Miracle-dz/UniFAR-FTUs huggingface dataset:
The Plausibility Paradox in Small-Scale Virtual Environments

This paper identifies a new phenomenon: when users interact with physically simulated objects in a virtual environment that is much smaller than usual, there is a mismatch between the object physics that they expect and the object physics that is actually correct. We report the findings of our study investigating the relationship between perceived realism and a physically correct approximation of realism in a small-scale virtual environment. We conducted a within-subjects experiment in which 44 subjects performed a simple interaction task with objects, using two physics simulation conditions while scaled down by a factor of ten in a virtual reality application. Although both conditions gave the visual impression of a scaled-down user interacting in a normal-sized environment, the physics conditions affecting the objects were different by simulating either correct behavior at that scale, or incorrect behavior similar to as if a normal sized user was interacting in a world that had been scaled up instead. We found that the significant majority of the users considered the latter condition to be the realistic one. We argue that our findings have implications on many virtual reality and telepresence applications involving operation with physically simulated or physical objects in small scales.
Superconformal Generalizations of $R^n$--$φ^p$ Interpolating Inflationary Theories

We present the supersymmetric completion of the auxiliary vector modified polynomial $f(R)$ theories that interpolate between the polynomial $f(R)$ theories and chaotic inflation with the power-law potential $V(\phi) \propto \phi^p$. The supersymmetrization is achieved in two steps: First, we introduce a superconformal theory for three chiral multiplets by choosing a conformal Kahler potential and a conformal superpotential. In the second step, we use one of the chiral multiplets to compensate for the superconformal symmetries and achieve the Kahler potential and the superpotential while the other two are used to realize inflation with a stable inflationary trajectory. The stability of the inflationary trajectory requires certain deformations to the Kahler potential which we discuss their compatibility against the inflationary observables from the latest Planck data.
A probabilistic assessment of the Indo-Aryan Inner-Outer Hypothesis

This paper uses a novel data-driven probabilistic approach to address the century-old Inner-Outer hypothesis of Indo-Aryan. I develop a Bayesian hierarchical mixed-membership model to assess the validity of this hypothesis using a large data set of automatically extracted sound changes operating between Old Indo-Aryan and Modern Indo-Aryan speech varieties. I employ different prior distributions in order to model sound change, one of which, the logistic normal distribution, has not received much attention in linguistics outside of Natural Language Processing, despite its many attractive features. I find evidence for cohesive dialect groups that have made their imprint on contemporary Indo-Aryan languages, and find that when a logistic normal prior is used, the distribution of dialect components across languages is largely compatible with a core-periphery pattern similar to that proposed under the Inner-Outer hypothesis.
Experiences of Front-line Shelter Workers in Providing Services to Immigrant Women Impacted by Family Violence York University, Graduate Programme in Social Work Practice-based Research Paper GS/SOWK 6100 3.00 Faculty Advisor: Saddeiqa Holder

INTRODUCTION Purpose and Rationale of Study Research Issues and Questions LITERATURE REVIEW METHODOLOGY Research Approach Key Concepts Sampling Data Collection Methods Data Analysis Ethics Limitations FINDINGS AND DATA ANALYSIS Training Challenges Changes IMPLICATIONS AND RECOMMENDATIONS Recommendations for One-to-One Support Recommendations for Shelters Recommendations for Social Services Recommendations for Larger Systems LIMITATIONS AND FUTURE RESEARCH DISSEMINATION CONCLUSION REFERENCES BIBLIOGRAPHY APPENDICES Appendix A: Information Notice Appendix B: Shelter Consent Form Appendix C: Participant Consent Form Appendix D: Interview Guide Appendix E: Pamphlet
Polynomial time guarantees for the Burer-Monteiro method

The Burer-Monteiro method is one of the most widely used techniques for solving large-scale semidefinite programs (SDP). The basic idea is to solve a nonconvex program in $Y$, where $Y$ is an $n \times p$ matrix such that $X = Y Y^T$. In this paper, we show that this method can solve SDPs in polynomial time in an smoothed analysis setting. More precisely, we consider an SDP whose domain satisfies some compactness and smoothness assumptions, and slightly perturb the cost matrix and the constraints. We show that if $p \gtrsim \sqrt{2(1+\eta)m}$, where $m$ is the number of constraints and $\eta>0$ is any fixed constant, then the Burer-Monteiro method can solve SDPs to any desired accuracy in polynomial time, in the setting of smooth analysis. Our bound on $p$ approaches the celebrated Barvinok-Pataki bound in the limit as $\eta$ goes to zero, beneath which it is known that the nonconvex program can be suboptimal. Previous analyses were unable to give polynomial time guarantees for the Burer-Monteiro method, since they either assumed that the criticality conditions are satisfied exactly, or ignored the nontrivial problem of computing an approximately feasible solution. We address the first problem through a novel connection with tubular neighborhoods of algebraic varieties. For the feasibility problem we consider a least squares formulation, and provide the first guarantees that do not rely on the restricted isometry property.
Learning Deep Generative Models with Short Run Inference Dynamics

