Miracle-dz/UniFAR-FTUs huggingface dataset:
Natural gradient Variational Bayes without matrix inversion

This paper presents an approach for efficiently approximating the inverse of Fisher information, a key component in variational Bayes inference. A notable aspect of our approach is the avoidance of analytically computing the Fisher information matrix and its explicit inversion. Instead, we introduce an iterative procedure for generating a sequence of matrices that converge to the inverse of Fisher information. The natural gradient variational Bayes algorithm without matrix inversion is provably convergent and achieves a convergence rate of order O(log s/s), with s the number of iterations. We also obtain a central limit theorem for the iterates. Our algorithm exhibits versatility, making it applicable across a diverse array of variational Bayes domains, including Gaussian approximation and normalizing flow Variational Bayes. We offer a range of numerical examples to demonstrate the efficiency and reliability of the proposed variational Bayes method.
Extra Dimensions Beyond the Horizon

We discuss an extra-dimensional braneworld with a 5th dimension compactified on a circle. As a characteristic feature, the warp factor is hyperbolic and separates the hidden and visible branes by a bulk horizon without a singularity. The two most widely separated scales of 4D physics - the 4D Planck mass and 4D cosmological constant - are determined by two physical scales in the extra dimension, namely: $(i)$ the proper size of the extra dimension, $R$, and, $(ii)$ the distance between the visible brane and the horizon, $R_0$. A realistic scale hierarchy between 4D Planck mass and 4D cosmological constant is obtained for $R/R_0\sim2.34$. The usual fine tuning is not reduced but promoted to a fine tuning of two separate brane energy densities that must approach the fundamental scale of the model with very high precision. Our scenario is based on an exact solution to the 5D Einstein equations with a strictly empty bulk and Friedmann-Lema\^itre-Robertson-Walker metric on the 4D branes. This requires positive 4D brane energy densities and describes an adiabatic runaway solution in agreement with the de Sitter swampland conjecture. The Kaluza-Klein (KK) graviton states are solutions of a modified P\"oschl-Teller potential which permits a discrete graviton spectrum of $\textit{exactly two}$ modes. In addition to the usual massless graviton, our scenario predicts an extra massive spin-2 graviton with a mass gap of $m_1=\sqrt{2}H_0\approx2\times10^{-33}\,\mathrm{eV}$ which might be detectable in the foreseeable future. A KK tower of gravitons, or a possible continuum of massive graviton states, is prohibited by unitarity with respect to the horizon. We discuss hurdles in turning this model into a realistic cosmology at all times, which points us towards 4D brane tensions that that must be raising towards the fundamental scale of the model, while the observable 4D expansion rate is decreasing.
SRMAC -- Smoothed Recursive Moving Average Crossover for Real-Time Systolic Peak Detection in Photoplethysmography

Purpose. Photoplethysmography (PPG) is a non-invasive technique that measures changes in blood flow volume through optical means. Previous research has established the feasibility of PPG peak detection based on the crossover of moving averages. This paper proposes the Smoothed Recuarsive Moving Average Crossover, which eliminates the need for post-processing and nonlinear pre-processing of previous crossover-based peak detectors. The proposed model is advantageous regarding memory and computational complexity, making it attractive for implementations on embedded devices. Methods. Along with this paper, we make available a novel dataset comprising 66 minutes of PPG recordings. The optimization and assessment of the proposed peak detection model use this dataset. Its optimization is accomplished with the simple random search heuristic, while the leave-subject-out cross-validation method provides the means to assess its performance. The source code for all experiments reported in this research is also available in an online repository. Results. The experimental study examines the performance of the proposed model considering different arrangements of the PPG data. The experiments show that the proposed model performs better than the previous crossover-based approach from the literature regarding the precision and recall metrics. More specifically, our model has an average precision of 0.9937 and an average recall of 0.9968. Conclusion. The contribution of this research to the scientific community and literature is twofold. The dataset we collected is open for any researcher, and we improve upon the leading edge on crossover-based PPG peak detection. This improvement comes in terms of performance metrics and computational cost.
Design of Secure NOMA Against Full-Duplex Proactive Eavesdropping

