Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial robustness in discontinuous spaces via alternating sampling & descent

Several works have shown that deep learning models are vulnerable to adversarial attacks where seemingly simple label-preserving changes to the input image lead to incorrect predictions. To combat this, gradient based adversarial training is generally employed as a standard defense mechanism. However, in cases where the loss landscape is discontinuous with respect to a given perturbation set, first order methods get stuck in local optima, and fail to defend against threat. This is often a problem for many physically realizable perturbation sets such as 2D affine transformations and 3D scene parameters. To work in such settings, we introduce a new optimization framework that alternates between global zeroth order sampling and local gradient updates to compute strong adversaries that can be used to harden the model against attack. Further, we design a powerful optimization algorithm using this framework, called Alternating Evolutionary Sampling and Descent (ASD), which combines an evolutionary search strategy (viz. covariance matrix adaptation) with gradient descent. We consider two settings with discontinuous/discrete and non-convex loss landscapes to evaluate ASD: a) 3D scene parameters and b) 2D patch attacks, and find that it achieves state-of-the-art results on adversarial robustness.
Monotone Advection on the Sphere: An Eulerian Versus Semi-Lagrangian Approach

Abstract The purpose of this paper is twofold. First, a formalism is presented that extends the conceptual framework identified by Ritchie as the “semi-Lagrangian method without interpolation. ” While his words for this concept refer to a particular class of semi-Lagrangian approximations, the idea is actually much more general. The formalism may be used to convert any advection algorithm into the semi-Lagrangian format, and it makes most algorithms sufficient for the integration of flows characterized by large Courant numbers. The formalism is presented in an arbitrary curvilinear system of coordinates. Second, exploiting the generality of the theoretical considerations, the formalism is implemented in solving a practical problem of scalar advection in spherical geometry. Rather than elaborating on Ritchie's semi-Lagrangian techniques employing centered-in-time differencing, the focus is on the alternative of forward-in-time, dissipative finite-difference schemes. This class of schemes offers attractive c.
The forbidden oxygen lines in comets

High spatial resolution observations of comet Bradfield (1979X) with the IUE spectrograph have made it possible to tentatively identify a feature at 2972 A as the 1S-3P forbidden transition of oxygen atoms. It is shown here that the most likely source of this emission is the photodissociation of water molecules, mainly by solar Lyman-alpha photons, but that a significant contribution of CO2 cannot be excluded.
A Remark on the Guessing Game

The Guessing Game on numbers between zero and N and with a non negative parameter is considered in a 1-shot setting with k levels of thinking. The model proposed is based on two assumptions: 1. ) Players consider intervals rather than numbers, parametrized by a non negative parameter called the confidence parameter; 2. ) Each player readjusts her guess successively during the sequence of iterations according to her recent belief. Under mild conditions, in this model the expected Winning Number is found to be different form zero and the "classical" result 17 is obtained for a quite wide interval of confidence, after 4 levels of thinking only. Further, if the confidence parameter has a skew distribution on [0,1], then we also obtain a skew distribution for the guessed number. This matched to the real data on the Guessing Game.
Simulation of Incompressible Flows in Two-Sided Lid-Driven Square Cavities. Part I - FDM

The present paper computes the flow in a two-sided lid-driven square cavity by the Lattice Boltzmann Method (LBM). For some aspect ratios there exists a multiplicity of steady solutions, but the square cavity problem gives only a single steady solution for both the parallel and antiparallel motion of the walls. It is found that for parallel motion of the walls, there appears a pair of counter-rotating secondary vortices of equal size near the centre of a wall. Because of symmetry, this pair of counter-rotating vortices has similar shapes and their detailed study as to how they grow with increasing Reynolds number has not yet been made by lattice Boltzmann Method. Such a study is attempted in this paper through the LBM, as the results of the problem have the potential of being used for testing various solution methods for incompressible viscous flows. The results for the antiparallel motion of the walls are also presented in some detail. To lend credibility to the LBM results they are further compared with those obtained from a finite difference method (FDM).
Combined Admittance Control With Type II Singularity Evasion for Parallel Robots Using Dynamic Movement Primitives

