Miracle-dz/UniFAR-FTUs huggingface dataset:
How to detect higher order Van Hove singularities in multi-band Hamiltonians in practice

We present a practical method to detect and diagnose higher order Van Hove singularities in multiband systems, with no restrictions on number of bands and hopping terms. The method allows us to directly compute the Taylor expansion of the dispersion of any band at arbitrary k points, using an extension of the Feynman Hellmann theorem. Being fairly general in scope, it also allows us to incorporate and analyse the effect of tuning parameters on the low energy dispersions, which can greatly aid the search for higher order Van Hove singularities. A restricted class of degenerate bands can be handled within this framework, which can be readily computationally implemented. Subsequently, we demonstrate the use of the method, taking the Haldane model as an example.
Perturbation Inactivation Based Adversarial Defense for Face Recognition

Deep learning-based face recognition models are vulnerable to adversarial attacks. To curb these attacks, most defense methods aim to improve the robustness of recognition models against adversarial perturbations. However, the generalization capacities of these methods are quite limited. In practice, they are still vulnerable to unseen adversarial attacks. Deep learning models are fairly robust to general perturbations, such as Gaussian noises. A straightforward approach is to inactivate the adversarial perturbations so that they can be easily handled as general perturbations. In this paper, a plug-and-play adversarial defense method, named perturbation inactivation (PIN), is proposed to inactivate adversarial perturbations for adversarial defense. We discover that the perturbations in different subspaces have different influences on the recognition model. There should be a subspace, called the immune space, in which the perturbations have fewer adverse impacts on the recognition model than in other subspaces. Hence, our method estimates the immune space and inactivates the adversarial perturbations by restricting them to this subspace. The proposed method can be generalized to unseen adversarial perturbations since it does not rely on a specific kind of adversarial attack method. This approach not only outperforms several state-of-the-art adversarial defense methods but also demonstrates a superior generalization capacity through exhaustive experiments. Moreover, the proposed method can be successfully applied to four commercial APIs without additional training, indicating that it can be easily generalized to existing face recognition systems.
Federated Learning for THz Channel Estimation

—This paper addresses two major challenges in terahertz (THz) channel estimation: the beam-split phenomenon, i.e., beam misalignment because of frequency-independent analog beamformers, and computational complexity because of the usage of ultra-massive number of antennas to compensate propagation losses. Data-driven techniques are known to mitigate the complexity of this problem but usually require the transmission of the datasets from the users to a central server entailing huge communication overhead. In this work, we employ federated learning (FL), wherein the users transmit only the model parameters instead of the whole dataset, for THz channel estimation to improve the communications-efﬁciency. In order to accurately estimate the channel despite beam-split, we propose a beamspace support alignment (BSA) technique. By exploiting the sparsity of the THz channel, the proposed approach is implemented with fewer pilot signals than the traditional techniques. Compared to the previous works, our FL-BSA approach provides higher channel estimation accuracy as well as approximately 68 (32) times lower model (channel) training overhead, respectively.
Enhanced Security and Privacy via Fragmented Federated Learning

In federated learning (FL), a set of participants share updates computed on their local data with an aggregator server that combines updates into a global model. However, reconciling accuracy with privacy and security is a challenge to FL. On the one hand, good updates sent by honest participants may reveal their private local information, whereas poisoned updates sent by malicious participants may compromise the model's availability and/or integrity. On the other hand, enhancing privacy via update distortion damages accuracy, whereas doing so via update aggregation damages security because it does not allow the server to filter out individual poisoned updates. To tackle the accuracy-privacy-security conflict, we propose fragmented FL (FFL), in which participants randomly exchange and mix fragments of their updates before sending them to the server. To achieve privacy, we design a lightweight protocol that allows participants to privately exchange and mix encrypted fragments of their updates so that the server can neither obtain individual updates nor link them to their originators. To achieve security, we design a reputation-based defense tailored for FFL that builds trust in participants and their mixed updates based on the quality of the fragments they exchange and the mixed updates they send. Since the exchanged fragments' parameters keep their original coordinates and attackers can be neutralized, the server can correctly reconstruct a global model from the received mixed updates without accuracy loss. Experiments on four real data sets show that FFL can prevent semi-honest servers from mounting privacy attacks, can effectively counter-poisoning attacks, and can keep the accuracy of the global model.
Confounding-adjustment methods for the diﬀerence in medians

