Miracle-dz/UniFAR-FTUs huggingface dataset:
Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging

Decentralized optimization is increasingly popular in machine learning for its scalability and efficiency. Intuitively, it should also provide better privacy guarantees, as nodes only observe the messages sent by their neighbors in the network graph. But formalizing and quantifying this gain is challenging: existing results are typically limited to Local Differential Privacy (LDP) guarantees that overlook the advantages of decentralization. In this work, we introduce pairwise network differential privacy, a relaxation of LDP that captures the fact that the privacy leakage from a node $u$ to a node $v$ may depend on their relative position in the graph. We then analyze the combination of local noise injection with (simple or randomized) gossip averaging protocols on fixed and random communication graphs. We also derive a differentially private decentralized optimization algorithm that alternates between local gradient descent steps and gossip averaging. Our results show that our algorithms amplify privacy guarantees as a function of the distance between nodes in the graph, matching the privacy-utility trade-off of the trusted curator, up to factors that explicitly depend on the graph topology. Finally, we illustrate our privacy gains with experiments on synthetic and real-world datasets.
Generalised moment closure for multistate dynamics on networks

In the study of dynamics on networks, moment closure is a commonly used method to obtain low-dimensional evolution equations amenable to analysis. The variables in the evolution equations are mean counts of subgraph states and are referred to as moments. Due to interaction between neighbours, each moment equation is a function of higher-order moments, such that an inﬁnite hierarchy of equations arises. Hence, the derivation requires truncation at a given order, and, an approximation of the highest-order moments in terms of lower-order ones, known as a closure formula. Recent systematic approximations have either restricted focus to closed moment equations for SIR epidemic spreading or to unclosed moment equations for arbitrary dynamics. In this paper, we develop a general procedure that automates both derivation and closure of arbitrary order moment equations for dynamics with nearest-neighbour interactions on undirected networks. Automation of the closure step was made possible by our generalised closure scheme, which systematically decomposes the largest subgraphs into their smaller components. We show that this decomposition is exact if these components form a tree, there is independence at distances beyond their graph diameter, and there is spatial homogeneity. Testing our method for SIS epidemic spreading on lattices and random networks conﬁrms that biases are larger for networks with many short loops in regimes with long-range dependence. A Mathematica script that automates the moment closure is available for download.
xView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagery

Unsustainable fishing practices worldwide pose a major threat to marine resources and ecosystems. Identifying vessels that do not show up in conventional monitoring systems -- known as ``dark vessels'' -- is key to managing and securing the health of marine environments. With the rise of satellite-based synthetic aperture radar (SAR) imaging and modern machine learning (ML), it is now possible to automate detection of dark vessels day or night, under all-weather conditions. SAR images, however, require a domain-specific treatment and are not widely accessible to the ML community. Maritime objects (vessels and offshore infrastructure) are relatively small and sparse, challenging traditional computer vision approaches. We present the largest labeled dataset for training ML models to detect and characterize vessels and ocean structures in SAR imagery. xView3-SAR consists of nearly 1,000 analysis-ready SAR images from the Sentinel-1 mission that are, on average, 29,400-by-24,400 pixels each. The images are annotated using a combination of automated and manual analysis. Co-located bathymetry and wind state rasters accompany every SAR image. We also provide an overview of the xView3 Computer Vision Challenge, an international competition using xView3-SAR for ship detection and characterization at large scale. We release the data (\href{https://iuu.xview.us/}{https://iuu.xview.us/}) and code (\href{https://github.com/DIUx-xView}{https://github.com/DIUx-xView}) to support ongoing development and evaluation of ML approaches for this important application.
Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations

Offline reinforcement learning has shown great promise in leveraging large pre-collected datasets for policy learning, allowing agents to forgo often-expensive online data collection. However, offline reinforcement learning from visual observations with continuous action spaces remains under-explored, with a limited understanding of the key challenges in this complex domain. In this paper, we establish simple baselines for continuous control in the visual domain and introduce a suite of benchmarking tasks for offline reinforcement learning from visual observations designed to better represent the data distributions present in real-world offline RL problems and guided by a set of desiderata for offline RL from visual observations, including robustness to visual distractions and visually identifiable changes in dynamics. Using this suite of benchmarking tasks, we show that simple modifications to two popular vision-based online reinforcement learning algorithms, DreamerV2 and DrQ-v2, suffice to outperform existing offline RL methods and establish competitive baselines for continuous control in the visual domain. We rigorously evaluate these algorithms and perform an empirical evaluation of the differences between state-of-the-art model-based and model-free offline RL methods for continuous control from visual observations. All code and data used in this evaluation are open-sourced to facilitate progress in this domain.
GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions

We develop data-driven methods incorporating geometric and topological information to learn parsimonious representations of nonlinear dynamics from observations. We develop approaches for learning nonlinear state space models of the dynamics for general manifold latent spaces using training strategies related to Variational Autoencoders (VAEs). Our methods are referred to as Geometric Dynamic (GD) Variational Autoencoders (GD-VAEs). We learn encoders and decoders for the system states and evolution based on deep neural network architectures that include general Multilayer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), and Transpose CNNs (T-CNNs). Motivated by problems arising in parameterized PDEs and physics, we investigate the performance of our methods on tasks for learning low dimensional representations of the nonlinear Burgers equations, constrained mechanical systems, and spatial fields of reaction-diffusion systems. GD-VAEs provide methods for obtaining representations for use in diverse learning tasks involving dynamics.
Energy conditions in extended f(R, G, T) gravity

In this paper, we consider the flat FriedmannLematreRobertson-Walker metric in the presence of perfect fluid models and extended f(R, G, T) gravity (where R is the Ricci scalar, G is the Gauss Bonnet invariant and T stands for trace of energy momentum tensor). In this context, we assume some specific realistic f(R, G, T) models configuration that could be used to explore the finite-time future singularities that arise in late-time cosmic accelerating phases. In this scenario, we choose the most recent estimated values for the Hubble, deceleration, snap and jerk parameters to develop the viability and bounds on the models parameters induced by different energy conditions.
Biosensors based on Novel Material Field Effect Transistors

For different purposes, state-of-the-art antigen detection mechanisms like ELISA are not sufficient in accuracy. Therefore, new approaches have to be developed to get highly sensitive technologies. Graphene’s outstanding electrical properties, like ultrahigh charge mobility and sensitivity to dopants, make it to a promising candidate for new electronic devices and their utilization for sensing technologies. Recent progress in nanotechnology has enabled new possibilities and, thus, has allowed the construction of graphene field effect transistors (gFET). In this work, gFET arrays with high yield and good device-to-device characteristic have been fabricated. Siliconoxide (SiO2) and aluminiumoxide (Al2O3) have been used as insulating oxide layers. To utilize the gFET device, a chemical linker was attached by π − π stacking interactions. The linker himself binds antibody fragments, so called single chain variable fragments (scFv). In this approach, detection of HER3, a well known breast cancer biomarker, was of interest. Therefore, matching HER3 immunoglobulin G-antigen complexes were used. Conclusions about the concentration of HER3 antigens down to as small as ng/ml were deducible by monitoring the Dirac voltages of the gFET devices.
Relationship between occupational physical activity and subclinical vascular damage in high-altitude dwellers

were found to be better predictors of PWV than FRS alone (R=0.24, p=0.039). Conclusion : High-intensity occupational PA shows a positive, independent association with arterial stiffness in Himalayan moderate altitude dwellers. This study suggests how occupational PA may be a new CV risk factor and thus should be included in the assessment of overall CV risk
RMHP_A_310938 4789..4797

