Miracle-dz/UniFAR-FTUs huggingface dataset:
Structured agents for physical construction

Physical construction---the ability to compose objects, subject to physical dynamics, to serve some function---is fundamental to human intelligence. We introduce a suite of challenging physical construction tasks inspired by how children play with blocks, such as matching a target configuration, stacking blocks to connect objects together, and creating shelter-like structures over target objects. We examine how a range of deep reinforcement learning agents fare on these challenges, and introduce several new approaches which provide superior performance. Our results show that agents which use structured representations (e.g., objects and scene graphs) and structured policies (e.g., object-centric actions) outperform those which use less structured representations, and generalize better beyond their training when asked to reason about larger scenes. Model-based agents which use Monte-Carlo Tree Search also outperform strictly model-free agents in our most challenging construction problems. We conclude that approaches which combine structured representations and reasoning with powerful learning are a key path toward agents that possess rich intuitive physics, scene understanding, and planning.
Moving Object Detection Under Discontinuous Change in Illumination Using Tensor Low-Rank and Invariant Sparse Decomposition

Although low-rank and sparse decomposition based methods have been successfully applied to the problem of moving object detection using structured sparsity-inducing norms, they are still vulnerable to significant illumination changes that arise in certain applications. We are interested in moving object detection in applications involving time-lapse image sequences for which current methods mistakenly group moving objects and illumination changes into foreground. Our method relies on the multilinear (tensor) data low-rank and sparse decomposition framework to address the weaknesses of existing methods. The key to our proposed method is to create first a set of prior maps that can characterize the changes in the image sequence due to illumination. We show that they can be detected by a k-support norm. To deal with concurrent, two types of changes, we employ two regularization terms, one for detecting moving objects and the other for accounting for illumination changes, in the tensor low-rank and sparse decomposition formulation. Through comprehensive experiments using challenging datasets, we show that our method demonstrates a remarkable ability to detect moving objects under discontinuous change in illumination, and outperforms the state-of-the-art solutions to this challenging problem.
Social Media as a Promotional Tool Towards SME’s Development: Evidence from the Financial Industry in a Developing Economy

Abstract The notable contribution of small and medium enterprises (SMEs) in the development of socio-economics has been very encouraging in terms of its GDP contribution and employment creation. Both developed and developing countries are living testimonies of the contributions by SMEs. The primary aim of this paper is to assess the use of social media as a promotional tool for SME development. To achieve this objective, a total of 800 structured questionnaires were developed through an intercept and online survey to obtain data for the exercise. A non-probability sampling technique was employed for the collection of the data of which 648 respondents were dully accurate for the data processing/analysis. Using PLS-SEM with the help of ADANCO software v 2.2. 1, findings revealed that the use of social media as an advertising tool has a significant effect on a firm’s financial performance, business motivation, customers’ perspective/attraction, and an increase in market share, thus, a key in reflecting a positive change in their marketing and promotional activities in the consumer market. The findings of the study are both beneficial to practitioners/owners of SMEs and the social media marketing literature in general. This study further strengthens the need for businesses to invest in digital marketing tools and employ them effectively to stay competitive in the long term for the firm’s growth and survival.
Lattice QCD Method To Study Parton To Hadron Fragmentation Function

In the literature it is assumed that the parton to hadron fragmentation function cannot be studied by using the lattice QCD method because of the sum over the (unobserved) outgoing hadronic states. However, in this paper we find that since the hadron formation from the partons can be studied by using the lattice QCD method, the parton to hadron fragmentation function can be studied by using the lattice QCD method by using the LSZ reduction formula for the partonic processes.
Pleasantness, Activation, and Negative Affectivity: A Test of Extended UTAUT Model

To better understand how positive and negative emotion influences the intention to use software applications, we introduce two affective constructs namely core affective experience and negative affectivity to the UTAUT (unified theory of adoption and use of technology). We first conducted a pilot study in a lab setting to replicate the UTAUT and the results are encouraging. We then conduced two more lab studies with inexperience and experience users, respectively. The results of the proposed affective augmentation of UTAUT are promising. The proposed relationships between the core affective experience (i.e., activation and pleasantness) and intention to use is significant, similarly the proposed relationship between negative affectivity is also significant. Furthermore, bringing core affective experience into the model makes UTAUT more robust.
Open Issues in Combating Fake News: Interpretability as an Opportunity