This paper studies the fundamental problem of learning deep generative models that consist of one or more layers of latent variables organized in top-down architectures. Learning such a generative model requires inferring the latent variables for each training example based on the posterior distribution of these latent variables. The inference typically requires Markov chain Monte Caro (MCMC) that can be time consuming. In this paper, we propose to use short run inference dynamics guided by the log-posterior, such as finite-step gradient descent algorithm initialized from the prior distribution of the latent variables, as an approximate sampler of the posterior distribution, where the step size of the gradient descent dynamics is optimized by minimizing the Kullback-Leibler divergence between the distribution produced by the short run inference dynamics and the posterior distribution. Our experiments show that the proposed method outperforms variational auto-encoder (VAE) in terms of reconstruction error and synthesis quality. The advantage of the proposed method is that it is natural and automatic, even for models with multiple layers of latent variables.
Neural Academic Paper Generation

In this work, we tackle the problem of structured text generation, specifically academic paper generation in $\LaTeX{}$, inspired by the surprisingly good results of basic character-level language models. Our motivation is using more recent and advanced methods of language modeling on a more complex dataset of $\LaTeX{}$ source files to generate realistic academic papers. Our first contribution is preparing a dataset with $\LaTeX{}$ source files on recent open-source computer vision papers. Our second contribution is experimenting with recent methods of language modeling and text generation such as Transformer and Transformer-XL to generate consistent $\LaTeX{}$ code. We report cross-entropy and bits-per-character (BPC) results of the trained models, and we also discuss interesting points on some examples of the generated $\LaTeX{}$ code.
Differentiation of Blackbox Combinatorial Solvers

Achieving fusion of deep learning with combinatorial algorithms promises transformative changes to artificial intelligence. One possible approach is to introduce combinatorial building blocks into neural networks. Such end-to-end architectures have the potential to tackle combinatorial problems on raw input data such as ensuring global consistency in multi-object tracking or route planning on maps in robotics. In this work, we present a method that implements an efficient backward pass through blackbox implementations of combinatorial solvers with linear objective functions. We provide both theoretical and experimental backing. In particular, we incorporate the Gurobi MIP solver, Blossom V algorithm, and Dijkstra's algorithm into architectures that extract suitable features from raw inputs for the traveling salesman problem, the min-cost perfect matching problem and the shortest path problem.
Active Learning of SVDD Hyperparameter Values

Support Vector Data Description (SVDD) is a popular one-class classifier, and well-suited for outlier detection. However, the effectiveness of SVDD depends on selecting good hyperparameter values – a difficult problem that has received significant attention in the literature. Since SVDD is an unsupervised classifier, tuning of hyperparameter values is difficult. This has motivated several methods to estimate hyperparameter values based on data characteristics. But existing methods are purely heuristic, and the conditions under which they work well are largely unclear. This has created a situation where instead of selecting hyperparameter values, one has to choose among several, equally plausible heuristics.In this article, we make some strides towards a principled approach to estimate SVDD hyperparameter values. We propose LAMA (Local Active Min-Max Alignment), the first method to select SVDD hyperparameter values by active learning. The core idea is based on kernel alignment, which we adapt to active learning with small sample sizes. LAMA provides estimates for both of the SVDD hyperparameters. These estimates are evidence-based, i.e., rely on actual class labels, and come with a quality score. This eliminates the need for manual validation, an issue with current heuristics. LAMA outperforms state-of-theart competitors in extensive experiments on real-world data. In several cases, LAMA even yields results close to the empirical upper bound.
Evaluating the Data Quality of Eye Tracking Signals from a Virtual Reality System: Case Study using SMI's Eye-Tracking HTC Vive

