Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient and Privacy-Preserving Federated Learning based on Full Homomorphic Encryption

Since the first theoretically feasible full homomorphic encryption (FHE) scheme was proposed in 2009, great progress has been achieved. These improvements have made FHE schemes come off the paper and become quite useful in solving some practical problems. In this paper, we propose a set of novel Federated Learning Schemes by utilizing the latest homomorphic encryption technologies, so as to improve the security, functionality and practicality at the same time. Comparisons have been given in four practical data sets separately from medical, business, biometric and financial fields, covering both horizontal and vertical federated learning scenarios. The experiment results show that our scheme achieves significant improvements in security, efficiency and practicality, compared with classical horizontal and vertical federated learning schemes.
Importance sampling for rare event tracking within the ensemble Kalman filtering framework

In this work we employ importance sampling (IS) techniques to track a small over-threshold probability of a running maximum associated with the solution of a stochastic differential equation (SDE) within the framework of ensemble Kalman filtering (EnKF). Between two observation times of the EnKF, we propose to use IS with respect to the initial condition of the SDE, IS with respect to the Wiener process via a stochastic optimal control formulation, and combined IS with respect to both initial condition and Wiener process. Both IS strategies require the approximation of the solution of Kolmogorov Backward equation (KBE) with boundary conditions. In multidimensional settings, we employ a Markovian projection dimension reduction technique to obtain an approximation of the solution of the KBE by just solving a one dimensional PDE. The proposed ideas are tested on two illustrative examples: Double Well SDE and Langevin dynamics, and showcase a significant variance reduction compared to the standard Monte Carlo method and another sampling-based IS technique, namely, multilevel cross entropy.
Sebastian, Basti, Wastl?! Recognizing Named Entities in Bavarian Dialectal Data

Named Entity Recognition (NER) is a fundamental task to extract key information from texts, but annotated resources are scarce for dialects. This paper introduces the first dialectal NER dataset for German, BarNER, with 161K tokens annotated on Bavarian Wikipedia articles (bar-wiki) and tweets (bar-tweet), using a schema adapted from German CoNLL 2006 and GermEval. The Bavarian dialect differs from standard German in lexical distribution, syntactic construction, and entity information. We conduct in-domain, cross-domain, sequential, and joint experiments on two Bavarian and three German corpora and present the first comprehensive NER results on Bavarian. Incorporating knowledge from the larger German NER (sub-)datasets notably improves on bar-wiki and moderately on bar-tweet. Inversely, training first on Bavarian contributes slightly to the seminal German CoNLL 2006 corpus. Moreover, with gold dialect labels on Bavarian tweets, we assess multi-task learning between five NER and two Bavarian-German dialect identification tasks and achieve NER SOTA on bar-wiki. We substantiate the necessity of our low-resource BarNER corpus and the importance of diversity in dialects, genres, and topics in enhancing model performance.
Totally Geodesic Surfaces in Hyperbolic 3-Manifolds: Algorithms and Examples

Finding a totally geodesic surface, an embedded surface where the geodesics in the surface are also geodesics in the surrounding manifold, has been a problem of interest in the study of 3-manifolds. This has especially been of interest in hyperbolic 3-manifolds and knot complements, complements of piecewise-linearly embedded circles in the 3-sphere. This is due to Menasco-Reid's conjecture stating that hyperbolic knot complements do not contain such surfaces. Here, we present an algorithm that determines whether a given surface is totally geodesic and an algorithm that checks whether a given 3-manifold contains a totally geodesic surface. We applied our algorithm on over 150,000 3-manifolds and discovered nine 3-manifolds with totally geodesic surfaces. Additionally, we verified Menasco-Reid's conjecture for knots up to 12 crossings.
Investigating Chromium, Manganese, Calcium and Nickel Abundances in SN1604

The purpose of this study is to investigate the abundance distribution of specific elements throughout the volume Kepler’s SNR, using an extensive dataset from the Chandra observatory archive. Manganese and chromium were used to determine an estimate of the metallicity parameter that characterized the progenitor star by way of the free neutron flux released after the explosion. The resulting value, Z = 0.082 −0.047, placed the progenitor into the solar or supersolar metallicity categories. Furthermore, abundances of calcium and nickel were computed in key regions of the remnant. Higher calcium concentrations were identified at higher radii, as a result of the environment’s enrichment with alpha particles from cosmic rays. For the determination of nickel abundances, an additional dataset was employed, taken from the XMM-Newton archive.
Xylooligosaccharide supplementation alters gut bacteria in both healthy and prediabetic adults: a pilot study