With continuous outcomes, the average causal eﬀect is typically deﬁned using a contrast of mean potential outcomes. However, for skewed outcome data the mean may no longer be a meaningful summary statistic and the deﬁnition of the causal eﬀect should be considered more closely. In practice the typical approach is to either “ignore or transform” – ignore the skewness in the data entirely or transform the outcome to obtain a more symmetric distribution. In many practical settings neither approach is entirely satisfactory. Alternatively, the causal eﬀect could be deﬁned using a contrast of median potential outcomes, although discussion or availability of confounding-adjustment methods to estimate this parameter is currently limited. To address this gap, we described and evaluated confounding-adjustment methods to estimate the causal diﬀerence in medians, speciﬁcally multivariable quantile regression, an inverse probability weighted (IPW) estimator, weighted quantile regression and adaptations of the g-computation approach. Performance of these methods was assessed within a simulation study under varying degrees of skewness in the outcome, and within an empirical study motivated by the Longitudinal Study of Australian Children. Results indicated the IPW estimator and weighted quantile regression were the best performing across all simulation settings if the propensity score model is correctly speciﬁed. Other methods had similar or higher bias than an unadjusted analysis. Application to the empirical study yielded more consistent estimates across methods. The methods presented provide appealing alternatives to the common “ignore or transform” approach, enhancing our capability to obtain meaningful causal eﬀect estimates with skewed outcome data.
Collaboration-Aware Graph Convolutional Networks for Recommendation Systems

By virtue of the message-passing that implicitly injects collaborative effect into the embedding process, Graph Neural Networks (GNNs) have been successfully adopted in recommendation systems (e.g., LightGCN, GTN, and UltraGCN). Nevertheless, most of existing message-passing mechanisms in recommendation are directly inherited from GNNs without any recommendation-tailored modification. Although some efforts have been made towards simplifying GNNs to improve the performance/efficiency of recommendation, no study has comprehensively scrutinized how message-passing captures collaborative effect and whether the captured effect would benefit the prediction of user preferences over items. Therefore, in this work we aim to demystify the collaborative effect captured by message-passing in GNNs and develop new insights towards customizing message-passing for recommendation. First, we theoretically analyze how message-passing captures and leverages the collaborative effect in predicting user preferences. Then, to determine whether the captured collaborative effect would benefit the prediction of user preferences, we propose a recommendation-oriented topological metric, Common Interacted Ratio (CIR), which measures the level of interaction between a specific neighbor of a node with the rest of its neighborhood set. Inspired by our theoretical and empirical analysis, we propose a recommendation-tailored GNN, Augmented Collaboration-Aware Graph Convolutional Network (CAGCN*), that extends upon the LightGCN framework and is able to selectively pass information of neighbors based on their CIR via the Collaboration-Aware Graph Convolution. Experimental results on six benchmark datasets show that CAGCN* outperforms the most representative GNN-based recommendation model, LightGCN, by 9% in Recall@20 and also achieves more than 79% speedup. Our code is publicly available at https://github
Conflict graphs of maximally planar subgraphs of Petersen family graphs

The purpose of this paper is to show that all maximally planar subgraphs of graphs in the Petersen Family have associated conflict graphs unbalanced. All but three strong conflict graphs arising from Petersen Family Graphs are unbalanced, and the three that are balanced all come from $K_{4,4}-e$.
A General Framework for Partial to Full Image Registration

