Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Sneaking as a Playful Input Modality for Virtual Environments

Using virtual reality setups, users can fade out of their surroundings and dive fully into a thrilling and appealing virtual environment. The success of such immersive experiences depends heavily on natural and engaging interactions with the virtual world. As developers tend to focus on intuitive hand controls, other aspects of the broad range of full-body capabilities are easily left vacant. One repeatedly overlooked input modality is the user's gait. Even though users may walk physically to explore the environment, it usually does not matter how they move. However, gait-based interactions, using the variety of information contained in human gait, could offer interesting benefits for immersive experiences. For instance, stealth VR-games could profit from this additional range of interaction fidelity in the form of a sneaking-based input modality. In our work, we explore the potential of sneaking as a playful input modality for virtual environments. Therefore, we discuss possible sneaking-based gameplay mechanisms and develop three technical approaches, including precise foot-tracking and two abstraction levels. Our evaluation reveals the potential of sneaking-based inter-actions in IVEs, offering unique challenges and thrilling gameplay. For these interactions, precise tracking of individual footsteps is unnecessary, as a more abstract approach focusing on the players' intention offers the same experience while providing better comprehensible feedback. Based on these findings, we discuss the broader potential and individual strengths of our gait-centered interactions.
Bayesian Fusion: Scalable unification of distributed statistical analyses

There has recently been considerable interest in addressing the problem of unifying distributed statistical analyses into a single coherent inference. This problem naturally arises in a number of situations, including in big-data settings, when working under privacy constraints, and in Bayesian model choice. The majority of existing approaches have relied upon convenient approximations of the distributed analyses. Although typically being computationally efficient, and readily scaling with respect to the number of analyses being unified, approximate approaches can have significant shortcomings -- the quality of the inference can degrade rapidly with the number of analyses being unified, and can be substantially biased even when unifying a small number of analyses that do not concur. In contrast, the recent Fusion approach of Dai et al. (2019) is a rejection sampling scheme which is readily parallelisable and is exact (avoiding any form of approximation other than Monte Carlo error), albeit limited in applicability to unifying a small number of low-dimensional analyses. In this paper we introduce a practical Bayesian Fusion approach. We extend the theory underpinning the Fusion methodology and, by embedding it within a sequential Monte Carlo algorithm, we are able to recover the correct target distribution. By means of extensive guidance on the implementation of the approach, we demonstrate theoretically and empirically that Bayesian Fusion is robust to increasing numbers of analyses, and coherently unifying analyses which do not concur. This is achieved while being computationally competitive with approximate schemes.
On Entropy and Bit Patterns of Ring Oscillator Jitter

Thermal jitter (phase noise) from a free-running ring oscillator is a common, easily implementable physical randomness source in True Random Number Generators (TRNGs). We show how to evaluate entropy, autocorrelation, and bit pattern distributions of ring oscillator noise sources, even with low jitter levels or some bias. Entropy justification is required in NIST 800-90B and AIS-31 testing and for applications such as the RISC-V entropy source extension. Our numerical evaluation algorithms outperform Monte Carlo simulations in speed and accuracy. We also propose a new lower bound estimation formula for the entropy of ring oscillator sources which applies more generally than previous ones.
TAD: Trigger Approximation based Black-box Trojan Detection for AI

An emerging amount of intelligent applications have been developed with the surge of Machine Learning (ML). Deep Neural Networks (DNNs) have demonstrated unprecedented performance across various fields such as medical diagnosis and autonomous driving. While DNNs are widely employed in security-sensitive fields, they are identified to be vulnerable to Neural Trojan (NT) attacks that are controlled and activated by the stealthy trigger. We call this vulnerable model adversarial artificial intelligence (AI). In this paper, we target to design a robust Trojan detection scheme that inspects whether a pre-trained AI model has been Trojaned before its deployment. Prior works are oblivious of the intrinsic property of trigger distribution and try to reconstruct the trigger pattern using simple heuristics, i.e., stimulating the given model to incorrect outputs. As a result, their detection time and effectiveness are limited. We leverage the observation that the pixel trigger typically features spatial dependency and propose TAD, the first trigger approximation based Trojan detection framework that enables fast and scalable search of the trigger in the input space. Furthermore, TAD can also detect Trojans embedded in the feature space where certain filter transformations are used to activate the Trojan. We perform extensive experiments to investigate the performance of the TAD across various datasets and ML models. Empirical results show that TAD achieves a ROC-AUC score of 0:91 on the public TrojAI dataset 1 and the average detection time per model is 7:1 minutes.
Discovery of Beryllium in White Dwarfs Polluted by Planetesimal Accretion

