Miracle-dz/UniFAR-FTUs huggingface dataset:
Solving Inefficiency of Self-supervised Representation Learning

Self-supervised learning (especially contrastive learning) has attracted great interest due to its huge potential in learning discriminative representations in an unsupervised manner. Despite the acknowledged successes, existing contrastive learning methods suffer from very low learning efficiency, e.g., taking about ten times more training epochs than supervised learning for comparable recognition accuracy. In this paper, we reveal two contradictory phenomena in contrastive learning that we call under-clustering and over-clustering problems, which are major obstacles to learning efficiency. Under-clustering means that the model cannot efficiently learn to discover the dissimilarity between inter-class samples when the negative sample pairs for contrastive learning are insufficient to differentiate all the actual object classes. Over-clustering implies that the model cannot efficiently learn features from excessive negative sample pairs, forcing the model to over-cluster samples of the same actual classes into different clusters. To simultaneously overcome these two problems, we propose a novel self-supervised learning framework using a truncated triplet loss. Precisely, we employ a triplet loss tending to maximize the relative distance between the positive pair and negative pairs to address the under-clustering problem; and we construct the negative pair by selecting a negative sample deputy from all negative samples to avoid the over-clustering problem, guaranteed by the Bernoulli Distribution model. We extensively evaluate our framework in several large-scale benchmarks (e.g., ImageNet, SYSU-30k, and COCO). The results demonstrate our model’s superiority (e.g., the learning efficiency) over the latest state-of-the-art methods by a clear margin. See Codes1.
Improving Attribution Methods by Learning Submodular Functions

This work explores the novel idea of learning a submodular scoring function to improve the specificity/selectivity of existing feature attribution methods. Submodular scores are natural for attribution as they are known to accurately model the principle of diminishing returns. A new formulation for learning a deep submodular set function that is consistent with the real-valued attribution maps obtained by existing attribution methods is proposed. The final attribution value of a feature is then defined as the marginal gain in the induced submodular score of the feature in the context of other highly attributed features, thus decreasing the attribution of redundant yet discriminatory features. Experiments on multiple datasets illustrate that the proposed attribution method achieves higher specificity along with good discriminative power. The implementation of our method is publicly available at https://github.com/Piyushi-0/SEA-NN.
Network modeling methods for precision medicine

We discuss in this survey several network modeling methods and their applicability to precision medicine. We review several network centrality methods (degree centrality, closeness centrality, eccentricity centrality, betweenness centrality, and eigenvector-based prestige) and two systems controllability methods (minimum dominating sets and network structural controllability). We demonstrate their applicability to precision medicine on three multiple myeloma patient disease networks. Each network consists of protein-protein interactions built around a specific patient's mutated genes, around the targets of the drugs used in the standard of care in multiple myeloma, and around multiple myeloma-specific essential genes. For each network we demonstrate how the network methods we discuss can be used to identify personalized, targeted drug combinations uniquely suited to that patient.
Significance of the strain-dominated region around a vortex on induced aerodynamic loads

Abstract The ability of vortices to induce aerodynamic loads on proximal surfaces plays a significant role in a wide variety of flows. However, most studies of vortex-induced effects primarily focus on analysing the influence of the rotation-dominated cores of vortices. In this work, we show that not only are vortices in viscous flows surrounded by strain-dominated regions, but these regions are dynamically important and can sometimes even dictate the induced aerodynamic loads. We demonstrate this for a pitching airfoil, which exhibits dynamic stall and generates several force-inducing vortices. Using a data-driven force partitioning method (FPM), we quantify the influence of vortices as well as vortex-associated strain to show that our current understanding of vortex-dominated phenomena, such as dynamic stall, is incomplete without considering the substantial effect of strain-dominated regions that are associated with vortices.
Robust Embeddings Via Distributions

Despite recent monumental advances in the field, many Natural Language Processing (NLP) models still struggle to perform adequately on noisy domains. We propose a novel probabilistic embedding-level method to improve the robustness of NLP models. Our method, Robust Embeddings via Distributions (RED), incorporates information from both noisy tokens and surrounding context to obtain distributions over embedding vectors that can express uncertainty in semantic space more fully than any deterministic method. We evaluate our method on a number of downstream tasks using existing state-of-the-art models in the presence of both natural and synthetic noise, and demonstrate a clear improvement over other embedding approaches to robustness from the literature.
Time-inhomogeneous Quantum Walks with Decoherence on Discrete Infinite Spaces