—Image registration is a research field in which images must be compared and aligned independently of the point of view or camera characteristics. In some applications (such as forensic biometrics, satellite photography or outdoor scene identification) classical image registration systems fail due to one of the images compared represents a tiny piece of the other image. For instance, in forensics palmprint recognition, it is usual to find only a small piece of the palmprint, but in the database, the whole palmprint has been enrolled. The main reason of the poor behaviour of classical image registration methods is the gap between the amounts of salient points of both images, which is related to the number of points to be considered as outliers. Usually, the difficulty of finding a good match increases when the image that represents the tiny part of the scene has been drastically rotated. Again, in the case of palmprint forensics, it is difficult to decide a priori the orientation of the found tiny palmprint image. We present a rotation invariant registration method that explicitly considers that the image to be matched is a small piece of a larger image. We have experimentally validated our method in two different scenarios; palmprint identification and outdoor image registration.
Searches for Massive Neutrinos with Mechanical Quantum Sensors

The development of quantum optomechanics now allows mechanical sensors with femtogram masses to be controlled and measured in the quantum regime. If the mechanical element contains isotopes that undergo nuclear decay, measuring the recoil of the sensor following the decay allows reconstruction of the total momentum of all emitted particles, including any neutral particles that may escape detection in traditional detectors. As an example, for weak nuclear decays the momentum of the emitted neutrino can be reconstructed on an event-by-event basis. We present the concept that a single nanometer-scale, optically levitated sensor operated with sensitivity near the standard quantum limit can search for heavy sterile neutrinos in the keV-MeV mass range with sensitivity significantly beyond existing constraints. We also comment on the possibility that mechanical sensors operated well into the quantum regime might ultimately reach the sensitivities required to provide an absolute measurement of the mass of the light neutrino states.
Employing Feature Selection Algorithms to Determine the Immune State of Mice with Rheumatoid Arthritis

Background: The immune response is a dynamic process by which the body determines whether an antigen is self or nonself. The state of this dynamic process is deﬁned by the relative balance and population of inﬂammatory and regulatory actors which comprise this decision making process. The goal of immunotherapy as applied to, e.g. Rheumatoid Arthritis (RA), then, is to bias the immune state in favor of the regulatory actors - thereby shutting down autoimmune pathways in the response. While there are several known approaches to immunotherapy, the eﬀectiveness of the therapy will depend on how this intervention alters the evolution of this state. Unfortunately, this process is determined not only by the dynamics of the process, but the state of the system at the time of intervention - a state which is diﬃcult if not impossible to determine prior to application of the therapy. Results: We consider a mouse model of RA immunotherapy; collect high dimensional data on T cell markers and populations of mice after treatment with a recently developed immunotherapy for RA; and use feature selection algorithms to select a lower dimensional subset of this data which can be used to predict both the full set of T cell markers and populations, along with the progression of the immunotherapy treatment. Conclusion: In this paper, we describe a process to identify a subset of immune cell populations that eﬀectively represent the state of the immune response - which can measure the eﬀectiveness of immunotherapy approaches to treating RA from measured T cell markers. We use a set of mouse-model experiments to obtain a robust dataset of T cell markers and populations and apply machine learning and feature selection techniques to determine speciﬁc T cells populations and models that predict the progression of RA, and can reconstruct any discarded features.
A Comparison of the Role of PPAR and RAR on CYP26 Regulation