Background: It has been suggested that gut microbiota is altered in Type 2 Diabetes Mellitus (T2DM) patients. Objective: This study was to evaluate the effect of the prebiotic xylooligosaccharide (XOS) on the gut microbiota in both healthy and prediabetic (Pre-DM) subjects, as well as impaired glucose tolerance (IGT) in Pre-DM. Subjects/Methods: Pre-DM (n = 13) or healthy (n = 16) subjects were randomized to receive 2 g/day XOS or placebo for 8-weeks. In Pre-DM subjects, body composition and oral glucose tolerance test (OGTT) was done at baseline and week 8. Stool from Pre-DM and healthy subjects at baseline and week 8 was analyzed for gut microbiota characterization using Illumina MiSeq sequencing. Results: We identified 40 Pre-DM associated bacterial taxa. Among them, the abundance of the genera Enterorhabdus, Howardella, and Slackia was higher in Pre-DM. XOS significantly decreased or reversed the increase in abundance of Howardella, Enterorhabdus, and Slackia observed in healthy or Pre-DM subjects. Abundance of the species Blautia hydrogenotrophica was lower in pre-DM subjects, while XOS increased its abundance. In Pre-DM, XOS showed a tendency to reduce OGTT 2-h insulin levels (P = 0.13), but had no effect on body composition, HOMA-IR, serum glucose, triglyceride, satiety hormones, and TNFα. Conclusion: This is the first clinical observation of modifications of the gut microbiota by XOS in both healthy and Pre-DM subjects in a pilot study. Prebiotic XOS may be beneficial in reversing changes in the gut microbiota during the development of diabetes. Clinical trial registration: NCT01944904 (https://clinicaltrials.gov/ct2/show/NCT01944904).
Are Clowns Good for Everyone? The Influence of Trait Cheerfulness on Emotional Reactions to a Hospital Clown Intervention

Trait cheerfulness predicts individual differences in experiences and behavioral responses in various humor experiments and settings. The present study is the first to investigate whether trait cheerfulness also influences the impact of a hospital clown intervention on the emotional state of patients. Forty-two adults received a clown visit in a rehabilitation center and rated their emotional state and trait cheerfulness afterward. Facial expressions of patients during the clown visit were coded with the Facial Action Coding System. Looking at the total sample, the hospital clown intervention elicited more frequent facial expressions of genuine enjoyment (Duchenne smiles) than other smiles (Non-Duchenne smiles), and more Duchenne smiles went along with more perceived funniness, a higher level of global positive feelings and transcendence. This supports the notion that overall, hospital clown interventions are beneficial for patients. However, when considering individual differences in the receptiveness to humor, results confirmed that high trait cheerful patients showed more Duchenne smiles than low trait cheerful patients (with no difference in Non-Duchenne smiles), and reported a higher level of positive emotions than low trait cheerful individuals. In summary, although hospital clown interventions on average successfully raise the patients’ level of positive emotions, not all patients in hospitals are equally susceptible to respond to humor with amusement, and thus do not equally benefit from a hospital clown intervention. Implications for research and practitioners are discussed.
Bayesian Optimization Sequential Surrogate (BOSS) Algorithm: Fast Bayesian Inference for a Broad Class of Bayesian Hierarchical Models

Approximate Bayesian inference based on Laplace approximation and quadrature methods have become increasingly popular for their efficiency at fitting latent Gaussian models (LGM), which encompass popular models such as Bayesian generalized linear models, survival models, and spatio-temporal models. However, many useful models fall under the LGM framework only if some conditioning parameters are fixed, as the design matrix would vary with these parameters otherwise. Such models are termed the conditional LGMs with examples in change-point detection, non-linear regression, etc. Existing methods for fitting conditional LGMs rely on grid search or Markov-chain Monte Carlo (MCMC); both require a large number of evaluations of the unnormalized posterior density of the conditioning parameters. As each evaluation of the density requires fitting a separate LGM, these methods become computationally prohibitive beyond simple scenarios. In this work, we introduce the Bayesian optimization sequential surrogate (BOSS) algorithm, which combines Bayesian optimization with approximate Bayesian inference methods to significantly reduce the computational resources required for fitting conditional LGMs. With orders of magnitude fewer evaluations compared to grid or MCMC methods, Bayesian optimization provides us with sequential design points that capture the majority of the posterior mass of the conditioning parameters, which subsequently yields an accurate surrogate posterior distribution that can be easily normalized. We illustrate the efficiency, accuracy, and practical utility of the proposed method through extensive simulation studies and real-world applications in epidemiology, environmental sciences, and astrophysics.
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when elicited via distal joints. Associated reactions in the wrist/ﬁnger ﬂexors were stronger than those of other paretic muscles and were the only ones whose response depended on whether the non-paretic contraction was at a proximal or distal joint. Results provide indirect evidence linking the inﬂuence of brainstem motor pathways to abnormal motor behaviors post-stroke, and they demonstrate the need to examine whole-limb behavior when studying or seeking to rehabilitate the paretic upper limb.
Crystalformer: Infinitely Connected Attention for Periodic Structure Encoding