We investigate the problem of secure non-orthogonal multiple access (NOMA) against full-duplex proactive eavesdropping, where the eavesdropper performs passive eavesdropping and active jamming simultaneously to interrupt the NOMA transmissions. To avoid the transmission outage caused by the unknown jamming level from the eavesdropper, we propose a novel transmission outage constrained scheme to limit the transmission outage probabilities of the users to a maximum tolerable threshold, which is also helpful in reducing the secrecy outage. We derive analytical expressions for the secrecy outage probability and secrecy diversity order to characterize the secrecy performance. Simulation results are provided to demonstrate the accuracy of the derived analytical results and the efficiency of the proposed scheme.
Entropy Causal Graphs for Multivariate Time Series Anomaly Detection

Many multivariate time series anomaly detection frameworks have been proposed and widely applied. However, most of these frameworks do not consider intrinsic relationships between variables in multivariate time series data, thus ignoring the causal relationship among variables and degrading anomaly detection performance. This work proposes a novel framework called CGAD, an entropy Causal Graph for multivariate time series Anomaly Detection. CGAD utilizes transfer entropy to construct graph structures that unveil the underlying causal relationships among time series data. Weighted graph convolutional networks combined with causal convolutions are employed to model both the causal graph structures and the temporal patterns within multivariate time series data. Furthermore, CGAD applies anomaly scoring, leveraging median absolute deviation-based normalization to improve the robustness of the anomaly identification process. Extensive experiments demonstrate that CGAD outperforms state-of-the-art methods on real-world datasets with a 15% average improvement based on three different multivariate time series anomaly detection metrics.
Flavoured Majorana Dark Matter then and now: From freeze-out scenarios to LHC signatures

We study a simplified Dark Matter model in the Dark Minimal Flavour Violation framework. Our model complements the Standard Model with a flavoured Dark Matter Majorana triplet and a coloured scalar mediator that share a Yukawa coupling with the right-handed up-type quarks with the coupling matrix $\lambda$. We extend previous work on this topic by exploring a large range of cosmologically viable parameter space, including the coannihilation region and, in particular, the region of conversion-driven freeze-out, while considering constraints from $D^0-\bar D^0$ mixing as well as constraints from direct and indirect Dark Matter searches. We find various realisations of conversion-driven freeze-out within the model, that open up allowed windows of parameter space towards small mass splittings and very weak Dark Matter couplings. Finally, we probe the model by reinterpreting current LHC searches for missing energy and long-lived particles. We point out gaps in the coverage of current constraints as well as new opportunities to search for the model at the LHC, in particular, the charge asymmetry in single-top production associated with jets and missing energy.
Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space

Federated Learning (FL) involves training a model over a dataset distributed among clients, with the constraint that each client’s dataset is localized and possibly heterogeneous. In FL, small and noisy datasets are common, highlighting the need for well-calibrated models that represent the uncertainty of predictions. The closest FL techniques to achieving such goals are the Bayesian FL methods which collect parameter samples from local posteriors, and aggregate them to approximate the global posterior. To improve scalability for larger models, one common Bayesian approach is to approximate the global predictive posterior by multiplying local predictive posteriors. In this work, we demonstrate that this method gives systematically overconfident predictions, and we remedy this by proposing β-Predictive Bayes, a Bayesian FL algorithm that interpolates between a mixture and product of the predictive posteriors, using a tunable parameter β. This parameter is tuned to improve the global ensemble’s calibration, before it is distilled to a single model. Our method is evaluated on a variety of regression and classification datasets to demonstrate its superiority in calibration to other baselines, even as data heterogeneity increases. Code available at https://github.com/hasanmohsin/betaPredBayesFL. Our paper's full version is at https://arxiv.org/abs/2312.09817.
Testing a Neural Network for Anomaly Detection in the CMS Global
Trigger Test Crate during Run 3

