Miracle-dz/UniFAR-FTUs huggingface dataset:
XPS binding energy shifts as a function of bond distances: a case study of CO

The O(1s) and C(1s) XPS core-level binding energies. BEs, have been studied as a function of the C–O internuclear distance for a large range of distances. The BE(r) for both BEs show considerable variation over the distances studied which is, however, different for the O(1s) and C(1s) BEs. The origin of the dependence on C–O distance is established and shown to involve more than the electric field generated because of the charge separation within CO being C+q and O−q . Furthermore, the BE(r) is shown to be different for Hartree–Fock and correlated wavefunctions indicating that the BE(r) can provide evidence of how electron correlation modifies the valence charge distribution. The difference between the O(1s) and C(1s) BEs is examined and it is proposed that this difference can be used as a measure of the accuracy of theoretically predicted BEs. It is believed that the features found for CO may be representative for the BE variations with geometry for other systems; an effect that has been mostly overlooked.
BENCHMARK OF GENERATIVE ADVERSARIAL NETWORKS FOR FAST HEP CALORIMETER SIMULATIONS

Highly precise simulations of elementary particles interaction and processes are fundamental toaccurately reproduce and interpret the experimental results in High Energy Physics (HEP) detectorsand to correctly reconstruct the particle flows. Today, detector simulations typically rely on MonteCarlo-based methods which are extremely demanding in terms of computing resources. The need forsimulated data at future experiments - like the ones that will run at the High Luminosity Large HadronCollider (HL-LHC) - are expected to increase by orders of magnitude, increasing drastically thecomputational challenge. This expectation motivates the research for alternative deep learning-basedsimulation strategies.In this research we speed-up HEP detector simulations for the specific case of calorimeters usingGenerative Adversarial Networks (GANs) with a huge factor of over 150 000x compared to thestandard Monte Carlo simulations. This could only be achieved by designing smart convolutional 2Dnetwork architectures for generating 3D images representing the detector volume. Detailed physicsevaluation shows an accuracy similar to the Monte Carlo simulation.Furthermore, we quantize the data format for the neural network architecture (float32) with the IntelLow Precision Optimization tool (LPOT) to a reduced precision (int8) data format. This results in anadditional 1.8x speed-up on modern Intel hardware while maintaining the physics accuracy. Theseexcellent results consolidate the beneficial use of GANs for future fast detector simulations
Model‐based clustering of semiparametric temporal exponential‐family random graph models

Model‐based clustering of time‐evolving networks has emerged as one of the important research topics in statistical network analysis. It is a fundamental research question to model time‐varying network parameters. However, due to difficulties in modelling functional network parameters, there is little progress in the current literature to model time‐varying network parameters effectively. In this work, we model network parameters as univariate nonparametric functions instead of constants. We effectively estimate those functional network parameters in temporal exponential‐family random graph models using a kernel regression technique and a local likelihood approach. Furthermore, we propose a semiparametric finite mixture of temporal exponential‐family random graph models by adopting finite mixture models, which simultaneously allows both modelling and detecting groups in time‐evolving networks. Also, we use a conditional likelihood to construct an effective model selection criterion and network cross‐validation to choose an optimal bandwidth. The power of our method is demonstrated in simulation studies and real‐world applications to dynamic international trade networks and dynamic arm trade networks.
Optimal Information Asymmetry and Team Performance Measurement

This paper studies the effect of agents’ access to pre-decision information, depending on whether only group performance is rewarded or individual performance is rewarded. Pre-decision information only affects agents’ incentives, which in turn depend on the type of performance evaluation system. With public information and group performance measures, the principal prefers a perfect signal if the high productivity state is more informative, and otherwise prefers no information. With group measures, only team success is rewarded, but cooperation is more costly in the higher productive states when the high state is more informative, so the principal prefers to be able to distinguish the states. With individual performance measures, the principal can replicate the group performance measure or induce competition, and in both cases prefers no information. A private signal exacerbates the control problem, and generally, with either performance evaluation system, the principal prefers uninformed agents. However, if agents can become at least slightly informed, then with individual performance measures the principal is indifferent to the level of information conveyed in the signal. Individual performance measures better balance the incentive effects of private information. With group measures, by definition the principal is restricted to a cooperative performance based pay and always prefers the lowest level of information.
Research on Methodology of Operational Efficiency Evaluation Based on Combat Simulation Data