Predicting physical properties of materials from their crystal structures is a fundamental problem in materials science. In peripheral areas such as the prediction of molecular properties, fully connected attention networks have been shown to be successful. However, unlike these finite atom arrangements, crystal structures are infinitely repeating, periodic arrangements of atoms, whose fully connected attention results in infinitely connected attention. In this work, we show that this infinitely connected attention can lead to a computationally tractable formulation, interpreted as neural potential summation, that performs infinite interatomic potential summations in a deeply learned feature space. We then propose a simple yet effective Transformer-based encoder architecture for crystal structures called Crystalformer. Compared to an existing Transformer-based model, the proposed model requires only 29.4% of the number of parameters, with minimal modifications to the original Transformer architecture. Despite the architectural simplicity, the proposed method outperforms state-of-the-art methods for various property regression tasks on the Materials Project and JARVIS-DFT datasets.
Relational Representation Learning Network for Cross-Spectral Image Patch Matching

Recently, feature relation learning has drawn widespread attention in cross-spectral image patch matching. However, existing related research focuses on extracting diverse relations between image patch features and ignores sufficient intrinsic feature representations of individual image patches. Therefore, an innovative relational representation learning idea is proposed for the first time, which simultaneously focuses on sufficiently mining the intrinsic features of individual image patches and the relations between image patch features. Based on this, we construct a lightweight Relational Representation Learning Network (RRL-Net). Specifically, we innovatively construct an autoencoder to fully characterize the individual intrinsic features, and introduce a Feature Interaction Learning (FIL) module to extract deep-level feature relations. To further fully mine individual intrinsic features, a lightweight Multi-dimensional Global-to-Local Attention (MGLA) module is constructed to enhance the global feature extraction of individual image patches and capture local dependencies within global features. By combining the MGLA module, we further explore the feature extraction network and construct an Attention-based Lightweight Feature Extraction (ALFE) network. In addition, we propose a Multi-Loss Post-Pruning (MLPP) optimization strategy, which greatly promotes network optimization while avoiding increases in parameters and inference time. Extensive experiments demonstrate that our RRL-Net achieves state-of-the-art (SOTA) performance on multiple public datasets. Our code will be made public later.
Effect of Leaders Voice on Financial Market: An Empirical Deep Learning Expedition on NASDAQ, NSE, and Beyond

Financial market like the price of stock, share, gold, oil, mutual funds are affected by the news and posts on social media. In this work deep learning based models are proposed to predict the trend of financial market based on NLP analysis of the twitter handles of leaders of different fields. There are many models available to predict financial market based on only the historical data of the financial component but combining historical data with news and posts of the social media like Twitter is the main objective of the present work. Substantial improvement is shown in the result. The main features of the present work are: a) proposing completely generalized algorithm which is able to generate models for any twitter handle and any financial component, b) predicting the time window for a tweets effect on a stock price c) analyzing the effect of multiple twitter handles for predicting the trend. A detailed survey is done to find out the latest work in recent years in the similar field, find the research gap, and collect the required data for analysis and prediction. State-of-the-art algorithm is proposed and complete implementation with environment is given. An insightful trend of the result improvement considering the NLP analysis of twitter data on financial market components is shown. The Indian and USA financial markets are explored in the present work where as other markets can be taken in future. The socio-economic impact of the present work is discussed in conclusion.
Data Selection for User Topic Model in Twitter-Like Service

Twitter-like services are now a popular kind of online social networking services, in which user can express themselves, share contents, and follow others they are interested in. User modeling, building a model for user's interests, is a key problem in many social networking applications, such as recommendation, advertisement, etc. This paper focuses on data selection for user modeling in Twitter-like services. That is, we study the problem of how to select useful data to model a user's interests. Using different data, three user modeling methods are proposed and experiments on a real Twitter-like service are conducted to verify the effectiveness of proposed approaches. Experimental results shows that modeling user's interests with what he/she wrote and selectively what he read performs the best among the three methods we proposed.
Effects of strangeness on the chiral pseudo-critical line

Within a 2+1 flavor Nambu\textendash Jona-Lasinio model, we have calculated the curvature coefficients and checked them against available lattice QCD estimations. With the observation that the flavor mixing due to the `t Hooft determinant term significantly affects the $\kappa_{2}^{S}$, we explore the effect of $\mu_{S}$ on the $T-\mu_{B}$ cross-over lines. With the novel determination of negative $\kappa_{2}^{B}$ at large $\mu_{S}$, we advocate the importance of studying the same in lattice QCD.
Motivic Hilbert zeta functions for curve singularities and related invariants

