Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional Outlier Detection for Density-Valued Data with Application to Robustify Distribution-to-Distribution Regression

Abstract Distributional data analysis, concerned with the statistical analysis of data objects consisting of random probability distributions in the framework of functional data analysis (FDA), has received considerable interest in recent years and is increasingly applied in various fields including engineering. Outlier detection and robustness are of great practical interest; however, these aspects remain unexplored for distributional data. To this end, this study focuses on density-valued outlier detection and its application in robust distributional regression. Specifically, we propose a transformation-based approach for single-dataset outlying density detection with an emphasis on converting the less detectable shape outliers to easily detectable magnitude outliers. We also propose a distributional regression-based approach for detecting the abnormal associations of the density-valued two-tuples associated with two datasets. Then, the proposed outlier detection methods are applied to robustify a distribution-to-distribution regression method used in engineering, and we develop a robust estimator for the regression operator by downweighting the detected outliers. The proposed methods are validated and evaluated via extensive simulation studies. The relevant results reveal the superiority of our method over other competitors in distributional outlier detection. A case study in structural health monitoring demonstrates the great potential of our proposal in engineering applications. Supplementary materials for this article are available online.
OAuthShield: Efficient Security Checking for OAuth Service Provider Implementations

—OAuth protocols have been widely adopted to simplify user authentication and service authorization for third-party applications. However, little effort has been devoted to automatically checking the security of libraries that are widely used by service providers. In this paper, we formalize the OAuth speciﬁcations and security best practices, and design OAuthShield, an automated static analyzer, to ﬁnd logical ﬂaws and identify vulnerabilities in the implementation of OAuth authorization server libraries. To efﬁciently detect OAuth violations in a large codebase, OAuthShield employs a demand-driven algorithm for answering queries about OAuth speciﬁcations. To demonstrate the effectiveness of OAuthShield, we evaluate it on ten popular OAuth libraries that have millions of downloads. Among these high-proﬁle libraries, OAuthShield has identiﬁed 47 vulnerabilities from ten classes of logical ﬂaws, 24 of which were previously unknown. We got acknowledged by the developers of six libraries, and had three accepted CVEs.
Inducing Equilibria via Incentives: Simultaneous Design-and-Play Finds Global Optima

To induce a desired equilibrium in a social system comprised of self-interested agents, economic incentives (e.g., taxes, tolls and subsidies) are often required to correct an ineﬃ-cient outcome. Such an incentive design problem naturally possesses a bi-level structure, in which an upper-level “designer” revises the payoﬀs of the agents with incentives while anticipating the response of the agents, who play a non-cooperative game at the lower level. The existing bi-level optimization algorithms developed in machine learning raise a dilemma when applied to this problem: anticipating how incentives aﬀect the agents at equilibrium requires solving the equilibrium problem repeatedly, which is computationally ineﬃcient; bypassing the time-consuming step of equilibrium-ﬁnding can reduce the computational cost, but may lead to a sub-optimal solution. Therefore, we propose an eﬃcient method that tackles the designer’s and agents’ problems simultaneously in a single loop. At each iteration, both the designer and the agents only move one step based on the ﬁrst-order information. In the proposed scheme, although the designer does not solve the equilibrium problem repeatedly, it can anticipate the overall inﬂuence of the incentives on the agents, which guarantees optimality. We prove that the algorithm converges to the global optima at a sublinear rate for a broad class of games.
Cognitive Control as a Multivariate Optimization Problem

Abstract A hallmark of adaptation in humans and other animals is our ability to control how we think and behave across different settings. Research has characterized the various forms cognitive control can take—including enhancement of goal-relevant information, suppression of goal-irrelevant information, and overall inhibition of potential responses—and has identified computations and neural circuits that underpin this multitude of control types. Studies have also identified a wide range of situations that elicit adjustments in control allocation (e.g., those eliciting signals indicating an error or increased processing conflict), but the rules governing when a given situation will give rise to a given control adjustment remain poorly understood. Significant progress has recently been made on this front by casting the allocation of control as a decision-making problem. This approach has developed unifying and normative models that prescribe when and how a change in incentives and task demands will result in changes in a given form of control. Despite their successes, these models, and the experiments that have been developed to test them, have yet to face their greatest challenge: deciding how to select among the multiplicity of configurations that control can take at any given time. Here, we will lay out the complexities of the inverse problem inherent to cognitive control allocation, and their close parallels to inverse problems within motor control (e.g., choosing between redundant limb movements). We discuss existing solutions to motor control's inverse problems drawn from optimal control theory, which have proposed that effort costs act to regularize actions and transform motor planning into a well-posed problem. These same principles may help shed light on how our brains optimize over complex control configuration, while providing a new normative perspective on the origins of mental effort.
Multiphase mean curvature flows approximation : the case of non harmonically additive mobilities