Department of Dermatology, Affiliated Hospital of Zunyi Medical University,149 Dalian Road, Huichuan District, Guizhou, 563003, People’s Republic of China; Department of Dermatology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210000, People’s Republic of China; Department of Endocrinology, Affiliated Hospital of Zunyi Medical University, Guizhou, 563003, People’s Republic of China; Neonatal Department, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, 200011, People’s Republic of China; Department of Pediatrics, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 21000, People’s Republic of China Purpose: To identify potential risk factors for herpes zoster infection in type 2 diabetes mellitus in southeast Chinese population. Patients and Methods: We built a model involving 266 herpes zoster patients collecting data from January 2018 to December 2019. The least absolute shrinkage and selection operator (Lasso) predictive model was used to test herpes zoster virus risk using the patient data. Multivariate regression was conducted to decide which variable would be the strongest to decrease the Lasso penalty. The predictive model was tested using the C-index, a calibration plot, and decision curve study. External validity was verified by bootstrapping by counting probabilities. Results: In the prediction nomogram, the prediction variables included age, sex, weight, length of hospital stay, infection, and blood pressure. The C-index of 0.844 (0.798–0.896) indicated substantial variability and thus the model was adjusted appropriately. A score of 0.825 was achieved somewhere in the above interval. Examination of the decision curve estimated that herpes zoster nomogram was useful when the intervention was determined at the 16 percent of the herpes zoster infection potential threshold. Conclusion: The herpes zoster nomogram combines age, weight, position of the rash, 2-hour plasma glucose, glycosuria, serum creatinine, length of the hospital stay, and hypertension. This calculator can be used to assess the individual herpes zoster risks in patients diagnosed with type 2 diabetes mellitus.
Lymphomaa Preclinical Model of Non-Hodgkin's CD19 × CD3 and CD19 × CD16 Diabodies in Synergistic Antitumor Effect of Bispecific

To target NK cells against non-Hodgkin’s lymphoma, we constructed a bispeciﬁc diabody (BsDb) with reactivity against both human CD19 and Fc (cid:2) RIII (CD16). Bacterially produced CD19 (cid:1) CD16 BsDb speciﬁcally interacted with both CD19 (cid:3) and CD16 (cid:3) cells and exhibited signiﬁcantly higher apparent afﬁnity and slower dissociation from the tumor cells than from effector cells. It was able to induce speciﬁc lysis of tumor cells in the presence of isolated human NK cells or nonfractionated PBLs. The combination of the CD19 (cid:1) CD16 BsDb with a previously described CD19 (cid:1) CD3 BsDb and CD28 costimulation signiﬁcantly increased the lytic potential of human PBLs. Treatment of SCID mice bearing an established Burkitt’s lymphoma (5 mm in diameter) with human PBLs, CD19 (cid:1) CD16 BsDb, CD19 (cid:1) CD3 BsDb, and anti-CD28 mAb resulted in the complete elimination of tumors in 80% of animals. In contrast, mice receiving human PBLs in combination with either diabody alone showed only partial tumor regression. These data clearly demonstrate the synergistic effect of small recombinant bispeciﬁc molecules recruiting different populations of human effector cells to the same tumor target. The Journal of Immunology, 2002, 169: 137–144. for anti-human CD16 counterparts (29) Nco I/ Hin dIII and Hin dIII/ Xba I bispeciﬁc molecule, we used CD19 (cid:2) CD3 BsDb as a control in all ﬂow cytometry experiments. Direct binding and cell surface retention on CD19 (cid:3) JOK-1 cells were practically indistinguishable for both BsDbs. The calculated K eq and t 1/2 values were 5.7 nM and 10.8 min, respectively, for CD19 (cid:2) CD3 BsDb, and 6.1 nM and 10.6 min for CD19 (cid:2) CD16 BsDb. These results indicate that the second speciﬁcity present in the BsDb molecule does not signiﬁcantly affect the afﬁnity for binding to CD19.
SEROLOGICAL ANALYSIS OF THYMUS AND SPLEEN GRAFTS* BY MICHAEL SCHLESINGER, M.D., Am) DAHLIA HURVITZ

