Miracle-dz/UniFAR-FTUs huggingface dataset:
Long Range 3D with Quadocular Thermal (LWIR) Camera

Long Wave Infrared (LWIR) cameras provide images regardles of the ambient illumination, they tolerate fog and are not blinded by the incoming car headlights. These features make LWIR cameras attractive for autonomous navigation, security and military applications. Thermal images can be used similarly to the visible range ones, including 3D scene reconstruction with two or more such cameras mounted on a rigid frame. There are two additional challenges for this spectral range: lower image resolution and lower contrast of the textures. In this work, we demonstrate quadocular LWIR camera setup, calibration, image capturing and processing that result in long range 3D perception with 0.077 pix disparity error over 90% of the depth map. With low resolution (160 x 120) LWIR sensors we achieved 10% range accuracy at 28 m with 56 degrees horizontal field of view (HFoV) and 150 mm baseline. Scaled to the now-standard 640 x 512 resolution and 200 mm baseline suitable for head-mounted application the result would be 10% accuracy at 130 m.
Optimal perturbations and transition energy thresholds in boundary layer shear flows

Subcritical transition to turbulence in spatially developing boundary layer flows can be triggered efficiently by finite amplitude perturbations. In this work, we employ adjoint-based optimization to identify optimal initial perturbations in the Blasius boundary layer, culminating in the computation of the subcritical transition critical energy threshold and the associated fully localized critical optimum in a spatially extended configuration, the so called minimal seed. By dynamically rescaling the variables with the local boundary layer thickness, we show that the identified edge trajectory approaches the same attracting phase space region as previously reported edge trajectories, and reaches the region more efficiently.
Electronic properties and polaronic dynamics of semi-Dirac system within Ladder approximation

We investigate the electronic properties of the semi-Dirac system and its polaronic dynamics when coupled with a Fermi bath with quadratic dispersion. The electronic anisotropic transport properties and the semiclassical dynamics of the semi-Dirac system are studied, including the density-of-states, conductivity, transport relaxation rate, specific heat, electrical current density, and free energy. The attractive polaron formed as the semi-Dirac impurity dressed with the particle–hole excitations in a two-dimensional system are studied both analytically and numerically. The pair propagator, self-energy, spectral function are being detailly calculated and discussed. The method of medium T-matrix approximation (nonself-consistent), which equivalent to the partially dressed interaction vertex by summing over all ladder diagrams, is applied, and compared with some other methods (for many-body problem), like the leading-order 1/N expansion (GW approximation), Hartree–Fock theory, and the Nozieres–Schmitt–Rink theory. Since we focus on the weak-coupling region, the mean-field approximation is also applicable. The polaron properties is related to the anisotropic effective masses of the semi-Dirac system. That’s in contrast to the polarons formed in the surface of normal Dirac systems which has an isotropic dispersion, since the anisotropic dispersion of the semi-Dirac systems results in anisotropic effective mass and anisotropic charge carrier transport. Besides, the symmetry between electron and hole is also broken since the effective masses of the electron and hole are different, which are affected by the polaronic effect when the semi-Dirac material is deposited on a polar substrate, like hBN. The self-localization, short-range potential, and experimental methods as well as the possible formation of the Bose polaron on the surface of semi-Dirac system are also discussed in the end. Our results are useful also for the investigation of two/three-dimensional bosonic polaron as well as the polarons in other solid state systems, like the magnetic matter or the topological systems.
Learning Similarity Attention

We consider the problem of learning similarity functions. While there has been substantial progress in learning suitable distance metrics, these techniques in general lack decision reasoning, i.e., explaining why the input set of images is similar or dissimilar. In this work, we solve this key problem by proposing the first method to generate generic visual similarity explanations with gradient-based attention. We demonstrate that our technique is agnostic to the specific similarity model type, e.g., we show applicability to Siamese, triplet, and quadruplet models. Furthermore, we make our proposed similarity attention a principled part of the learning process, resulting in a new paradigm for learning similarity functions. We demonstrate that our learning mechanism results in more generalizable, as well as explainable, similarity models. Finally, we demonstrate the generality of our framework by means of experiments on a variety of tasks, including image retrieval, person re-identification, and low-shot semantic segmentation.
Fair Division of Mixed Divisible and Indivisible Goods

