oversell the classifier's quality. Moreover, XGL leverages global explanations to open up the black-box of human-initiated interaction, enabling supervisors to select informative examples that challenge the learned model. By drawing a link to interactive machine teaching, we show theoretically that global explanations are a viable approach for guiding supervisors. Our simulations show that explanatory guided learning avoids overselling the model's quality and performs comparably or better than machine- and human-initiated interactive learning strategies in terms of model quality.
Spillovers of Program Benefits with Mismeasured Networks

In studies of program evaluation under network interference, correctly measuring spillovers of the intervention is crucial for making appropriate policy recommendations. However, increasing empirical evidence has shown that network links are often measured with errors. This paper explores the identification and estimation of treatment and spillover effects when the network is mismeasured. I propose a novel method to nonparametrically point-identify the treatment and spillover effects, when two network observations are available. The method can deal with a large network with missing or misreported links and possesses several attractive features: (i) it allows heterogeneous treatment and spillover effects; (ii) it does not rely on modelling network formation or its misclassification probabilities; and (iii) it accommodates samples that are correlated in overlapping ways. A semiparametric estimation approach is proposed, and the analysis is applied to study the spillover effects of an insurance information program on the insurance adoption decisions.
Kernel-Based Smoothness Analysis of Residual Networks

A major factor in the success of deep neural networks is the use of sophisticated architectures rather than the classical multilayer perceptron (MLP). Residual networks (ResNets) stand out among these powerful modern architectures. Previous works focused on the optimization advantages of deep ResNets over deep MLPs. In this paper, we show another distinction between the two models, namely, a tendency of ResNets to promote smoother interpolations than MLPs. We analyze this phenomenon via the neural tangent kernel (NTK) approach. First, we compute the NTK for a considered ResNet model and prove its stability during gradient descent training. Then, we show by various evaluation methodologies that the NTK of ResNet, and its kernel regression results, are smoother than the ones of MLP. The better smoothness observed in our analysis may explain the better generalization ability of ResNets and the practice of moderately attenuating the residual blocks.