ABSTRACT. This paper concerns the robust approximation of multi-phase mean curvature flow by phase fields even when the phase mobility are highly contrasted. Recent work suggested that harmonically additive mobilities could be incorporated in the metric of the associated gradient flow. We generalize this approach to arbitrary mobilities, by splitting them as a sum of a harmonically additive mobilities. We establish the consistency of the resulting method, by analyzing the sharp interface limit of the flow : a formal expansion of the phase field shows that the method is of order 2. Finally, we present some numerical experiments in dimensions 2 and 3 that illustrate the interest of our method, in particular in the modeling of flows in which some of the phases have 0 mobility.
Maximum-Entropy Multi-Agent Dynamic Games: Forward and Inverse Solutions

In this article, we study the problem of multiple stochastic agents interacting in a dynamic game scenario with continuous state and action spaces. We define a new notion of stochastic Nash equilibrium for boundedly rational agents, which we call the entropic cost equilibrium (ECE). We show that ECE is a natural extension to multiple agents of maximum entropy optimality for a single agent. We solve both the “forward” and “inverse” problems for the multi-agent ECE game. For the forward problem, we provide a Riccati algorithm to compute closed-form ECE feedback policies for the agents, which are exact in the linear-quadratic-gaussian case. We give an iterative variant to find locally ECE feedback policies for the nonlinear case. For the inverse problem, we present an algorithm to infer the cost functions of the multiple interacting agents given noisy, boundedly rational input and state trajectory examples from agents acting in an ECE. The effectiveness of our algorithms is demonstrated in a simulated multi-agent collision avoidance scenario, and with data from the INTERACTION traffic dataset. In both cases, we show that, by taking into account the agents' game theoretic interactions using our algorithm, a more accurate model of agents' costs can be learned, compared with standard inverse optimal control methods.
Economics of Semantic Communication System in Wireless Powered Internet of Things

The semantic communication system enables wireless devices to communicate effectively with the semantic meaning of the data. Wireless powered Internet of Things (IoT) that adopts the semantic communication system relies on harvested energy to transmit semantic information. However, the issue of energy constraint in the semantic communication system is not well studied. In this paper, we propose a semantic-based energy valuation and take an economic approach to solve the energy allocation problem as an incentive mechanism design. In our model, IoT devices (bidders) place their bids for the energy and power transmitter (auctioneer) decides the winner and payment by using deep learning based optimal auction. Results show that the revenue of wireless power transmitter is maximized while satisfying Individual Rationality (IR) and Incentive Compatibility (IC).
Massive-MIMO MF Beamforming with or without Grouped STBC for Ultra-Reliable Single-Shot Transmission Using Aged CSIT

The technology of using massive transmit-antennas to enable ultra-reliable single-shot transmission (URSST) is challenged by the transmitter-side channel knowledge (i.e., CSIT) imperfection. When the imperfectness mainly comes from the channel time-variation, the outage probability of the matched filter (MF) transmitter beamforming is investigated based on the first-order Markov model of the aged CSIT. With a fixed transmit-power, the transmitter-side uncertainty of the instantaneous signal-to-noise ratio (iSNR) is mathematically characterized. In order to guarantee the outage probability for every single shot, a transmit-power adaptation approach is proposed to satisfy a pessimistic iSNR requirement, which is predicted using the Chernoff lower bound of the beamforming gain. Our numerical results demonstrate a remarkable transmit-power efficiency when comparing with power control approaches using other lower bounds. In addition, a combinatorial approach of the MF beamforming and grouped space-time block code (G-STBC) is proposed to further mitigate the detrimental impact of the CSIT uncertainty. It is shown, through both theoretical analysis and computer simulations, that the combinatorial approach can further improve the transmit-power efficiency with a good tradeoff between the outage probability and the latency.
Partner-Aware Algorithms in Decentralized Cooperative Bandit Teams

When humans collaborate with each other, they often make decisions by observing others and considering the consequences that their actions may have on the entire team, instead of greedily doing what is best for just themselves. We would like our AI agents to effectively collaborate in a similar way by capturing a model of their partners. In this work, we propose and analyze a decentralized Multi-Armed Bandit (MAB) problem with coupled rewards as an abstraction of more general multi-agent collaboration. We demonstrate that naive extensions of single-agent optimal MAB algorithms fail when applied for decentralized bandit teams. Instead, we propose a Partner-Aware strategy for joint sequential decision-making that extends the well-known single-agent Upper Confidence Bound algorithm. We analytically show that our proposed strategy achieves logarithmic regret, and provide extensive experiments involving human-AI and human-robot collaboration to validate our theoretical findings. Our results show that the proposed partner-aware strategy outperforms other known methods, and our human subject studies suggest humans prefer to collaborate with AI agents implementing our partner-aware strategy.
Audio Captioning Using Sound Event Detection

