OpenRaiser/Intern-Atlas huggingface dataset:
Analog Programmable-Photonic Computation
Venue: arXiv cs.ET 2022
Year: 2022

Digital electronics is a technological cornerstone in our modern society which has covered the increasing demand in computing power during the last decades thanks to a periodic doubling of transistor density and power efficiency in integrated circuits. Currently, such scaling laws are reaching their fundamental limits, leading to the emergence of a large gamut of applications that cannot be supported by digital electronics, specifically, those that involve real time analog multi-data processing, e.g., medical diagnostic imaging, robotic control and remote sensing, among others. In this scenario, an analog computing approach implemented in a real-time reconfigurable non-electronic hardware such as programmable integrated photonics (PIP) can be more efficient than digital electronics to perform these emerging applications. However, actual analog computing models such as quantum and neuromorphic computation were not conceived to extract the benefits of PIP technology (and integrated photonics in general). Here, we present the foundations of a new computation theory, termed Analog Programmable-Photonic Computation (APC), explicitly designed to unleash the full potential of PIP. Interestingly, APC enables overcoming basic theoretical and technological limitations of existing computational models, can be implemented in other technologies (e.g. in electronics, acoustics or using metamaterials) and, consequently, exhibits the potential to spark a ground-breaking impact on our information society.
Robust No-Regret Learning in Min-Max Stackelberg Games
Venue: arXiv cs.GT 2022
Year: 2022

The behavior of no-regret learning algorithms is well understood in two-player min-max (i.e, zero-sum) games. In this paper, we investigate the behavior of no-regret learning in min-max games with dependent strategy sets, where the strategy of the first player constrains the behavior of the second. Such games are best understood as sequential, i.e., min-max Stackelberg, games. We consider two settings, one in which only the first player chooses their actions using a no-regret algorithm while the second player best responds, and one in which both players use no-regret algorithms. For the former case, we show that no-regret dynamics converge to a Stackelberg equilibrium. For the latter case, we introduce a new type of regret, which we call Lagrangian regret, and show that if both players minimize their Lagrangian regrets, then play converges to a Stackelberg equilibrium. We then observe that online mirror descent (OMD) dynamics in these two settings correspond respectively to a known nested (i.e., sequential) gradient descent-ascent (GDA) algorithm and a new simultaneous GDA-like algorithm, thereby establishing convergence of these algorithms to Stackelberg equilibrium. Finally, we analyze the robustness of OMD dynamics to perturbations by investigating online min-max Stackelberg games. We prove that OMD dynamics are robust for a large class of online min-max games with independent strategy sets. In the dependent case, we demonstrate the robustness of OMD dynamics experimentally by simulating them in online Fisher markets, a canonical example of a min-max Stackelberg game with dependent strategy sets.
Benchmarking Deep AUROC Optimization: Loss Functions and Algorithmic Choices
Venue: arXiv cs.LG 2022
Year: 2022

The area under the ROC curve (AUROC) has been vigorously applied for imbalanced classification and moreover combined with deep learning techniques. However, there is no existing work that provides sound information for peers to choose appropriate deep AUROC maximization techniques. In this work, we fill this gap from three aspects. (i) We benchmark a variety of loss functions with different algorithmic choices for deep AUROC optimization problem. We study the loss functions in two categories: pairwise loss and composite loss, which includes a total of 10 loss functions. Interestingly, we find composite loss, as an innovative loss function class, shows more competitive performance than pairwise loss from both training convergence and testing generalization perspectives. Nevertheless, data with more corrupted labels favors a pairwise symmetric loss. (ii) Moreover, we benchmark and highlight the essential algorithmic choices such as positive sampling rate, regularization, normalization/activation, and optimizers. Key findings include: higher positive sampling rate is likely to be beneficial for deep AUROC maximization; different datasets favors different weights of regularizations; appropriate normalization techniques, such as sigmoid and $\ell_2$ score normalization, could improve model performance. (iii) For optimization aspect, we benchmark SGD-type, Momentum-type, and Adam-type optimizers for both pairwise and composite loss. Our findings show that although Adam-type method is more competitive from training perspective, but it does not outperform others from testing perspective.
mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations
Venue: arXiv cs.LG 2022
Year: 2022

