Miracle-dz/UniFAR-FTUs huggingface dataset:
FC-DCNN: A densely connected neural network for stereo estimation

We propose a novel lightweight network for stereo estimation. Our network consists of a fully-convolutional densely connected neural network (FC-DCNN) that computes matching costs between rectified image pairs. Our FC-DCNN method learns expressive features and performs some simple but effective postprocessing steps. The densely connected layer structure connects the output of each layer to the input of each subsequent layer. This network structure and the fact that we do not use any fully-connected layers or 3D convolutions leads to a very lightweight network. The output of this network is used in order to calculate matching costs and create a cost-volume. Instead of using time and memory-inefficient cost-aggregation methods such as semiglobal matching or conditional random fields in order to improve the result, we rely on filtering techniques, namely median filter and guided filter. By computing a left-right consistency check we get rid of inconsistent values. Afterwards we use a watershed foreground-background segmentation on the disparity image with removed inconsistencies. This mask is then used to refine the final prediction. We show that our method works well for both challenging indoor and outdoor scenes by evaluating it on the Middlebury, KITTI and ETH3D benchmarks respectively. Our full framework is available at https://github.com/thedodo/FC-DCNN
Effects of magnetic perturbations and radiation on the runaway avalanche

The electron runaway phenomenon in plasmas depends sensitively on the momentum- space dynamics. However, efficient simulation of the global evolution of systems involving runaway electrons typically requires a reduced fluid description. This is needed, for example, in the design of essential runaway mitigation methods for tokamaks. In this paper, we present a method to include the effect of momentum-dependent spatial transport in the runaway avalanche growth rate. We quantify the reduction of the growth rate in the presence of electron diffusion in stochastic magnetic fields and show that the spatial transport can raise the effective critical electric field. Using a perturbative approach, we derive a set of equations that allows treatment of the effect of spatial transport on runaway dynamics in the presence of radial variation in plasma parameters. This is then used to demonstrate the effect of spatial transport in current quench simulations for ITER-like plasmas with massive material injection. We find that in scenarios with sufficiently slow current quench, owing to moderate impurity and deuterium injection, the presence of magnetic perturbations reduces the final runaway current considerably. Perturbations localised at the edge are not effective in suppressing the runaways, unless the runaway generation is off-axis, in which case they may lead to formation of strong current sheets at the interface of the confined and perturbed regions.
Fundamental Physical Constants at Low and High Redshifts

Spacetime variations of physical constants can be associated with the existence of Higgs-like scalar field(s) that couple non-universally to the baryonic matter. Recent results of astronomical spectral measurements of the fractional changes in the electron-to-proton mass ratio, mu = m_e/m_p, at low (z ~ 0) and high (z ~ 6.5) redshifts are discussed. It is shown that the distribution of the most accurate estimates of Delta mu/mu = (mu_obs - mu_lab)/mu_lab ranging between z = 0 and z ~ 1100 can be approximated by a power low Delta mu/mu = k_mu (1+z)^p, with k_mu = (1.7 +/- 0.3)x10^-8 and p = 1.99 +/- 0.03, implying a dynamical nature of the scalar field(s).
Neural Mixture Distributional Regression

We present neural mixture distributional regression (NMDR), a holistic framework to estimate complex finite mixtures of distributional regressions defined by flexible additive predictors. Our framework is able to handle a large number of mixtures of potentially different distributions in high-dimensional settings, allows for efficient and scalable optimization and can be applied to recent concepts that combine structured regression models with deep neural networks. While many existing approaches for mixture models address challenges in optimization of such and provide results for convergence under specific model assumptions, our approach is assumption-free and instead makes use of optimizers well-established in deep learning. Through extensive numerical experiments and a high-dimensional deep learning application we provide evidence that the proposed approach is competitive to existing approaches and works well in more complex scenarios.
Fission dynamics of compound nuclei: Pairing versus fluctuations