The element beryllium is detected for the first time in white dwarf stars. This discovery in the spectra of two helium-atmosphere white dwarfs was made possible only because of the remarkable overabundance of Be relative to all other elements, heavier than He, observed in these stars. The measured Be abundances, relative to chondritic, are by far the largest ever seen in any astronomical object. We anticipate that the Be in these accreted planetary bodies was produced by spallation of one or more of O, C, and N in a region of high fluence of particles of MeV or greater energy.
KMT-2018-BLG-1025Lb: microlensing super-Earth planet orbiting a low-mass star

Aims. We aim to find missing microlensing planets hidden in the unanalyzed lensing events of previous survey data. Methods. For this purpose, we conducted a systematic inspection of high-magnification microlensing events, with peak magnifications of Apeak ≳ 30, in the data collected from high-cadence surveys in and before the 2018 season. From this investigation, we identified an anomaly in the lensing light curve of the event KMT-2018-BLG-1025. The analysis of the light curve indicates that the anomaly is caused by a very low mass-ratio companion to the lens. Results. We identify three degenerate solutions, in which the ambiguity between a pair of solutions (solutions B) is caused by the previously known close–wide degeneracy, and the degeneracy between these and the other solution (solution A) is a new type that has not been reported before. The estimated mass ratio between the planet and host is q ~ 0.8 × 10−4 for solution A and q ~ 1.6 × 10−4 for solutions B. From the Bayesian analysis conducted with measured observables, we estimate that the masses of the planet and host and the distance to the lens are (Mp, Mh, DL) ~ (6.1 M⊕, 0.22 M⊙, 6.7 kpc) for solution A and ~(4.4 M⊕, 0.08 M⊙, 7.5 kpc) for solutions B. The planet mass is in the category of a super-Earth regardless of the solutions, making the planet the eleventh super-Earth planet, with masses lying between those of Earth and the Solar System’s ice giants, which were discovered by microlensing.
Learning to Match Mathematical Statements with Proofs

We introduce a novel task consisting in assigning a proof to a given mathematical statement. The task is designed to improve the processing of research-level mathematical texts. Applying Natural Language Processing (NLP) tools to research level mathematical articles is both challenging, since it is a highly specialized domain which mixes natural language and mathematical formulae. It is also an important requirement for developing tools for mathematical information retrieval and computer-assisted theorem proving. We release a dataset for the task, consisting of over 180k statement-proof pairs extracted from mathematical research articles. We carry out preliminary experiments to assess the difficulty of the task. We first experiment with two bag-of-words baselines. We show that considering the assignment problem globally and using weighted bipartite matching algorithms helps a lot in tackling the task. Finally, we introduce a self-attention-based model that can be trained either locally or globally and outperforms baselines by a wide margin.
Learning to Select External Knowledge With Multi-Scale Negative Sampling

The Track-1 of DSTC9 aims to effectively answer user requests or questions during task-oriented dialogues, which are out of the scope of APIs/DB. By leveraging external knowledge resources, relevant information can be retrieved and encoded into the response generation for these out-of-API-coverage queries. In this work, we have explored several advanced techniques to enhance the utilization of external knowledge and boost the quality of response generation, including schema guided knowledge decision, negatives enhanced knowledge selection, and knowledge grounded response generation. To evaluate the performance of our proposed method, comprehensive experiments have been carried out on the publicly available dataset. Our approach was ranked as the best in human evaluation of DSTC9 Track-1.
Thin film growth of phase-separating phthalocyanine-fullerene blends: A combined experimental and computational study

