Miracle-dz/UniFAR-FTUs huggingface dataset:
On error estimates and adaptivity in elastoplastic solids: Applications to the numerical simulation of strain localization in classical and Cosserat continua

The a posteriori error estimates based on the post-processing approach are introduced for elastoplastic solids. The standard energy norm error estimate established for linear elliptic problems is generalized here to account for the presence of internal variables through the norm associated with the complementary free energy. This is known to represent a natural metric for the class of elastoplastic problems of evolution. In addition, the intrinsic dissipation functional is utilized as a basis for a complementary a posteriori error estimates. A posteriori error estimates and adaptive refinement techniques are applied to the finite element analysis of a strain localization problem. As a model problem, the constitutive equations describing a generalization of standard J2-elastoplasticity within the Cosserat continuum are used to overcome serious limitations exhibited by classical continuum models in the post-instability region. The proposed a posteriori error estimates are appropriately modified to account for the Cosserat continuum model and linked with adaptive techniques in order to simulate strain localization problems. Superior behaviour of the Cosserat continuum model in comparison to the classical continuum model is demonstrated through the finite element simulation of the localization in a plane strain tensile test for an elastopiastic softening material, resulting in convergent solutions with an h-refinement and almost uniform error distribution in all considered error norms.
Quantum dot resonant cavity light emitting diode operating near 1300 nm

The first demonstration of resonant cavity enhancement of optical emission from an InAs-GaAs quantum dot light emitting diode is reported. For emission around 1300 nm efficiency enhancements of greater than 10 for low current injection and greater than 3 for higher current injection for light coupled into a multimode optical fibre are observed.
Gamma-ray observations of Cygnus X-1 and Cygnus X-3 using a coded-aperture telescope

A balloon-borne coded-aperture telescope, measuring gamma-ray photons in the 160 keV to 9.3 MeV range, was used to observe the Cygnus region of the sky on October 1 and 2, 1984. In the 2-9.3-MeV band, evidence is found for a hard spectral component with a mean flux level at the top of the atmosphere of 7.4 + or - 2.5 x 10 to the -7th photons/sq cm per s per keV, inconsistent with the predictions of the inverse Compton models normally used to describe the X-ray emission. Both Cyg X-1 and Cyg X-3 could be observed simultaneously with the telescope. The results are used to establish 1-sigma upper flux limits on the spectral emission from Cyg X-3.
Vortex sound in confined flows

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Customer Disloyalty: An Interactive Introspection

Contemporary marketing thought and practice of customer loyalty seem to like happy endings. A considerable amount of literature has been published on customer loyalty with most of these studies examining how to build loyalty. However, much less is known about how loyalty weakens and terminates. In this chapter, we explore the idea of customer disloyalty. We use this term to refer to the situations when customers stop or reduce their repeat purchase behaviour and/or decrease their psychological commitment to a brand, product, service, or idea. To investigate customer disloyalty, we use interactive introspection which included authors writing reflective journals and interviewing each other about their described experiences. Such approach intentionally diverts from well-trodden path of using quantitative approaches to explore customer loyalty. Our findings, providing insights into the non-monolithic nature of customer disloyalty, demonstrate the insightfulness of using interactive introspection to better understand customer (dis)loyalty from the view of both customers and marketing academics. Besides providing ideas for future research, this chapter facilitates the use of interactive introspection by describing the procedures involved in conducting this under-used but theoretically and practically valuable approach.
Spatial Modeling With Spatially Varying Coefficient Processes

