nils-herrmann/clean_scirepeval huggingface dataset:
A Unified Framework of Robust Submodular Optimization

In this paper, we shall study a unified framework of robust submodular optimization. We study this problem both from a minimization and maximization perspective (previous work has only focused on variants of robust submodular maximization). We do this under a broad range of combinatorial constraints including cardinality, knapsack, matroid as well as graph based constraints such as cuts, paths, matchings and trees. Furthermore, we also study robust submodular minimization and maximization under multiple submodular upper and lower bound constraints. We show that all these problems are motivated by important machine learning applications including robust data subset selection, robust co-operative cuts and robust co-operative matchings. In each case, we provide scalable approximation algorithms and also study hardness bounds. Finally, we empirically demonstrate the utility of our algorithms on real world applications.
Absorption by black hole remnants in metric-affine gravity

Using numerical methods, we investigate the absorption properties of a family of nonsingular solutions {which arise in different metric-affine theories, such as quadratic and Born-Infeld gravity.} These solutions continuously interpolate between Schwarzschild black holes and naked solitons with wormhole topology. The resulting spectrum is characterized by a series of quasibound states excitations, associated with the existence of a stable photonsphere.
Value Functions for Depth-Limited Solving in Zero-Sum Imperfect-Information Games

Depth-limited look-ahead search is an essential tool for agents playing perfect-information games. In imperfect information games, the lack of a clear notion of a value of a state makes designing theoretically sound depth-limited solving algorithms substantially more difficult. Furthermore, most results in this direction only consider the domain of poker. We consider two-player zero-sum extensive form games in general. We provide a domain-independent definitions of optimal value functions and prove that they can be used for depth-limited look-ahead game solving. We prove that the minimal set of game states necessary to define the value functions is related to common knowledge of the players. We show the value function may be defined in several structurally different ways. None of them is unique, but the set of possible outputs is convex, which enables approximating the value function by machine learning models.
Fixing the train-test resolution discrepancy

Data-augmentation is key to the training of neural networks for image classification. This paper first shows that existing augmentations induce a significant discrepancy between the typical size of the objects seen by the classifier at train and test time. We experimentally validate that, for a target test resolution, using a lower train resolution offers better classification at test time. 
We then propose a simple yet effective and efficient strategy to optimize the classifier performance when the train and test resolutions differ. It involves only a computationally cheap fine-tuning of the network at the test resolution. This enables training strong classifiers using small training images. For instance, we obtain 77.1% top-1 accuracy on ImageNet with a ResNet-50 trained on 128x128 images, and 79.8% with one trained on 224x224 image. In addition, if we use extra training data we get 82.5% with the ResNet-50 train with 224x224 images. 
Conversely, when training a ResNeXt-101 32x48d pre-trained in weakly-supervised fashion on 940 million public images at resolution 224x224 and further optimizing for test resolution 320x320, we obtain a test top-1 accuracy of 86.4% (top-5: 98.0%) (single-crop). To the best of our knowledge this is the highest ImageNet single-crop, top-1 and top-5 accuracy to date.
Principled Frameworks for Evaluating Ethics in NLP Systems

We critique recent work on ethics in natural language processing. Those discussions have focused on data collection, experimental design, and interventions in modeling. But we argue that we ought to first understand the frameworks of ethics that are being used to evaluate the fairness and justice of algorithmic systems. Here, we begin that discussion by outlining deontological and consequentialist ethics, and make predictions on the research agenda prioritized by each.
Linear-time Hierarchical Community Detection

Community detection in graphs has many important and fundamental applications including in distributed systems, compression, image segmentation, divide-and-conquer graph algorithms such as nested dissection, document and word clustering, circuit design, among many others. Finding these densely connected regions of graphs remains an important and challenging problem. Most work has focused on scaling up existing methods to handle large graphs. These methods often partition the graph into two or more communities. In this work, we focus on the problem of hierarchical community detection (i.e., finding a hierarchy of dense community structures going from the lowest granularity to the largest) and describe an approach that runs in linear time with respect to the number of edges and thus fast and efficient for large-scale networks. The experiments demonstrate the effectiveness of the approach quantitatively. Finally, we show an application of it for visualizing large networks with hundreds of thousands of nodes/links.
Towards Empathetic Planning