This technical report proposes an audio captioning system for DCASE 2021 Task 6 audio captioning challenge. Our proposed model is based on an encoder-decoder architecture with bi-directional Gated Recurrent Units (BiGRU) using pretrained audio features and sound event detection. A pretrained neural network (PANN) is used to extract audio features and Word2Vec is selected with the aim of extracting word embeddings from the audio captions. To create semantically meaningful captions, we extract sound events from the audio clips and feed the encoder-decoder architecture with sound events in addition to PANNs features. Our experiments on the Clotho dataset show that our proposed method significantly achieves better results than the challenge baseline model across all evaluation metrics.
Accelerated forward-backward method with fast convergence rate for nonsmooth convex optimization beyond differentiability

We propose an accelerated forward-backward method with fast convergence rate for finding a minimizer of a decomposable nonsmooth convex function over a closed convex set, and name it smoothing accelerated proximal gradient (SAPG) algorithm. The proposed algorithm combines the smoothing method with the proximal gradient algorithm with extrapolation $\frac{k-1}{k+\alpha-1}$ and $\alpha>3$. The updating rule of smoothing parameter $\mu_k$ is a smart scheme and guarantees the global convergence rate of $o(\ln^{\sigma}k/k)$ with $\sigma\in(\frac{1}{2},1]$ on the objective function values. Moreover, we prove that the sequence is convergent to an optimal solution of the problem. Furthermore, we introduce an error term in the SAPG algorithm to get the inexact smoothing accelerated proximal gradient algorithm. And we obtain the same convergence results as the SAPG algorithm under the summability condition on the errors. Finally, numerical experiments show the effectiveness and efficiency of the proposed algorithm.
End-to-End Complex-Valued Multidilated Convolutional Neural Network for Joint Acoustic Echo Cancellation and Noise Suppression

Echo and noise suppression is an integral part of a full-duplex communication system. Many recent acoustic echo cancellation (AEC) systems rely on a separate adaptive filtering module for linear echo suppression and a neural module for residual echo suppression. However, in practice, adaptive filtering modules require time to converge and remain susceptible to changes in the acoustic environment. This introduces unnecessary delays to AEC systems using this two-stage framework, despite neural modules already having the capability to suppress both linear and nonlinear echo components. In this paper, we exploit the offset-compensating property of complex time-frequency masks and propose an end-to-end complex-valued neural network architecture. The building block of the proposed model is a pseudocomplex extension of the densely-connected multidilated DenseNet (D3Net), resulting in a very small network of only 354K parameters. The architecture utilized the multi-resolution nature of the D3Net to eliminate the need for pooling, allowing feature extraction using large receptive fields without any loss of output resolution. We also propose a dual-mask technique for joint echo and noise suppression with simultaneous speech enhancement. Evaluation on both synthetic and real test sets demonstrated promising results across multiple energy-based metrics and perceptual proxies.
LawSum: A weakly supervised approach for Indian Legal Document Summarization

Unlike the courts in western countries, public records of Indian judiciary are completely unstructured and noisy. No large scale publicly available annotated datasets of Indian legal documents exist till date. This limits the scope for legal analytics research. In this work, we propose a new dataset consisting of over 10,000 judgements delivered by the supreme court of India and their corresponding hand written summaries. The proposed dataset is pre-processed by normalising common legal abbreviations, handling spelling variations in named entities, handling bad punctuations and accurate sentence tokenization. Each sentence is tagged with their rhetorical roles. We also annotate each judgement with several attributes like date, names of the plaintiffs, defendants and the people representing them, judges who delivered the judgement, acts/statutes that are cited and the most common citations used to refer the judgement. Further, we propose an automatic labelling technique for identifying sentences which have summary worthy information. We demonstrate that this auto labeled data can be used effectively to train a weakly supervised sentence extractor with high accuracy. Some possible applications of this dataset besides legal document summarization can be in retrieval, citation analysis and prediction of decisions by a particular judge.
Exploiting Pre-Trained ASR Models for Alzheimer's Disease Recognition Through Spontaneous Speech