Energy dependence of fission observables is a key issue for wide nuclear applications. We studied real-time fission dynamics from low-energy to high excitations in the compound nucleus $^{240}$Pu with the time-dependent Hartree-Fock+BCS approach. It is shown that the evolution time of the later phase of fission towards scission is considerably lengthened at finite temperature. As the role of dynamical pairing is vanishing at high excitations, the random transition between single-particle levels around the Fermi surface to mimic thermal fluctuations is indispensable to drive fission. The obtained fission yields and total kinetic energies with fluctuations can be divided into two asymmetric scission channels, namely S1 and S2, which explain well experimental results, and give microscopic support to the Brosa model. With increasing fluctuations, S2 channel takes over S1 channel and the spreading fission observables are obtained.
Walks Obeying Two-Step Rules on the Square Lattice: Full, Half and Quarter Planes

We consider walks on the edges of the square lattice $\mathbb Z^2$ which obey two-step rules, which allow (or forbid) steps in a given direction to be followed by steps in another direction. We classify these rules according to a number of criteria, and show how these properties affect their generating functions, asymptotic enumerations and limiting shapes, on the full lattice as well as the upper half plane. For walks in the quarter plane, we only make a few tentative first steps. We propose candidates for the group of a model, analogous to the group of a regular short-step quarter plane model, and investigate which models have finite versus infinite groups. We demonstrate that the orbit sum method used to solve a number of the original models can be made to work for some models here, producing a D-finite solution. We also generate short series for all models and guess differential or algebraic equations where possible. In doing so, we find that there are possibilities here which do not occur for the regular short-step models, including cases with algebraic or D-finite generating functions but infinite groups, as well as models with non-D-finite generating functions but finite groups.
Computing L-polynomials of Picard curves from Cartier-Manin matrices

We study the sequence of zeta functions $Z(C_p,T)$ of a generic Picard curve $C:y^3=f(x)$ defined over $\mathbb{Q}$ at primes $p$ of good reduction for $C$. By determining the density of the set of primes of ordinary reduction, we prove that, for all but a density zero subset of primes, the Zeta function $Z(C_p,T)$ is uniquely determined by the Cartier--Manin matrix $A_p$ of $C$ modulo $p$, the irreducibility of $f$ modulo $p$ (or the failure thereof), and the exponent of the Jacobian of $C$ modulo $p$; we also show that for primes $\equiv 1 \pmod{3}$ the matrix $A_p$ suffices and that for primes $\equiv 2 \pmod{3}$ the genericity assumption on $C$ is unnecessary. By combining this with recent work of Sutherland, we obtain a practical probabilistic algorithm of Las Vegas type that computes $Z(C_p,T)$ for almost all primes $p \le N$ using $N\log(N)^{3+o(1)}$ expected bit operations. This is the first practical result of this type for curves of genus greater than 2.
An Alternative Thresholding Rule for Compressed Sensing

Compressed Sensing algorithms often make use of the hard thresholding operator to pass from dense vectors to their best s-sparse approximations. However, the output of the hard thresholding operator does not depend on any information from a particular problem instance. We propose an alternative thresholding rule, Look Ahead Thresholding, that does. In this paper we offer both theoretical and experimental justification for the use of this new thresholding rule throughout compressed sensing.
A Short Review on Graphene-Based Filters: Perspectives and Challenges

Graphene is an interesting debated topic between scientists because of its unique properties such as tunable conductivity. Graphene conductivity can be varied by either electrostatic or magnetostatic gating or via chemical doping, which leads to design of various photonic and electronic devices. Among various graphene-based structures, plasmonic graphene filters have attracted the attention of many researchers because of their various applications in THz frequencies. There are four main types of graphene filters proposed in the literature, which are: 1- Circular filters, 2- Planar filters, 3- Periodic filters, and 4- Other types of filters (which are not categorized in any of the other three groups). This paper aims to study and investigate various types of graphene-based filters published in the literature.
HeartBees: Visualizing Crowd Affects