Critical to successful human interaction is a capacity for empathy - the ability to understand and share the thoughts and feelings of another. As Artificial Intelligence (AI) systems are increasingly required to interact with humans in a myriad of settings, it is important to enable AI to wield empathy as a tool to benefit those it interacts with. In this paper, we work towards this goal by bringing together a number of important concepts: empathy, AI planning, and reasoning in the presence of knowledge and belief. We formalize the notion of Empathetic Planning which is informed by the beliefs and affective state of the empathizee. We appeal to an epistemic logic framework to represent the beliefs of the empathizee and propose AI planning-based computational approaches to compute empathetic solutions. We illustrate the potential benefits of our approach by conducting a study where we evaluate participants' perceptions of the agent's empathetic abilities and assistive capabilities.
Detecting Bias with Generative Counterfactual Face Attribute Augmentation

We introduce a simple framework for identifying biases of a smiling attribute classifier. Our method poses counterfactual questions of the form: how would the prediction change if this face characteristic had been different? We leverage recent advances in generative adversarial networks to build a realistic generative model of face images that affords controlled manipulation of specific image characteristics. We introduce a set of metrics that measure the effect of manipulating a specific property of an image on the output of a trained classifier. Empirically, we identify several different factors of variation that affect the predictions of a smiling classifier trained on CelebA.
High-Performance Deep Learning via a Single Building Block

Deep learning (DL) is one of the most prominent branches of machine learning. Due to the immense computational cost of DL workloads, industry and academia have developed DL libraries with highly-specialized kernels for each workload/architecture, leading to numerous, complex code-bases that strive for performance, yet they are hard to maintain and do not generalize. In this work, we introduce the batch-reduce GEMM kernel and show how the most popular DL algorithms can be formulated with this kernel as the basic building-block. Consequently, the DL library-development degenerates to mere (potentially automatic) tuning of loops around this sole optimized kernel. By exploiting our new kernel we implement Recurrent Neural Networks, Convolution Neural Networks and Multilayer Perceptron training and inference primitives in just 3K lines of high-level code. Our primitives outperform vendor-optimized libraries on multi-node CPU clusters, and we also provide proof-of-concept CNN kernels targeting GPUs. Finally, we demonstrate that the batch-reduce GEMM kernel within a tensor compiler yields high-performance CNN primitives, further amplifying the viability of our approach.
Tagged Back-Translation

Recent work in Neural Machine Translation (NMT) has shown significant quality gains from noised-beam decoding during back-translation, a method to generate synthetic parallel data. We show that the main role of such synthetic noise is not to diversify the source side, as previously suggested, but simply to indicate to the model that the given source is synthetic. We propose a simpler alternative to noising techniques, consisting of tagging back-translated source sentences with an extra token. Our results on WMT outperform noised back-translation in English-Romanian and match performance on English-German, redefining the state-of-the-art on the former.
Efficient Neural Network Approaches for Leather Defect Classification

Genuine leather, such as the hides of cows, crocodiles, lizards and goats usually contain natural and artificial defects, like holes, fly bites, tick marks, veining, cuts, wrinkles and others. A traditional solution to identify the defects is by manual defect inspection, which involves skilled experts. It is time consuming and may incur a high error rate and results in low productivity. This paper presents a series of automatic image processing processes to perform the classification of leather defects by adopting deep learning approaches. Particularly, the leather images are first partitioned into small patches,then it undergoes a pre-processing technique, namely the Canny edge detection to enhance defect visualization. Next, artificial neural network (ANN) and convolutional neural network (CNN) are employed to extract the rich image features. The best classification result achieved is 80.3 %, evaluated on a data set that consists of 2000 samples. In addition, the performance metrics such as confusion matrix and Receiver Operating Characteristic (ROC) are reported to demonstrate the efficiency of the method proposed.
Can Neural Networks Understand Monotonicity Reasoning?

Monotonicity reasoning is one of the important reasoning skills for any intelligent natural language inference (NLI) model in that it requires the ability to capture the interaction between lexical and syntactic structures. Since no test set has been developed for monotonicity reasoning with wide coverage, it is still unclear whether neural models can perform monotonicity reasoning in a proper way. To investigate this issue, we introduce the Monotonicity Entailment Dataset (MED). Performance by state-of-the-art NLI models on the new test set is substantially worse, under 55%, especially on downward reasoning. In addition, analysis using a monotonicity-driven data augmentation method showed that these models might be limited in their generalization ability in upward and downward reasoning.
Robust or Private? Adversarial Training Makes Models More Vulnerable to Privacy Attacks