Combating fake news needs a variety of defense methods. Although rumor detection and various linguistic analysis techniques are common methods to detect false content in social media, there are other feasible mitigation approaches that could be explored in the machine learning community. In this paper, we present open issues and opportunities in fake news research that need further attention. We first review different stages of the news life cycle in social media and discuss core vulnerability issues for news feed algorithms in propagating fake news content with three examples. We then discuss how complexity and unclarity of the fake news problem limit the advancements in this field. Lastly, we present research opportunities from interpretable machine learning to mitigate fake news problems with 1) interpretable fake news detection and 2) transparent news feed algorithms. We propose three dimensions of interpretability consisting of algorithmic interpretability, human interpretability, and the inclusion of supporting evidence that can benefit fake news mitigation methods in different ways.
The Impact of Persuasive Messages on the Disclosure of Personal Health Information

Individuals’ disclosure of personal health information (PHI) holds substantial benefits for providers, but users are often reluctant to disclose. While providers can employ persuasive messages, little is known about their effects in the sensitive context of PHI disclosure. To address this research gap, we conduct a web-based experiment with 529 non-users of health wearables (HWs) to examine the influences of persuasive messages (attribute framing and argument strength) on individuals’ PHI disclosure. We reveal that individuals tend to disclose more PHI when they experience persuasive messages with more positively framed HW attributes or messages with higher argument strength concerning data collection. We enable researchers to uncover the impact of persuasive messages in highly sensitive data environments and provide practitioners with workable suggestions to have individuals disclose more PHI.
Unsupervised Low Latency Speech Enhancement With RT-GCC-NMF

In this paper, we present RT-GCC-NMF: a real-time (RT), two-channel blind speech enhancement algorithm that combines the non-negative matrix factorization (NMF) dictionary learning algorithm with the generalized cross-correlation (GCC) spatial localization method. Using a pre-learned universal NMF dictionary, RT-GCC-NMF operates in a frame-by-frame fashion by associating individual dictionary atoms to target speech or background interference based on their estimated time-delay of arrivals. We evaluate RT-GCC-NMF on two-channel mixtures of speech and real-world noise from the signal separation and evaluation campaign (SiSEC). We demonstrate that this approach generalizes to new speakers, acoustic environments, and recording setups from very little training data, and outperforms all but one of the algorithms from the SiSEC challenge in terms of overall perceptual evaluation methods for audio source separation (PEASS) scores and compares favourably to the ideal binary mask baseline. Over a wide range of input signal to noise ratios (SNRs), we show that this approach simultaneously improves the PEASS and SNR-based blind source separation eval objective quality metrics as well as the short-time objective intelligibility (STOI) and extended STOI objective speech intelligibility metrics. A flexible, soft masking function in the space of NMF activation coefficients offers real-time control of the tradeoff between interference suppression and target speaker fidelity. Finally, we use an asymmetric short-time Fourier transform to reduce the inherent algorithmic latency of RT-GCC-NMF from 64 ms to 2 ms with no loss in performance. We demonstrate that latencies within the tolerable range for hearing aids are possible on current hardware platforms.
Local Average Sampling and Reconstruction with Fundamental Splines of Fractional Order

Abstract We analyze sampling and average sampling techniques for fractional spline subspaces of Fractional B-splines are extensions of Schoenberg’s polynomial splines of integral order to real order We present the interpolation with fundamental splines of fractional order for and the average sampling with fundamental splines of fractional order for Further, we generalize Kramer’s lemma in the context of local average sampling.
Constraining the rotational kinematic Sunyaev-Zel'dovich effect in massive galaxy clusters

We constrain the rotational kinematic Sunyaev-Zel'dovich (rkSZ) effect in Planck data using a sample of rotating galaxy clusters identified in the Sloan Digital Sky Survey (SDSS) . We extract cluster-centered cutouts from Planck cosmic microwave background (CMB) maps that have been cleaned of thermal SZ signal. Using previous constraints on the cluster rotation vectors determined from the motions of galaxies, we fit for the amplitude of the rkSZ effect in the CMB cutouts, marginalizing over parameters describing the cluster electron distribution. We also employ an alternative, less model-dependent approach to measuring the rkSZ signal that involves measuring the dipole induced by the rkSZ in rotation velocity-oriented CMB stacks. In both cases, we find roughly 2σ evidence for a rkSZ signal consistent with the expected amplitude and morphology. We comment on future directions for measurements of the rkSZ signal.
Compare More Nuanced: Pairwise Alignment Bilinear Network for Few-Shot Fine-Grained Learning