The P450 26 family is believed to be responsible for retinoic acid (RA) metabolism and elimination in the human fetus and adults. CYP26A1 and CYP26B1 mRNA is expressed in tissue specific manner and knockout mice of the CYP26 isoforms show distinct malformations and lethality. The aim of this study was to determine differences in CYP26A1 and CYP26B1 regulation and expression. Analysis of CYP26A1 and CYP26B1 expression in a panel of 57 human livers showed CYP26A1 being the major CYP26 isoform present in the liver and its expression being subject to large inter-individual variability between donors. CYP26A1 and RAR β were found to be greatly inducible by RA in HepG2 cells, whereas CYP26B1, RAR α and RAR γ induced to a much lesser extent. Based on treatments with RAR isoform selective ligands, RAR α is the major isoform responsible for CYP26A1 and RAR β induction in HepG2 cells. Classical P450 inducers did not affect CYP26 transcription whereas PPAR γ agonists, pioglitazone and rosiglitazone, up-regulated CYP26B1 transcription by as much as 209 ± 80 –fold and CYP26A1 by 10-fold. RAR β was also up-regulated by pioglitazone and rosiglitazone. CYP26B1 induction by PPAR γ agonists was abolished by the irreversible PPAR γ antagonist (GW9662) whereas RAR β and CYP26A1 induction was unaffected by GW9662. Overall, the results of this study suggest that CYP26B1 and CYP26A1 are regulated by different nuclear receptors resulting in tissue specific expression patterns. The fact that drugs can alter the expression of CYP26 enzymes may have toxicological and therapeutic importance.
DynaST: Dynamic Sparse Transformer for Exemplar-Guided Image Generation

One key challenge of exemplar-guided image generation lies in establishing fine-grained correspondences between input and guided images. Prior approaches, despite the promising results, have relied on either estimating dense attention to compute per-point matching, which is limited to only coarse scales due to the quadratic memory cost, or fixing the number of correspondences to achieve linear complexity, which lacks flexibility. In this paper, we propose a dynamic sparse attention based Transformer model, termed Dynamic Sparse Transformer (DynaST), to achieve fine-level matching with favorable efficiency. The heart of our approach is a novel dynamic-attention unit, dedicated to covering the variation on the optimal number of tokens one position should focus on. Specifically, DynaST leverages the multi-layer nature of Transformer structure, and performs the dynamic attention scheme in a cascaded manner to refine matching results and synthesize visually-pleasing outputs. In addition, we introduce a unified training objective for DynaST, making it a versatile reference-based image translation framework for both supervised and unsupervised scenarios. Extensive experiments on three applications, pose-guided person image generation, edge-based face synthesis, and undistorted image style transfer, demonstrate that DynaST achieves superior performance in local details, outperforming the state of the art while reducing the computational cost significantly. Our code is available at https://github.com/Huage001/DynaST
Many-body quantum non-Markovianity

We port the concept of non-Markovian quantum dynamics to the many-particle realm, by a suitable decomposition of the many-particle Hilbert space. We show how the specific structure of many-particle states determines the observability of non-Markovianity by single- or many-particle observables, and discuss a realization in a readily implementable few-particle set-up.
Study of exclusive di-pion production in diffractive ep interactions

Název práce: Studium exkluzivńı produkce dvou pion̊u v difrakčńıch ep interakćıc Autor: Richard Polifka Katedra (ústav): Ústav částicové a jaderné fyziky Vedoućı diplomové práce: RNDr.Alice Valkárová, DrSc. e-mail vedoućıho: avalkar@mail.desy. de Abstrakt: Byla studována produkce nerezonančńıch dvoupionových event̊u na detektoru H1 na urychlovači HERA. Kinematická oblast byla 4 < Q < 100 GeV 2 a energie v γp těžǐsťovém systému byla v rozmeźı 40 < W < 200 GeV . Spektrum invariantńıch hmot Mππ se nacházelo v oblasti 1.1 − 3.0 GeV . Kromě toho byly hledány daľśı kinematické řezy, aby se minimalizoval vliv rezonanćı. Bylo měřeno azimutálńı rozděleńı úhlu sv́ıraného leptonovou a pionovou rovinou v γp systému za účelem srovnáńı předpověd́ı dvou teoretických model̊u dvougluonové a pomeronové výměny. Pro srovnáńı bylo použito Monte Carlo simulované pro oba procesy. Kĺıčová slova: nerezonančńı piony, difrakčńı DIS, dvougluonová výměna, pomeronová výměna Title: Study of exclusive di-pion production in diffractive ep interactions Author: Richard Polifka Department: Institute of Particle and Nuclear Physics Supervisor: RNDr.Alice Valkárová, DrSc. Supervisor’s e-mail address: avalkar@mail.desy. de Abstract: Production of non resonant di-pion events was studied on the H1 detector at HERA. The kinematical region was 4 < Q < 100 GeV 2 and the γp centre-of-mass energy 40 < W < 200 GeV . The invariant mass spectrum Mππ was in the range 1.1 − 3.0 GeV . Other kinematical cuts have been applied to minimize the influence of resonances. The azimuthal distribution of the pion and lepton plane in the γp frame was measured to compare with the theoretical predictions of the pomeron exchange model and two gluon exchange model. The Monte Carlo for both processes was used for comparison.
DESIGNING MENTAL HEALTH WITH BRAIN DOMINANCE

