Miracle-dz/UniFAR-FTUs huggingface dataset:
A General Framework for Uncertainty Estimation in Deep Learning

Neural networks predictions are unreliable when the input sample is out of the training distribution or corrupted by noise. Being able to detect such failures automatically is fundamental to integrate deep learning algorithms into robotics. Current approaches for uncertainty estimation of neural networks require changes to the network and optimization process, typically ignore prior knowledge about the data, and tend to make over-simplifying assumptions which underestimate uncertainty. To address these limitations, we propose a novel framework for uncertainty estimation. Based on Bayesian belief networks and Monte-Carlo sampling, our framework not only fully models the different sources of prediction uncertainty, but also incorporates prior data information, e.g. sensor noise. We show theoretically that this gives us the ability to capture uncertainty better than existing methods. In addition, our framework has several desirable properties: (i) it is agnostic to the network architecture and task; (ii) it does not require changes in the optimization process; (iii) it can be applied to already trained architectures. We thoroughly validate the proposed framework through extensive experiments on both computer vision and control tasks, where we outperform previous methods by up to 23% in accuracy. The video available at https://youtu.be/X7n-bRS5vSM shows qualitative results of our experiments. The project's code is available at: https://tinyurl.com/s3nygw7.
Memory effects on two-dimensional overdamped Brownian dynamics

We consider the effects of memory on the stationary behavior of a two-dimensional Langevin dynamics in a confining potential. The system is treated in an overdamped approximation and the degrees of freedom are under the influence of distinct kinds of stochastic forces, described by Gaussian white and colored noises, as well as different effective temperatures. The joint distribution function is calculated exactly by means of time-averaging techniques, and the long-term behavior is analyzed. We determine, by using the stochastic thermodynamics formalism, the influence of noise temporal correlations on the energetics in the steady-state regime. As a result, we find that non-Markovian effects lead to a decaying heat exchange with spring force parameter, which is in contrast to the usual linear dependence obtained when only Gaussian white noises are presented in overdamped treatments. Also, the memory time-scale affects in a nontrivial fashion the entropy production rate associated with stationary states.
Radiative corrections to neutron and nuclear beta decays revisited

The universal radiative corrections common to neutron and super-allowed nuclear beta decays (also known as ``inner'' corrections) are revisited in light of a recent dispersion relation study that found $+2.467(22)\%$, i.e.~about $2.4\sigma$ larger than the previous evaluation. For comparison, we consider several alternative computational methods. All employ an updated perturbative QCD four-loop Bjorken sum rule (BjSR) defined QCD coupling supplemented with a nucleon form factor based Born amplitude to estimate axial-vector induced hadronic contributions. In addition, we now include hadronic contributions from low $Q^2$ loop effects based on duality considerations and vector meson resonance interpolators. Our primary result, $2.426(32)\%$ corresponds to an average of a Light Front Holomorphic QCD approach and a three resonance interpolator fit. It reduces the dispersion relation discrepancy to approximately $1.1\sigma$ and thereby provides a consistency check. Consequences of our new radiative correction estimate, along with that of the dispersion relation result, for CKM unitarity are discussed. The neutron lifetime-$g_A$ connection is updated and shown to suggest a shorter neutron lifetime $< 879$ s. We also find an improved bound on exotic, non-Standard Model, neutron decays or oscillations of the type conjectured as solutions to the neutron lifetime problem, $\text{BR}(n\to \text{exotics}) < 0.16 \%$.
Cosmological test of gravity using weak lensing voids

Modifications to general relativity often incorporate screening mechanisms in order to remain compatible with existing tests of gravity. The screening is less efficient in underdense regions, which suggests that cosmic voids can be a useful cosmological probe for constraining modified gravity models. In particular, weak lensing by voids has been proposed as a promising test of such theories. Usually, voids are identified from galaxy distributions, making them biased tracers of the underlying matter field. An alternative approach is to study voids identified in weak lensing maps – weak lensing voids – which have been shown to better correspond to true underdense regions. In this paper, we study the ability of weak lensing voids to detect the signatures of modified gravity. Focusing on the void abundance and weak lensing profiles, we find that both statistics are sensitive probes of gravity. These are quantified in terms of the signal-to-noise ratios (SNR) with which an LSST-like survey will be able to distinguish between different gravity models. We find that the tangential shear profiles of weak lensing voids are considerably better than galaxy voids at this, though voids have somewhat lower SNR than weak lensing peaks. The abundances of voids and peaks have, respectively, $\rm {SNR} = 50$ and 70 for a popular class of modified gravity in an LSST-like survey.
Attractors of nonlinear Hamiltonian partial differential equations