Thymic lymphocytes are continuously replaced by stem cells entering the thymus. The thymus of animals receiving lethal total body irradiation, followed by administration of bone marrow or spleen cells, is repopulated by cells derived from the inoculure (1-6). Similarly, repopulation of the unirradiated thymus eventually occurs in animals in which only the lower third of the body was irradiated prior to bone marrow administration (7). The thymus of mice (8) or of chicken embryos (9) joined by parabiosis contains cells derived from the partner. Analysis of thymus grafts has shown that the lymphoid elements are replaced by cells derived from the host (10-12). Thymic lymphocytes constantly display distinctive serological properties (see references 12 and 14) in spite of their replacement by the continuous entry of stem cells. Thymus cells of all strains of mice show a distinctive sensitivity to the cytotoxic effect of guinea pig serum (GPS) (15-19). The thymus cells of some strains of mice, such as the A strain, possess a thymus-specific antigen, the TL (thymus-leukemia) antigen, while other strains of mice, such as the C57BL strain, do not have this antigen on their thymus cells (20, 21).
An Improved Multi-Objective Genetic Algorithm for Tuning Linguistic Fuzzy Systems

This work proposes the use of MultiObjective Evolutionary Algorithms to obtain Fuzzy Rule-Based Systems with good accuracy-interpretability trade-o. To do this, we present a new post-processing method that performs rule selection and membership function tuning by focusing in the Pareto zone containing the most accurate solutions but with the least number of possible rules. This method is based on the well-known SPEA2 algorithm, applying an intelligent crossover operator, considering some modifications to concentrate the search in the desired Pareto zone and including an incest prevention mechanism in order to obtain more global optima. The results show that improving the trade-o between exploration and exploitation in the search process enhances the SPEA2 algorithm performance.
1 On the potential of silicon intermediate band solar 2 cells 3

Intermediate band solar cells (IBSCs) have an efficiency limit of 63.2%, which is 10 significantly higher than the 40.7% limit for conventional single gap solar cells. In order to achieve 11 the maximum efficiency, the total bandgap of the cell should be in the range of ~2 eV. However, 12 that fact does not prevent that other cells based on different semiconductors bandgaps can benefit 13 from the presence of an intermediate band (IB) within their bandgap. Since silicon (1.12 eV 14 bandgap) is the dominant material in solar cell technology, it is of interest to determine the limit 15 efficiency of a silicon-IBSC, because even a modest gain in efficiency could trigger a large 16 commercial interest if the IB is implemented at low cost. In this work we study the limit efficiency 17 of silicon based IBSCs considering operating conditions that include the use of non-ideal photon 18 casting between the optical transitions, different light intensities and Auger recombination. The 19 results lead to the conclusion that a silicon-IBSC, operating under the conventional model in which 20 the sub-bandgaps add to the total silicon gap, provides an efficiency gain if operated in the 21 medium-high concentration range. The performance of these devices is affected by Auger 22 recombination only under extremely high concentrations. 23
THE EFFECT OF FOLIAR FERTILIZATION WITH NITROFOSKA AND SEED INOCULATION WITH SOME BIO-FERTILIZERS ON GREEN POD YIELD OF PEA (Pisum sativum L.)

Two field experiments were carried out in winter seasons of 2012/2013 and 2013/ 2014 to study the effect of foliar fertilizer with nitrofoska and treating seeds with bio-fertilizers , i.e. nitrobein and potassiomag on plant growth, chemical composition and green pod yield and its components, as well as, pod quality (pod physical characters and nutritive value of seeds) of pea cv. Lincoln. The results showed that foliar fertilization with nitrofoska and bio-fertilization with nitrobein increased most of plant growth traits, minerals content of shoots (N,P and K as a concentration), yield and its components, as well as physical pod characters and nutritive value of seeds (N, P, K contents, protein, ascorbic acid, TSS and total carbohydrates), followed by nitofoska + potassiomag, then nitofoska + nitrobein+potassiomag, respectively, in most cases. Conclusively, it could be concluded that the combination treatment between nitrofoska and nitrobein, followed by nitrofoska and potassiomag , as well as, nitrofoska + nitrobein + potassiomag, respectively, enhancing the most studied characters of peas.
Rotating Bose-Einstein condensates in partially anisotropic traps