In many applications, the objective is to build regression models to explain a response variable over a region of interest under the assumption that the responses are spatially correlated. In nearly all of this work, the regression coefficients are assumed to be constant over the region. However, in some applications, coefficients are expected to vary at the local or subregional level. Here we focus on the local case. Although parametric modeling of the spatial surface for the coefficient is possible, here we argue that it is more natural and flexible to view the surface as a realization from a spatial process. We show how such modeling can be formalized in the context of Gaussian responses providing attractive interpretation in terms of both random effects and explaining residuals. We also offer extensions to generalized linear models and to spatio-temporal setting. We illustrate both static and dynamic modeling with a dataset that attempts to explain (log) selling price of single-family houses.
Myocardial Infarction; Early Prognostic Instrumental & Laboratory Markers: Single Cross Sectional Analysis

Background: Cardiovascular events remain one of the frequently reported causes of death globally. Aims: Assess the early laboratory and instrumental changes in systemic manner. Objectives: Patients with fresh myocardial infarction that has been occurred in less than 30 days before hospitalization in the rehabilitation hospital. Materials and methods: A single large scale retrospective cohort sectional study for patient with fresh myocardial infarction for the period of 2014-2019, data collected from Mordovia Rehabilitation Hospital. The study involved 154 patients and analyzed 76 parameters for each patient. The t-test, Pearson correlation coefficient, and ROC test has been used. For statistical analysis used Statistica program. Results: The sample included 154 patients with history of fresh myocardial infarction. Of 154. 51(33.11%) female and 103 (66.88 %) males. chronic heart failure (CHF), which is seen in 42 (27.27273 %) patients, 101 (65.58442 %) did not have a CHF, and 11 (7.14286 %) missing data. Diabetes mellitus (DM) has been observed in 28 (18.18182 %), 118 (76.62338 %) without DM, Hypertension has been seen in 118 (76.62338 %), 28 (18.18182%) did not have hypertension, chronic kidney disease (CKD) has been seen in 11 (7.14%) patients, 131 (85.06494 %) did not have CKD, Chronic obstructive pulmonary disease (COPD) existed in 14 (9.09091%), 132 (85.71429 %) did not have COPD. Post myocardial infarction (MI) CHF has been seen in 108 (70.12987%), 42 (27.27273 %) did not have CHF. Post MI arrythmia seen in 50 (32.47%) patients, 99 (64.28571%) did not have arrythmia. Early post MI complication such as aneurysm has been seen in 12 (7.79221%), 138 (89.61039 %) did not have aneurysm, Dressler syndrome; pericarditis has been seen in 4 (2.59740%) patients, pneumonitis seen in 1 (0.64935%) patient, and pleuritis have not been seen. Conclusions: Systemic manifestations include kidney function impairment or development of new kidney disease. Of these changes include increase in the aorta basement diameter, increase in the right ventricle wall thickness, decrease in the level of serum red blood cell, serum potassium level, and serum calcium level, with increase in the serum myoglobulin. Others: Females are more frequently affected with post MI urinary tract infection.
Eclipsing binary distances to the edge of the Local Group

Abstract Eclipsing binaries are powerful tools for determining the fundamental parameters of stars and, therefore, for measuring accurate, independent distances to nearby galaxies. I present distance measurements that are in progress based on early-type eclipsing binary systems in several Local Group galaxies at a range of metallicities, and discuss the strengths of the method, the limitations and possible sources of systematic error. The goal is to establish several local stepping stones along the extragalactic distance ladder, which will help calibrate the Cepheid period–luminosity relation and thus resolve the ongoing controversy about the distance scale, with its ramifications for cosmology and stellar ages.
Simulation of transient and steady-state magnetisation characteristics with hysteresis

A simulation technique is described to generate both transient and steady-state B/H characteristics including hysteresis. The basic linear model is defined by three constant parameters, and, by refining these parameters to be nonlinear functions, the accuracy of representation is then improved. Additional features to improve the modelling technique for recoil characteristics are discussed. Good agreement was obtained between predicted and measured values for a wide range of materials.
In silico discovery of potent inhibitors against monkeypox’s major structural proteins