We study the problem of fair division when the resources contain both divisible and indivisible goods. Classic fairness notions such as envy-freeness (EF) and envy-freeness up to one good (EF1) cannot be directly applied to the mixed goods setting. In this work, we propose a new fairness notion envy-freeness for mixed goods (EFM), which is a direct generalization of both EF and EF1 to the mixed goods setting. We prove that an EFM allocation always exists for any number of agents. We also propose efficient algorithms to compute an EFM allocation for two agents and for n agents with piecewise linear valuations over the divisible goods. Finally, we relax the envy-free requirement, instead asking for ϵ-envy-freeness for mixed goods (ϵ-EFM), and present an algorithm that finds an ϵ-EFM allocation in time polynomial in the number of agents, the number of indivisible goods, and 1/ϵ.
Learning Probably Approximately Correct Maximin Strategies in Simulation-Based Games with Infinite Strategy Spaces

We tackle the problem of learning equilibria in simulation-based games. In such games, the players' utility functions cannot be described analytically, as they are given through a black-box simulator that can be queried to obtain noisy estimates of the utilities. This is the case in many real-world games in which a complete description of the elements involved is not available upfront, such as complex military settings and online auctions. In these situations, one usually needs to run costly simulation processes to get an accurate estimate of the game outcome. As a result, solving these games begets the challenge of designing learning algorithms that can find (approximate) equilibria with high confidence, using as few simulator queries as possible. Moreover, since running the simulator during the game is unfeasible, the algorithms must first perform a pure exploration learning phase and, then, use the (approximate) equilibrium learned this way to play the game. In this work, we focus on two-player zero-sum games with infinite strategy spaces. Drawing from the best arm identification literature, we design two algorithms with theoretical guarantees to learn maximin strategies in these games. The first one works in the fixed-confidence setting, guaranteeing the desired confidence level while minimizing the number of queries. Instead, the second algorithm fits the fixed-budget setting, maximizing the confidence without exceeding the given maximum number of queries. First, we formally prove {\delta}-PAC theoretical guarantees for our algorithms under some regularity assumptions, which are encoded by letting the utility functions be drawn from a Gaussian process. Then, we experimentally evaluate our techniques on a testbed made of randomly generated games and instances representing simple real-world security settings.
A First Look at References from the Dark to Surface Web World

Tor is one of the most well-known networks that protects the identity of both content providers and their clients against any tracking or tracing on the Internet. So far, most research attention has been focused on investigating the security and privacy concerns of Tor and characterizing the topic or hyperlink structure of its hidden services. However, there is still lack of knowledge about the information leakage attributed to the linking from Tor hidden services to the surface Web. This work addresses this gap by presenting a broad evaluation of the network of referencing from Tor to surface Web and investigates to what extent Tor hidden services are vulnerable against this type of information leakage. The analyses also consider how linking to surface websites can change the overall hyperlink structure of Tor hidden services. They also provide reports regarding the type of information and services provided by Tor domains. Results recover the dark-to-surface network as a single massive connected component where over 90% of Tor hidden services have at least one link to the surface world despite their interest in being isolated from surface Web tracking. We identify that Tor directories have closest proximity to all other Web resources and significantly contribute to both communication and information dissemination through the network which emphasizes on the main application of Tor as information provider to the public. Our study is the product of crawling near 2 million pages from 23,145 onion seed addresses, over a three-month period.
Quantized Compressed Sensing by Rectified Linear Units

This work is concerned with the problem of recovering high-dimensional signals $\mathrm {x}\in \mathbb {R}^{\text {n}}$ which belong to a convex set of low complexity from a small number of quantized measurements. We propose to estimate the signals via a convex program based on rectified linear units (ReLUs) for two different quantization schemes, namely one-bit and uniform multi-bit quantization. Assuming that the linear measurement process can be modelled by a sensing matrix with i.i. d. subgaussian rows, we obtain for both schemes near-optimal uniform reconstruction guarantees by adding well-designed noise to the linear measurements prior to the quantization step. In the one-bit case, we show that the program is robust against adversarial bit corruptions as well as additive noise on the linear measurements. Further, our analysis quantifies precisely how the rate-distortion relationship of the program changes depending on whether we seek reconstruction accuracies above or below the level of additive noise. The proofs rely on recent results by Dirksen and Mendelson on non-Gaussian hyperplane tessellations. Finally, we complement our theoretical analysis with numerical experiments which compare our method to other state-of-the-art methodologies.
Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy Dovepress

php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy 2017:10 473–479 Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy Dovepress
Star–Gas Misalignment in Galaxies. I. The Properties of Galaxies from the Horizon-AGN Simulation and Comparisons to SAMI