Blended organic thin films have been studied during the last decades due to their applicability in organic solar cells. Although their optical and electronic features have been examined intensively, there is still lack of detailed knowledge about their growth processes and resulting morphologies, which play a key role for the efficiency of optoelectronic devices such as organic solar cells. In this study, pure and blended thin films of copper phthalocyanine (CuPc) and the Buckminster fullerene (C60) were grown by vacuum deposition onto a native silicon oxide substrate at two different substrate temperatures, 310 K and 400 K. The evolution of roughness was followed by in-situ real-time X-ray reflectivity. Crystal orientation, island densities and morphology were examined after the growth by X-ray diffraction experiments and microscopy techniques. The formation of a smooth wetting layer followed by rapid roughening was found in pure CuPc thin films, whereas C60 shows a fast formation of distinct islands at a very early stage of growth. The growth of needle-like CuPc crystals loosing their alignment with the substrate was identified in co-deposited thin films. Furthermore, the data demonstrates that structural features become larger and more pronounced and that the island density decreases by a factor of four when going from 310 K to 400 K. Finally, the key parameters roughness and island density were well reproduced on a smaller scale by kinetic Monte-Carlo simulations of a generic, binary lattice model with simple nearest-neighbor interaction energies.
A Bayesian Neural Network based on Dropout Regulation

Bayesian Neural Networks (BNN) have recently emerged in the Deep Learning world for dealing with uncertainty estimation in classification tasks, and are used in many application domains such as astrophysics, autonomous driving.BNN assume a prior over the weights of a neural network instead of point estimates, enabling in this way the estimation of both aleatoric and epistemic uncertainty of the model prediction.Moreover, a particular type of BNN, namely MC Dropout, assumes a Bernoulli distribution on the weights by using Dropout.Several attempts to optimize the dropout rate exist, e.g. using a variational approach.In this paper, we present a new method called"Dropout Regulation"(DR), which consists of automatically adjusting the dropout rate during training using a controller as used in automation.DR allows for a precise estimation of the uncertainty which is comparable to the state-of-the-art while remaining simple to implement.
Tracing non-Abelian anyons via impurity particles

Niccolò Baldelli, Bruno Juliá-Dı́az, 3 Utso Bhattacharya, 4 Maciej Lewenstein, 5 and Tobias Graß ICFO-Institut de Ciencies Fotoniques, The Barcelona Institute of Science and Technology, 08860 Castelldefels (Barcelona), Spain Departament de F́ısica Quàntica i Astrof́ısica, Facultat de F́ısica, Universitat de Barcelona, 08028 Barcelona, Spain. Institut de Ciències del Cosmos, Universitat de Barcelona, ICCUB, Mart́ı i Franquès 1, Barcelona 08028, Spain Max-Planck-Institut für Quantenoptik, D-85748 Garching, Germany ICREA, Pg. Lluis Companys 23, 08010 Barcelona, Spain (Dated: February 4, 2021)
Optimizing QoS for Erasure-Coded Wireless Data Centers

Cloud computing facilitates the access of applications and data from any location by a distributed storage system. Erasure codes offer better data replication technique with reduced storage costs for more reliability. This paper considers the erasure-coded data center with multiple servers in a wireless network where each is equipped with a base-station. The cause of latency in the file retrieval process is mainly due to queuing delays at each server. This work puts forth a stochastic optimization framework for obtaining the optimal scheduling policy that maximizes users’ quality of service (QoS) while adhering to the latency requirements. We further show that the problem has non-linear functions of expectations in objective and constraints and is impossible to solve with traditional SGD like algorithms. We propose a new algorithm that addresses compositional structure in the problem. Further, we show that the proposed algorithm achieves a faster convergence rate than the best-known results. Finally, we test the efficacy of the proposed method in a simulated environment.
Multi-Class Probabilistic Atlas-Based Whole Heart Segmentation Method in Cardiac CT and MRI