Abstract Monkeypox virus (MPXV) outbreak in non-endemic countries is a worldwide public health emergency. An enveloped double-stranded DNA virus belongs to the genus Orth poxvirus. A viral zoonotic infection known as monkeypox has been a serious risk to public health, especially in Africa. However, it has recently spread to other continents, so it might soon become a worldwide problem. There is an increased risk of transmission of the virus because there is a lack of effective treatment that cures the disease. To stop the multi-country outbreak from spreading, it is important to discover effective medications urgently. The objective of the current study is to swiftly find new treatments for the monkeypox virus using advanced computational approaches. By investigating five potential MPXV targets (DNA ligase, Palmytilated Extracellular Enveloped Virus (EEV) membrane protein, Scaffold protein D13, Thymidylate Kinase, and Viral core cysteine proteinase), this research was carried out using cutting-edge computational techniques against human monkeypox virus infection. Here we present the accurate 3D structures and their binding cavities of the selected targets with higher confidence using AlphaFold 2 and SiteMap analysis. Molecular docking and MD simulation analysis revealed the top five potential lead compounds with higher binding affinity and stability toward selected targets. Binding free energy calculations and other essential dynamics analysis supports the finding. The selected lead compounds utilizing virtual screening and drug repurposing approach reported in this study are beneficial for medical scientists and experimental biologists in drug development for the treatment of human MPXV. Communicated by Ramaswamy H. Sarma
Gastric cancer‐derived exosomes facilitate pulmonary metastasis by activating ERK‐mediated immunosuppressive macrophage polarization

Gastric cancer (GC) with pulmonary metastasis is one of the deadliest diseases in the world; however, the underlying pathological mechanisms and potential therapeutic targets remain to be elucidated. As exosomes play indispensable roles in the formation of premetastatic niches (PMN) and cancer metastasis. Therefore, investigating the underlying mechanisms of exosome‐mediated pulmonary metastasis of GC may shed new light on identifying novel therapeutic targets for GC treatment. GC‐derived exosomes were isolated from the conditioned medium of mouse forestomach carcinoma (MFC) cell line. The effects of MFC‐derived exosomes on pulmonary macrophage polarization were analyzed by reverse‐ transcription polymerase chain reaction and flow cytometry. Expression of PD‐L1 and other proteins was evaluated by Western blot. Exosomal microRNAs (miRNAs) were analyzed by microarray. GC‐derived exosomes (GC‐exo) accumulated in high numbers in the lungs and were ingested by macrophages. The extracellular‐signal‐regulated kinase (ERK) signaling pathway was activated by GC‐exo, inducing macrophage immunosuppressive‐phenotype differentiation and increased PD‐L1 expression. miRNA‐sequencing identified 130 enriched miRNAs in GC‐exo. Among the enriched miRNAs, miR‐92a‐3p plays a major role in activating ERK signaling via inhibition of PTEN expression. In addition, inhibiting ERK signaling with PD98059 significantly reduced the expression of PD‐L1 in macrophages and, therefore, reversed the immunosuppressive PMN and inhibited the colonization of GC cells in the lungs. This study identified a novel mechanism of GC‐exo mediated PD‐L1 expression in lung macrophages that facilitates lung PMN formation and GC pulmonary metastasis, which also provided a potential therapeutic target for GC with pulmonary metastasis treatment.
Noise-tolerant texture classification and image segmentation

Most previous work on image texture has not studied the effect of noise on texture classification or on textured image segmentation. Classification studies typically use samples of unknown textures that were digitized under the same noise-free conditions as the samples in the training set . In this paper noise-tolerant texture classification is demonstrated. Noise-tolerant texture classification allows the samples of unknown textures to be digitized under different noise and illumination conditions than the samples in the training set. The features extracted from the texture samples are relatively tolerant of noise and illumination gradients resulting in reliable classification. The method is based on edges found in texture samples using a noise-tolerant edge detector similar to the Canny operator [4]. Texture features are average distances separating edges in various orientations [10 11]. Since the edges are reliably extracted from noisy samples texture features based on these edges are noise-tolerant. This method is investigated experimentally and compared with cooccurrence matrix features [6] and cooccurrence matrix features of directionally-smoothed samples. The edge-based features may be used for texture-based image segmentation. This is demonstrated by partitioning an image into areas of uniform texture even when there are illumination and noise gradients over the image.
Passively addressed robotic morphing surface (PARMS) based on machine learning