We evaluated the data quality of SMI's tethered eye-tracking head-mounted display based on the HTC Vive (ET-HMD) during a random saccade task. We measured spatial accuracy, spatial precision, temporal precision, linearity, and crosstalk. We proposed the use of a non-parametric spatial precision measure based on the median absolute deviation (MAD). Our linearity analysis considered both the slope and adjusted R-squared of a best-fitting line. We were the first to test for a quadratic component to crosstalk. We prepended a calibration task to the random saccade task and evaluated 2 methods to employ this user-supplied calibration. For this, we used a unique binning approach to choose samples to be included in the recalibration analyses. We compared our quality measures between the ET-HMD and our EyeLink 1000 (SR-Research, Ottawa, Ontario, CA). We found that the ET-HMD had significantly better spatial accuracy and linearity fit than our EyeLink, but both devices had similar spatial precision and linearity slope. We also found that, while the EyeLink had no significant crosstalk, the ET-HMD generally exhibited quadratic crosstalk. Fourier analysis revealed that the binocular signal was a low-pass filtered version of the monocular signal. Such filtering resulted in the binocular signal being useless for the study of high-frequency components such as saccade dynamics.
EmbedMask: Embedding Coupling for One-stage Instance Segmentation

Current instance segmentation methods can be categorized into segmentation-based methods that segment first then do clustering, and proposal-based methods that detect first then predict masks for each instance proposal using repooling. In this work, we propose a one-stage method, named EmbedMask, that unifies both methods by taking advantages of them. Like proposal-based methods, EmbedMask builds on top of detection models making it strong in detection capability. Meanwhile, EmbedMask applies extra embedding modules to generate embeddings for pixels and proposals, where pixel embeddings are guided by proposal embeddings if they belong to the same instance. Through this embedding coupling process, pixels are assigned to the mask of the proposal if their embeddings are similar. The pixel-level clustering enables EmbedMask to generate high-resolution masks without missing details from repooling, and the existence of proposal embedding simplifies and strengthens the clustering procedure to achieve high speed with higher performance than segmentation-based methods. Without any bells and whistles, EmbedMask achieves comparable performance as Mask R-CNN, which is the representative two-stage method, and can produce more detailed masks at a higher speed. Code is available at github.com/yinghdb/EmbedMask.
UEFA Against the Champions? An Evaluation of the Recent Reform of the Champions League Qualification

The paper evaluates the impact of the only reform in the Champions Path of UEFA Champions League qualifying system, effective from the 2018/19 season. In contrast to previous studies, our methodology considers five seasons instead of only one to filter out any possible season-specific attributes. The chances of some national champions decrease much stronger than suggested by the reduction in the number of available slots. Since the negative effects depend to a large extent on the arbitrary cutoffs in the access list, we propose to introduce some randomness into the determination of entry stages.
Electromagnetic Transitions in Near-threshold Resonances

Near-threshold collectivization of continuum shell model eigenstates is investigated in $^{20}$O on the example of B(E$\lambda$) decays of 4$^+$ states in the vicinity of elastic and inelastic neutron threshold. Changes of the electromagnetic transition probabilities as a function of the continuum-coupling strength are explained by the corresponding evolution of the double poles of the scattering matrix.
Disk Instabilities Caused the 2018 Outburst of AG Draconis

Symbiotic binary AG~Draconis (AG~Dra) has an well-established outburst behavior based on an extensive observational history. Usually, the system undergoes a 9--15~yr period of quiescence with a constant average energy emitted, during which the system's orbital period of $\sim$550~d can be seen at shorter wavelengths (particularly in the U-band) as well as a shorter period of $\sim$355~d thought to be due to pulsations of the cool component. After a quiescent period, the marker of an active period is usually a major (cool) outburst of up to $\textrm{V}=8.4$~mag, followed by a series of minor (hot) outbursts repeating at a period of approximately 1~yr. However, in 2016 April after a 9-year period of quiescence AG~Dra exhibited unusual behavior: it began an active phase with a minor outburst followed by two more minor outbursts repeating at an interval of $\sim$1~yr. We present R-band observations of AG~Dra's 2018 April minor outburst and an analysis of the outburst mechanism and reports on the system's activity levels following the time of its next expected outburst. By considering the brightening and cooling times, the scale of the outburst, and its temperature evolution we have determined that this outburst was of disk instability nature.
A reflection on professional development of registrars completing a module in Health Care Practice

Background. A need for professional development in the training of registrars was identified by the School of Medicine, University of the Free State (UFS), Bloemfontein, South Africa, in 2007. Objective. To develop the module on Health Care Practice (HCP) (GPV703) to address these shortcomings. Methods. A quantitative study, enhanced by qualitative data, was conducted. A self-administered questionnaire that included a rating scale and open-ended questions was used. The quantitative responses were analysed using Microsoft Excel, and the qualitative data were edited, categorised and summarised. Results. The questionnaire was completed by 95% ( n =38/40) of registrars. The quantitative questions, regarding the orientation session, content and applicability of the content of the module, showed satisfactory to very good responses. Of the 40 surveys collected from registrars, 77.5% ( n =31/40) were completed by heads of department (HODs). The surveys showed a significant improvement in registrar competence: 17 were given an above average rating and 14 an average rating; there were no below-average ratings. Discussion. The module on HCP, which is part of the MMed programme, addressed aspects required by registrars to develop and/or enhance their skills, knowledge and professional behaviour with regard to ethics, practice management and patient communication. Registrars were generally satisfied with the content and presentation of the module. The open-ended questions raised concerns about aspects of patient communication and electronic learning. These need to be addressed to improve the quality of the module. Conclusion. The module on HCP (GPV703), as implemented by the UFS, is successful in addressing key aspects often neglected because of the strong clinical focus of a medical programme.
Elementary Laser‐Less Quantum Logic Operations with (Anti‐)Protons in Penning Traps