This article addresses a new way of generating compliant trajectories for control using movement primitives to allow physical human–robot interaction where parallel robots (PRs) are involved. PRs are suitable for tasks requiring precision and performance because of their robust behavior. However, two fundamental issues must be resolved to ensure safe operation: first, the force exerted on the human must be controlled and limited, and second, Type II singularities should be avoided to keep complete control of the robot. We offer a unified solution under the dynamic movement primitives (DMP) framework to tackle both tasks simultaneously. DMPs are used to get an abstract representation for movement generation and are involved in broad areas, such as imitation learning and movement recognition. For force control, we design an admittance controller intrinsically defined within the DMP structure, and subsequently, the Type II singularity evasion layer is added to the system. Both the admittance controller and the evader exploit the dynamic behavior of the DMP and its properties related to invariance and temporal coupling, and the whole system is deployed in a real PR meant for knee rehabilitation. The results show the capability of the system to perform safe rehabilitation exercises.
Role of Protein Kinase A Activation in the Immune System with an Emphasis on Lipopolysaccharide-Responsive and Beige-like Anchor Protein in B Cells

Cyclic AMP-dependent protein kinase A (PKA) is a ubiquitous enzymatic complex that is involved in a broad spectrum of intracellular receptor signaling. The activity of PKA depends on A-kinase anchoring proteins (AKAPs) that attach to PKAs close to their substrates to control signaling. Although the relevance of PKA-AKAP signaling in the immune system is evident in T cells, its relevance in B and other immune cells remains relatively unclear. In the last decade, lipopolysaccharide-responsive and beige-like anchor protein (LRBA) has emerged as an AKAP that is ubiquitously expressed in B and T cells, specifically after activation. A deficiency of LRBA leads to immune dysregulation and immunodeficiency. The cellular mechanisms regulated by LRBA have not yet been investigated. Therefore, this review summarizes the functions of PKA in immunity and provides the most recent information regarding LRBA deficiency to deepen our understanding of immune regulation and immunological diseases.
The Fourth Law of Thermodynamics: The Law of Maximum Entropy Production (LMEP)

More than 20 years ago, Swenson (1988) proposed and elaborated the Law of Maximum Entropy Production (LMEP) as the missing piece of physical or universal law that would account for the ubiquitous and opportunistic transformation from disordered, or less ordered, to more highly ordered states. Given Boltzmann's (1974) interpretation, the Second Law of Thermodynamics has generally been interpreted as a “law of disorder. ” Schrödinger (1945) and Bertalanffy (1952) had shown, however, that the Second Law, viewed from the classical perspective of Clausius (1865) and Thomson (1852), was not anathema to order. Ordered flow, including life, was permissible as long as it produced enough entropy to compensate for its own internal entropy reduction. The central problem remained, however: If the spontaneous production of order was “infinitely improbable,” as Boltzmann had surmised, then why were ordered systems such a fundamental and characteristic property of the visible world? LMEP provided the answer: Order production is inexorable because order produces entropy faster than disorder. In Swenson (1989d), LMEP was given expression as a precise law that could be demonstrated in falsifiable, experimental, physical terms. In Swenson and Turvey (1991), LMEP was tied explicitly to the progressive emergence of living things with their perception–action capabilities.
Multicomponent Training in Progressive Phases Improves Functional Capacity, Physical Capacity, Quality of Life, and Exercise Motivation in Community-Dwelling Older Adults: A Randomized Clinical Trial