We present the deployment and testing of an autoencoder trained for unbiased detection of new physics signatures in the CMS Level-1 Global Trigger (GT) test crate during LHC Run 3. The GT test crate is a copy of the main GT system, receiving the same input data, but whose output is not used to trigger the readout of CMS, providing a platform for thorough testing of new trigger algorithms on live data, but without interrupting data taking. We describe the integration of the Neural Network into the GT test crate, and the monitoring, testing, and validation of the algorithm during proton collisions.
The Bubble of Nothing under T-duality

The bubble of nothing is a solution to Einstein's equations where a circle shrinks and pinches off smoothly. As such, it is one of the simplest examples of a dynamical cobordism to nothing. We take a first step in studying how this solution transforms under T-duality in bosonic string theory. Applying Buscher's rules reveals that the dual solution features a singular, strongly coupled core, with a circle blowing-up rather than pinching off. This naive approach to T-duality solely accounts for the zero-modes of the fields after dimensional reduction on the circle. For this reason, we argue that this is not the full picture that the T-dual solution should depend non-trivially on the dual circle. We point out evidence to this effect both in the gravity description and on the worldsheet. A more complete description of the T-dual object would require a full-fledged sigma model for the bubble of nothing. Nevertheless, inspired by similar examples in the literature, we detail one possible scenario where the stringy bubble of nothing is mediated by closed string tachyon condensation and we discuss its T-duality.
Drivers and Barriers of AI Adoption and Use in Scientific Research

New technologies have the power to revolutionize science. It has happened in the past and is happening again with the emergence of new computational tools, such as artificial intelligence and machine learning. Despite the documented impact of these technologies, there remains a significant gap in understanding the process of their adoption within the scientific community. In this paper, we draw on theories of scientific and technical human capital to study the integration of AI in scientific research, focusing on the human capital of scientists and the external resources available within their network of collaborators and institutions. We validate our hypotheses on a large sample of publications from OpenAlex, covering all sciences from 1980 to 2020, and identify a set key drivers and inhibitors of AI adoption and use in science. Our results suggest that AI is pioneered by domain scientists with a `taste for exploration' and who are embedded in a network rich of computer scientists, experienced AI scientists and early-career researchers; they come from institutions with high citation impact and a relatively strong publication history on AI. The access to computing resources only matters for a few scientific disciplines, such as chemistry and medical sciences. Once AI is integrated into research, most adoption factors continue to influence its subsequent reuse. Implications for the organization and management of science in the evolving era of AI-driven discovery are discussed.
Connection formulae for orthogonal polynomials when the measure is modified by an even factor

In this paper we consider connection formulae for orthogonal polynomials in the context of Christoffel transformations for the case where a weight function, not necessarily even, is multiplied by an even function $c_{2k}(x),k\in \mathbb{N}_0$. When $p_n$ is orthogonal with respect to a weight $w(x)$ and $g_{n-m}$ is orthogonal with respect to the weight $c_{2k}(x)w(x)$, we show that $k\in\{0,1,\dots,m\}$ is a necessary and sufficient condition for existence of a connection formula involving a polynomial $G_{m-1}$ of degree $(m-1)$, such that the $(n-1)$ zeros of $G_{m-1}g_{n-m}$ and the $n$ zeros of $p_n$ interlace. We apply our result to investigate Stieltjes interlacing and bounds for the zeros of Meixner-Pollaczek and Pseudo-Jacobi polynomials.
The Optimal Strategy against Hedge Algorithm in Repeated Games