Wir untersuchen dreidimensionale schnell rotierende Bose-Einstein Kondensate in einem theoretischen Kontext. Die Hauptresultate sind untere und obere Schranken an die Gross-Pitaevskii Energie des verdunnten Bose Gases im Thomas-Fermi Limes. Ahnliche Resultate wurden bereits fur stark anharmonische ausere Potentiale in 2D und homogene ausere Potentiale in 2D und 3D entwickelt wohingegen wir die Betrachtung von teilweise anisotropen Fallen in 3D prasentieren. Weiters geben wir eine komplette Beschreibung und Illustration der Thomas-Fermi Dichte in diesem Grenzwert an. Wir stellen auch den Quelltext zur Erstellung der Illustrationen zur Verfugung.
Radar-Camera Sensor Fusion Based Object Detection for Smart Vehicles

—We propose a post-data processing scheme for radar sensors in a fusion system consisting of a camera and radar sensors. The proposed scheme is divided into the recursive least square filter, the ghost target and clutter cancellation, and region of interest (ROI) selection. Especially for the recursive least square filter, we determine whether detections are valid tracks or are new tracks to initialize or update the filter parameters. Next, we apply the valid detections to the filter to reduce detection errors. Next, we cancel ghost targets as comparing the current tracks and the last tracks, and suppress clutter using the detected radial velocity. Finally, we select the ROI and determine the transfer coordinates to provide these values to the camera sensor. To verify the proposed method, we use Delphi commercial radar and carry out the measurements in a chamber and on the real road.
A Mobile Mashup for Accessing Distributed Recycling Knowledge

—From the consumer perspective, classifying a product regarding its environmental impact is a difﬁcult task because relevant knowledge is usually not only diverse, but also distributed over several information sources. In this work, an analysis of mobile ”green” applications formed the basis of a mobile application, which aims at providing all recycling-related information in-situ. Its domain model integrates recycling knowledge from several information sources and is capable of disassembling a product into its elementary parts. An information extraction approach allows the automatic integration of relevant content from new Web sources, which were suggested by the user. The mobile application enables the user to initiate interaction with this model over three different ways of describing a product. Beside insights concerning information access and user interaction, a ﬁrst evaluation of the prototype indicates that the employed fused domain model may outperform results achieved with a traditional approach to web-based information search concerning recycling information. Based on the outcomes of the evaluation, a revised user interface is presented.
Global SM and BSM Fits using Results from LHC and other Experiments

Global ts which confront results of High-Energy-Physics experiments with accurate theoretical predictions can be used to provide indirect constraints on fundamental parameters of the Standard Model of particle physics or new physics models. Important examples are global ts of the electroweak sector of the Standard Model or ts of supersymetric extensions of it. In this article we review the results obtained using the public software tools Gfitter, Fittino, MasterCode, HiggsBounds and HiggsSignals and we highlight the physics results of global ts in the Standard Model, Supersymmetry and various other beyond-the-Standard-Model theories.
University of Birmingham Laterality Differences in Cerebellar-motor Cortex Connectivity

Lateralization of function is an important organizational feature of the human motor system. Each effector is predominantly controlled by the contralateral cerebral cortex and the ipsilateral cerebellar hemisphere. These two structures work together to implement coordinated movements. Transcranial magnetic stimulation studies have revealed hemispheric differences in the stimulation strength required to evoke a muscle response from the primary motor cortex (M1), with the dominant hand typically requiring less stimulation than the non-dominant. The current study assessed whether the strength of the connection between the cerebellum and M1 (CB-M1), known to change in association with motor learning, have hemispheric differences and whether these differences have any behavioral correlate. We observed, in right-handed individuals, that the connection between the right cerebellum and left M1 is typically stronger than the contralateral network. Behaviorally, we found that the strength of the CB-M1 connection is correlated with the amplitude variability of reaching movements. These findings indicate that hand-dominance lateralization in the motor system is present beyond the primary motor cortex, and points to an association between cerebellar M1 connectivity and movement execution precision.