This is a survey of the theory of attractors of nonlinear Hamiltonian partial differential equations since its appearance in 1990. Included are results on global attraction to stationary states, to solitons, and to stationary orbits, together with results on adiabatic effective dynamics of solitons and their asymptotic stability, and also results on numerical simulation. The results obtained are generalized in the formulation of a new general conjecture on attractors of -invariant nonlinear Hamiltonian partial differential equations. This conjecture suggests a novel dynamical interpretation of basic quantum phenomena: Bohr transitions between quantum stationary states, de Broglie’s wave-particle duality, and Born’s probabilistic interpretation. Bibliography: 212 titles.
MorphIC: A 65-nm 738k-Synapse/mm$^2$ Quad-Core Binary-Weight Digital Neuromorphic Processor With Stochastic Spike-Driven Online Learning

Recent trends in the field of neural network accelerators investigate weight quantization as a means to increase the resource- and power-efficiency of hardware devices. As full on-chip weight storage is necessary to avoid the high energy cost of off-chip memory accesses, memory reduction requirements for weight storage pushed toward the use of binary weights, which were demonstrated to have a limited accuracy reduction on many applications when quantization-aware training techniques are used. In parallel, spiking neural network (SNN) architectures are explored to further reduce power when processing sparse event-based data streams, while on-chip spike-based online learning appears as a key feature for applications constrained in power and resources during the training phase. However, designing power- and area-efficient SNNs still requires the development of specific techniques in order to leverage on-chip online learning on binary weights without compromising the synapse density. In this paper, we demonstrate MorphIC, a quad-core binary-weight digital neuromorphic processor embedding a stochastic version of the spike-driven synaptic plasticity (S-SDSP) learning rule and a hierarchical routing fabric for large-scale chip interconnection. The MorphIC SNN processor embeds a total of 2k leaky integrate-and-fire (LIF) neurons and more than two million plastic synapses for an active silicon area of 2.86 mm$^2$ in 65-nm CMOS, achieving a high density of 738k synapses/mm$^2$. MorphIC demonstrates an order-of-magnitude improvement in the area-accuracy tradeoff on the MNIST classification task compared to previously-proposed SNNs, while having no penalty in the energy-accuracy tradeoff.
A Short Note on the Kinetics-700 Human Action Dataset

We describe an extension of the DeepMind Kinetics human action dataset from 600 classes to 700 classes, where for each class there are at least 600 video clips from different YouTube videos. This paper details the changes introduced for this new release of the dataset, and includes a comprehensive set of statistics as well as baseline results using the I3D neural network architecture.
SOFIA/HAWC+ Traces the Magnetic Fields in NGC 1068

We report the first detection of galactic spiral structure by means of thermal emission from magnetically aligned dust grains. Our 89 μm polarimetric imaging of NGC 1068 with the High-resolution Airborne Wideband Camera/Polarimeter (HAWC+) on NASAs Stratospheric Observatory for Infrared Astronomy (SOFIA) also sheds light on magnetic field structure in the vicinity of the galaxy's inner-bar and active galactic nucleus (AGN). We find correlations between the 89 μm magnetic field vectors and other tracers of spiral arms, and a symmetric polarization pattern as a function of the azimuthal angle arising from the projection and inclination of the disk field component in the plane of the sky. The observations can be fit with a logarithmic spiral model with pitch angle of and a disk inclination of 48° ± 2°. We infer that the bulk of the interstellar medium from which the polarized dust emission originates is threaded by a magnetic field that closely follows the spiral arms. Inside the central starburst disk (<1.6 kpc), the degree of polarization is found to be lower than for far-infrared sources in the Milky Way, and has minima at the locations of most intense star formation near the outer ends of the inner-bar. Inside the starburst ring, the field direction deviates from the model, becoming more radial along the leading edges of the inner-bar. The polarized flux and dust temperature peak ∼3″–6″ NE of the AGN at the location of a bow shock between the AGN outflow and the surrounding interstellar medium, but the AGN itself is weakly polarized (<1%) at both 53 and 89 μm.
Quantifying Replicability and Consistency in Systematic Reviews

Abstract Systematic reviews and meta-analyses are important tools for synthesizing evidence from multiple studies. They serve to increase power and improve precision, in the same way that large studies can do, but also to establish the consistency of effects and replicability of results across studies. In this work we propose statistical tools to quantify replicability of effect signs (or directions) and their consistency. We suggest that these tools accompany the fixed-effect or random-effects meta-analysis, and we show that they convey important information for the assessment of the intervention under investigation. We motivate and demonstrate our approach and its implications by examples from systematic reviews from the Cochrane Library. Our tools make no assumptions on the distribution of the true effect sizes, so their inferential guarantees continue to hold even if the assumptions of the fixed-effect or random-effects models do not hold. We also develop a version of this tool under the fixed-effect assumption for cases where it is crucial and justified.
Integrating the Data Augmentation Scheme with Various Classifiers for Acoustic Scene Modeling

