Miracle-dz/UniFAR-FTUs huggingface dataset:
ISLET: Fast and Optimal Low-rank Tensor Regression via Importance Sketching

In this paper, we develop a novel procedure for low-rank tensor regression, namely \emph{\underline{I}mportance \underline{S}ketching \underline{L}ow-rank \underline{E}stimation for \underline{T}ensors} (ISLET). The central idea behind ISLET is \emph{importance sketching}, i.e., carefully designed sketches based on both the responses and low-dimensional structure of the parameter of interest. We show that the proposed method is sharply minimax optimal in terms of the mean-squared error under low-rank Tucker assumptions and under randomized Gaussian ensemble design. In addition, if a tensor is low-rank with group sparsity, our procedure also achieves minimax optimality. Further, we show through numerical study that ISLET achieves comparable or better mean-squared error performance to existing state-of-the-art methods while having substantial storage and run-time advantages including capabilities for parallel and distributed computing. In particular, our procedure performs reliable estimation with tensors of dimension $p = O(10^8)$ and is $1$ or $2$ orders of magnitude faster than baseline methods.
Characterizing dynamically varying acoustic scenes from egocentric audio recordings in workplace setting

Devices capable of detecting and categorizing acoustic scenes have numerous applications such as providing context-aware user experiences. In this paper, we address the task of characterizing acoustic scenes in a workplace setting from audio recordings collected with wearable microphones. The acoustic scenes, tracked with Bluetooth transceivers, vary dynamically with time from the egocentric perspective of a mobile user. Our dataset contains experience sampled long audio recordings collected from clinical providers in a hospital, who wore the audio badges during multiple work shifts. To handle the long egocentric recordings, we propose a Time Delay Neural Network~(TDNN)-based segment-level modeling. The experiments show that TDNN outperforms other models in the acoustic scene classification task. We investigate the effect of primary speaker's speech in determining acoustic scenes from audio badges, and provide a comparison between performance of different models. Moreover, we explore the relationship between the sequence of acoustic scenes experienced by the users and the nature of their jobs, and find that the scene sequence predicted by our model tend to possess similar relationship. The initial promising results reveal numerous research directions for acoustic scene classification via wearable devices as well as egocentric analysis of dynamic acoustic scenes encountered by the users.
Examining the weak cosmic censorship conjecture of RN-AdS black holes via the new version of the gedanken experiment

Based on the new version of the gedanken experiment proposed by Sorce and Wald, we investigate the weak cosmic censorship conjecture (WCCC) for Reissner-Nordström-AdS (RN-AdS) black holes under the second-order approximation of the perturbation that comes from the matter fields. First of all, we propose that the cosmological constant is a portion of the matter fields. It means that the cosmological constant can be seen as a variable, and its value will vary with the process of the perturbation. Moreover, we assume that the perturbation satisfies the stability condition, which states that after the perturbation, the spacetime geometry also belongs to the class of RN-AdS solutions. Based on the stability condition and the null energy condition, while using the method of the off-shell variation, the first two order perturbation inequalities are derived. In the two inequalities, the term which contains the thermodynamic pressure and volume is involved firstly. Furthermore, based on the first-order optimal option and the second-order inequality, the WCCC for nearly extremal RN-AdS black holes is examined. It is shown that considering the cosmological constant varying with the perturbation, the WCCC for RN-AdS black holes cannot be violated under the second-order approximation of the matter fields perturbation.
On Clustering Incomplete Data

We study fundamental clustering problems for incomplete data. In this setting, we are given a set of incomplete d-dimensional Boolean vectors (representing the rows of a matrix), and the goal is to complete the missing vector entries so that the set of complete vectors admits a partitioning into at most k clusters with radius or diameter at most r. We develop a toolkit and use it to give tight characterizations of the parameterized complexity of these problems with respect to the parameters k, r, and the minimum number of rows and columns needed to cover all the missing entries. We show that the aforementioned problems are fixed-parameter tractable when parameterized by the three parameters combined, and that dropping any of these three parameters results in parameterized intractability. We extend this toolkit to settle the parameterized complexity of other clustering problems, answering an open question along the way. We also show how our results can be extended to data over any constant-size domain. A byproduct of our results is that, for the complete data setting, all problems under consideration are fixed-parameter tractable parameterized by k+r.
Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets

Time series modeling has attracted extensive research efforts; however, achieving both reliable efficiency and interpretability from a unified model still remains a challenging problem. Among the literature, shapelets offer interpretable and explanatory insights in the classification tasks, while most existing works ignore the differing representative power at different time slices, as well as (more importantly) the evolution pattern of shapelets. In this paper, we propose to extract time-aware shapelets by designing a two-level timing factor. Moreover, we define and construct the shapelet evolution graph, which captures how shapelets evolve over time and can be incorporated into the time series embeddings by graph embedding algorithms. To validate whether the representations obtained in this way can be applied effectively in various scenarios, we conduct experiments based on three public time series datasets, and two real-world datasets from different domains. Experimental results clearly show the improvements achieved by our approach compared with 16 state-of-the-art baselines.
APPLICABILITY OF OPERATIONS RESEARCH AND ARTIFICIAL INTELLIGENCE APPROACHES TO NON- CONTACT ANTERIOR CRUCIATE LIGAMENT INJURY STUDIES

Numerous problems in biomechanics can be tackled using optimization methods. The primary objective of this paper is to explore the applicability and illustrate the importance of two major optimization approaches, namely, operation research (OR) and artificial intelligence (AI) to studies in non-contact anterior cruciate ligament (ACL) injury biomechanics. This paper focuses on the applicability of the two approaches to bring attention to the enabling capabilities that can be offered to address challenges faced in non-contact ACL injury studies. The differences and similarities, as well as, advantages and disadvantages of these two approaches are discussed. Some of the key techniques covered in the two different approaches are highlighted. As well, the area in which there is a common ground for both approaches is outlined. It was determined for a small search space and highly tailored problems, classical exhaustive OR methods usually suffice; however, for large search spaces an AI technique must be employed. Thus, an AI technique is better suited to tackle the challenges faced in non-contact ACL injury studies especially given the multifaceted nature of such problems.
Simulation of the internal kink mode in visco-resistive regimes

We present numerical simulation results of the nonlinear evolution of the (1,1) internal kink mode in the presence of various kinds of equilibrium plasma flows. The present studies are carried out in the framework of a two fluid model to extend our past investigations done with a reduced magneto-hydrodynamic (RMHD) model. Two-fluid effects are found to significantly influence the mode dynamics in a number of different ways. In the linear regime diamagnetic effects in combination with flows provide a synergistic stabilizing influence that also carries over to the nonlinear regime. In addition one observes novel symmetry breaking phenomena in the linear growth rates as well as in the nonlinear saturated states of the mode. Our study also explores the influence of strong viscosity on the mode evolution and finds interesting modifications in the real frequency of the mode in the linear regime.
Continuity with New Labour? Deconstructing the Triangulation of David Cameron's Conservatives

This paper examines the ideological nature of David Cameron’s Conservative party, specifically in relation to the ‘triangulated’ perspective of the ‘Third Way’ utilised by New Labour. Continuities between the ideas, discourses, and political tactics guiding Cameron’s approach and those used by New Labour are identified. However, while a level of continuity is recognised, it is argued that ‘Cameronism’ represents a distinctive ideological project in itself, which (selectively) utilises the ideas of New Labour and the Third Way, yet redirects this inherited framework in a deeply anti-statist, (partially) Thatcherite direction.
Does financial development increase rural‐urban income inequality? Cointegration analysis in the case of Indian economy

Purpose - The purpose of this paper is to investigate the impact of financial development on the rural-urban income inequality in India using annual data from 1965 to 2008. Design/methodology/approach - The Ng-Perron unit root test is utilised to check for the order of integration of the variables. The long run relation is examined by implementing the ARDL bounds testing approach to cointegration. Findings - The results confirm a relation among the variables. Evidence suggest that financial development, economic growth and consumer prices aggravate rural-urban income inequality in the long run. Research limitations/implications - The present study offers fresh insights to policy makers on crafting appropriate policies that reduce rural-urban income inequality in India. Originality/value - The contribution of this paper is lies in extending the literature in the context of India towards an extensively researched area of rural-urban divide but in time series framework and utilization of a better approach of time series approach, i.e. ARDL. Specifically, to the best of the authors' knowledge, this is the first empirical study to test poverty-finance nexus using the basic principles of the GJ hypothesis and provide evidence of short- and long-run dynamics on the postulated relation for India.
Teaching Duet in Social Sciences Education in Promoting Critical Thinking Abilities