This paper aims to solve the optimal strategy against a well-known adaptive algorithm, the Hedge algorithm, in a finitely repeated $2\times 2$ zero-sum game. In the literature, related theoretical results are very rare. To this end, we make the evolution analysis for the resulting dynamical game system and build the action recurrence relation based on the Bellman optimality equation. First, we define the state and the State Transition Triangle Graph (STTG); then, we prove that the game system will behave in a periodic-like way when the opponent adopts the myopic best response. Further, based on the myopic path and the recurrence relation between the optimal actions at time-adjacent states, we can solve the optimal strategy of the opponent, which is proved to be periodic on the time interval truncated by a tiny segment and has the same period as the myopic path. Results in this paper are rigorous and inspiring, and the method might help solve the optimal strategy for general games and general algorithms.
A Novel 1D Generative Adversarial Network-based Framework for Atrial Fibrillation Detection using Restored Wrist Photoplethysmography Signals

Atrial fibrillation (AF) increases the risk of stroke. Electrocardiogram (ECG) is used for AF detection, while photoplethysmography (PPG) is simple to use and appropriate for long-term monitoring. We have developed a novel approach to detect AF from smartwatch-based wrist PPG signals. To the best of our knowledge, this is the first study to employ 1D CycleGAN for the reconstruction of 1D wrist PPG signals, since the quality of wrist PPG signals is very poor due to motion artifacts and acquisition site limitations. Our proposed approach is validated on a dataset of 21,278 10s long wrist PPG segments. The dataset was divided into two halves and two experiments were conducted. One half was used for training and the other half for testing and vice versa. Our classification model (Self-AFNet) with 1D-CycleGAN for restoration achieves an accuracy of 96.41% and 97.09% for both splits, respectively. There is a noteworthy improvement in accuracy (2.94% and 5.08% for test splits, respectively) for restored signals as compared to unrestored PPG. AF detection task was also performed using ECG signals with the matched PPG signals to assess the validity of employing reconstructed PPG-based AF classification. Self-AFNet managed to achieve an accuracy of 98.07% and 98.97%, respectively using two ECG splits which is comparable to the performance of AF detection utilizing reconstructed PPG segments. This study reflects that reconstructed wrist PPG signals acquired from wearable devices can be used for reliable detection of atrial fibrillation, which can significantly help in the reduction of the risk of stroke.
Transport response of topological hinge modes in $\alpha$-Bi$_4$Br$_4$

Electronic topological phases are renowned for their unique properties, where conducting surface states exist on the boundary of an insulating three-dimensional bulk. While the transport response of the surface states has been extensively studied, the response of the topological hinge modes remains elusive. Here, we investigate a layered topological insulator $\alpha$-Bi$_4$Br$_4$, and provide the first evidence for quantum transport in gapless topological hinge states existing within the insulating bulk and surface energy gaps. Our magnetoresistance measurements reveal pronounced h/e periodic (where h denotes Planck's constant and e represents the electron charge) Aharonov-Bohm oscillation. The observed periodicity, which directly reflects the enclosed area of phase-coherent electron propagation, matches the area enclosed by the sample hinges, providing compelling evidence for the quantum interference of electrons circumnavigating around the hinges. Notably, the h/e oscillations evolve as a function of magnetic field orientation, following the interference paths along the hinge modes that are allowed by topology and symmetry, and in agreement with the locations of the hinge modes according to our scanning tunneling microscopy images. Remarkably, this demonstration of quantum transport in a topological insulator can be achieved using a flake geometry and we show that it remains robust even at elevated temperatures. Our findings collectively reveal the quantum transport response of topological hinge modes with both topological nature and quantum coherence, which can be directly applied to the development of efficient quantum electronic devices.
Modeling and Predicting Epidemic Spread: A Gaussian Process Regression Approach