Reconfigurable morphing surfaces provide new opportunities for advanced human-machine interfaces and bio-inspired robotics. Morphing into arbitrary surfaces on demand requires a device with a sufficiently large number of actuators and an inverse control strategy. Developing compact, efficient control interfaces and algorithms is vital for broader adoption. In this work, we describe a passively addressed robotic morphing surface (PARMS) composed of matrix-arranged ionic actuators. To reduce the complexity of the physical control interface, we introduce passive matrix addressing. Matrix addressing allows the control of N2 independent actuators using only 2N control inputs, which is substantially lower than traditional direct addressing (N2 control inputs). Using machine learning with finite element simulations for training, our control algorithm enables real-time, high-precision forward and inverse control, allowing PARMS to dynamically morph into arbitrary achievable predefined surfaces on demand. These innovations may enable the future implementation of PARMS in wearables, haptics, and augmented reality/virtual reality.
Learning stiff chemical kinetics using extended deep neural operators

We utilize neural operators to learn the solution propagator for the challenging chemical kinetics equation. Specifically, we apply the deep operator network (DeepONet) along with its extensions, such as the autoencoder-based DeepONet and the newly proposed Partition-of-Unity (PoU-) DeepONet to study a range of examples, including the ROBERS problem with three species, the POLLU problem with 25 species, pure kinetics of the syngas skeletal model for $CO/H_2$ burning, which contains 11 species and 21 reactions and finally, a temporally developing planar $CO/H_2$ jet flame (turbulent flame) using the same syngas mechanism. We have demonstrated the advantages of the proposed approach through these numerical examples. Specifically, to train the DeepONet for the syngas model, we solve the skeletal kinetic model for different initial conditions. In the first case, we parametrize the initial conditions based on equivalence ratios and initial temperature values. In the second case, we perform a direct numerical simulation of a two-dimensional temporally developing $CO/H_2$ jet flame. Then, we initialize the kinetic model by the thermochemical states visited by a subset of grid points at different time snapshots. Stiff problems are computationally expensive to solve with traditional stiff solvers. Thus, this work aims to develop a neural operator-based surrogate model to solve stiff chemical kinetics. The operator, once trained offline, can accurately integrate the thermochemical state for arbitrarily large time advancements, leading to significant computational gains compared to stiff integration schemes.
The variance-gamma ratio distribution

Let $X$ and $Y$ be independent variance-gamma random variables with zero location parameter; then the exact probability density function of the ratio $X/Y$ is derived. Some basic distributional properties are also derived, including identification of parameter regimes under which the density is bounded, asymptotic approximations of tail probabilities, and fractional moments; in particular, we see that the mean is undefined. In the case that $X$ and $Y$ are independent symmetric variance-gamma random variables, an exact formula is also given for the cumulative distribution function of the ratio $X/Y$.
On the complexity of computing Kronecker coecients and deciding positivity of Littlewood-Richardson coecients