Accurate and robust whole heart substructure segmentation is crucial in developing clinical applications, such as computer-aided diagnosis and computer-aided surgery. However, the segmentation of different heart substructures is challenging because of inadequate edge or boundary information, the complexity of the background and texture, and the diversity in different substructures’ sizes and shapes. This article proposes a framework for multi-class whole heart segmentation employing non-rigid registration-based probabilistic atlas incorporating the Bayesian framework. We also propose a non-rigid registration pipeline utilizing a multi-resolution strategy for obtaining the highest attainable mutual information between the moving and fixed images. We further incorporate non-rigid registration into the expectation-maximization algorithm and implement different deep convolutional neural network-based encoder-decoder networks for ablation studies. All the extensive experiments are conducted utilizing the publicly available dataset for the whole heart segmentation containing 20 MRI and 20 CT cardiac images. The proposed approach exhibits an encouraging achievement, yielding a mean volume overlapping error of 14.5% for CT scans exceeding the state-of-the-art results by a margin of 1.3% in terms of the same metric. As the proposed approach provides better results to delineate the different substructures of the heart, it can be a medical diagnostic aiding tool for helping experts with quicker and more accurate results.
Break Through the Limitation of Malus' Law with Plasmonic Polarizers

bandwidth. [ 14 ] Very recently, Xiao et al. attempted theoretically to use a pair of nonorthogonal plasmonic polarizers to rotate the linear polarization. [ 16 ] Nonetheless, the reported working bandwidth was very narrow (∼1%). Their results also showed that, when the two polarizers are orthogonal, the transmission and conversion effects are still ineffi cient. In this paper, we suggest that the aforementioned longstanding problem can be solved: two specially designed linear plasmonic polarizers, which are orthogonally oriented to each other, can induce both high transmission and 90-degree polarization conversion. We also present the fi rst experimental demonstration of such an effect in both optical frequencies and the microwave regime. The working system is of simple confi guration, high effi ciency, and larger bandwidth. Moreover, based on this effect, a super polarization converter that switches the incident polarization to an arbitrary direction has been designed and realized in the microwave band. The results represent a breakthrough of conventional effects and open the door for constructing new types of plasmonic devices, e.g., the polarization converters, one-way transmitters, subwavelength switches, modulators, etc. Figure 1 a–c show the schematic view of our design, where two linear plasmonic polarizers M with the thickness t are separated by a spacer layer I of thickness h (the spacer layer can be dielectric or air; the qualitative conclusion does not depend on the medium of spacer, unlike the case in a liquid crystal display). The linear polarizer is constructed by a metal fi lm milled with periodic rectangular holes (the lattice constant is d , the hole length is l , and the hole width is w ), with the polarization axis perpendicular to the long hole edges. If projected onto the xy plane, the rectangular holes in the two polarizers become orthogonal and form the L-shaped “crossing” (see Figure 1 c). Thus, the two perforated metal fi lms comprise a pair of orthogonal linear polarizers. Such a system can ensure that only the x -polarized component of electromagnetic wave can be coupled into the inlet and that only the y -polarized component can be produced from the exit. Hence, if there is any transmission in the system, it will inevitably lead to a rotation of linear polarization of 90 degrees. We start from one experiment carried out in the optical range. Because of the asymmetrical confi guration of the two polarizers with respect to the spacer layer, it may be a challenge to fabricate the system working in optical frequencies. By preparing a free-standing Au/SiN/Au sandwich structure, we successfully constructed the system with focused-ion-beam milling. Here, the thickness of the SiN fi lm is 50 nm, and the thickness of each gold fi lm is 100 nm. Figure 1 d shows the scanning electron microscope (SEM) image of the input polarizer. The fabricated hole pattern consists of 100 × 100 units Break Through the Limitation of Malus’ Law with Plasmonic Polarizers
Edvertisements: Adding Microlearning to Social News Feeds and Websites