This study is being revealed about the findings on mental health and brain dominance of the higher secondary students around Kumbakonam educational district, Thanjavur, Tamilnadu India. 167 subjects were taken for this study as the targeted population. The investigator used the tool for collecting the data for brain dominance as the alert scale of the cognitive style questionnaire was prepared by Loren D Crane (1989) and the mental health tool by Sahaya Sarojini (2020). Chi-square for the goodness of fit, t-test for gender, ANOVA, Chi-square, and correlation test were used to analyze this research. The major findings of the study were the populations of the higher secondary students were not equally distributed concerning the factor of mental health and brain dominance. Concerning the t-test, boys and girls higher secondary students concerning left-brain dominance and right brain dominance were significantly different and ANOVA and Chi-square test and correlation there was no significant difference found concerning the factors of mental health and brain dominance. style teachers the resulting study revealed that there is a significant difference between male and female high school teachers in their leadership style of male high school teachers in the autocratic and democratic dimensions and it is significantly different between male and female high school teachers in their brain dominance.
STUDY OF EFFECT OF BMI AND WAIST CIRCUMFERENCE ON BLOOD PRESSURE IN FIRST YEAR MEDICAL STUDENTS

: The present study is undertaken to evaluate any association between BMI, WC and Blood pressure and any tendency to develop prehypertension. The study comprises of 100 medical students of NRI medical college, Sangivalasa near Visakhapatnam.It was carried out ongirl students with age of 17 years. Their BMI, WC and Blood pressure were determined. The examination included measurement of weight, height of the student to determine BMI, measurement of waist circumference, and measurement of blood pressure by sphygmomanometer. In the present study the results are consistent with early clinical studies reporting that there is elevated systolic and diastolic blood pressure with increasing BMI and waist circumference and there is tendency to develop prehypertension in students with higher BMI. Modification of life style factors should be emphasized.
Data Science Models for Weather Forecasting State of the art review

Weather forecasting has become an essential part of both the private and the professional sector. Individuals are planning their activities for the upcoming weekends depending on weather forecasts, for the professional sector, the weather can influence the scheduled tasks for the next days. Some industries like the winter road maintenance may also want to plan their staff depending on how much precipitation is expected. The scientific study of meteorology started developing after the first measuring instruments became available, e.g. the mercury barometer which was invented in the mid-17 th century [5]. The progress that has been made in the field of meteorology since that time is based on a wide variety of physical and mathematical inventions/laws. In the 19 th century, with the development of the electric telegraph, it was possible to create synoptic weather maps by exchanging weather data and observations [5]. After World War I, a British scientist completed a numerical forecast, which is an objective calculation of changes to the weather map based on equations [5]. This numerical forecast took years, since all calculations were done by hand [5]. The general approach of the numerical forecast was accepted decades later because the electronic computer got invented [5]. Today, there are 3 overall different approaches on how weather forecasting can be done nowadays. The first category can be described as Statistical Methods like NWP. Numerical weather prediction is a set of equations used to describe the flow of fluids [2]. Numerical weather forecasting is generally known as a method that requires a considerable amount of computing power, because of the complexity and number of equations that have to be processed [2]. Another statistical model that is often used in weather forecasting is the autoregressive integrated moving average (ARIMA) model [1]. With the evolving techniques like machine learning and deep learning, a trend towards intelligent forecasting models evolved