Static magnetic field gradients superimposed on the electromagnetic trapping potential of a Penning trap can be used to implement laser‐less spin–motion couplings that allow the realization of elementary quantum logic operations in the radio‐frequency regime. An important scenario of practical interest is the application to g‐factor measurements with single (anti‐)protons to test the fundamental charge, parity, time reversal (CPT) invariance as pursued in the Baryon Antibaryon Symmetry Experiment (BASE) collaboration. The classical and quantum behavior of a charged particle in a Penning trap with a superimposed magnetic field gradient is discussed. Using analytic and numerical calculations, it is found that it is possible to carry out a SWAP gate between the spin and the motional qubit of a single (anti‐)proton with high fidelity, provided the particle has been initialized in the motional ground state. The implications of the findings for the realization of quantum logic spectroscopy in this system are discussed.
Moments of Student's t-distribution: A Unified Approach

In this note, we derive the closed form formulae for moments of Student's t-distribution in the one dimensional case as well as in higher dimensions through a unified probability framework. Interestingly, the closed form expressions for the moments of Student's t-distribution can be written in terms of the familiar Gamma function, Kummer's confluent hypergeometric function, and the hypergeometric function.
Genera of knots in the complex projective plane

Our goal is to systematically compute the [Formula: see text]-genus of as many prime knots up to 8-crossings as possible. We obtain upper bounds on the [Formula: see text]-genus via coherent band surgery. We obtain lower bounds by obstructing homological degrees of potential slice discs. The obstructions are pulled from a variety of sources in low-dimensional topology and adapted to [Formula: see text]. There are 27 prime knots and distinct mirrors up to 7-crossings. We now know the [Formula: see text]-genus of all of these knots. There are 64 prime knots and distinct mirrors up to 8-crossings. We now know the [Formula: see text]-genus of all but 6 of these knots, where the [Formula: see text]-genus was not determined explicitly, it was narrowed down to 2 possibilities. As a consequence of this work, we show an infinite family of knots such that the [Formula: see text]-genus of each knot differs from that of its mirror.
Asymptotics for Strassen's Optimal Transport Problem

In this paper, we consider Strassen's version of optimal transport problem. That is, we minimize the excess-cost probability (i.e., the probability that the cost is larger than a given value) over all couplings of two distributions. We derive large deviation, moderate deviation, and central limit theorems for this problem. Our approach is based on Strassen's dual formulation of the optimal transport problem, Sanov's theorem on the large deviation principle (LDP) of empirical measures, as well as the moderate deviation principle (MDP) and central limit theorems (CLT) of empirical measures. In order to apply the LDP, MDP, and CLT to Strassen's optimal transport problem, a nested optimal transport formula for Strassen's optimal transport problem is derived. In this nested formula, the cost function of the outer optimal transport subproblem is set to the optimal transport functional (i.e., the mapping from a pair of distributions to the optimal optimal transport cost for this pair of distributions) of the inner optimal transport subproblem. Based on this nested formula, we carefully design asymptotically optimal solutions to Strassen's optimal transport problem and its dual formulation. Finally, we connect Strassen's optimal transport problem to the empirical optimal transport problem, which hence provides an application for our results.
L3 Fusion: Fast Transformed Convolutions on CPUs

Fast convolutions via transforms, either Winograd or FFT, had emerged as a preferred way of performing the computation of convolutional layers, as it greatly reduces the number of required operations. Recent work shows that, for many layer structures, a well--designed implementation of fast convolutions can greatly utilize modern CPUs, significantly reducing the compute time. However, the generous amount of shared L3 cache present on modern CPUs is often neglected, and the algorithms are optimized solely for the private L2 cache. In this paper we propose an efficient `L3 Fusion` algorithm that is specifically designed for CPUs with significant amount of shared L3 cache. Using the hierarchical roofline model, we show that in many cases, especially for layers with fewer channels, the `L3 fused` approach can greatly outperform standard 3 stage one provided by big vendors such as Intel. We validate our theoretical findings, by benchmarking our `L3 fused` implementation against publicly available state of the art.