Operational efficiency evaluation is the premise of all kinds of combat simulation applications,including weaponry system demonstration,operational concepts and tactical research,operational plans verification,battlefield situation and battle result prediction,war fighting capability analysis,and so on.But for a long time,the application of simulation data had only stayed in the level of simple statistics.Starting from the breakthrough of the traditional operational efficiency evaluation concept and guided by system theory,a new operational efficiency evaluation methodology integrating the overall evaluation and the efficiency analysis was put forward.And the contents and processes of operational efficiency evaluation based on combat simulation were clarified,the cores of which were overall evaluation,information efficiency analysis,sensitivity analysis,contribution analysis,causal traceability analysis.Finally,the demands of combat simulation data content,granularity and structure were educed by the above evaluation demands,and the combat simulation structures supporting the operational efficiency evaluation were designed.
THE ROLE OF ORGANIZATIONAL AND ECONOMIC MECHANISM OF STRATEGIC COMPANY MANAGEMENT IN THE NATIONAL ECONOMY

Abstract. The COVID-19 pandemic caused an economic downturn, and business development strategies based on globalization and integration failed. So it is necessary to develop new organizational and economic mechanisms of strategic company management, which will compensate for the losses of foreign markets. The focus should be on operation within the national economy as a major alternative to globalised markets. The purpose was to develop proposals for improving the mechanism of strategic company management in the context of implementation of investment policy. The study involved scientific developments that consider the theoretical and practical aspects of development, analysis and implementation of dynamic models of company management. The study showed that the main purpose of strategic company management is to maximize its value to shareholders. The methodological framework of the study is systems theory, a systems approach to strategic company management, theory and practice of simulation. The mechanism of strategic company management based on investment activity is suggested. An algorithm for improving strategic investment management is suggested. The mechanism allows the management to identify and analyze possible consequences in implementation of investment strategies, as well as take into account the impact of these factors on the stable company operation. The proposed mechanism of strategic management also allows linking the full range of strategic actions, strategic marketing research, company’s investment policy, measures to control, identify and take into account the degree of risk, evaluate strategies and adjust management decisions to ensure company’s sustainable development. Keywords: innovations, investment policy, management, national economy, organizational and economic mechanism, sustainable economic development. JEL Classification G30 Formulas: 4; fig.: 3; tabl. : 0; bibl. : 25.
Collagen mineralization with lepidocrocite via Fe(OH)2 addition

The mineralization of collagen in vitro has been extensively investigated for hydroxyapatite, silica, calcium carbonate and lepidocrocite (γ-FeOOH). Henceforth, it is interesting to investigate whether collagen also could serve as a generic mineralization template for other minerals, like magnetite. To this end, and inspired by the partial oxidation approach, first a ferrous hydroxide (Fe(OH)2) intermediate is synthesized via the titration of base to a solution of Fe2+. Subsequently, the Fe(OH)2 is mixed with collagen fibrils and poly(aspartic acid) is added to promote the formation of intrafibrillar crystals. Platelet-shaped lepidocrocite crystals being present throughout the entire thickness of the collagen fibrils can be realized, as was confirmed with electron tomography. The formation of lepidocrocite, which is an Fe3+ compound, is hypothesized to be induced via oxidation of the Fe2+ species and, therefore, the oxygen concentration during titration, TEM sample preparation and drying of TEM samples are investigated. Although the reaction is sensitive to small changes in experimental conditions, highly mineralized collagen fibers can be realized.
Energy Imbalance Gap, Anthropometric Measures, Lifestyle, and Sociodemographic Correlates in Latin American Adults—Results from the ELANS Study

Overweight and obesity are often explained by an imbalance between energy intake and expenditure. This, in addition to metabolic effects, makes it difficult to assess the real state of individual energy balance. This study aims to analyze the energy gaps between intake and expenditure in the adult population of Latin America, as well as its relationships with sociodemographic variables and nutrition status, to draw an epidemiological perspective based on the trends observed. The energy imbalance gap was used to this end. The difference between energy intake and expenditure can be applied as a reference to explain whether weight equilibrium can prevent weight gain. Moreover, the energy imbalance gap allows for a better understanding of the design of public health policies. Using data from the Latin American Study of Nutrition and Health, the energy imbalance gap in adult population from eight Latin-American countries was assessed in 5994 subjects aged from 19–65. Usual dietary intake was measured using two non-consecutive 24 h dietary recalls. The sociodemographic questionnaire was supplemented by anthropometric measurements. Physical activity was measured through the long International Physical Activity Questionnaire. Energy expenditure was obtained using the basal metabolic rate. For the overall sample, the mean energy intake was 1939.1 kcal (95% CI: 1926.9; 1951.3), the mean of energy expenditure was 1915.7 kcal (95% CI: 1906.4; 1924.9), and the mean of energy imbalance gap was 23.4 kcal (95% CI: 11.9; 35.0). Results show that energy intake and expenditure were higher in men. Moreover, subjects aged 19–34, of high socioeconomic level, who completed high school, were mestizos and were of normal weight consumed the highest number of calories. Overall, a positive energy imbalance gap was observed. Overweight and obese from Argentina, Costa Rica, Ecuador, Peru, and Venezuela showed a significantly lower energy imbalance gap than underweight subjects. These findings confirm the high variability of energy imbalance gap and the accompanying correlates of energy intake and expenditure. Further research is needed to specifically address interventions in low and middle-income countries such as many in Latin America, to help reduce the prevalence of obesity and eradicate undernutrition.
A Cross-Country Analysis of the Effectiveness of COVID-19 Vaccines in Reducing Mortality Rates within the EU