Modeling and prediction of epidemic spread are critical to assist in policy-making for mitigation. Therefore, we present a new method based on Gaussian Process Regression to model and predict epidemics, and it quantifies prediction confidence through variance and high probability error bounds. Gaussian Process Regression excels in using small datasets and providing uncertainty bounds, and both of these properties are critical in modeling and predicting epidemic spreading processes with limited data. However, the derivation of formal uncertainty bounds remains lacking when using Gaussian Process Regression in the setting of epidemics, which limits its usefulness in guiding mitigation efforts. Therefore, in this work, we develop a novel bound on the variance of the prediction that quantifies the impact of the epidemic data on the predictions we make. Further, we develop a high probability error bound on the prediction, and we quantify how the epidemic spread, the infection data, and the length of the prediction horizon all affect this error bound. We also show that the error stays below a certain threshold based on the length of the prediction horizon. To illustrate this framework, we leverage Gaussian Process Regression to model and predict COVID-19 using real-world infection data from the United Kingdom.
Peer Learning: Learning Complex Policies in Groups from Scratch via Action Recommendations

Peer learning is a novel high-level reinforcement learning framework for agents learning in groups. While standard reinforcement learning trains an individual agent in trial-and-error fashion, all on its own, peer learning addresses a related setting in which a group of agents, i.e., peers, learns to master a task simultaneously together from scratch. Peers are allowed to communicate only about their own states and actions recommended by others: "What would you do in my situation? ". Our motivation is to study the learning behavior of these agents. We formalize the teacher selection process in the action advice setting as a multi-armed bandit problem and therefore highlight the need for exploration. Eventually, we analyze the learning behavior of the peers and observe their ability to rank the agents' performance within the study group and understand which agents give reliable advice. Further, we compare peer learning with single agent learning and a state-of-the-art action advice baseline. We show that peer learning is able to outperform single-agent learning and the baseline in several challenging discrete and continuous OpenAI Gym domains. Doing so, we also show that within such a framework complex policies from action recommendations beyond discrete action spaces can evolve.
One Self-Configurable Model to Solve Many Abstract Visual Reasoning Problems

Abstract Visual Reasoning (AVR) comprises a wide selection of various problems similar to those used in human IQ tests. Recent years have brought dynamic progress in solving particular AVR tasks, however, in the contemporary literature AVR problems are largely dealt with in isolation, leading to highly specialized task-specific methods. With the aim of developing universal learning systems in the AVR domain, we propose the unified model for solving Single-Choice Abstract visual Reasoning tasks (SCAR), capable of solving various single-choice AVR tasks, without making any a priori assumptions about the task structure, in particular the number and location of panels. The proposed model relies on a novel Structure-Aware dynamic Layer (SAL), which adapts its weights to the structure of the considered AVR problem. Experiments conducted on Raven's Progressive Matrices, Visual Analogy Problems, and Odd One Out problems show that SCAR (SAL-based models, in general) effectively solves diverse AVR tasks, and its performance is on par with the state-of-the-art task-specific baselines. What is more, SCAR demonstrates effective knowledge reuse in multi-task and transfer learning settings. To our knowledge, this work is the first successful attempt to construct a general single-choice AVR solver relying on self-configurable architecture and unified solving method. With this work we aim to stimulate and foster progress on task-independent research paths in the AVR domain, with the long-term goal of development of a general AVR solver.
Amphion: An Open-Source Audio, Music and Speech Generation Toolkit

Amphion is an open-source toolkit for Audio, Music, and Speech Generation, targeting to ease the way for junior researchers and engineers into these fields. It presents a unified framework that is inclusive of diverse generation tasks and models, with the added bonus of being easily extendable for new incorporation. The toolkit is designed with beginner-friendly workflows and pre-trained models, allowing both beginners and seasoned researchers to kick-start their projects with relative ease. Additionally, it provides interactive visualizations and demonstrations of classic models for educational purposes. The initial release of Amphion v0.1 supports a range of tasks including Text to Speech (TTS), Text to Audio (TTA), and Singing Voice Conversion (SVC), supplemented by essential components like data preprocessing, state-of-the-art vocoders, and evaluation metrics. This paper presents a high-level overview of Amphion.