Objective: To evaluate the effect of a multicomponent progressive training program (MPTP) on functionality, quality of life (QoL) and motivation to exercise (EM) in a group of older adults (OA) of a community. Methods: A total of 55 participants of 69.42 ± 6.01 years of age were randomized into two groups; experimental (EG:35) and control (CG:20), and subjected to 27 weeks of MPTP. Functionality (pre/post-intervention) was assessed using the Short Physical Performance Battery (SPPB), Time Up and Go (TUG), Walking While Talking Test (WWT), Manual Dynamometry (MD), Forced Expiratory Volume in the first second (FEV1), Sit and Reach (SR), Back Scratch (BS), and walk for 2 min (2 mST). QoL was assessed using the SF-36 questionnaire and EM using the BREQ-3. The Kolmogorov–Smirnov and Levene tests were applied. A two-way repeated measures ANOVA was applied. A significance level of p < 0.05 was accepted for all comparisons. Results: The EG compared to the CG improved in SPPB (ΔEG/CG: 29.67%/p < 0.001), TUG (ΔEG/CG: 35.70%/p < 0.05), WWT (ΔEG/CG: 42.93%/p < 0.001), MD (ΔEG/CG: 20.40%/p < 0.05), FEV1 (ΔEG/CG: 21.37%/p < 0.05), BS (ΔEG/CG: 80.34%/p < 0.05), 2 mST (ΔEG/CG: 33.02%/p < 0.05), SF-36 (ΔEG/CG: 13.85%/p < 0.001), and Intrinsic Regulation (ΔEG/CG: 27.97%/p < 0.001); Identified by regulation (ΔEG/CG: 9.29%/p < 0.05). Conclusion: An MPTP improves functionality, QoL and EM, and is a safe and effective method for community OAs.
Subspecific Nomenclature of Giardia duodenalis in the Light of a Compared Population Genomics of Pathogens

Genetic and genomic data have long recognized that the species Giardia duodenalis is subdivided into at least eight genetic clusters that have been named “assemblages” by specialists in the field. Some of these assemblages have been given the status of species, with Linnean binames. In the framework of the predominant clonal evolution model (PCE), we have shown that, from an evolutionary point of view, G. duodenalis assemblages are equatable to “near-clades”, that is to say: clades whose discreteness is somewhat clouded by occasional genetic exchange, but remain discrete and stable in space and time. The implications of this evolutionary status for the species described within G. duodenalis are discussed in light of the most recent genetic and genomic studies. The pattern of this species’ subspecific genetic variability and genetic clustering appears to be very similar to the ones of various parasitic, fungal and bacteria species. This underlines the relevance of a compared population genomics of pathogenic species allowed by the broad framework of the PCE model.
Integrated T&D Voltage Stability Assessment Considering Impact of DERs and Distribution Network Topology

Electric distribution systems are going through a major upgrade with increasing penetration of distributed energy resources (DERs) and impacting transmission system operational analysis. Traditionally, the transmission system’s voltage stability margin is computed by performing a continuation power flow (CPF) analysis. In transmission-CPF, loads in distribution systems are aggregated at the transmission-distribution (T&D) interface buses, and the distribution system’s electrical characteristics, like feeder length, increasing penetration of DERs, and associated control, are not considered. The traditional CPF thus considers the distribution network as a purely passive network without active network management and distributed energy resources. In this paper, two integrated T&D CPF methods are implemented using a co-simulation framework that uses individual transmission and distribution solvers. The first method is iteratively-coupled T&D CPF (IC-TDCPF), where the iterative coupling concept forces convergence of boundary variables at T&D PCC (Point of common coupling) at every CPF step. The second method, Loosely Coupled T&D CPF (LC-TDCPF), relaxes the boundary convergence constraints at PCC to achieve faster calculation speed. Moreover, a reduced distribution network equivalent is also developed to enable faster computation of VSM by reducing the number of integrated T&D buses. The developed T&D CPF co-simulation framework uses an open-source platform MATPOWER for solving transmission system and open-source software OPENDSS for solving distribution system. Three large integrated T&D systems with 4196, 10587, and 12295 buses are developed to demonstrate the impact of DERs, voltage-dependent load models, and distribution network topology on integrated voltage stability margin using the proposed T&D CPF and co-simulation.
Design and Development of a Big Data Platform for Disease Burden Based on the Spark Engine

