Miracle-dz/UniFAR-FTUs huggingface dataset:
Auxeticity of cubic materials under pressure

The global maximum and global minimum Poisson's ratio (PR) surfaces, regions of different auxetic behaviour and the domains of the different extreme directions of cubic materials are shown and discussed in terms of the elastic moduli ratios, $X = G/K,Y = G/W$, where K is the bulk modulus and $G,W$ are shear moduli. A straightforward way is given to classify any cubic material as being auxetic, nonauxetic and partially auxetic, as well as calculating its extreme PR values. A decomposition of the XY plane with the elastic constant ratio ${\lambda} = C_{12} /C_{44} $ is introduced to facilitate the interpretation of cubic materials under hydrostatic pressure, $P> 0$, and isotropic tension, $P< 0$, which are two qualitatively different situations. Using this representation it is demonstrated that cubic materials with ${\lambda} > 0$ can be auxetic only under ‘negative’ pressure ($P< 0$). It is shown that the influence of pressure on the auxetic behaviour is different for the positive and negative ${\lambda} $ cases. In particular, a cubic material with $0> {\lambda} > {-} 1$ may be auxetic at negative as well as positive pressure. The work demonstrates the crucial role of P in obtaining desired auxetic behaviour. Microscopic mechanisms which tune the cubic system to targeted regions in the XY plane are investigated with a fcc crystal of particles interacting via the pairwise spherically symmetric potential, ${\phi} (r)$. It is found that all fcc static models in which the range of interaction is only between nearest neighbour particles are placed on a universal curve in the XY plane. It is shown that auxetic behaviours can be achieved with simple static ${\phi} (r)$ analytic forms. The influence of the next neighbour interactions on the universal curve is also considered. In order to assess the role of thermal fluctuations, molecular dynamic simulations for two different ${\phi} (r)$ (the Lennard–Jones (LJ) and the tethered particle potentials) were performed. At low temperatures the studied systems are well represented by the universal curve and on increasing temperature a systematic departure from it is observed.
Detecting Adversarial Examples on Deep Neural Networks With Mutual Information Neural Estimation

Despite achieving exceptional performance, deep neural networks (DNNs) suffer from the harassment caused by adversarial examples, which are produced by corrupting clean examples with tiny perturbations. Many powerful defense methods have been presented such as training data augmentation and input reconstruction which, however, usually rely on the prior knowledge of the targeted models or attacks. In this paper, we propose a novel approach for detecting adversarial images, which can protect any pre-trained DNN classifiers and resist an endless stream of new attacks. Specifically, we first adopt a dual autoencoder to project images to a latent space. The dual autoencoder uses the self-supervised learning to ensure that small modifications to samples do not significantly alter their latent representations. Next, the mutual information neural estimation is utilized to enhance the discrimination of the latent representations. We then leverage the prior distribution matching to regularize the latent representations. To easily compare the representations of examples in the two spaces, and not rely on the prior knowledge of the targeted model, a simple fully connected neural network is used to embed the learned representations into an eigenspace, which is consistent with the output eigenspace of the targeted model. Through the distribution similarity of an input example in the two eigenspaces, we can judge whether the input example is adversarial or not. Extensive experiments on MNIST, CIFAR-10, and ImageNet show that the proposed method has superior defense performance and transferability than state-of-the-arts.
Alzheimer's disease pathology concomitant with memory impairment in late‐onset multiple system atrophy

Multiple system atrophy (MSA) is characterised pathologically by the accumulation of abnormal α -synuclein in oligodendrocytes (glial cytoplasmic inclusions) and neurons (neuronal cytoplasmic inclusions [NCIs]) in affected regions. Patients with MSA can develop a wider array of symptoms than thought previously — including cognitive impairment — in addition to parkinsonism, cerebellar ataxia and auto-nomic dysfunction. We investigated 148 patients with pathologically proven MSA, who were referred to Queen Square Brain Bank (QSBB) between 2002 and 2018. Thirty of these patients (20%) had manifested cognitive impairment (frontal subcortical dysfunction and/or memory impairment) during life [1]. Among those patients, the degree of memory impairment had been mild to moderate in 18. In addition, pathological examinations had revealed more NCIs in the hippocampus in MSA patients with memory impairment than in those without [1]. We further investigated the mechanism of memory impairment in MSA using a human α -synuclein-inducible MSA mouse model and human MSA cases with or without memory impairment [2]. We found that α -synuclein oligomers in the hippocampus caused synaptic dysfunction, leading to the development of memory impairment in MSA [1, 2]
DeepEIT: Deep Image Prior Enabled Electrical Impedance Tomography