The growing amount of data and advances in data science have created a need for a new kind of cloud platform that provides users with flexibility, strong security, and the ability to couple with supercomputers and edge devices through high-performance networks. We have built such a nation-wide cloud platform, called "mdx" to meet this need. The mdx platform's virtualization service, jointly operated by 9 national universities and 2 national research institutes in Japan, launched in 2021, and more features are in development. Currently mdx is used by researchers in a wide variety of domains, including materials informatics, geo-spatial information science, life science, astronomical science, economics, social science, and computer science. This paper provides an the overview of the mdx platform, details the motivation for its development, reports its current status, and outlines its future plans.
Approximately Counting Subgraphs in Data Streams
Venue: arXiv cs.DS 2022
Year: 2022

Estimating the number of subgraphs in data streams is a fundamental problem that has received great attention in the past decade. In this paper, we give improved streaming algorithms for approximately counting the number of occurrences of an arbitrary subgraph $H$, denoted $\# H$, when the input graph $G$ is represented as a stream of $m$ edges. To obtain our algorithms, we provide a generic transformation that converts constant-round sublinear-time graph algorithms in the query access model to constant-pass sublinear-space graph streaming algorithms. Using this transformation, we obtain the following results. 1. We give a $3$-pass turnstile streaming algorithm for $(1\pm ε)$-approximating $\# H$ in $\tilde{O}(\frac{m^{ρ(H)}}{ε^2\cdot \# H})$ space, where $ρ(H)$ is the fractional edge-cover of $H$. This improves upon and generalizes a result of McGregor et al. [PODS 2016], who gave a $3$-pass insertion-only streaming algorithm for $(1\pm ε)$-approximating the number $\# T$ of triangles in $\tilde{O}(\frac{m^{3/2}}{ε^2\cdot \# T})$ space if the algorithm is given additional oracle access to the degrees. 2. We provide a constant-pass streaming algorithm for $(1\pm ε)$-approximating $\# K_r$ in $\tilde{O}(\frac{mλ^{r-2}}{ε^2\cdot \# K_r})$ space for any $r\geq 3$, in a graph $G$ with degeneracy $λ$, where $K_r$ is a clique on $r$ vertices. This resolves a conjecture by Bera and Seshadhri [PODS 2020]. More generally, our reduction relates the adaptivity of a query algorithm to the pass complexity of a corresponding streaming algorithm, and it is applicable to all algorithms in standard sublinear-time graph query models, e.g., the (augmented) general model.
"This is Fake News": Characterizing the Spontaneous Debunking from Twitter Users to COVID-19 False Information
Venue: arXiv cs.SI 2022
Year: 2022

False information spreads on social media, and fact-checking is a potential countermeasure. However, there is a severe shortage of fact-checkers; an efficient way to scale fact-checking is desperately needed, especially in pandemics like COVID-19. In this study, we focus on spontaneous debunking by social media users, which has been missed in existing research despite its indicated usefulness for fact-checking and countering false information. Specifically, we characterize the tweets with false information, or fake tweets, that tend to be debunked and Twitter users who often debunk fake tweets. For this analysis, we create a comprehensive dataset of responses to fake tweets, annotate a subset of them, and build a classification model for detecting debunking behaviors. We find that most fake tweets are left undebunked, spontaneous debunking is slower than other forms of responses, and spontaneous debunking exhibits partisanship in political topics. These results provide actionable insights into utilizing spontaneous debunking to scale conventional fact-checking, thereby supplementing existing research from a new perspective.
A Unified Study of Machine Learning Explanation Evaluation Metrics
Venue: arXiv cs.LG 2022
Year: 2022