Objective This study attempts to build a big data platform for disease burden that can realize the deep coupling of artificial intelligence and public health. This is a highly open and shared intelligent platform, including big data collection, analysis, and result visualization. Methods Based on data mining theory and technology, the current situation of multisource data on disease burden was analyzed. Putting forward the disease burden big data management model, functional modules, and technical framework, Kafka technology is used to optimize the transmission efficiency of the underlying data. This will be an efficient and highly scalable data analysis platform through embedding embedded Sparkmlib in the Hadoop ecosystem. Results With the concept of “Internet + medical integration,” the overall architecture design of the big data platform for disease burden management was proposed based on the Spark engine and Python language. The main system composition and application scenarios are given at four levels: multisource data collection, data processing, data analysis, and the application layer, according to application scenarios and use requirements. Conclusion The big data platform of disease burden management helps to promote the multisource convergence of disease burden data and provides a new path for the standardized paradigm of disease burden measurement. Provide methods and ideas for the deep integration of medical big data and the formation of a broader standard paradigm.
On the PMU Placement Optimization for the Detection of False Data Injection Attacks

The graph signal processing (GSP) approaches could facilitate the design of detectors for false data injection attacks (FDIAs) on smart grids. However, enhancing the performance of FDIA detection by optimizing the placement of phasor measurement units (PMUs) on smart grids remains an open problem. We formulate herein the detection of FDIAs as a binary hypothesis testing problem within the Grid-GSP framework and develop a subspace-based projection detection (SPD) algorithm, incorporating a quasi-optimal PMU placement strategy and two double thresholds. The proposed quasi-optimal PMU placement strategy enhances the performance of the SPD in FDIA detection. We further propose to enhance the SPD with the dynamic double threshold for high dynamic load profiles beyond a simple yet efficient static double threshold. Illustrative simulations based on the IEEE 118 bus test grid and the ERCOT dataset validate the superior performance of the proposed quasi-optimal PMU placement strategy along with the two double thresholds.
Antimicrobial Potential, Antioxidant Activity, and Phenolic Content of Grape Seed Extracts from Four Grape Varieties

The aim of this study was to evaluate the total phenolic content, composition, and antioxidant and antibacterial activities of four grape seed extracts (Cabernet Sauvignon, Marselan, Pinot Noir, and Tamyanka). The total phenolic content (TPC) and flavonoid, anthocyanin, procyanidin, ascorbic acid, DPPH, and ABTS antioxidant capacities of the grape seed extracts (GSEs) were determined. The extracts showed high TPC values (79.06–111.22 mg GAE/g). The individual components in the GSEs were determined using HPLC. High contents of catechin, epicatechin, and procyanidin B1 were found in the extracts. The antimicrobial activity of the obtained GSEs against Staphylococcus aureus, Bacillus cereus, and Escherichia coli was evaluated using the agar diffusion test and a test to determine the minimum inhibitory concentration (MIC). According to the effect on the growth of pathogens, the extracts were ranked in the following order: Pinot Noir > Marselan > Cabernet Sauvignon > Tamyanka. The tested bacteria showed high sensitivity to the extracts (MIC = 0.12–0.50 mg/mL). According to the MIC values, the bacteria were in the following order: S. aureus > B. cereus > E. coli. A correlation was found between the phenolic content of the GSEs and their antibacterial potential. The obtained results show that the studied GSEs have good potential as antioxidant and antimicrobial agents.
CG-NeRF: Conditional Generative Neural Radiance Fields for 3D-aware Image Synthesis

Recent generative models based on neural radiance fields (NeRF) achieve the generation of diverse 3D-aware images. Despite the success, their applicability can be further expanded by incorporating with various types of user-specified conditions such as text and images. In this paper, we propose a novel approach called the conditional generative neural radiance fields (CG-NeRF), which generates multi-view images that reflect multimodal input conditions such as images or text. However, generating 3D-aware images from multimodal conditions bears several challenges. First, each condition type has different amount of information - e.g., the amount of information in text and color images are significantly different. Furthermore, the pose-consistency is often violated when diversifying the generated images from input conditions. Addressing such challenges, we propose 1) a unified architecture that effectively handles multiple types of conditions, and 2) the pose-consistent diversity loss for generating various images while maintaining the view consistency. Experimental results show that the proposed method maintains consistent image quality on various multimodal condition types and achieves superior fidelity and diversity compared to the existing NeRF-based generative models.
Chemokine Receptors—Structure-Based Virtual Screening Assisted by Machine Learning