We use a linear mixed model in order to estimate the effect of the number of people vaccinated against COVID-19 on the overall death toll on a monthly basis. We limit our analysis for the duration of the year 2021 and within 25 countries which are current or former (UK) members of the EU since these countries follow similar approaches to testing and reporting different COVID-19 related statistics. We explored the effect in question by comparing the total number of people vaccinated up to the end of each month and the total number of deaths occurring during the next month while controlling for several measures including number of new COVID-19 cases, diabetes prevalence, cardio vascular death rates and time trends among others. Our results indicated that one percentage point monthly increase in the total number of vaccinated people was associated, on average, with a decrease of two deaths per general population of 1 million for the next month with the effect being highly significant.
Curved factor analysis with the Ellipsoid-Gaussian distribution

There is a need for new models for characterizing dependence in multivariate data. The multivariate Gaussian distribution is routinely used, but cannot characterize nonlinear relationships in the data. Most non-linear extensions tend to be highly complex; for example, involving estimation of a non-linear regression model in latent variables. In this article, we propose a relatively simple class of Ellipsoid-Gaussian multivariate distributions, which are derived by using a Gaussian linear factor model involving latent variables having a von Mises-Fisher distribution on a unit hyper-sphere. We show that the Ellipsoid-Gaussian distribution can flexibly model curved relationships among variables with lower-dimensional structures. Taking a Bayesian approach, we propose a hybrid of gradient-based geodesic Monte Carlo and adaptive Metropolis for posterior sampling. We derive basic properties and illustrate the utility of the Ellipsoid-Gaussian distribution on a variety of simulated and real data applications. An accompanying R package is also available.
Instance-Dependent Confidence and Early Stopping for Reinforcement Learning

Various algorithms for reinforcement learning (RL) exhibit dramatic variation in their convergence rates as a function of problem structure. Such problem-dependent behavior is not captured by worst-case analyses and has accordingly inspired a growing effort in obtaining instance-dependent guarantees and deriving instance-optimal algorithms for RL problems. This research has been carried out, however, primarily within the confines of theory, providing guarantees that explain \textit{ex post} the performance differences observed. A natural next step is to convert these theoretical guarantees into guidelines that are useful in practice. We address the problem of obtaining sharp instance-dependent confidence regions for the policy evaluation problem and the optimal value estimation problem of an MDP, given access to an instance-optimal algorithm. As a consequence, we propose a data-dependent stopping rule for instance-optimal algorithms. The proposed stopping rule adapts to the instance-specific difficulty of the problem and allows for early termination for problems with favorable structure.
Deep Reinforcement Learning Under Signal Temporal Logic Constraints Using Lagrangian Relaxation

Deep reinforcement learning (DRL) has attracted much attention as an approach to solve optimal control problems without mathematical models of systems. On the other hand, in general, constraints may be imposed on optimal control problems. In this study, we consider the optimal control problems with constraints to complete temporal control tasks. We describe the constraints using signal temporal logic (STL), which is useful for time sensitive control tasks since it can specify continuous signals within bounded time intervals. To deal with the STL constraints, we introduce an extended constrained Markov decision process (CMDP), which is called a <inline-formula> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula>-CMDP. We formulate the STL-constrained optimal control problem as the <inline-formula> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula>-CMDP and propose a two-phase constrained DRL algorithm using the Lagrangian relaxation method. Through simulations, we also demonstrate the learning performance of the proposed algorithm.
Understanding and Detecting Hateful Content using Contrastive Learning