Many long-term goals, such as learning a language, require people to regularly practice every day to achieve mastery. At the same time, people regularly surf the web and read social news feeds in their spare time. We have built a browser extension that teaches vocabulary to users in the context of Facebook feeds and arbitrary websites, by showing users interactive quizzes they can answer without leaving the website. On Facebook, the quizzes show up as part of the news feed, while on other sites, the quizzes appear where advertisements normally would. In our user study, we examined the effectiveness of inserting microlearning tasks into social news feeds. We compared vocabulary learning rates when we inserted interactive quizzes into feeds, versus inserting links that lead them to a website where they could do the quizzes. Our results suggest that users engage with and learn from our embedded quizzes, and engagement increases when the quizzes can be done directly within their feeds.
A few statistical principles for data science

In any other circumstance, it might make sense to define the extent of the terrain (Data Science) first, and then locate and describe the landmarks (Principles). But this data revolution we are experiencing defies a cadastral survey. Areas are continually being annexed into Data Science. For example, biometrics was traditionally statistics for agriculture in all its forms but now, in Data Science, it means the study of characteristics that can be used to identify an individual. Examples of non‐intrusive measurements include height, weight, fingerprints, retina scan, voice, photograph/video (facial landmarks and facial expressions) and gait. A multivariate analysis of such data would be a complex project for a statistician, but a software engineer might appear to have no trouble with it at all. In any applied‐statistics project, the statistician worries about uncertainty and quantifies it by modelling data as realisations generated from a probability space. Another approach to uncertainty quantification is to find similar data sets, and then use the variability of results between these data sets to capture the uncertainty. Both approaches allow ‘error bars’ to be put on estimates obtained from the original data set, although the interpretations are different. A third approach, that concentrates on giving a single answer and gives up on uncertainty quantification, could be considered as Data Engineering, although it has staked a claim in the Data Science terrain. This article presents a few (actually nine) statistical principles for data scientists that have helped me, and continue to help me, when I work on complex interdisciplinary projects.
AHAR: Adaptive CNN for Energy-Efficient Human Activity Recognition in Low-Power Edge Devices

Human activity recognition (HAR) is one of the key applications of health monitoring that requires continuous use of wearable devices to track daily activities. This article proposes an adaptive convolutional neural network for energy-efficient HAR (AHAR) suitable for low-power edge devices. Unlike traditional adaptive (early-exit) architecture that makes the early-exit decision based on classification confidence, AHAR proposes a novel adaptive architecture that uses an output block predictor to select a portion of the baseline architecture to use during the inference phase. The experimental results show that traditional adaptive architecture suffer from performance loss whereas our adaptive architecture provides similar or better performance as the baseline one while being energy efficient. We validate our methodology in classifying locomotion activities from two data sets—1) Opportunity and 2) w-HAR. Compared to the fog/cloud computing approaches for the Opportunity data set, our baseline and adaptive architectures show a comparable weighted F1 score of 91.79%, and 91.57%, respectively. For the w-HAR data set, our baseline and adaptive architectures outperform the state-of-the-art works with a weighted F1 score of 97.55%, and 97.64%, respectively. Evaluation on real hardware shows that our baseline architecture is significantly energy efficient (<inline-formula> <tex-math notation="LaTeX">$422.38\times $ </tex-math></inline-formula> less) and memory-efficient (<inline-formula> <tex-math notation="LaTeX">$14.29\times $ </tex-math></inline-formula> less) compared to the works on the Opportunity data set. For the w-HAR data set, our baseline architecture requires <inline-formula> <tex-math notation="LaTeX">$2.04\times $ </tex-math></inline-formula> less energy and <inline-formula> <tex-math notation="LaTeX">$2.18\times $ </tex-math></inline-formula> less memory compared to the state-of-the-art work. Moreover, experimental results show that our adaptive architecture is 12.32% (Opportunity) and 11.14% (w-HAR) energy efficient than our baseline while providing similar (Opportunity) or better (w-HAR) performance with no significant memory overhead.