Alzheimer's disease (AD) is a progressive neurodegenerative disease and recently attracts extensive attention worldwide. Speech technology is considered a promising solution for the early diagnosis of AD and has been enthusiastically studied. Most recent works concentrate on the use of advanced BERT-like classifiers for AD detection. Input to these classifiers are speech transcripts produced by automatic speech recognition (ASR) models. The major challenge is that the quality of transcription could degrade significantly under complex acoustic conditions in the real world. The detection performance, in consequence, is largely limited. This paper tackles the problem via tailoring and adapting pre-trained neural-network based ASR model for the downstream AD recognition task. Only bottom layers of the ASR model are retained. A simple fully-connected neural network is added on top of the tailored ASR model for classification. The heavy BERT classifier is discarded. The resulting model is light-weight and can be fine-tuned in an end-to-end manner for AD recognition. Our proposed approach takes only raw speech as input, and no extra transcription process is required. The linguistic information of speech is implicitly encoded in the tailored ASR model and contributes to boosting the performance. Experiments show that our proposed approach outperforms the best manual transcript-based RoBERTa by an absolute margin of 4.6% in terms of accuracy. Our best-performing models achieve the accuracy of 83.2% and 78.0% in the long-audio and short-audio competition tracks of the 2021 NCMMSC Alzheimer's Disease Recognition Challenge, respectively.
Biogas Emission from an Anaerobic Reactor

Anaerobic denitrification is accompanied by biogas emissions, which may orient greenhouse gases or air pollutants. Ferrous iron play an important role in wastewater treatment. However, the effect of ferrous ion on biogas emission and the relationship between microbial community and nitrogen removal performance are not fully understood, especially in up-flow anaerobic sludge blanket (UASB) reactors. Here, the results revealed that nitrogen gas increased rapidly with Fe(II) addition. And ferrous ions enhanced denitrification and COD removal efficiency with more than 60% and 10%, respectively. Moreover, Ferrous ions addition evidently increased Hydrogenophaga, Methylotenera, Zoogloea and Fluviicola during the whole UASB reactor operation progress. The correlation coefficient analysis further confirmed that Fe(II) was positively correlated with the abundances of Hydrogenophaga, Methylotenera and Fluviicola. Based on these results of chemicals transformation kinetics, microbial community and the correlations coefficient analysis, a hypothetical mechanism is proposed: In the UASB system with Fe(II) and NH4+, firstly Paludibacter made a contribution to NH4+ oxidation and generated NO3–, and then Hydrogenophaga, played an important role in NO3– reduction, Zoogloea coupled NO3– reduction to Fe(II) oxidation, Fluviicola combined with Methylotenera conducted NO2– reduction together with Fe(II) oxidation. This study will improve our understanding of ferrous ion’s influence on biogas emission, denitrification process and corresponding microbial community.
Universality of breath figures on two-dimensional surfaces: An experimental study

Droplet condensation on surfaces produces patterns, called breath figures. Their evolution into self-similar structures is a classical example of self-organization. It is described by a scaling theory with scaling functions whose universality has recently been challenged by numerical work. Here, we provide a thorough experimental testing, where we inspect substrates with vastly different chemical properties, stiffness, and condensation rates. We critically survey the size distributions, and the related time-asymptotic scaling of droplet number and surface coverage. In the time-asymptotic regime they admit a data collapse: the data for all substrates and condensation rates lie on universal scaling functions.
Drone Charging Stations Deployment in Rural Areas for Better Wireless Coverage: Challenges and Solutions

While fifth-generation (5G) cellular is meant to deliver gigabit peak data speeds, low latency, and connection to billions of devices, and 6G is already on the way, half of the world population living in rural areas are still facing challenges connecting to the internet. Compared to urban areas, users in rural areas are greatly impacted by low income, high cost of backhaul connectivity, limited resources, extreme weather, and natural geographical limitations. Therefore, how to connect the rural areas and the difficulties of providing connectivity draw great attention. This article first provides a brief discussion about existing technologies and strategies for enhancing the network coverage in rural areas, and their advantages, limitations, and cost. Next, we mainly focus on the UAV-assisted network in resource-limited regions. Considering the limitation of the onboard battery of UAVs and the electricity supply scarcity in some rural regions, we investigate the possibility and performance enhancement of the deployment of renewable energy (RE) charging stations. We outline three practical scenarios, and use simulation results to demonstrate that RE charging stations can be a possible solution to address the limited onboard battery of UAVs in rural areas, especially when they can harvest and store enough energy. Finally, future works and challenges are discussed.
Local quantum uncertainty of two gravitational cat states in inhomogeneous magnetic field

This paper investigates the local quantum correlations (LQU), including entanglement, of two gravitational cat states subjected to an inhomogeneous magnetic field. We derived the LQU expression from the physical quantities associated with the selected system. Our findings suggest that temperature, magnetic field, and magnetic field inhomogeneity may all play a role in determining the degree of intricacy between the gravcats to some extent. Furthermore, these conclusions suggest that the thermal LQU captures a stronger quantum correlation than the entanglement. Especially true for low external magnetic field levels combined with low field inhomogeneity or high-temperature domains. Besides, we obtained the states’ separability for large values of field inhomogeneity. Moreover, the correlation of the states obtained is maximal for small magnetic field values at low temperatures. Finally, we note that the state’s systems become non-entangled and separable when the gap between the fundamental level and the first excited level becomes large. PACS numbers: 03.65. Ud