Recent integral field spectroscopy observations have found that about 11% of galaxies show star–gas misalignment. The misalignment possibly results from external effects such as gas accretion, interaction with other objects, and other environmental effects, hence providing clues to these effects. We explore the properties of misaligned galaxies using Horizon-AGN, a large-volume cosmological simulation, and compare the results with those of the Sydney-AAO Multi-object integral field spectrograph (SAMI) Galaxy Survey. Horizon-AGN can match the overall misalignment fraction and reproduces the distribution of misalignment angles found by observations surprisingly closely. The misalignment fraction is found to be highly correlated with galaxy morphology both in observations and in the simulation: early-type galaxies are substantially more frequently misaligned than late-type galaxies. The gas fraction is another important factor associated with misalignment in the sense that misalignment increases with decreasing gas fraction. However, there is a significant discrepancy between the SAMI and Horizon-AGN data in the misalignment fraction for the galaxies in dense (cluster) environments. We discuss possible origins of misalignment and disagreement.
Genus Two Quasi--Siegel Modular Forms and Gromov--Witten Theory of Toric Calabi-Yau Threefolds

We first develop theories of differential rings of quasi-Siegel modular and quasi-Siegel Jacobi forms for genus two. Then we apply them to the Eynard-Orantin topological recursion of certain local Calabi-Yau threefolds equipped with branes, whose mirror curves are genus two hyperelliptic curves. By the proof of the Remodeling Conjecture, we prove that the corresponding open- and closed- Gromov-Witten potentials are essentially quasi-Siegel Jacobi and quasi-Siegel modular forms for genus two, respectively.
Wavelet-Based Moment-Matching Techniques for Inertial Sensor Calibration

The task of inertial sensor calibration has required the development of various techniques to take into account the sources of measurement error coming from such devices. The calibration of the stochastic errors of these sensors has been the focus of increasing amount of research in which the method of reference has been the so-called “Allan variance (AV) slope method” which, in addition to not having appropriate statistical properties, requires a subjective input which makes it prone to mistakes. To overcome this, recent research has started proposing “automatic” approaches where the parameters of the probabilistic models underlying the error signals are estimated by matching functions of the AV or wavelet variance with their model-implied counterparts. However, given the increased use of such techniques, there has been no study or clear direction for practitioners on which approach is optimal for the purpose of sensor calibration. This article, for the first time, formally defines the class of estimators based on this technique and puts forward theoretical and applied results that, comparing with estimators in this class, suggest the use of the Generalized method of Wavelet moments (GMWM) as an optimal choice. In addition to analytical proofs, experiment-driven Monte Carlo simulations demonstrated the superior performance of this estimator. Further analysis of the error signal from a gyroscope was also provided to further motivate performing such analyses, as real-world observed error signals may show significant deviation from manufacturer-provided error models.
Adaptive Multi-Scale Detection of Acoustic Events

The goal of acoustic (or sound) events detection (AED or SED) is to predict the temporal position of target events in given audio segments. This task plays a significant role in safety monitoring, acoustic early warning and other scenarios. However, the deficiency of data and diversity of acoustic event sources make the AED task a tough issue, especially for prevalent data-driven methods. In this article, we start from analyzing acoustic events according to their time-frequency domain properties, showing that different acoustic events have different time-frequency scale characteristics. Inspired by the analysis, we propose an adaptive multi-scale detection (AdaMD) method. By taking advantage of hourglass neural network and gated recurrent unit (GRU) module, our AdaMD produces multiple predictions at different temporal and frequency resolutions. An adaptive training algorithm is subsequently adopted to combine multi-scale predictions to enhance the overall capability. Experimental results on Detection and Classification of Acoustic Scenes and Events 2017 (DCASE 2017) Task 2, DCASE 2016 Task 3 and DCASE 2017 Task 3 demonstrate that the AdaMD outperforms published state-of-the-art competitors in terms of the metrics of event error rate (ER) and F1-score. The verification experiment on our collected factory mechanical dataset also proves the noise-resistant capability of the AdaMD, providing the possibility for it to be deployed in the complex environment.
Biological Value of Centaurea damascena: Minireview