Littlewood-Richardson coefficients are the multiplicities in the tensor product decomposition of two irreducible representations of the general linear group GLn(C). Kronecker coefficients are the multiplicities in the tensor product decomposition of two irreducible representations of the symmetric group Sn. Both coefficients have a wide variety of interpretations in combinatorics, representation theory, geometry and in the theory of symmetric functions. It is known that the problem of computing Littlewood-Richardson coefficients is hard. More specifically, it is #P-complete. This means that the existence of a polynomial time algorithm for this problem is equivalent to the existence of a polynomial time algorithm for evaluating permanents, which is considered unlikely. Our first result shows that the problem of computing Kronecker coefficients is computationally hard as well. More specifically, we prove that this problem is GapP-complete. Quite surprisingly, as first pointed out by Mulmuley and Sohoni, it is possible to decide the positivity of Littlewood-Richardson coefficients in polynomial time. This follows by combining the facts that Knutson and Tao proved the Saturation Conjecture (1999) and that linear optimization is solvable in polynomial time. In the second part of this work, we design an explicit combinatorial polynomial time algorithm for deciding the positivity of Littlewood-Richardson coefficients. This algorithm is highly adapted to the problem and uses ideas from the theory of optimizing flows in networks. This algorithm also yields a proof of the Saturation Conjecture and a proof of a conjecture by Fulton, which was proved by Knutson, Tao and Woodward (2004). We further give a polynomial-time algorithm for deciding multiplicity freeness, i.e. whether a Littlewood-Richardson coefficient is exactly 1.
Language-Driven Representation Learning for Robotics

Recent work in visual representation learning for robotics demonstrates the viability of learning from large video datasets of humans performing everyday tasks. Leveraging methods such as masked autoencoding and contrastive learning, these representations exhibit strong transfer to policy learning for visuomotor control. But, robot learning encompasses a diverse set of problems beyond control including grasp affordance prediction, language-conditioned imitation learning, and intent scoring for human-robot collaboration, amongst others. First, we demonstrate that existing representations yield inconsistent results across these tasks: masked autoencoding approaches pick up on low-level spatial features at the cost of high-level semantics, while contrastive learning approaches capture the opposite. We then introduce Voltron, a framework for language-driven representation learning from human videos and associated captions. Voltron trades off language-conditioned visual reconstruction to learn low-level visual patterns, and visually-grounded language generation to encode high-level semantics. We also construct a new evaluation suite spanning five distinct robot learning problems $\unicode{x2013}$ a unified platform for holistically evaluating visual representations for robotics. Through comprehensive, controlled experiments across all five problems, we find that Voltron's language-driven representations outperform the prior state-of-the-art, especially on targeted problems requiring higher-level features.
The linear stability and basic reproduction numbers for autonomous FDEs

In this paper, we first prove the stability equivalence between a linear autonomous and cooperative functional differential equation (FDE) and its associated autonomous and cooperative system without time delay. Then we present the theory of basic reproduction number $\mathcal{R}_0$ for general autonomous FDEs. As an illustrative example, we also establish the threshold dynamics for a time-delayed population model of black-legged ticks in terms of $\mathcal{R}_0$.
Active Velocity Estimation using Light Curtains via Self-Supervised Multi-Armed Bandits

To navigate in an environment safely and autonomously, robots must accurately estimate where obstacles are and how they move. Instead of using expensive traditional 3D sensors, we explore the use of a much cheaper, faster, and higher resolution alternative: programmable light curtains. Light curtains are a controllable depth sensor that sense only along a surface that the user selects. We adapt a probabilistic method based on particle filters and occupancy grids to explicitly estimate the position and velocity of 3D points in the scene using partial measurements made by light curtains. The central challenge is to decide where to place the light curtain to accurately perform this task. We propose multiple curtain placement strategies guided by maximizing information gain and verifying predicted object locations. Then, we combine these strategies using an online learning framework. We propose a novel self-supervised reward function that evaluates the accuracy of current velocity estimates using future light curtain placements. We use a multi-armed bandit framework to intelligently switch between placement policies in real time, outperforming fixed policies. We develop a full-stack navigation system that uses position and velocity estimates from light curtains for downstream tasks such as localization, mapping, path-planning, and obstacle avoidance. This work paves the way for controllable light curtains to accurately, efficiently, and purposefully perceive and navigate complex and dynamic environments. Project website: https://siddancha.github. io/projects/active-velocity-estimation/