The spread of hate speech and hateful imagery on the Web is a significant problem that needs to be mitigated to improve our Web experience. This work contributes to research efforts to detect and understand hateful content on the Web by undertaking a multimodal analysis of Antisemitism and Islamophobia on 4chan’s /pol/ using OpenAI’s CLIP. This large pre-trained model uses the Contrastive Learning paradigm. We devise a methodology to identify a set of Antisemitic and Islamophobic hateful textual phrases using Google’s Perspective API and manual annotations. Then, we use OpenAI’s CLIP to identify images that are highly similar to our Antisemitic/Islamophobic textual phrases. By running our methodology on a dataset that includes 66M posts and 5.8M images shared on 4chan’s /pol/ for 18 months, we detect 173K posts containing 21K Antisemitic/Islamophobic images and 246K posts that include 420 hateful phrases. Among other things, we find that we can use OpenAI’s CLIP model to detect hateful content with an accuracy score of 0.81 (F1 score = 0.54). By comparing CLIP with two baselines proposed by the literature, we find that CLIP outperforms them, in terms of accuracy, precision, and F1 score, in detecting Antisemitic/Islamophobic images. Also, we find that Antisemitic/Islamophobic imagery is shared in a similar number of posts on 4chan’s /pol/ compared to Antisemitic/Islamophobic textual phrases, highlighting the need to design more tools for detecting hateful imagery. Finally, we make available (upon request) a dataset of 246K posts containing 420 Antisemitic/Islamophobic phrases and 21K likely Antisemitic/Islamophobic images (automatically detected by CLIP) that can assist researchers in further understanding Antisemitism and Islamophobia.
Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learning

In recent years, the amount of secure information being stored on mobile devices has grown exponentially. However, current security schemas for mobile devices such as physiological biometrics and passwords are not secure enough to protect this information. Behavioral biometrics have been heavily researched as a possible solution to this security deficiency for mobile devices. This study aims to contribute to this innovative research by evaluating the performance of a multi-modal behavioral biometric based user authentication scheme using touch dynamics and phone movement. This study uses a fusion of two popular publicly available datasets - the Hand Movement Orientation and Grasp (HMOG) dataset and the BioIdent dataset. This study evaluates our model's performance using three common machine learning algorithms; Random Forest, Support Vector Machine, and K-Nearest Neighbor reaching accuracy rates as high as 82%, with each algorithm performing respectively for all success metrics reported.
TOFU: Towards Obfuscated Federated Updates by Encoding Weight Updates into Gradients from Proxy Data

Advances in Federated Learning and an abundance of user data have enabled rich collaborative learning between multiple clients, without sharing user data. This is done via a central server that aggregates learning in the form of weight updates. However, this comes at the cost of repeated expensive communication between the clients and the server, and concerns about compromised user privacy. The inversion of gradients into the data that generated them is termed data leakage. Encryption techniques can be used to counter this leakage, but at added expense. To address these challenges of communication efficiency and privacy, we propose TOFU, a novel algorithm which generates proxy data that encodes the weight updates for each client in its gradients. Instead of weight updates, this proxy data is now shared. Since input data is far lower in dimensional complexity than weights, this encoding allows us to send much lesser data per communication round. Additionally, the proxy data resembles noise, and even perfect reconstruction from data leakage attacks would invert the decoded gradients into unrecognizable noise, enhancing privacy. We show that TOFU enables learning with less than 1% and 7% accuracy drops on MNIST and on CIFAR-10 datasets, respectively. This drop can be recovered via a few rounds of expensive encrypted gradient exchange. This enables us to learn to near-full accuracy in a federated setup, while being 4x and 6.6x more communication efficient than the standard Federated Averaging algorithm on MNIST and CIFAR-10, respectively.
Taxonomy Enrichment with Text and Graph Vector Representations

Knowledge graphs such as DBpedia, Freebase or Wikidata always contain a taxonomic backbone that allows the arrangement and structuring of various concepts in accordance with hypo-hypernym (“class-subclass”) relationship. With the rapid growth of lexical resources for specific domains, the problem of automatic extension of the existing knowledge bases with new words is becoming more and more widespread. In this paper, we address the problem of taxonomy enrichment which aims at adding new words to the existing taxonomy. We present a new method which allows achieving high results on this task with little effort. It uses the resources which exist for the majority of languages, making the method universal. We extend our method by incorporating deep representations of graph structures like node2vec, Poincaré embeddings, GCN etc. that have recently demonstrated promising results on various NLP tasks. Furthermore, combining these representations with word embeddings allows us to beat the state of the art. We conduct a comprehensive study of the existing approaches to taxonomy enrichment based on word and graph vector representations and their fusion approaches. We also explore the ways of using deep learning architectures to extend taxonomic backbones of knowledge graphs. We create a number of datasets for taxonomy extension for English and Russian. We achieve state-of-the-art results across different datasets and provide an in-depth error analysis of mistakes.