This technical report describes the IOA team's submission for TASK1A of DCASE2019 challenge. Our acoustic scene classification (ASC) system adopts a data augmentation scheme employing generative adversary networks. Two major classifiers, 1D deep convolutional neural network integrated with scalogram features and 2D fully convolutional neural network integrated with Mel filter bank features, are deployed in the scheme. Other approaches, such as adversary city adaptation, temporal module based on discrete cosine transform and hybrid architectures, have been developed for further fusion. The results of our experiments indicates that the final fusion systems A-D could achieve an accuracy higher than 85% on the officially provided fold 1 evaluation dataset.
Influence of the low energies resonances on the $n^{11}B$ capture reaction rate

Within the framework of the modified potential cluster model (MPCM) with a classification of orbital states according to Young schemes (diagrams), the possibility of describing experimental data for total cross sections of radiative n11B capture on the ground state (GS) of 12B nucleus at energies from 10 meV (1 meV = 10-3 eV) to 7 MeV was considered. It was shown that, taking into account only the E1-transition from the S-state of n11B scattering to the ground state of the 12B nucleus in the n11B channel, it is quite possible to explain the magnitude of the known experimental cross sections at energies from 25.3 meV to 70 keV. Further, on the basis of the total cross sections from 10 meV to 7 MeV, but excluding resonances above 5 MeV, the reaction rate is calculated in the temperature range from 0.01 to 10.0 T9. It is shown that the inclusion of low-lying resonant states makes a significant contribution to the reaction rate, start-ing already with temperatures of 0.2-0.3 T9.
Isolated Singularities of Yang–Mills–Higgs Fields on Surfaces

We study isolated singularities of 2D Yang–Mills–Higgs (YMH) fields defined on a fiber bundle, where the fiber space is a compact Riemannian manifold and the structure group is a compact connected Lie group. In general, the singularity cannot be removed due to possibly non-vanishing limit holonomy around the singular points. We establish a sharp asymptotic decay estimate of the YMH field near a singular point, where the decay rate is precisely determined by the limit holonomy. Our result can be viewed as a generalization of the classical removable singularity theorem of 2D harmonic maps.
Support for Communication-Based Work Processes in Virtual Work

Distributed work teams are increasingly being implemented to enhance organizational flexibility and competitiveness in today's complex and rapidly changing work environment. Though an increasing number of employees work cooperatively with individuals in a variety of different locations and types of virtual work environments, there is little investigation of the performance of communication-based work processes and their effect on team performance in these distributed environments. The objective of this research is to investigate the various communication-enabled work processes necessary in different distributed work teams, and the ways in which different types of communication modes can be used most effectively to support these processes. Our study takes into consideration individuals' multiple and often overlapping memberships in different team structures. In this paper we report initial findings from the research, which was conducted through interviews in two sales divisions of service units of two major companies in the information technology industry.
An interactive demonstration of electromagnetic wave propagation using time-domain finite differences

The finite difference time-domain (FDTD) method is one of the most widely used computational methods in electromagnetics. Using FDTF, Maxwell's equations are solved directly in the time domain via finite differences and time stepping. the basic approach is relatively easy to understand and is an alternative to the more usual frequency-domain approaches. In order to take advantage of this, an interactive personal computer program based on FDTD has been developed. The program directly solves Maxwell's equation via finite differences. The solution is for one dimension, corresponding to normal incidence propagation through a planar stratified medium. The program displays an electromagnetic pulse as it propagates through the medium. Since Maxwell's equations are solved directly, the reflected and transmitted pulse amplitudes demonstrate how the reflection and transmission coefficients determine reflected and transmitted wave amplitudes. Since lossy material layers can be included, frequency dispersion can be demonstrated. >
A Bird's Eye View of Nonlinear System Identification

This text aims at providing a bird's eye view of system identification with special attention to nonlinear systems. The driving force is to give a feeling for the philosophical problems facing those that build mathematical models from data. Special attention will be given to grey-box approaches in nonlinear system identification. In this text, grey-box methods use auxiliary information such as the system steady-state data, possible symmetries, some bifurcations and the presence of hysteresis. The text ends with a sample of applications. No attempt is made to be thorough nor to survey such an extensive and mature field as system identification. In most parts references will be provided for a more detailed study.
Utility-Aware and Privacy-Preserving Mobile Query Services