Affective sharing within groups strengthens coordination and empathy, leads to better health outcomes, and increases productivity and performance. Existing tools for affective sharing face one main challenge: creating a representation of collective emotional states that is relatable and universally accessible. To overcome this challenge, we propose HeartBees, a bio-feedback system for visualizing collective emotional states, which maps a multi-dimensional emotion model into a metaphorical visualization of flocks of birds. Grounded on Affective Computing literature and physiological sensing, we mapped physiological indicators that could be obtained from wearable devices into a multi-dimensional emotion model, which, in turn, our HeartBees can make use of. We evaluated our nature-inspired interactive system with 353 online participants, whose responses showed good consensus in the way they subjectively perceived the visualizations. Last, we discuss practical applications of HeartBees.
A Review of Cyber-Ranges and Test-Beds: Current and Future Trends

Cyber situational awareness has been proven to be of value in forming a comprehensive understanding of threats and vulnerabilities within organisations, as the degree of exposure is governed by the prevailing levels of cyber-hygiene and established processes. A more accurate assessment of the security provision informs on the most vulnerable environments that necessitate more diligent management. The rapid proliferation in the automation of cyber-attacks is reducing the gap between information and operational technologies and the need to review the current levels of robustness against new sophisticated cyber-attacks, trends, technologies and mitigation countermeasures has become pressing. A deeper characterisation is also the basis with which to predict future vulnerabilities in turn guiding the most appropriate deployment technologies. Thus, refreshing established practices and the scope of the training to support the decision making of users and operators. The foundation of the training provision is the use of Cyber-Ranges (CRs) and Test-Beds (TBs), platforms/tools that help inculcate a deeper understanding of the evolution of an attack and the methodology to deploy the most impactful countermeasures to arrest breaches. In this paper, an evaluation of documented CRs and TBs platforms is evaluated. CRs and TBs are segmented by type, technology, threat scenarios, applications and the scope of attainable training. To enrich the analysis of documented CRs and TBs research and cap the study, a taxonomy is developed to provide a broader comprehension of the future of CRs and TBs. The taxonomy elaborates on the CRs/TBs dimensions, as well as, highlighting a diminishing differentiation between application areas.
Variational Approximation of Factor Stochastic Volatility Models

Estimation and prediction in high dimensional multivariate factor stochastic volatility models is an important and active research area because they allow a parsimonious representation of multivariate stochastic volatility. Such factor models are usually estimated by Markov chain Monte Carlo or particle methods, which are usually slow for high dimensional or long time series because of the large number of parameters and latent states involved. Our article proposes fast batch and sequential variational methods to approximate the posterior distribution of the states and parameters in a factor stochastic volatility model. It also obtains one-step and multi-step ahead variational forecast distributions. The method is applied to simulated and real datasets and shown to produce good approximate inference and prediction compared to the latest particle Markov chain Monte Carlo approaches, but is much faster.
Development of cooling system of solid state target for irradiation under proton beam of C18 cyclotron

In recent years, the possibility of direct production of the 99mTc isotope (bypassing the parent 99Mo stage) for medical purposes using nuclear reactions on charged particle beams has been actively discussed around the world [1,2]. At A.I. Alikhanyan National Science Laboratory (Yerevan Physics Institute), an activity is underway to develop a technology for producing the 99mTc isotope by irradiating a molybdenum target of 100Mo, pressed into a titanium base, with a proton beam of a C18 cyclotron [3,4,5]. One of the limitations of this technique is the utilization of heat released in the target as a result of proton energy loss [6,7]. The task of this work was the improvement of the thermal regime of a standard target for the C18 cyclotron due to a series of parallel grooves. Heat transfer experiments with prototypes of targets were carried out on a specially made test bench providing water cooling of its back side. A special Plexiglas thermal block was made to study the thermal processes in the target. The measurement results show that the above mentioned technique of processing the base of the target leads to a significant increase in the rate of cooling of the target, which will allow to irradiate at significantly higher proton beam intensities, which in its turn will increase the irradiation efficiency and reduce the cost of the final product.
Heights and arithmetic dynamics over finitely generated fields.