Adversarial training was introduced as a way to improve the robustness of deep learning models to adversarial attacks. This training method improves robustness against adversarial attacks, but increases the models vulnerability to privacy attacks. In this work we demonstrate how model inversion attacks, extracting training data directly from the model, previously thought to be intractable become feasible when attacking a robustly trained model. The input space for a traditionally trained model is dominated by adversarial examples - data points that strongly activate a certain class but lack semantic meaning - this makes it difficult to successfully conduct model inversion attacks. We demonstrate this effect using the CIFAR-10 dataset under three different model inversion attacks, a vanilla gradient descent method, gradient based method at different scales, and a generative adversarial network base attacks.
Touching the stars: improving NASA 3D printed data sets with blind and visually impaired audiences

Astronomy has been an inherently visual area of science for millenia, yet a majority of its significant discoveries take place in wavelengths beyond human vision. There are many people, including those with low or no vision, who cannot participate fully in such discoveries if visual media is the primary communication mechanism. Numerous efforts have worked to address equity of accessibility to such knowledge sharing, such as through the creation of three-dimensional (3D) printed data sets. This paper describes progress made through technological and programmatic developments in tactile 3D models using NASA's Chandra X-ray Observatory to improve access to data.
Correlating Twitter Language with Community-Level Health Outcomes

We study how language on social media is linked to mortal diseases such as atherosclerotic heart disease (AHD), diabetes and various types of cancer. Our proposed model leverages state-of-the-art sentence embeddings, followed by a regression model and clustering, without the need of additional labelled data. It allows to predict community-level medical outcomes from language, and thereby potentially translate these to the individual level. The method is applicable to a wide range of target variables and allows us to discover known and potentially novel correlations of medical outcomes with life-style aspects and other socioeconomic risk factors.
Speeding Up VP9 Intra Encoder With Hierarchical Deep Learning-Based Partition Prediction

In VP9 video codec, the sizes of blocks are decided during encoding by recursively partitioning $64\times 64$ superblocks using rate-distortion optimization (RDO). This process is computationally intensive because of the combinatorial search space of possible partitions of a superblock. Here, we propose a deep learning based alternative framework to predict the intra-mode superblock partitions in the form of a four-level partition tree, using a hierarchical fully convolutional network (H-FCN). We created a large database of VP9 superblocks and the corresponding partitions to train an H-FCN model, which was subsequently integrated with the VP9 encoder to reduce the intra-mode encoding time. The experimental results establish that our approach speeds up intra-mode encoding by 69.7% on average, at the expense of a 1.71% increase in the Bjøntegaard-Delta bitrate (BD-rate). While VP9 provides several built-in speed levels which are designed to provide faster encoding at the expense of decreased rate-distortion performance, we find that our model is able to outperform the fastest recommended speed level of the reference VP9 encoder for the good quality intra encoding configuration, in terms of both speedup and BD-rate.
Calibration of local‐stochastic volatility models by optimal transport

In this paper, we study a semi‐martingale optimal transport problem and its application to the calibration of local‐stochastic volatility (LSV) models. Rather than considering the classical constraints on marginal distributions at initial and final time, we optimize our cost function given the prices of a finite number of European options. We formulate the problem as a convex optimization problem, for which we provide a PDE formulation along with its dual counterpart. Then we solve numerically the dual problem, which involves a fully non‐linear Hamilton–Jacobi–Bellman equation. The method is tested by calibrating a Heston‐like LSV model with simulated data and foreign exchange market data.
Option Pricing via Multi-path Autoregressive Monte Carlo Approach

The pricing of financial derivatives, which requires massive calculations and close-to-real-time operations under many trading and arbitrage scenarios, were largely infeasible in the past. However, with the advancement of modern computing, the efficiency has substantially improved. In this work, we propose and design a multi-path option pricing approach via autoregression (AR) process and Monte Carlo Simulations (MCS). Our approach learns and incorporates the price characteristics into AR process, and re-generates the price paths for options. We apply our approach to price weekly options underlying Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and compare the results with prior practiced models, e.g., Black-Scholes-Merton and Binomial Tree. The results show that our approach is comparable with prior practiced models.
A formal approach for customization of schema.org based on SHACL

this http URL is a widely adopted vocabulary for semantic annotation of content and data. However, its generic nature makes it complicated for data publishers to pick right types and properties for a specific domain and task. In this paper we propose a formal approach, a domain specification process that generates domain specific patterns by applying operators implemented in SHACL to the this http URL vocabulary. These patterns can support knowledge generation and assessment processes for specific domains and tasks. We demonstrated our approach with use cases in tourism domain.
Gait transition in swimming