In quantum computation theory, quantum random walks have been utilized by many quantum search algorithms which provide improved performance over their classical counterparts. However, due to the importance of the quantum decoherence phenomenon, decoherent quantum walks and their applications have been studied on a wide variety of structures. Recently, a unified timeinhomogeneous coin-turning random walk with rescaled limiting distributions, Bernoulli, uniform, arcsine and semi-circle laws as parameter varies have been obtained. In this paper we study the quantum analogue of these models. We obtained a representation theorem for time-inhomogeneous quantum walk on discrete infinite state space. Additionally, the convergence of the distributions of the decoherent quantum walks are numerically estimated as an application of the representation theorem, and the convergence in distribution of the quantum analogues of Bernoulli, uniform, arcsine and semicircle laws are statistically analyzed. 1 ar X iv :2 10 4. 09 10 4v 1 [ qu an tph ] 1 9 A pr 2 02 1
Bayesian statistical framework for identifying strongly lensed gravitational-wave signals

It is expected that gravitational waves, similar to electromagnetic waves, can be gravitationally lensed by intervening matters, producing multiple instances of the same signal arriving at different times from different apparent luminosity distances with different phase shifts compared to the un-lensed signal due to lensing. If unaccounted for, these lensed signals will masquerade as separate systems with higher mass and lower redshift. Here we present a Bayesian statistical framework for identifying strongly-lensed gravitational-wave signals that incorporates astrophysical information and accounts for selection effects. We also propose a two-step hierarchical analysis for more efficient computations of the probabilities and inferences of source parameters free from bias introduced by lensing. We show with examples on how changing the astrophysical models could shift one's interpretation on the origin of the observed gravitational waves, and possibly lead to indisputable evidence of strong lensing of the observed waves. In addition, we demonstrate the improvement in the sky localization of the source of the lensed signals, and in some cases the identification of the Morse indices of the lensed signals. If confirmed, lensed gravitational waves will allow us to probe the Universe at higher redshift, and to constrain the polarization contents of the waves with fewer detectors.
Generating Related Work

Communicating new research ideas involves highlighting similarities and differences with past work. Authors write fluent, often long sections to survey the distinction of a new paper with related work. In this work we model generating related work sections while being cognisant of the motivation behind citing papers. Our content planning model generates a tree of cited papers before a surface realization model lexicalizes this skeleton. Our model outperforms several strong state-of-the-art summarization and multi-document summarization models on generating related work on an ACL Anthology (AA) based dataset which we contribute.
Modeling Ideological Agenda Setting and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity

The increasing polarization of online political discourse calls for computational tools that are able to automatically detect and monitor ideological divides in social media. Here, we introduce a minimally supervised method that directly leverages the network structure of online discussion forums, specifically Reddit, to detect polarized concepts. We model polarization along the dimensions of agenda setting and framing, drawing upon insights from moral psychology. The architecture we propose combines graph neural networks with structured sparsity and results in representations for concepts and subred-dits that capture phenomena such as ideological radicalization and subreddit hijacking. We also create a new dataset of political discourse covering 12 years and more than 600 online groups with different ideologies.
Attacking Text Classifiers via Sentence Rewriting Sampler

Most adversarial attack methods on text classiﬁcation can change the classiﬁer’s prediction by synonym substitution. We propose the adversarial sentence rewriting sampler ( ASRS ), which rewrites the whole sentence to generate more similar and higher-quality adversarial examples. Our method achieves a better attack success rate on 4 out of 7 datasets, as well as signiﬁcantly better sentence quality on all 7 datasets. ASRS is an indispensable supplement to the existing attack methods, because classiﬁers cannot resist the attack from ASRS unless they are trained on adversarial examples found by ASRS .
Deformation and relaxation of viscous thin films under bouncing drops

Abstract Thin, viscous liquid films subjected to impact events can deform. Here we investigate free-surface oil-film deformations that arise owing to the build up of air under the impacting and rebouncing of water drops. Using digital holographic microscopy, we measure the three-dimensional surface topography of the deformed film immediately after the drop rebound, with a resolution down to 20 nm. We first discuss how the film is initially deformed during impact, as a function of film thickness, film viscosity and drop impact speed. Subsequently, we describe the slow relaxation process of the deformed film after the rebound. Scaling laws for the broadening of the width and the decay of the amplitude of the perturbations are obtained experimentally and found to be in excellent agreement with the results from a lubrication analysis. We finally arrive at a detailed spatio–temporal description of the oil-film deformations that arise during the impact and rebouncing of water drops.
News Meets Microblog: Hashtag Annotation via Retriever-Generator

Hashtag annotation for microblog posts has been recently formulated as a sequence generation problem to handle emerging hashtags that are unseen in the training set. The state-of-the-art method leverages conversations initiated by posts to enrich contextual information for the short posts. However, it is unrealistic to assume the existence of conversations before the hashtag annotation itself. Therefore, we propose to leverage news articles published before the microblog post to generate hashtags following a Retriever-Generator framework. Extensive experiments on English Twitter datasets demonstrate superior performance and significant advantages of leveraging news articles to generate hashtags.
Downconversion of quantum fluctuations of photonic heat current in a circuit