The purpose of this research is to identify the effects of teaching duet among social sciences students on the frequency of the critical thinking abilities used. This research are conducted in order to employ the current tools of conventional methods with integration of group activities and learning video in order to deliver the teaching and learning content. There are only selected topics from the subjects content are taking part in this teaching duet approach. This research is conducted using quasi experimental design involving 45 students in one of the universities in Malaysia. The research focuses on the frequency of critical thinking abilities used while answering the assessment test after the teaching duet occur according to Ennis’s critical thinking abilities. By using thematic analysis, the researcher has analyzed and categorized the answers and compared them with the Ennis’s critical thinking abilities. Among 12 abilities contain in Ennis, there are 10 abilities were obtain at the end of the research for the treatment group while there are 7 abilities contains in control group. This result suggest that the teaching duet approach have a potential in promoting the students to use more critical thinking abilities compare to conventional learning method.
Language Model-Driven Unsupervised Neural Machine Translation

Unsupervised neural machine translation(NMT) is associated with noise and errors in synthetic data when executing vanilla back-translations. Here, we explicitly exploits language model(LM) to drive construction of an unsupervised NMT system. This features two steps. First, we initialize NMT models using synthetic data generated via temporary statistical machine translation(SMT). Second, unlike vanilla back-translation, we formulate a weight function, that scores synthetic data at each step of subsequent iterative training; this allows unsupervised training to an improved outcome. We present the detailed mathematical construction of our method. Experimental WMT2014 English-French, and WMT2016 English-German and English-Russian translation tasks revealed that our method outperforms the best prior systems by more than 3 BLEU points.
Multi-Perspective Inferrer: Reasoning Sentences Relationship from Holistic Perspective

Natural Language Inference (NLI) aims to determine the logic relationships (i.e., entailment, neutral and contradiction) between a pair of premise and hypothesis. Recently, the alignment mechanism effectively helps NLI by capturing the aligned parts (i.e., the similar segments) in the sentence pairs, which imply the perspective of entailment and contradiction. However, these aligned parts will sometimes mislead the judgment of neutral relations. Intuitively, NLI should rely more on multiple perspectives to form a holistic view to eliminate bias. In this paper, we propose the Multi-Perspective Inferrer (MPI), a novel NLI model that reasons relationships from multiple perspectives associated with the three relationships. The MPI determines the perspectives of different parts of the sentences via a routing-by-agreement policy and makes the final decision from a holistic view. Additionally, we introduce an auxiliary supervised signal to ensure the MPI to learn the expected perspectives. Experiments on SNLI and MultiNLI show that 1) the MPI achieves substantial improvements on the base model, which verifies the motivation of multi-perspective inference; 2) visualized evidence verifies that the MPI learns highly interpretable perspectives as expected; 3) more importantly, the MPI is architecture-free and compatible with the powerful BERT.
A Reinforced Generation of Adversarial Samples for Neural Machine Translation

Neural machine translation systems tend to fail on less de-cent inputs despite its great efficacy, which may greatly harm the credibility of these systems. Fathoming how and when neural-based systems fail in such cases is critical for industrial maintenance. Instead of collecting and analyzing bad cases using limited handcrafted error features, here we investigate this issue by generating adversarial samples via a new paradigm based on reinforcement learning. Our paradigm could expose pitfalls for a given performance metric, e.g.BLEU, and could target any given neural machine translation architecture. We conduct experiments of adversarial attacks on two mainstream neural machine translation architectures, RNN-search and Transformer. The results show that our method efficiently produces stable attacks with meaning-preserving adversarial samples. We also present a qualitative and quantitative analysis for the preference pattern of the attack, showing its capability of pitfall exposure.
A filamentary cascade model of the inertial range

This paper develops a simple model of the inertial range of turbulent flow, based on a cascade of vortical filaments. The filaments are taken to be helical, one turn of the helix playing the role of a turbulent eddy. A binary branching structure is proposed, involving the splitting of filaments at each step into pairs of daughter filaments with differing properties, in effect two distinct simultaneous cascades. Neither of the cascades of this bimodal structure, in isolation, has the Richardson exponent of 1/3. If cascades are assumed to be initiated continuously and throughout space we obtain a model of the inertial range of stationary turbulence. We impose the constraint associated with Kolmogorov’s four-fifths law and then adjust the splitting to achieve good agreement with the observed structure exponents ζp. The presence of two elements to the cascade is responsible for the non-linear dependence of ζp upon p. The proposed binary branching cascade provides a model for the initial-value problem of the Navier–Stokes equations in the limit of vanishing viscosity. To simulate this limit we let the cascade continue indefinitely, energy removal occurring in the limit. We are thus able to compute the decay of energy in the model.
Classical linear logic, cobordisms and categorial grammars