The skill to swim fast results from the interplay between generating high thrust while minimizing drag. In front crawl, swimmers achieve this goal by adapting their inter-arm coordination according to the race pace. A transition has been observed from a catch-up pattern of coordination (i.e. lag time between the propulsion of the two arms) to a superposition pattern of coordination as the velocity increases. Expert swimmers choose a catch-up coordination pattern at low velocities with a constant relative lag time of glide during the cycle and switch to a maximum propulsion force strategy at higher velocities. This transition is explained using a burst-and-coast model. At low velocities, the choice of coordination can be understood through two parameters: the time of propulsion and the gliding effectiveness. These parameters can characterize a swimmer and help to optimize their technique.
Attributed Graph Clustering: A Deep Attentional Embedding Approach

Graph clustering is a fundamental task which discovers communities or groups in networks. Recent studies have mostly focused on developing deep learning approaches to learn a compact graph embedding, upon which classic clustering methods like k-means or spectral clustering algorithms are applied. These two-step frameworks are difficult to manipulate and usually lead to suboptimal performance, mainly because the graph embedding is not goal-directed, i.e., designed for the specific clustering task. In this paper, we propose a goal-directed deep learning approach, Deep Attentional Embedded Graph Clustering (DAEGC for short). Our method focuses on attributed graphs to sufficiently explore the two sides of information in graphs. By employing an attention network to capture the importance of the neighboring nodes to a target node, our DAEGC algorithm encodes the topological structure and node content in a graph to a compact representation, on which an inner product decoder is trained to reconstruct the graph structure. Furthermore, soft labels from the graph embedding itself are generated to supervise a self-training graph clustering process, which iteratively refines the clustering results. The self-training process is jointly learned and optimized with the graph embedding in a unified framework, to mutually benefit both components. Experimental results compared with state-of-the-art algorithms demonstrate the superiority of our method.
MV-C3D: A Spatial Correlated Multi-View 3D Convolutional Neural Networks

As the development of deep neural networks, 3D object recognition is becoming increasingly popular in the computer vision community. Many multi-view-based methods are proposed to improve the category recognition accuracy. These approaches mainly rely on multi-view images that are rendered with the whole circumference. In real-world applications, however, 3D objects are mostly observed from partial viewpoints in a less range. Therefore, we propose a multi-view-based 3D convolutional neural network that takes only part of contiguous multi-view images as input and can still maintain high accuracy. Moreover, our model takes these view images as a joint variable to better learn the spatially correlated features using 3D convolution and 3D max-pooling layers. The experimental results on ModelNet10 and ModelNet40 datasets show that our MV-C3D technique can achieve outstanding performance with multi-view images that are captured from partial angles with less range. The results on 3D-rotated real image dataset MIRO further demonstrate that MV-C3D is more adaptable in real-world scenarios. The classification accuracy can be further improved with the increasing number of view images.
Image-Based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era

3D reconstruction is a longstanding ill-posed problem, which has been explored for decades by the computer vision, computer graphics, and machine learning communities. Since 2015, image-based 3D reconstruction using convolutional neural networks (CNN) has attracted increasing interest and demonstrated an impressive performance. Given this new era of rapid evolution, this article provides a comprehensive survey of the recent developments in this field. We focus on the works which use deep learning techniques to estimate the 3D shape of generic objects either from a single or multiple RGB images. We organize the literature based on the shape representations, the network architectures, and the training mechanisms they use. While this survey is intended for methods which reconstruct generic objects, we also review some of the recent works which focus on specific object classes such as human body shapes and faces. We provide an analysis and comparison of the performance of some key papers, summarize some of the open problems in this field, and discuss promising directions for future research.
ASKAP detection of periodic and elliptically polarized radio pulses from UV Ceti

Active M dwarfs are known to produce bursty radio emission, and multiwavelength studies have shown that solar-like magnetic activity occurs in these stars. However, coherent bursts from active M dwarfs have often been difficult to interpret in the solar activity paradigm. We present Australian Square Array Pathfinder (ASKAP) observations of UV Ceti at a central frequency of 888 MHz. We detect several periodic, coherent pulses occurring over a time-scale consistent with the rotational period of UV Ceti. The properties of the pulsed emission show that they originate from the electron cyclotron maser instability, in a cavity at least 7 orders of magnitude less dense than the mean coronal density at the estimated source altitude. These results confirm that auroral activity can occur in active M dwarfs, suggesting that these stars mark the beginning of the transition from solar-like to auroral magnetospheric behaviour. These results demonstrate the capabilities of ASKAP for detecting polarized, coherent bursts from active stars and other systems.