In the present paper, we show that the motivic Hilbert zeta function for a curve singularity yields the generating functions for Euler numbers of punctual Hilbert schemes when any punctual Hilbert scheme admits an affine cell decomposition. This fact allows us to derive the relations among the motivic Hilbert zeta function and other invariants such as the generating function for semi-modules of the semi-group of the singularity, the HOMFLY polynomial and the degrees of Severi strata of the miniversal deformation of the singularity. As an application of the fact above, we also generalize Kawai's result regarding the generating function for the Euler numbers of a singular curve.
MAC Advice for Facility Location Mechanism Design

Algorithms with predictions have attracted much attention in the last years across various domains, including variants of facility location, as a way to surpass traditional worst-case analyses. We study the $k$-facility location mechanism design problem, where the $n$ agents are strategic and might misreport their location. Unlike previous models, where predictions are for the $k$ optimal facility locations, we receive $n$ predictions for the locations of each of the agents. However, these predictions are only"mostly"and"approximately"correct (or MAC for short) -- i.e., some $\delta$-fraction of the predicted locations are allowed to be arbitrarily incorrect, and the remainder of the predictions are allowed to be correct up to an $\varepsilon$-error. We make no assumption on the independence of the errors. Can such predictions allow us to beat the current best bounds for strategyproof facility location? We show that the $1$-median (geometric median) of a set of points is naturally robust under corruptions, which leads to an algorithm for single-facility location with MAC predictions. We extend the robustness result to a"balanced"variant of the $k$ facilities case. Without balancedness, we show that robustness completely breaks down, even for the setting of $k=2$ facilities on a line. For this"unbalanced"setting, we devise a truthful random mechanism that outperforms the best known result of Lu et al. [2010], which does not use predictions. En route, we introduce the problem of"second"facility location (when the first facility's location is already fixed). Our findings on the robustness of the $1$-median and more generally $k$-medians may be of independent interest, as quantitative versions of classic breakdown-point results in robust statistics.
CASPER: Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation

Spatiotemporal time series is the foundation of understanding human activities and their impacts, which is usually collected via monitoring sensors placed at different locations. The collected data usually contains missing values due to various failures, which have significant impact on data analysis. To impute the missing values, a lot of methods have been introduced. When recovering a specific data point, most existing methods tend to take into consideration all the information relevant to that point regardless of whether they have a cause-and-effect relationship. During data collection, it is inevitable that some unknown confounders are included, e.g., background noise in time series and non-causal shortcut edges in the constructed sensor network. These confounders could open backdoor paths between the input and output, in other words, they establish non-causal correlations between the input and output. Over-exploiting these non-causal correlations could result in overfitting and make the model vulnerable to noises. In this paper, we first revisit spatiotemporal time series imputation from a causal perspective, which shows the causal relationships among the input, output, embeddings and confounders. Next, we show how to block the confounders via the frontdoor adjustment. Based on the results of the frontdoor adjustment, we introduce a novel Causality-Aware SPatiotEmpoRal graph neural network (CASPER), which contains a novel Spatiotemporal Causal Attention (SCA) and a Prompt Based Decoder (PBD). PBD could reduce the impact of confounders and SCA could discover the sparse causal relationships among embeddings. Theoretical analysis reveals that SCA discovers causal relationships based on the values of gradients. We evaluate Casper on three real-world datasets, and the experimental results show that Casper outperforms the baselines and effectively discovers causal relationships.
A $C^\ast$-algebraic view on the interaction of real- and reciprocal space topology in skyrmion crystals

Understanding the interaction of real- and reciprocal space topology in skyrmion crystals is an open problem. We approach it from the viewpoint of $C^\ast$-algebras and calculate all admissible Chern numbers of a strongly coupled tight-binding skyrmion system on a triangular lattice as a function of Fermi energy and texture parameters. Our analysis reveals the topological complexity of electronic states coupled to spin textures, and the failure of the adiabatic picture to account for it in terms of emergent electromagnetism. On the contrary, we explain the discontinuous jumps in the real-space winding number in terms of collective evolution in real-, reciprocal, and mixed space Chern numbers. Our work sets the stage for further research on topological dynamics in complex dynamic spin textures coupled to external fields.
A new framework for constrained optimization via feedback control of Lagrange multipliers

The continuous-time analysis of existing iterative algorithms for optimization has a long history. This work proposes a novel continuous-time control-theoretic framework for equality-constrained optimization. The key idea is to design a feedback control system where the Lagrange multipliers are the control input, and the output represents the constraints. The system converges to a stationary point of the constrained optimization problem through suitable regulation. Regarding the Lagrange multipliers, we consider two control laws: proportional-integral control and feedback linearization. These choices give rise to a family of different methods. We rigorously develop the related algorithms, theoretically analyze their convergence and present several numerical experiments to support their effectiveness concerning the state-of-the-art approaches.