Chemokines modulate the immune response by regulating the migration of immune cells. They are also known to participate in such processes as cell–cell adhesion, allograft rejection, and angiogenesis. Chemokines interact with two different subfamilies of G protein-coupled receptors: conventional chemokine receptors and atypical chemokine receptors. Here, we focused on the former one which has been linked to many inflammatory diseases, including: multiple sclerosis, asthma, nephritis, and rheumatoid arthritis. Available crystal and cryo-EM structures and homology models of six chemokine receptors (CCR1 to CCR6) were described and tested in terms of their usefulness in structure-based drug design. As a result of structure-based virtual screening for CCR2 and CCR3, several new active compounds were proposed. Known inhibitors of CCR1 to CCR6, acquired from ChEMBL, were used as training sets for two machine learning algorithms in ligand-based drug design. Performance of LightGBM was compared with a sequential Keras/TensorFlow model of neural network for these diverse datasets. A combination of structure-based virtual screening with machine learning allowed to propose several active ligands for CCR2 and CCR3 with two distinct compounds predicted as CCR3 actives by all three tested methods: Glide, Keras/TensorFlow NN, and LightGBM. In addition, the performance of these three methods in the prediction of the CCR2/CCR3 receptor subtype selectivity was assessed.
Preliminary Toxicity Evaluation of a Porphyrin Photosensitizer in an Alternative Preclinical Model

In photodynamic therapy (PDT), a photosensitizer (PS) excited with a specific wavelength, and in the presence of oxygen, gives rise to photochemical reactions that lead to cell damage. Over the past few years, larval stages of the G. mellonella moth have proven to be an excellent alternative animal model for in vivo toxicity testing of novel compounds and virulence testing. In this article, we report a series of preliminary studies on G. mellonella larvae to evaluate the photoinduced stress response by a porphyrin (PS) (TPPOH). The tests performed evaluated PS toxicity on larvae and cytotoxicity on hemocytes, both in dark conditions and following PDT. Cellular uptake was also evaluated by fluorescence and flow cytometry. The results obtained demonstrate how the administration of PS and subsequent irradiation of larvae affects not only larvae survival rate, but also immune system cells. It was also possible to verify PS’s uptake and uptake kinetics in hemocytes, observing a maximum peak at 8 h. Given the results obtained in these preliminary tests, G. mellonella appears to be a promising model for preclinical PS tests.
An Efficient Integrity Checking Scheme With Full Identity Anonymity for Cloud Data Sharing

Cloud storage services can support data sharing for a group of users. However, the cloud server may lose some of the shared data when attacked. The integrity of stored data is a key issue for cloud storage. However, identity anonymity, an important problem in data integrity, has not been fully investigated yet. Furthermore, the existing schemes of data integrity checking are usually based on public key infrastructure and thus have to perform complex certificate management. In this paper, we propose an efficient integrity checking scheme to achieve full identity anonymity. Additionally, the proposed scheme can reduce the workloads of certificate management and simplify key management. Inspired by the idea of attribute-based signature, we devise a common predicate to manage the identities of legitimate users and disable illegal users from joining the data sharing. By adopting the technique of the monotone span program, the legitimate user can compute the valid authenticators of shared data for all attributes in the common predicate without the need of binding his attribute set to the shared data. Whereupon, the user's identity remains anonymous to all parties in the data sharing because he can share the data and authenticators without revealing his attribute set to other parties. The extensive experimental results demonstrate that the proposed scheme has less computational and communication overhead compared with the existing schemes while achieving full identity anonymity.