The recognition ability of human beings is developed in a progressive way. Usually, children learn to discriminate various objects from coarse to fine-grained with limited supervision. Inspired by this learning process, we propose a simple yet effective model for the Few-Shot Fine-Grained (FSFG) recognition, which tries to tackle the challenging fine-grained recognition task using meta-learning. The proposed method, named Pairwise Alignment Bilinear Network (PABN), is an end-to-end deep neural network. Unlike traditional deep bilinear networks for fine-grained classification, which adopt the self-bilinear pooling to capture the subtle features of images, the proposed model uses a novel pairwise bilinear pooling to compare the nuanced differences between base images and query images for learning a deep distance metric. In order to match base image features with query image features, we design feature alignment losses before the proposed pairwise bilinear pooling. Experiment results on four fine-grained classification datasets and one generic few-shot dataset demonstrate that the proposed model outperforms both the state-of-the-art few-shot fine-grained and general few-shot methods.
On the controllability and Stabilization of the Benjamin Equation

The aim of this paper is to study the controllability and stabilization for the Benjamin equation on a periodic domain $\mathbb{T}$. We show that the Benjamin equation is globally exactly controllable and globally exponentially stabilizable in $H_{p}^{s}(\mathbb{T}),$ with $s\geq 0. $ First we prove propagation of compactness, propagation of regularity of solution in Bourgain's spaces and unique continuation property, and use them to obtain the global exponential stabilizability corresponding to a natural feedback law. Combining the global exponential stability and the local controllability result we prove the global controllability as well. Also, we prove that the closed-loop system with a different feedback control law is locally exponentially stable with an arbitrary decay rate. Finally, a time-varying feedback law is designed to guarantee a global exponential stability with an arbitrary decay rate. The results obtained here extend the ones we proved for the linearized Benjamin equation in \cite{Vielma and Panthee}.
Scalar field dark matter with a cosh potential, revisited

Dark matter models in which the constituent particle is an ultra-light boson have become part of the mainstream discussion in cosmology and astrophysics. At the classical level, the models are represented by the dynamics of a (real or complex) scalar field endowed with a potential that contains its self-interactions, and for this reason are generically known as scalar field dark matter models. Here, we revisit the properties of such a model with a cosh potential and compare it with other known examples in the literature. Within the cosmological context, the self-interaction in the potential induces a radiation-like behavior at early times of the scalar field density, which is followed by a proper matter-like behavior at the onset of rapid field oscillations around the minimum of the potential. The solutions are found by numerical means, and from them we obtain information about the cosmological observables up to the level of linear density perturbations. We also study the general properties of self-gravitating objects in the non-relativistic limit and determine the role in them of the self-interaction obtained from the cosh potential. An overall conclusion is that, for the range of values in its parameters allowed by different constraints, a cosh potential behaves almost indistinguishable from the simpler quadratic one, which also means that the two models suffer the same tight constraints from cosmological and astrophysical observations.
Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification

Adversarial examples are carefully constructed modifications to an input that completely change the output of a classifier but are imperceptible to humans. Despite these successful attacks for continuous data (such as image and audio samples), generating adversarial examples for discrete structures such as text has proven significantly more challenging. In this paper we formulate the attacks with discrete input on a set function as an optimization task. We prove that this set function is submodular for some popular neural network text classifiers under simplifying assumption. This finding guarantees a $1-1/e$ approximation factor for attacks that use the greedy algorithm. Meanwhile, we show how to use the gradient of the attacked classifier to guide the greedy search. Empirical studies with our proposed optimization scheme show significantly improved attack ability and efficiency, on three different text classification tasks over various baselines. We also use a joint sentence and word paraphrasing technique to maintain the original semantics and syntax of the text. This is validated by a human subject evaluation in subjective metrics on the quality and semantic coherence of our generated adversarial text.
Evaluation and Optimal Calibration of Purchase Time Recommendation Services

rice Comparison Sites enable customers to make better – more informed, less costly – buying decisions through providing price information and offering buying advice in the form of prediction services. While these services differ to some extent, they are comparable regarding their prediction target and usually monitor every arbitrarily small price decrease. We use a large data set of daily minimum prices for 272 smartphones consisting of 198,560 daily price movements from a Price Comparison Site to show that the standard prediction setting is not optimal. A custom evaluation framework allows the maximization of the achievable savings by altering the calibration of the forecasting service to monitor changes that exceed a certain threshold. Additionally, we show that time series features calculated in a calibration period can be used to obtain precise out of sample estimates of the saving optimal forecasting setting.
Enhanced AC Quasi-steady State Cascading Failure Model for Grid Vulnerability Analysis under Wind Uncertainty