Neural networks (NNs) have been widely applied in tomographic imaging through data-driven training and image processing. One of the main challenges in using NNs in real medical imaging is the requirement of massive amounts of training data – which are not always available in clinical practice. In this article, we demonstrate that, on the contrary, one can directly execute image reconstruction using NNs without training data. The key idea is to bring in the recently introduced deep image prior (DIP) and merge it with electrical impedance tomography (EIT) reconstruction. DIP provides a novel approach to the regularization of EIT reconstruction problems by compelling the recovered image to be synthesized from a given NN architecture. Then, by relying on the NN's built-in back-propagation and the finite element solver, the conductivity distribution is optimized. Quantitative results based on simulation and experimental data show that the proposed method is an effective unsupervised approach capable of outperforming state-of-the-art alternatives.
Minimizing Certain Convex Functions

Let us consider the following iterative procedure: we arbitrarily choose a point x(? ) = (x1(0), . , Xn(0)) in X. In general, given an integer t > 0 and a point x(" in X, we choose as xi (4+1) a point which minimizes +(-x, x2(t), * , over X1, then we choose as x2(^+1) a point which minimizes 4(x1(i+1), X2 , X3 , , * X Xn (t))) over X2, etc. Clearly, each step of the iteration either lowers or leaves unchanged the value of +(x) and thus +(x~t)) is nonincreasing. In view of the convexity of +(x), it is natural to conjecture that the value c(x(? ) will actually converge to Minm x (x). It can be easily seen that this conjecture is not generally true. Consider, indeed, the following case: X =X2 = [0, 1] (the closed interval from 0 to 1 on the real axis) and
Detection of moving targets from a moving ground platform

Semi-autonomous operation of intelligent vehicles may require that such platforms maintain a basic situational awareness with respect to people, other vehicles and their intent. These vehicles should be able to operate safely among people and other vehicles, and be able to perceive threats and respond accordingly. A key requirement is the ability to detect people and vehicles from a moving platform. We have developed one such algorithm using video cameras mounted on the vehicle. Our person detection algorithms model the shape and appearance of the person instead of modeling the background. This algorithm uses histogram of oriented gradients (HOG), which model shape and appearance using image edge histograms. These HOG descriptors are computed on an exhaustive set of image windows, which are then classified as person/non-person using a support vector machine classifier. The image windows are computed using camera calibration, which provides approximate size of people with respect to their location in the imagery. The algorithm is flexible and has been trained for different domains such as urban, rural and wooded scenes. We have designed a sensor platform that can be mounted on a moving vehicle to collect video data of pedestrians. Using manually annotated ground-truth data we have evaluated the person detection algorithm in terms of true positive and false positive rates. This paper provides a detailed overview of the algorithm, describes the experiments conducted and reports on algorithmic performance.
Design‐Based Inference in a Mixture Model for Ordinal Variables for a Two Stage Stratified Design

In this paper we present methods for inference on data selected by a complex sampling design for a class of statistical models for the analysis of ordinal variables. Specifically, assuming that the sampling scheme is not ignorable, we derive for the class of cub models (Combination of discrete Uniform and shifted Binomial distributions) variance estimates for a complex two stage stratified sample. Both Taylor linearization and repeated replication variance estimators are presented. We also provide design‐based test diagnostics and goodness‐of‐fit measures. We illustrate by means of real data analysis the differences between survey‐weighted and unweighted point estimates and inferences for cub model parameters.
Measurement of effective electron mass in biaxial tensile strained silicon on insulator