We discuss the non-zero frequency noise of heat current with the explicit example of energy carried by thermal photons in a circuit. Instead of the standard circuit modelling that gives a convenient way of predicting time-averaged heat current, we describe a setup composed of two resistors forming the heat baths by collections of bosonic oscillators. In terms of average heat transport this model leads to identical results with the conventional one, but besides this, it yields a convenient way of dealing with noise as well. The non-zero frequency heat current noise does not vanish in equilibrium even at zero temperature, the result that is known for, e.g., electron tunneling. We present a modulation method that can convert the difficult-to-measure high frequency quantum noise down to zero frequency.
Deep Learning in Beyond 5G Networks with Image-based Time-Series Representation

Towards the network innovation, the Beyond Five-Generation (B5G) networks envision the use of machine learning (ML) methods to predict the network conditions and performance indicators in order to best make decisions and allocate resources. In this paper, we propose a new ML approach to accomplish predictions in B5G networks. Instead of handling the time-series in the network domain of values, we transform them into image thus allowing to apply advanced ML methods of Computer Vision field to reach better predictions in B5G networks. Particularly, we analyze different techniques to transform time-series of network measures into image representation, e.g., Recurrence Plots, Markov Transition Fields, and Gramian Angular Fields. Then, we apply deep neural networks with convolutional layers to predict different 5G radio signal quality indicators. When comparing with other ML-based solutions, experimental results from 5G transmission datasets showed the feasibility and small prediction errors of the proposed approach.
Attention in Attention Network for Image Super-Resolution

Convolutional neural networks have allowed remarkable advances in single image super-resolution (SISR) over the last decade. Among recent advances in SISR, attention mechanisms are crucial for high-performance SR models. However, the attention mechanism remains unclear on why and how it works in SISR. In this work, we attempt to quantify and visualize attention mechanisms in SISR and show that not all attention modules are equally beneficial. We then propose attention in attention network (A$^2$N) for more efficient and accurate SISR. Specifically, A$^2$N consists of a non-attention branch and a coupling attention branch. A dynamic attention module is proposed to generate weights for these two branches to suppress unwanted attention adjustments dynamically, where the weights change adaptively according to the input features. This allows attention modules to specialize to beneficial examples without otherwise penalties and thus greatly improve the capacity of the attention network with few parameters overhead. Experimental results demonstrate that our final model A$^2$N could achieve superior trade-off performances comparing with state-of-the-art networks of similar sizes. Codes are available at https://github.com/haoyuc/A2N.
Beyond tight deadlines: what are the business causes for technical debt?

What are the business causes behind tight deadlines? What drives the prioritization of features that pushes quality matters to the back burner? We conducted a survey with 71 experienced practitioners and did a thematic analysis of the open-ended answers to the question: “Could you give examples of how business may contribute to technical debt? ” Business-related causes were organized into two categories: pure-business and business/IT gap, and they were related to ‘tight deadlines’ and ‘features over quality’, the most frequently cited management reasons for technical debt. We contribute a cause-effect model which relates the various business causes of technical debt to each other and explains their impact on technical debt.
Neural Unsupervised Semantic Role Labeling

The task of semantic role labeling (SRL) is dedicated to finding the predicate-argument structure. Previous works on SRL are mostly supervised and do not consider the difficulty in labeling each example which can be very expensive and time-consuming. In this article, we present the first neural unsupervised model for SRL. To decompose the task as two argument related subtasks, identification and clustering, we propose a pipeline that correspondingly consists of two neural modules. First, we train a neural model on two syntax-aware statistically developed rules. The neural model gets the relevance signal for each token in a sentence, to feed into a BiLSTM, and then an adversarial layer for noise-adding and classifying simultaneously, thus enabling the model to learn the semantic structure of a sentence. Then we propose another neural model for argument role clustering, which is done through clustering the learned argument embeddings biased toward their dependency relations. Experiments on the CoNLL-2009 English dataset demonstrate that our model outperforms the previous state-of-the-art baseline in terms of non-neural models for argument identification and classification.
Temperature-Dependent Reverse Recovery Characterization of SiC MOSFETs Body Diode for Switching Loss Estimation in a Half-Bridge