We develop a theory of vector-valued heights and intersections defined relative to finitely generated extensions K/k. These generalize both number field and geometric heights. When k is Q or F_p, or when a non-isotriviality condition holds, we obtain Northcott-type results. We then prove a version of the Hodge Index Theorem for vector-valued intersections, and use it to prove a rigidity theorem for polarized dynamical systems over any field.
Spatial-Slepian Transform on the Sphere

We present spatial-Slepian transform (SST) for the joint spatial-Slepian domain representation of signals on the sphere to support localized signal analysis. We employ well-optimally concentrated Slepian functions, which are obtained as a solution of the Slepian spatial-spectral concentration problem of finding bandlimited and spatially optimally concentrated functions on the sphere, to formulate the proposed transform. Due to optimal energy concentration of Slepian functions in the spatial domain, the proposed spatial-Slepian transform allows us to probe spatially localized content of the signal. Furthermore, we present an inverse transform to recover the signal from its spatial-Slepian coefficients, formulate an algorithm for fast computation of SST, and carry out computational complexity analysis. We compute the spatial variance of spatial-Slepian coefficients and conduct experiments to show that spatial-Slepian coefficients have better spatial localization than scale-discretized wavelet coefficients. We present the formulation of SST for zonal Slepian functions, which are spatially optimally concentrated in the axisymmetric polar cap region, and provide an illustration using a bandlimited Earth topography map. To demonstrate the utility of the proposed transform, we carry out localized variation analysis, in which we employ SST to detect hidden localized variations in the signal. We illustrate, through a toy example, that spatial-Slepian transform yields a much better estimate of the underlying region of hidden localized variations than scale-discretized wavelet transform.
Globular clusters as tracers of the dark matter content of dwarfs in galaxy clusters

Globular clusters (GCs) are often used to estimate the dark matter content of galaxies, especially dwarfs, where other kinematic tracers are lacking. These estimates typically assume spherical symmetry and dynamical equilibrium, assumptions that may not hold for the sparse GC population of dwarfs in galaxy clusters. We use a catalog of GCs tagged onto the Illustris simulation to study the accuracy of GC-based mass estimates. We focus on galaxies in the stellar mass range 10$^{8} - 10^{11.8}$ M$_{\odot}$ identified in $9$ simulated Virgo-like clusters. Our results indicate that mass estimates are, on average, quite accurate in systems with GC numbers $N_{\rm GC} \geq 10$ and where the uncertainty of individual GC line-of-sight velocities is smaller than the inferred velocity dispersion, $\sigma_{\rm GC}$. In cases where $N_{\rm GC} \leq 10$, however, biases may result depending on how $\sigma_{\rm GC}$ is computed. We provide calibrations that may help alleviate these biases in methods widely used in the literature. As an application, we find a number of dwarfs with $M_{*} \sim 10^{8.5}\, M_{\odot}$ (comparable to the ultradiffuse galaxy DF2, notable for the low $\sigma_{GC}$ of its $10$ GCs) with $\sigma_{\rm GC} \sim 7$ - $15\; \rm km \rm s^{-1}$. These DF2 analogs correspond to relatively massive systems at their infall time ($M_{200} \sim 1$ - $3 \times 10^{11}$ $M_{\odot}$) which have retained only $3$-$17$ GCs and have been stripped of more than 95$\%$ of their dark matter. Our results suggest that extreme tidal mass loss in otherwise normal dwarf galaxies may be a possible formation channel for ultradiffuse objects like DF2.
The RR Lyrae projected density distribution from the Galactic centre to the halo