We present measurements of the effective electron mass in biaxial tensile strained silicon on insulator (SSOI) material with 1.2 GPa stress and in unstrained SOI. Hall-bar metal oxide semiconductor field effect transistors on 60 nm SSOI and SOI were fabricated and Shubnikov–de Haas oscillations in the temperature range of T=0.4–4 K for magnetic fields of B=0–10 T were measured. The effective electron mass in SSOI and SOI samples was determined as mt=(0.20±0.01)m0. This result is in excellent agreement with first-principles calculations of the effective electron mass in the presence of strain.
A constraint preconditioner for solving symmetric positive definite systems and application to the helmholtz equations and poisson equations

Abstract In this paper, first, by using the diagonally compensated reduction and incomplete Cholesky factorization methods, we construct a constraint preconditioner for solving symmetric positive definite linear systems and then we apply the preconditioner to solve the Helmholtz equations and Poisson equations. Second, according to theoretical analysis, we prove that the preconditioned iteration method is convergent. Third, in numerical experiments, we plot the distribution of the spectrum of the preconditioned matrix M−1A and give the solution time and number of iterations comparing to the results of [5, 19].
3D Effects on Minority Carrier Recombination in Homogeneous Silicon Wafers

Calculation of three-dimensional recombination effects in homogeneous silicon wafers is performed. The current continuity equation for minority carriers with surface recombination boundary conditions is solved in cylindrical coordinates. The two most important three-dimensional recombination effects are discussed. Lateral diffusion of minority carriers gives rise to a characteristic decay inversely proportional to time. Shell surface recombination should be taken into account when measuring within the minority carrier diffusion length from the wafer edge. The discrepancy between the one-dimensional and the three-dimensional models is discussed.
FUNCTIONAL OUTCOME OF MEDIAL GASTROCNEMIUS MUSCLE PEDICLE FLAP FOR SOFT TISSUE RECONSTRUCTION AROUND THE KNEE AND PROXIMAL LEG

Background: Soft tissue coverage around the knee and a proximal third leg can be done using a medial Gastrocnemius Muscle Pedicle Flap (GMPF). There are variable results in the literature about its outcome and such study is lacking in our country. The present study was undertaken to evaluate the outcome of the medial GMPF to reconstruct the defect around the knee and proximal 3rd leg. Methods: It was a retrospective study of 21 cases of medial GMPF reconstruction to cover the defect around the knee and proximal third leg over 2 years in tertiary care hospital. The outcome variables measured were flap and graft take up, fracture union, knee range of motion, and complications. Data was analyzed using SPSS software version 20.0 with descriptive statistics for categorical and discrete variables. Results: The mean age of the patient was 37.19 years (range 21-65). Out of a total of 21 cases, 20 (95.2%) were traumatic wound defects, whereas 1 case was complications of necrotizing fasciitis. Flap and graft take-up was good in all the cases. The mean time for union of tibia fracture was 24.57 weeks (range 16-36). The mean knee range of motion was 125 degrees (range 90-145). Two cases had a superficial infection and one had a deep infection. Conclusions: Medial GMPF gives satisfactory results in soft tissue reconstruction about knee and proximal 3rd leg and it can be performed well by trained orthopedic surgeons also with consistently good results.
fcc-hcp phase transformation in Co nanoparticles induced by swift heavy-ion irradiation

We demonstrate a face-centered cubic (fcc) to hexagonally close-packed (hcp) phase transformation in spherical Co nanoparticles achieved via swift heavy-ion irradiation. Co nanoparticles of mean diameter 13.2 nm and fcc phase were first formed in amorphous SiO{sub 2} by ion implantation and thermal annealing and then irradiated at room temperature with 9-185 MeV Au ions. The crystallographic phase was identified with x-ray absorption spectroscopy and electron diffraction and quantified, as functions of the irradiation energy and fluence, with the former. The transformation was complete at low fluence prior to any change in nanoparticle shape or size and was governed by electronic stopping. A direct-impact mechanism was identified with the transformation interaction cross-section correlated with that of a molten ion track in amorphous SiO{sub 2}. We suggest the shear stress resulting from the rapid thermal expansion about an ion track in amorphous SiO{sub 2} was sufficient to initiate the fcc-to-hcp phase transformation in the Co nanoparticles.
Metabolic Imaging and Molecular Biology Reveal the Interplay between Lipid Metabolism and DHA-Induced Modulation of Redox Homeostasis in RPE Cells