In a hard switched <sc>mosfet</sc> based converter, turn-<sc>on</sc> energy losses is predominant in the total switching loss. At higher junction temperature the turn-<sc>on</sc> energy loss further increases due to the reverse recovery effect of the complementary <sc>mosfet</sc>s body diode in a half-bridge configuration. Estimation of the switching loss under different operating conditions at an early design stage is essential for optimizing the thermal design. Analytical switching loss models available in literature are generally used for estimating the switching losses due to its accuracy and simplicity. In this article, the inaccuracy in the reported loss models due to non-inclusion of temperature-dependent reverse recovery characteristics of body diode, is investigated. A structured method to determine the temperature-dependent switching loss of a SiC <sc>mosfet</sc> in a half-bridge is presented. A simple methodology has been proposed to analyze the carrier lifetime's temperature dependencies of a SiC <sc>mosfet</sc>s body diode. Device parameters from a <inline-formula><tex-math notation="LaTeX">$\text{1.2}\,\text{kV}/36\,\text{A}$</tex-math></inline-formula> SiC <sc>mosfet</sc>s datasheet are used for developing the loss model and experimental validation of the model.
On the temperature and density dependence of dislocation drag from phonon wind

At extreme strain rates, where fast moving dislocations govern plastic deformation, anharmonic phonon scattering imparts a drag force on the dislocations. In this paper, we present calculations of the dislocation drag coefficients of aluminum and copper as functions of temperature and density. We discuss the sensitivity of the drag coefficients to changes in the third-order elastic constants with temperature and density.
Crossing the Conversational Chasm: A Primer on Multilingual Task-Oriented Dialogue Systems

Despite the fact that natural language conversations with machines represent one of the central objectives of AI, and despite the massive increase of research and development efforts in conversational AI, task-oriented dialogue (T O D) – i.e., conversations with an ar-tiﬁcial agent with the aim of completing a concrete task – is currently limited to a few narrow domains (e.g., food ordering, ticket booking) and a handful of major languages (e.g., English, Chinese). In this work, we provide an extensive overview of existing efforts in multilingual T O D and analyse the factors preventing the development of truly multilingual T O D systems. We identify two main challenges that combined hinder the faster progress in multi-lingual T O D: (1) current state-of-the-art T O D models based on large pretrained neural language models are data hungry; at the same time (2) data acquisition for T O D use cases is expensive and tedious. Most existing approaches to multilingual T O D thus rely on (zero-or few-shot) cross-lingual transfer from resource-rich languages (in T O D, this is basically only English), either by means of (i) machine translation or (ii) multilingual representation spaces. However, such approaches are currently not a viable solution for a large number of low-resource languages without parallel data and/or limited monolingual corpora. Finally, we discuss critical challenges and potential solutions by drawing parallels between T O D and other cross-lingual and multilingual NLP research.
Exploring Deep Learning for Joint Audio-Visual Lip Biometrics

Audio-visual (AV) lip biometrics is a promising authentication technique that leverages the benefits of both the audio and visual modalities in speech communication. Previous works have demonstrated the usefulness of AV lip biometrics. However, the lack of a sizeable AV database hinders the exploration of deep-learning-based audio-visual lip biometrics. To address this problem, we compile a moderate-size database using existing public databases. Meanwhile, we establish the DeepLip AV lip biometrics system realized with a convolutional neural network (CNN) based video module, a time-delay neural network (TDNN) based audio module, and a multimodal fusion module. Our experiments show that DeepLip outperforms traditional speaker recognition models in context modeling and achieves over 50% relative improvements compared with our best single modality baseline, with an equal error rate of 0.75% and 1.11% on the test datasets, respectively.
Automatic Stroke Classification of Tabla Accompaniment in Hindustani Vocal Concert Audio

The tabla is a unique percussion instrument due to the combined harmonic and percussive nature of its timbre, and the contrasting harmonic frequency ranges of its two drums. This allows a tabla player to uniquely emphasize parts of the rhythmic cycle (theka) in order to mark the salient positions. An analysis of the loudness dynamics and timing deviations at various cycle positions is an important part of musicological studies on the expressivity in tabla accompaniment. To achieve this at a corpus-level, and not restrict it to the few recordings that manual annotation can afford, it is helpful to have access to an automatic tabla transcription system. Although a few systems have been built by training models on labeled tabla strokes, the achieved accuracy does not necessarily carry over to unseen instruments. In this article, we report our work towards building an instrument-independent stroke classification system for accompaniment tabla based on the more easily available tabla solo audio tracks. We present acoustic features that capture the distinctive characteristics of tabla strokes and build an automatic system to predict the label as one of a reduced, but musicologically motivated, target set of four stroke categories. To address the lack of sufficient labeled training data, we turn to common data augmentation methods and find the use of pitch-shifting based augmentation to be most promising. We then analyse the important features and highlight the problem of their instrument-dependence while motivating the use of more task-specific data augmentation strategies to improve the diversity of training data.