The family Asteraceae include large number of Centaurea species which have been applied in folk medicine. One of the family Asteraceae members is the Centaurea damascena which authentically been tested for its antibacterial activity. The aim of the study was to discuss antibacterial activities of essential oil composition and methanolic extract of the same plant aerial part leaves. Thirty-seven components were characterized with 86% of oxygenated terpenes. The composition was dominated by 11.45% Fokienol, 8.8% thymol, 8.21% Alpha Terpineol, 7.24% Chrysanthemumic acid, 7.13% Terpinen-4-ol and 6.59% Borneol with a high degree of polymorphism in the occurrence of these compounds as compared with the different species of centaurea. Free radical scavenging capacity of the C. damascna methanol extract was calculated by DPPH and FRAP test. DPPH radicals were scavenged with an IC50 value of 17.08 µg /ml. Antioxidant capacities obtained by the FRAP was 51.9 and expressed in mg Trolox g-1 dry weight. The total phenolic compounds of the methanol extracts of aerial parts, as estimated by Folin–Ciocalteu reagent method, was about 460 mg GAE/ g. The phenolic contents in the extracts highly correlate with their antioxidant activity, (R2 = 0.976) confirming that the antioxidant activity of this plant extracts is considerably phenolic contents-dependent.
Fine-Grained Neural Architecture Search

We present an elegant framework of fine-grained neural architecture search (FGNAS), which allows to employ multiple heterogeneous operations within a single layer and can even generate compositional feature maps using several different base operations. FGNAS runs efficiently in spite of significantly large search space compared to other methods because it trains networks end-to-end by a stochastic gradient descent method. Moreover, the proposed framework allows to optimize the network under predefined resource constraints in terms of number of parameters, FLOPs and latency. FGNAS has been applied to two crucial applications in resource demanding computer vision tasks---large-scale image classification and image super-resolution---and demonstrates the state-of-the-art performance through flexible operation search and channel pruning.
Spin networks and cosmic strings in 3+1 dimensions

Spin networks, the quantum states of discrete geometry in loop quantum gravity, are directed graphs whose links are labeled by irreducible representations of SU(2), or spins. Cosmic strings are 1-dimensional topological defects carrying distributional curvature in an otherwise flat spacetime. In this paper we prove that the classical phase space of spin networks coupled to cosmic strings may obtained as a straightforward discretization of general relativity in 3+1 spacetime dimensions. We decompose the continuous spatial geometry into 3-dimensional cells, which are dual to a spin network graph in a unique and well-defined way. Assuming that the geometry may only be probed by holonomies (or Wilson loops) located on the spin network, we truncate the geometry such that the cells become flat and the curvature is concentrated at the edges of the cells, which we then interpret as a network of cosmic strings. The discrete phase space thus describes a spin network coupled to cosmic strings. This work proves that the relation between gravity and spin networks exists not only at the quantum level, but already at the classical level. Two appendices provide detailed derivations of the Ashtekar formulation of gravity as a Yang–Mills theory and the distributional geometry of cosmic strings in this formulation.
Parametric Graph-Based Separable Transforms For Video Coding

In many video coding systems, separable transforms (such as two-dimensional DCT-2) have been used to code block residual signals obtained after prediction. This paper proposes a parametric approach to build graph-based separable transforms (GBSTs) for video coding. Specifically, a GBST is derived from a pair of line graphs, whose weights are determined based on two non-negative parameters. As certain choices of those parameters correspond to the discrete sine and cosine transform types used in recent video coding standards (including DCT-2, DST-7 and DCT-8), this paper further optimizes these graph parameters to better capture residual block statistics and improve video coding efficiency. The proposed GBSTs are tested on the Versatile Video Coding (VVC) reference software, and the experimental results show that about 0.4% average coding gain is achieved over the existing set of separable transforms constructed based on DCT-2, DST-7 and DCT-8 in VVC.
IDEALEM: Statistical Similarity Based Data Reduction

Many applications such as scientific simulation, sensing, and power grid monitoring tend to generate massive amounts of data, which should be compressed first prior to storage and transmission. These data, mostly comprised of floating-point values, are known to be difficult to compress using lossless compression. A few compression methods based on lossy compression have been proposed to compress this seemingly incompressible data. Unfortunately, they are all designed to minimize the Euclidean distance between the original data and the decompressed data, which fundamentally limits compression performance. We recently proposed a new class of lossy compression based on statistical similarity, called IDEALEM, which was also provided as a software package. IDEALEM has demonstrated its performance by reducing data volume much more than state-of-the-art compression methods while preserving unique patterns of data. IDEALEM can operate in two different modes depending on the stationarity of input data. This paper presents compression performance analyses of these two modes, and investigates the difference between two transform techniques targeted for non-stationary data. This paper also discusses the data reconstruction quality of IDEALEM using spectral analysis and shows that important frequency components in application domain are well preserved. We expand the capability of IDEALEM by adding a new min/max check that facilitates preserving significant patterns lasting only for a brief duration which were previously hard to capture. This min/max check also accelerates the encoding process significantly. Experiments show IDEALEM preserves significant patterns in the original data with faster encoding time.