The projected density distribution of type ab RR Lyrae (RRab) stars was characterised from the innermost regions of the Milky Way to the halo, with the aim of placing constraints on the Galaxy’s evolution. The compiled sample (NRRab = 64 850) stems from fundamental mode RR Lyrae variables identified by the VVV, OGLE, and Gaia surveys. The distribution is well fitted by three power laws over three radial intervals. In the innermost region (R < 2.2°) the distribution follows ΣRRab[1] ∝ R−0.94 ± 0.051, while in the external region the distribution adheres to ΣRRab[2] ∝ R−1.50 ± 0.019 for 2.2° < R < 8.0° and ΣRRab[3] ∝ R−2.43 ± 0.043 for 8.0° < R < 30.0°. Conversely, the cumulative distribution of red clump (RC) giants exhibits a more concentrated distribution in the mean, but in the central R < 2.2° the RRab population is more peaked, whereas globular clusters (GCs) follow a density power law (ΣGCs ∝ R−1.59 ± 0.060 for R < 30.0°) similar to that of RRab stars, especially when considering a more metal-poor subsample ([Fe/H] < −1.1 dex). The main conclusion emerging from the analysis is that the RRab distribution favours the star cluster infall and merger scenario for creating an important fraction (> 18%) of the central Galactic region. The radii containing half of the populations (half populations radii) are RH RRab = 6.8° (0.99 kpc), RH RC = 4.2° (0.61 kpc), and RH GCs = 11.9° (1.75 kpc) for the RRab stars, RC giants, and GCs, respectively. Finally, merely ∼1% of the stars have been actually discovered in the innermost region (R < 35 pc) out of the expected (based on our considerations) total number of RRab therein: N ∼ 1562. That deficit will be substantially ameliorated with future space missions like the Nancy Grace Roman Space Telescope (formerly WFIRST).
Re-evaluating Evaluation in Text Summarization

Automated evaluation metrics as a stand-in for manual evaluation are an essential part of the development of text-generation tasks such as text summarization. However, while the field has progressed, our standard metrics have not -- for nearly 20 years ROUGE has been the standard evaluation in most summarization papers. In this paper, we make an attempt to re-evaluate the evaluation method for text summarization: assessing the reliability of automatic metrics using top-scoring system outputs, both abstractive and extractive, on recently popular datasets for both system-level and summary-level evaluation settings. We find that conclusions about evaluation metrics on older datasets do not necessarily hold on modern datasets and systems.
Development of Open Informal Dataset Affecting Autonomous Driving

This document is a document that has written procedures and methods for collecting objects and unstructured dynamic data on the road for the development of object recognition technology for self-driving cars, and outlines the methods of collecting data, annotation data, object classifier criteria, and data processing methods. On-road object and unstructured dynamic data were collected in various environments, such as weather, time and traffic conditions, and additional reception calls for police and safety personnel were collected. Finally, 100,000 images of various objects existing on pedestrians and roads, 200,000 images of police and traffic safety personnel, 5,000 images of police and traffic safety personnel, and data sets consisting of 5,000 image data were collected and built.
Privacy-Preserving Object Detection & Localization Using Distributed Machine Learning: A Case Study of Infant Eyeblink Conditioning

Distributed machine learning is becoming a popular model-training method due to privacy, computational scalability, and bandwidth capacities. In this work, we explore scalable distributed-training versions of two algorithms commonly used in object detection. A novel distributed training algorithm using Mean Weight Matrix Aggregation (MWMA) is proposed for Linear Support Vector Machine (L-SVM) object detection based in Histogram of Orientated Gradients (HOG). In addition, a novel Weighted Bin Aggregation (WBA) algorithm is proposed for distributed training of Ensemble of Regression Trees (ERT) landmark localization. Both algorithms do not restrict the location of model aggregation and allow custom architectures for model distribution. For this work, a Pool-Based Local Training and Aggregation (PBLTA) architecture for both algorithms is explored. The application of both algorithms in the medical field is examined using a paradigm from the fields of psychology and neuroscience - eyeblink conditioning with infants - where models need to be trained on facial images while protecting participant privacy. Using distributed learning, models can be trained without sending image data to other nodes. The custom software has been made available for public use on GitHub: this https URL. Results show that the aggregation of models for the HOG algorithm using MWMA not only preserves the accuracy of the model but also allows for distributed learning with an accuracy increase of 0.9% compared with traditional learning. Furthermore, WBA allows for ERT model aggregation with an accuracy increase of 8% when compared to single-node models.