This paper presents several enhancements on a mixed OPF-stochastic cascading failure model to study the impacts of renewable energy resource uncertainty on grid vulnerability. The improved quasi-steady state (QSS) cascading failure model incorporates AC power flow calculations thus allowing us to simulate voltagerelated failures in the grid. The under-voltage load shedding (UVLS) relays are modeled along with a stochastic time-inverse overload relay to accurately simulate the protective system response. In addition, more realistic assumptions are considered in the modeling of wind power penetration using geographical information of grid topology and wind potential map for a given geographical area. The effectiveness of the proposed framework is evaluated on a 500-bus synthetic network developed based on the footprints of South Carolina. The enhanced model allows us to more accurately simulate cascades in the power system with high penetration of erratic renewables and identify weak points.
Near-linear time approximation schemes for Steiner tree and forest in low-dimensional spaces

We give an algorithm that computes a (1+є)-approximate Steiner forest in near-linear time n · 2(1/є)O(ddim2) (loglogn)2, where ddim is the doubling dimension of the metric space. This improves upon the best previous result due to Chan et al. (SIAM J. Comput. 4 (2018)), who gave a runtime of about n2O(ddim) · 2(ddim/є)O(ddim) √logn. For Steiner tree our methods achieve an even better runtime n (logn)(1/є)O(ddim2).
Understanding the Effect of Task Descriptions on User Participation in Crowdsourcing Contests: A Linguistic Style Perspective

Many employers are struggling with how to deliver attractive tasks on crowdsourcing platforms, where users can be effectively integrated into a company’s tasks. In this study, the linguistic style of crowdsourcing task descriptions is investigated, and an analysis is conducted on how such linguistic styles are related to a task description’s success in attracting participants. Based on uncertainty reduction theory as well as source credibility theory, an empirical analysis of 2,014 designing contests demonstrates that certain linguistic styles will reduce the uncertainty perceived by crowdsourcing solvers and increase employers’ credibility, generating positive effects on participation. It is also found that these observed effects are moderated by the magnitude of the rewards offered for completing crowdsourcing tasks. The results of this study inform the theories concerned on crowdsourcing participation, linguistics, as well as psychological processes, while offering the industry insight on how to describe their own crowdsourcing tasks better.
Investigating Antecedents and Development of Trust in Ridesharing Platform: A Case from China

Trust is playing an important role in the sharing economy-driven business. However, little research has been conducted measuring trust as a dynamic process in the sharing economy era. This research is a preliminary exploration investigating trust antecedents and the dynamic development of clients’ trust in ridesharing platforms. We used 102 two-stage interviews collected from clients of a major Chinese ridesharing platform. Then we conducted a qualitative content analysis and proposed a theoretical model based on literature and data analysis. We found that antecedents of trust in two stages are different. Personality-based beliefs and cognition-based beliefs are the main antecedents of initial trust, while knowledge-based beliefs, institution-based beliefs, and calculus-based beliefs are the main antecedents of ongoing trust. These findings could help us understand how trust evolves over time and enable us to explore several viewpoints on ridesharing platforms.
"Friending" in Online Fitness Communities: Exploring Activity-Based Online Network Structure

Individuals are influenced by both direct and indirect interaction with their social contacts. While peer influence is known to affect health-related outcomes such as exercise, limited work has fully explored how social networks are structured to support (or inhibit) interaction that could lead to positive health behaviors. With the development of pervasive technology and rise of personal health and wellness tracking, increasing attention has been paid to promoting positive fitness behaviors through social interaction mechanisms in online fitness communities. This trend offers a unique opportunity to understand the opportunity structures for personal health and wellness support. Utilizing a large-scale behavioral trace dataset from the online fitness community Strava, we examine how the size of people’s personal network is structured by demographics (e.g. gender and age) and an economic indicator (i.e. if they pay for a premium account). We employ stochastic process models to characterize the empirical network degree distributions in this population of fitness community members. We find that gender, age and account status are associated with distinct network structure. Results have implications in the analysis and the design of health interventions that make use of network relationships in online settings.
Metal-ion batteries for electric vehicles: current state of the technology, issues and future perspectives

The batteries based on metals-ions have the potential to meet the future needs of electric vehicle (EV) applications. This article reviews the key technological developments and scientific challenges of a broad range of Li-ion, Mg-ion and Al-ion batteries for electric vehicles. The fundamental configurations and corresponding reaction mechanisms of metal-ion strategies are tangibly discussed in this review article. After a brief revision of the fundamentals, the performance is analysed among Li-ion, Mg-ion and Al-ion battery technologies. The key parameters for the present compilation are the abundance, the volumetric capacity, the gravimetric capacity, the cycling life, cost and safety. Further, it summarizes the recycling methodologies, strengths and limitations of these batteries. Finally, future directions of all these batteries are highlighted and discussed.