We propose a categorial grammar based on classical multiplicative linear logic. This can be seen as an extension of abstract categorial grammars (ACG) and is at least as expressive. However, constituents of {\it linear logic grammars (LLG)} are not abstract ${\lambda}$-terms, but simply tuples of words with labeled endpoints and supplied with specific {\it plugging instructions}: the sets of endpoints are subdivided into the {\it incoming} and the {\it outgoing} parts. We call such objects {\it word cobordisms}. A key observation is that word cobordisms can be organized in a category, very similar to the familiar category of topological cobordisms. This category is symmetric monoidal closed and compact closed and thus is a model of linear $\lambda$-calculus and classical, as well as intuitionistic linear logic. This allows us using linear logic as a typing system for word cobordisms. At least, this gives a concrete and intuitive representation of ACG. We think, however, that the category of word cobordisms, which has a rich structure and is independent of any grammar, might be interesting on its own right.
Stylized Text Generation Using Wasserstein Autoencoders with a Mixture of Gaussian Prior

Wasserstein autoencoders are effective for text generation. They do not however provide any control over the style and topic of the generated sentences if the dataset has multiple classes and includes different topics. In this work, we present a semi-supervised approach for generating stylized sentences. Our model is trained on a multi-class dataset and learns the latent representation of the sentences using a mixture of Gaussian prior without any adversarial losses. This allows us to generate sentences in the style of a specified class or multiple classes by sampling from their corresponding prior distributions. Moreover, we can train our model on relatively small datasets and learn the latent representation of a specified class by adding external data with other styles/classes to our dataset. While a simple WAE or VAE cannot generate diverse sentences in this case, generated sentences with our approach are diverse, fluent, and preserve the style and the content of the desired classes.
Prosodic properties of formality in conversational Japanese

This paper examines potential prosodic cues for level of formality in Japanese conversational speech using speech data gathered via one-on-one interviews. Based on previous work on the phonetic properties of formality in Japanese (Ofuka et al. 2000, Ito 2002), further studies of Korean (Winter & Grawunder 2012) and Catalan Spanish (Hübscher, Borràs-Comes & Prieto 2017), and on a lab-based pilot study, the study examined properties of f0, intensity, pause frequency, and articulation rate via mixed effects regression models and a functional data analysis (Grabe, Kochanski & Coleman 2007, Ramsay 2006). Analysis of the speech data shows significant relationships between a number of prosodic variables and level of formality, and suggests that some of these relationships may apply cross-linguistically.
State space models for non‐stationary intermittently coupled systems: an application to the North Atlantic oscillation

We develop Bayesian state space methods for modelling changes to the mean level or temporal correlation structure of an observed time series due to intermittent coupling with an unobserved process. Novel intervention methods are proposed to model the effect of repeated coupling as a single dynamic process. Latent time varying auto‐regressive components are developed to model changes in the temporal correlation structure. Efficient filtering and smoothing methods are derived for the resulting class of models. We propose methods for quantifying the component of variance attributable to an unobserved process, the effect during individual coupling events and the potential for skilful forecasts. The methodology proposed is applied to the study of winter time variability in the dominant pattern of climate variation in the northern hemisphere: the North Atlantic oscillation. Around 70% of the interannual variance in the winter (December–January–February) mean level is attributable to an unobserved process. Skilful forecasts for the winter (December–January–February) mean are possible from the beginning of December.
Zero Censorship! Who Are We Kidding?An exploratory analysis of the opinions and experiences of Queensland-based public librarians with regard to the censorship of materials in public library collections

Public librarians have long upheld the social justice philosophy of free access to information for all people. The issue of censorship is related to both the professional principles of the Australian Library and Information Association and to the role public librarians have to play in nurturing social capital in our communities. Until now there has been little Australian data on this topic. This paper considers the philosophy of free access to information in the context of contemporary item selection and classification processes within public libraries. The findings of a survey of Queensland public librarians are used to identify current industry attitudes towards the public right to information, and to determine the degree to which censorship mechanisms are currently practiced or prevented in public libraries. The findings support those of overseas researchers that stated anti-censorship attitudes are not always indicative of censorship behaviours, and that some librarians employ self-censorship with regard to controversial materials in order to avoid censorship challenges.
Privacy driven internet ecosystem

The dominant business model of today's Internet is built upon advertisements; users can access Internet services while the providers show ads to them. Although significant efforts have been made to model and analyze the economic aspects of this ecosystem, the heart of the current status quo, namely privacy, has not received the attention of the research community yet. Accordingly, we propose an economic model of the privacy driven Internet ecosystem where privacy is handled as an asset that can be traded. Expressing the entropy of privacy as the service providers' fitness value and applying a dynamic network formation model based on the preferential attachment principle allow the analysis of the providers' economic interactions in a realistic framework, whose properties are illustrated based on extensive simulations.