The growing need for trustworthy machine learning has led to the blossom of interpretability research. Numerous explanation methods have been developed to serve this purpose. However, these methods are deficiently and inappropriately evaluated. Many existing metrics for explanations are introduced by researchers as by-products of their proposed explanation techniques to demonstrate the advantages of their methods. Although widely used, they are more or less accused of problems. We claim that the lack of acknowledged and justified metrics results in chaos in benchmarking these explanation methods -- Do we really have good/bad explanation when a metric gives a high/low score? We split existing metrics into two categories and demonstrate that they are insufficient to properly evaluate explanations for multiple reasons. We propose guidelines in dealing with the problems in evaluating machine learning explanation and encourage researchers to carefully deal with these problems when developing explanation techniques and metrics.
Budget-Constrained Reinforcement of Ranked Objects
Venue: arXiv cs.GT 2022
Year: 2022

Commercial entries, such as hotels, are ranked according to score by a search engine or recommendation system, and the score of each can be improved upon by making a targeted investment, e.g., advertising. We study the problem of how a principal, who owns or supports a set of entries, can optimally allocate a budget to maximize their ranking. Representing the set of ranked scores as a probability distribution over scores, we treat this question as a game between distributions. We show that, in the general case, the best ranking is achieved by equalizing the scores of several disjoint score ranges. We show that there is a unique optimal reinforcement strategy, and provide an efficient algorithm implementing it.
On the Linear Components Space of S-boxes Generated by Orthogonal Cellular Automata
Venue: arXiv cs.CR 2022
Year: 2022

We investigate S-boxes defined by pairs of Orthogonal Cellular Automata (OCA), motivated by the fact that such CA always define bijective vectorial Boolean functions, and could thus be interesting for the design of block ciphers. In particular, we perform an exhaustive search of all nonlinear OCA pairs of diameter $d=4$ and $d=5$, which generate S-boxes of size $6\times 6$ and $8\times 8$, respectively. Surprisingly, all these S-boxes turn out to be linear, and thus they are not useful for the design of confusion layers in block ciphers. However, a closer inspection of these S-boxes reveals a very interesting structure. Indeed, we remark that the linear components space of the OCA-based S-boxes found by our exhaustive search are themselves the kernels of linear CA, or, equivalently, \emph{polynomial codes}. We finally classify the polynomial codes of the S-boxes obtained in our exhaustive search and observe that, in most cases, they actually correspond to the cyclic code with generator polynomial $X^{b}+1$, where $b=d-1$. Although these findings rule out the possibility of using OCA to design good S-boxes in block ciphers, they give nonetheless some interesting insights for a theoretical characterization of nonlinear OCA pairs, which is still an open question in general.
MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering
Venue: arXiv cs.CL 2022
Year: 2022

This paper introduces MedMCQA, a new large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions. More than 194k high-quality AIIMS \& NEET PG entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token length of 12.77 and high topical diversity. Each sample contains a question, correct answer(s), and other options which requires a deeper language understanding as it tests the 10+ reasoning abilities of a model across a wide range of medical subjects \& topics. A detailed explanation of the solution, along with the above information, is provided in this study.
Code Review Practices for Refactoring Changes: An Empirical Study on OpenStack
Venue: arXiv cs.SE 2022
Year: 2022