Diabetes-induced oxidative stress induces the development of vascular complications, which are significant causes of morbidity and mortality in diabetic patients. Among these, diabetic retinopathy (DR) is often caused by functional changes in the blood–retinal barrier (BRB) due to harmful oxidative stress events in lipids, proteins, and DNA. Docosahexaenoic acid (DHA) has a potential therapeutic effect against hyperglycemia-induced oxidative damage and apoptotic pathways in the main constituents of BRB, retinal pigment epithelium cells (ARPE-19). Effective antioxidant response elicited by DHA is driven by the activation of the Nrf2/Nqo1 signaling cascade, which leads to the formation of NADH, a reductive agent found in the cytoplasm. Nrf2 also induces the expression of genes encoding enzymes involved in lipid metabolism. This study, therefore, aims at investigating the modulation of lipid metabolism induced by high-glucose (HG) on ARPE-19 cells through the integration of metabolic imaging and molecular biology to provide a comprehensive functional and molecular characterization of the mechanisms activated in the disease, as well the therapeutic role of DHA. This study shows that HG augments RPE metabolic processes by enhancing lipid metabolism, from fatty acid uptake and turnover to lipid biosynthesis and β-oxidation. DHA exerts its beneficial effect by ameliorating lipid metabolism and reducing the increased ROS production under HG conditions. This investigation may provide novel insight for formulating novel treatments for DR by targeting lipid metabolism pathways.
Synthesis, In Silico Study, Antibacterial and Antifungal Activities of N-phenylbenzamides

Antibiotic and antifungal resistance problems have been prevalent in recent decades. One of the efforts to solve the problems is to develop new medicines with more potent antibacterial and antifungal activity. N-phenylbenzamides have the potential to be developed as antibacterial and antifungal medicine. This study aimed to synthesize N-phenylbenzamides and evaluate their in silico and in vitro antibacterial and antifungal activities. The in silico studies conducted absorption, distribution, metabolism, excretion and toxicity (ADMET) predictions along with molecular docking studies. ADMET predictions used pkCSM software online, while the docking studies used MVD software (Molegro ® Virtual Docker version 5.5) on Aminoglycosid-2 ″-phosphotransferase-IIa (APH2 ″-IIa) enzyme with protein data bank (PDB) ID code 3HAV as antibacterial and aspartic proteinases enzyme (Saps) with PDB ID code 2QZX as an antifungal. In vitro, antibacterial and antifungal tests were carried out using the zone of inhibition (ZOI) method. The five N-phenylbenzamides (3a–e) were successfully synthesized with a high yield. Based on in silico and in vitro studies, compounds 3a–e have antibacterial and antifungal activities, where they can inhibit the growth of Gram-positive bacteria (Staphylococcus aureus), Gram-negative (Escherichia coli), and Candida albicans. Therefore, compounds 3a–e can be developed as a topical antibacterial and antifungal agent.
Rule adaptation of fuzzy sliding mode controller design

A fuzzy sliding mode controller (FSMC) synthesized through rule adaptation is proposed. There are two sets of control rule bases. The first rule base is utilized to approach the equivalent control of sliding mode control (SMC) according to an adaptive scheme. Another rule base is used to attenuate the hitting control of SMC in the sense of an heuristic. In particular, only one tuning factor is characterized to adapt the control rules, instead of several tuning parameters used by the conventional adaptive approach. The results show that the rule adaptive FSMC system is stable in the sense of Lyapunov under a common Lyapunov function. We apply this FSMC to a nonlinear inverted pendulum system for demonstrating the availability of the proposed approach.
High-speed metal-insulator transition in vanadium dioxide films induced by an electrical pulsed voltage over nano-gap electrodes