Location-based queries enable fundamental services for mobile users. While the benefits of location-based services (LBS) are numerous, exposure of mobile users’ locations to untrusted LBS providers may lead to privacy concerns. This article proposes <sc>StarCloak</sc>, a utility-aware and attack-resilient location anonymization service for privacy-preserving LBS usage. <sc>StarCloak</sc> combines several desirable properties. First, unlike conventional approaches which are indifferent to underlying road network structure, <sc>StarCloak</sc> uses the concept of <italic>star</italic>s and proposes <italic>cloaking graphs</italic> for effective location cloaking on road networks. Second, <sc>StarCloak</sc> supports user-specified <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="gursoy-ieq1-3170007.gif"/></alternatives></inline-formula>-user anonymity and <inline-formula><tex-math notation="LaTeX">$l$</tex-math><alternatives><mml:math><mml:mi>l</mml:mi></mml:math><inline-graphic xlink:href="gursoy-ieq2-3170007.gif"/></alternatives></inline-formula>-segment indistinguishability, for enabling personalized privacy protection and for serving users with varying privacy preferences. Third, <sc>StarCloak</sc> achieves strong attack-resilience against replay and query injection attacks through randomized star selection and pruning. Finally, to enable efficient query processing with high throughput and low bandwidth overhead, <sc>StarCloak</sc> makes cost-aware star selection decisions by considering query evaluation and network communication costs. We evaluate <sc>StarCloak</sc> on two datasets using real-world road networks, under various privacy and utility constraints. Results show that <sc>StarCloak</sc> achieves improved query success rate and throughput, reduced anonymization time and network usage, and higher attack-resilience in comparison to <sc>XStar</sc>, its most relevant competitor.
Agent Technologies Designed to Facilitate Interactive Knowledge Construction

During the last decade, interdisciplinary researchers have developed technologies with animated pedagogical agents that interact with the student in language and other communication channels (such as facial expressions and gestures). These pedagogical agents model good learning strategies and coach the students in actively constructing knowledge during learning. This article describes computer technologies that have been developed during the last decade with tutors that attempt to facilitate deep comprehension (e.g., causal explanations, plans, logical justifications), reasoning in natural language, and inquiry (i.e., question asking, question answering, hypothesis testing). These tutors target high school and college students who learn about topics in science and technology. The primary example is AutoTutor, a system on the Internet that helps students compose answers to deep-reasoning questions and solutions to problems by holding a conversation. AutoTutor's dialogue moves include feedback (positive, neutral, and negative), pumps for more information (“Tell me more.”), hints, prompts to fill in missing words, summaries, corrections of student misconceptions, and answers to student questions. Other learning technologies with agents include the Human Use Regulatory Affairs Advisor (HURAA); Source, Evidence, Explanation, and Knowledge (SEEK) Web Tutor; Interactive Strategy Trainer for Active Reading and Thinking (iSTART); Instruction with Deep-level Reasoning questions In Vicarious Environments (iDRIVE); and Acquiring Research Investigative and Evaluative Skills (ARIES). These systems have been tested on their effectiveness in facilitating knowledge construction. They also have uncovered insights on the prospects of designing agents to effectively communicate in language and discourse.
Variation in the Concentration of Ellagitannins and cis- and trans-ß-Methyl-γ-Octalactone Extracted From Oak Wood (Quercus robur L., Quercus petraea Liebl.) Under Model Wine Cask Conditions

In this study we examine the chemical composition in ellagitannins and oak lactones of 12% ethanol solutions placed in model casks for 200 days, in contact with disks of unheated oak wood. The correlations observed between the wood anatomy and the concentration of extractives are not able to explain the enormous variability found among the samples. The results suggest that the effect of wood anatomy on the extraction of tannins and lactones is less important than the intrinsic differences in the levels of these extractives found in the same wood tissue of different trees and between the two species Quercus robur and Quercus petraea . Species is suggested as a better indicator than wood ring width ("grain") of the chemical cask effects which may affect wine and spirit quality.
Learning through remote practical experiments over the internet: a case study from teacher education

Abstract The article focuses on a cross-national Internet module employing a remote device for learning about control and intended to promote the professional development of a group of student teachers from England and the Netherlands. The practical activity was one component of an integrated conferencing package that sought to encourage shared pedagogical discussions of the teaching of control technology in schools. Microanalysis of the activities highlighted a number of difficulties affecting the execution of the tasks. An instrument of analysis based on the non-neutrality of technology is described which offered a particular lens with which to interpret participant actions and the extent to which learning took place. The article addresses ways in which remote access transformed those practical experiments and affected the learning experiences.