Modern code review is a widely used technique employed in both industrial and open-source projects to improve software quality, share knowledge, and ensure adherence to coding standards and guidelines. During code review, developers may discuss refactoring activities before merging code changes in the code base. To date, code review has been extensively studied to explore its general challenges, best practices and outcomes, and socio-technical aspects. However, little is known about how refactoring is being reviewed and what developers care about when they review refactored code. Hence, in this work, we present a quantitative and qualitative study to understand what are the main criteria developers rely on to develop a decision about accepting or rejecting a submitted refactored code, and what makes this process challenging. Through a case study of 11,010 refactoring and non-refactoring reviews spread across OpenStack open-source projects, we find that refactoring-related code reviews take significantly longer to be resolved in terms of code review efforts. Moreover, upon performing a thematic analysis on a significant sample of the refactoring code review discussions, we built a comprehensive taxonomy consisting of 28 refactoring review criteria. We envision our findings reaffirming the necessity of developing accurate and efficient tools and techniques that can assist developers in the review process in the presence of refactorings.
SMP-PHAT: Lightweight DoA Estimation by Merging Microphone Pairs
Venue: arXiv cs.SD 2022
Year: 2022

This paper introduces SMP-PHAT, which performs direction of arrival (DoA) of sound estimation with a microphone array by merging pairs of microphones that are parallel in space. This approach reduces the number of pairwise cross-correlation computations, and brings down the number of flops and memory lookups when searching for DoA. Experiments on low-cost hardware with commonly used microphone arrays show that the proposed method provides the same accuracy as the former SRP-PHAT approach, while reducing the computational load by 39% in some cases.
OTFace: Hard Samples Guided Optimal Transport Loss for Deep Face Representation
Venue: arXiv cs.CV 2022
Year: 2022

Face representation in the wild is extremely hard due to the large scale face variations. To this end, some deep convolutional neural networks (CNNs) have been developed to learn discriminative feature by designing properly margin-based losses, which perform well on easy samples but fail on hard samples. Based on this, some methods mainly adjust the weights of hard samples in training stage to improve the feature discrimination. However, these methods overlook the feature distribution property which may lead to better results since the miss-classified hard samples may be corrected by using the distribution metric. This paper proposes the hard samples guided optimal transport (OT) loss for deep face representation, OTFace for short. OTFace aims to enhance the performance of hard samples by introducing the feature distribution discrepancy while maintain the performance on easy samples. Specifically, we embrace triplet scheme to indicate hard sample groups in one mini-batch during training. OT is then used to characterize the distribution differences of features from the high level convolutional layer. Finally, we integrate the margin-based-softmax (e.g. ArcFace or AM-Softmax) and OT to guide deep CNN learning. Extensive experiments are conducted on several benchmark databases. The quantitative results demonstrate the advantages of the proposed OTFace over state-of-the-art methods.
Optimization of Directional Landmark Deployment for Visual Observer on SE(3)
Venue: arXiv cs.CV 2022
Year: 2022

An optimization method is proposed in this paper for novel deployment of given number of directional landmarks (location and pose) within a given region in the 3-D task space. This new deployment technique is built on the geometric models of both landmarks and the monocular camera. In particular, a new concept of Multiple Coverage Probability (MCP) is defined to characterize the probability of at least n landmarks being covered simultaneously by a camera at a fixed position. The optimization is conducted with respect to the position and pose of the given number of landmarks to maximize MCP through globally exploration of the given 3-D space. By adopting the elimination genetic algorithm, the global optimal solutions can be obtained, which are then applied to improve the convergent performance of the visual observer on SE(3) as a demonstration example. Both simulation and experimental results are presented to validate the effectiveness of the proposed landmark deployment optimization method.
EnCBP: A New Benchmark Dataset for Finer-Grained Cultural Background Prediction in English
Venue: arXiv cs.CL 2022
Year: 2022