We report the fabrication of VO2 -based two terminal devices with 125-nm gaps between the two electrodes, using a simple, cost-effective method employing optical lithography and shadow evaporation. Current-voltage characteristics of the obtained devices show a main abrupt metal-insulator transition (MIT) in the VO2 ﬁlm with voltage threshold values of several volts, followed by secondary MIT steps due to the nanostructured morphology of the layer. By applying to the two-terminal device a pulsed voltage over the MIT threshold, the measured switching time was as low as 4.5 ns and its value does not signiﬁcantly change with device temperature, supporting the evidence of an electronically driven
Real-Time Reconstruction of 3D Videos From Single-Photon LiDaR Data in the Presence of Obscurants

Single-photon methods are emerging as a key approach to 3D imaging. This paper introduces a two step statistical based approach for real-time image reconstruction applicable to a transmission medium with extreme light scattering conditions. The first step is an optional target detection method to select informative pixels which have photons reflected from the target, hence allowing data compression. The second is a reconstruction algorithm that exploits data statistics and multiscale information to deliver clean depth and reflectivity images together with associated uncertainty maps. Both methods involve independent operations that are implemented in parallel on graphics processing units (GPUs), which enables real-time data processing of moving scenes at more than 50 depth frames per second for an image of $128 \times 128$ pixels. Comparisons with state-of-the-art algorithms on simulated and real underwater data demonstrate the benefit of the proposed framework for target detection, and for fast and robust depth estimation at multiple frames per second.
Three-dimensional reconstruction from optical flow using temporal integration

Image flow the apparent motion of brightness patterns on the image plane can provide important visual information such as distance shape surface orientation and boundaries. It can be determined by either feature tracking or spatio-temporal analysis. The optical flow thus determined can be used to reconstruct the 3-D scene by determining the depth from camera of every point in the scene. However the optical flow determined by either of the methods mentioned above will be noisy. As a result the depth information obtained from optical flow can not be successfully used in practical applications such as image segmentation 3-D reconsiruction path planning etc. By using temporal integration we can increase the accuracy of both the optical flow and the depth determined from optical flow. In this work we describe an incremental integration scheme called the running average method to temporally integrate the image flow. We integrate the depth from camera obtained using optical flow determined from gradient based methods and show that the results of temporal integration are much more useful in practical applications than the results from local edge operators. Finally we consider an image segmentation example and show the advantages of temporal integration. 1.
Exploiting quasiperiodic order in the design of optical devices

In this work we present a prospective study on the potential capabilities of optical devices based on Fibonacci dielectric multilayers. We perform a detailed analytical comparison of the linear optical response of periodic versus quasiperiodic multilayers. Based on this study we will suggest the use of hybrid-order devices, composed of both periodic and quasiperiodic subunits to design microcavities of practical interest, and we provide some illustrative examples. From our study we conclude that the inclusion of quasiperiodically ordered subunits substantially widens the possibilities of engineering modular optical structures.
Performance Analysis of Vilnius Chaos Oscillator-Based Digital Data Transmission Systems for IoT

The current work is devoted to chaos oscillator employment in digital communication systems for IoT applications. The paper presents a comparative performance analysis of two different chaos data transmission systems: frequency-modulated chaos shift keying (FM-CSK) and quadrature chaos phase-shift keying (QCPSK), and a comparison to their non-chaotic counterparts: frequency-shift keying (FSK) and quadrature amplitude modulation (QAM). For both chaotic communication systems, the Vilnius oscillator and substitution method of chaotic synchronization are chosen due to simple circuitry implementation and low power consumption properties. The performance of the systems in the fading channel with additive white Gaussian noise (AWGN) is evaluated. Also, the systems’ performance in the case phase noise is investigated, and the benefits of chaotic waveforms employment for data transmission are demonstrated.
Comparison of Historical Satellite-Based Estimates of Solar Radiation Resources with Recent Rotating Shadowband Radiometer Measurements: Preprint

The availability of rotating shadow band radiometer measurement data at several new stations provides an opportunity to compare historical satellite-based estimates of solar resources with measurements. We compare mean monthly daily total (MMDT) solar radiation data from eight years of NSRDB and 22 years of NASA hourly global horizontal and direct beam solar estimates with measured data from three stations, collected after the end of the available resource estimates.