While cultural backgrounds have been shown to affect linguistic expressions, existing natural language processing (NLP) research on culture modeling is overly coarse-grained and does not examine cultural differences among speakers of the same language. To address this problem and augment NLP models with cultural background features, we collect, annotate, manually validate, and benchmark EnCBP, a finer-grained news-based cultural background prediction dataset in English. Through language modeling (LM) evaluations and manual analyses, we confirm that there are noticeable differences in linguistic expressions among five English-speaking countries and across four states in the US. Additionally, our evaluations on nine syntactic (CoNLL-2003), semantic (PAWS-Wiki, QNLI, STS-B, and RTE), and psycholinguistic tasks (SST-5, SST-2, Emotion, and Go-Emotions) show that, while introducing cultural background information does not benefit the Go-Emotions task due to text domain conflicts, it noticeably improves deep learning (DL) model performance on other tasks. Our findings strongly support the importance of cultural background modeling to a wide variety of NLP tasks and demonstrate the applicability of EnCBP in culture-related research.
ANNA: Enhanced Language Representation for Question Answering
Venue: arXiv cs.CL 2022
Year: 2022

Pre-trained language models have brought significant improvements in performance in a variety of natural language processing tasks. Most existing models performing state-of-the-art results have shown their approaches in the separate perspectives of data processing, pre-training tasks, neural network modeling, or fine-tuning. In this paper, we demonstrate how the approaches affect performance individually, and that the language model performs the best results on a specific question answering task when those approaches are jointly considered in pre-training models. In particular, we propose an extended pre-training task, and a new neighbor-aware mechanism that attends neighboring tokens more to capture the richness of context for pre-training language modeling. Our best model achieves new state-of-the-art results of 95.7\% F1 and 90.6\% EM on SQuAD 1.1 and also outperforms existing pre-trained language models such as RoBERTa, ALBERT, ELECTRA, and XLNet on the SQuAD 2.0 benchmark.
Pyramid Feature Alignment Network for Video Deblurring
Venue: arXiv cs.CV 2022
Year: 2022

Video deblurring remains a challenging task due to various causes of blurring. Traditional methods have considered how to utilize neighboring frames by the single-scale alignment for restoration. However, they typically suffer from misalignment caused by severe blur. In this work, we aim to better utilize neighboring frames with high efficient feature alignment. We propose a Pyramid Feature Alignment Network (PFAN) for video deblurring. First, the multi-scale feature of blurry frames is extracted with the strategy of Structure-to-Detail Downsampling (SDD) before alignment. This downsampling strategy makes the edges sharper, which is helpful for alignment. Then we align the feature at each scale and reconstruct the image at the corresponding scale. This strategy effectively supervises the alignment at each scale, overcoming the problem of propagated errors from the above scales at the alignment stage. To better handle the challenges of complex and large motions, instead of aligning features at each scale separately, lower-scale motion information is used to guide the higher-scale motion estimation. Accordingly, a Cascade Guided Deformable Alignment (CGDA) is proposed to integrate coarse motion into deformable convolution for finer and more accurate alignment. As demonstrated in extensive experiments, our proposed PFAN achieves superior performance with competitive speed compared to the state-of-the-art methods.
Computer Science Named Entity Recognition in the Open Research Knowledge Graph
Venue: arXiv cs.CL 2022
Year: 2022

Domain-specific named entity recognition (NER) on Computer Science (CS) scholarly articles is an information extraction task that is arguably more challenging for the various annotation aims that can beset the task and has been less studied than NER in the general domain. Given that significant progress has been made on NER, we believe that scholarly domain-specific NER will receive increasing attention in the years to come. Currently, progress on CS NER -- the focus of this work -- is hampered in part by its recency and the lack of a standardized annotation aim for scientific entities/terms. This work proposes a standardized task by defining a set of seven contribution-centric scholarly entities for CS NER viz., research problem, solution, resource, language, tool, method, and dataset. Following which, its main contributions are: combines existing CS NER resources that maintain their annotation focus on the set or subset of contribution-centric scholarly entities we consider; further, noting the need for big data to train neural NER models, this work additionally supplies thousands of contribution-centric entity annotations from article titles and abstracts, thus releasing a cumulative large novel resource for CS NER; and, finally, trains a sequence labeling CS NER model inspired after state-of-the-art neural architectures from the general domain NER task. Throughout the work, several practical considerations are made which can be useful to information technology designers of the digital libraries.