Miracle-dz/UniFAR-FTUs huggingface dataset:
Majorana versus Dirac Constraints on the Neutrino Dipole Moments

Massive neutrinos are guaranteed to have nonzero electromagnetic moments and, since there are at least three neutrino species, these dipole moments define a matrix. Here, we estimate the current upper bounds on all independent neutrino electromagnetic moments, concentrating on Earth-bound experiments and measurements with solar neutrinos, including the very recent results reported by XENONnT. We make no simplifying assumptions and compare the hypotheses that neutrinos are Majorana fermions or Dirac fermions. In particular, we fully explore constraints in the Dirac-neutrino parameter space. Majorana and Dirac neutrinos are different; for example, the upper bounds on the magnitudes of the elements of the dipole moment matrix are weaker for Dirac neutrinos, relative to Majorana neutrinos. The potential physics reach of next-generation experiments also depends on the nature of the neutrino. We find that a next-generation experiment two orders of magnitude more sensitive to the neutrino electromagnetic moments via $\nu_{\mu}$ elastic scattering may discover that the neutrino electromagnetic moments are nonzero if the neutrinos are Dirac fermions. Instead, if the neutrinos are Majorana fermions, such a discovery is ruled out by existing solar neutrino data, unless there are more than three light neutrinos.
E-LMC: Extended Linear Model of Coregionalization for Spatial Field Prediction

Physical simulations based on partial differential equations typically generate spatial fields results, which are utilized to calculate specific properties of a system for engineering design and optimization. Due to the intensive computational burden of the simulations, a surrogate model mapping the low-dimensional inputs to the spatial fields are commonly built based on a relatively small dataset. To resolve the challenge of predicting the whole spatial field, the popular linear model of coregional-ization (LMC) can disentangle complicated correlations within the high-dimensional spatial field outputs and deliver accurate predictions. However, LMC fails if the spatial field cannot be well approximated by a linear combination of base functions with latent processes. In this paper, we present the Extended Linear Model of Coregionalization (E-LMC) by introducing an invertible neural network to linearize the highly complex and nonlinear spatial fields so that the LMC can easily generalize to nonlinear problems while preserving the traceability and scalability. Several real-world applications demonstrate that E-LMC can exploit spatial correlations effectively, showing a maximum improvement of about 40% over the original LMC and outperforming the other state-of-the-art spatial field models.
CLaCLab at SocialDisNER: Using Medical Gazetteers for Named-Entity Recognition of Disease Mentions in Spanish Tweets

This paper summarizes the CLaC submission for SMM4H 2022 Task 10 which concerns the recognition of diseases mentioned in Spanish tweets. Before classifying each token, we encode each token with a transformer encoder using features from Multilingual RoBERTa Large, UMLS gazetteer, and DISTEMIST gazetteer, among others. We obtain a strict F1 score of 0.869, with competition mean of 0.675, standard deviation of 0.245, and median of 0.761.
Strong Optimistic Solving for Dynamic Symbolic Execution

Dynamic symbolic execution (DSE) is an effective method for automated program testing and bug detection. It is increasing the code coverage by the complex branches exploration during hybrid fuzzing. DSE tools invert the branches along some execution path and help fuzzer examine previously unavailable program parts. DSE often faces over- and underconstraint problems. The first one leads to significant analysis complication while the second one causes inaccurate symbolic execution.We propose strong optimistic solving method that eliminates irrelevant path predicate constraints for target branch inversion. We eliminate such symbolic constraints that the target branch is not control dependent on. Moreover, we separately handle symbolic branches that have nested control transfer instructions that pass control beyond the parent branch scope, e.g. return, goto, break, etc. We implement the proposed method in our dynamic symbolic execution tool Sydr.We evaluate the strong optimistic strategy, the optimistic strategy that contains only the last constraint negation, and their combination. The results show that the strategies combination helps increase either the code coverage or the average number of correctly inverted branches per one minute. It is optimal to apply both strategies together in contrast with other configurations.
Beyond Random Split for Assessing Statistical Model Performance

Even though a train/test split of the dataset randomly performed is a common practice, could not always be the best approach for estimating performance generalization under some scenarios. The fact is that the usual machine learning methodology can sometimes overestimate the generalization error when a dataset is not representative or when rare and elusive examples are a fundamental aspect of the detection problem. In the present work, we analyze strategies based on the predictors' variability to split in training and testing sets. Such strategies aim at guaranteeing the inclusion of rare or unusual examples with a minimal loss of the population's representativeness and provide a more accurate estimation about the generalization error when the dataset is not representative. Two baseline classifiers based on decision trees were used for testing the four splitting strategies considered. Both classifiers were applied on CTU19 a low-representative dataset for a network security detection problem. Preliminary results showed the importance of applying the three alternative strategies to the Monte Carlo splitting strategy in order to get a more accurate error estimation on different but feasible scenarios.
FADE: Enabling Large-Scale Federated Adversarial Training on Resource-Constrained Edge Devices

Adversarial Training (AT) has been proven to be an effective method of introducing strong adversarial robustness into deep neural networks. However, the high computational cost of AT prohibits the deployment of large-scale AT on resource-constrained edge devices, e.g., with limited computing power and small memory footprint, in Federated Learning (FL) applications. Very few previous studies have tried to tackle these constraints in FL at the same time. In this paper, we propose a new framework named Federated Adversarial Decoupled Learning (FADE) to enable AT on resource-constrained edge devices in FL. FADE reduces the computation and memory usage by applying Decoupled Greedy Learning (DGL) to federated adversarial training such that each client only needs to perform AT on a small module of the entire model in each communication round. In addition, we improve vanilla DGL by adding an auxiliary weight decay to alleviate objective inconsistency and achieve better performance. FADE offers a theoretical guarantee for the adversarial robustness and convergence. The experimental results also show that FADE can signiﬁcantly reduce the computing resources consumed by AT while maintaining almost the same accuracy and robustness as fully joint training.
Analytic automorphism group and similar representation of analytic functions

In geometry group theory, one of the milestones is M. Gromov's polynomial growth theorem: Finitely generated groups have polynomial growth if and only if they are virtually nilpotent. Inspired by M. Gromov's work, we introduce the growth types of weighted Hardy spaces. In this paper, we focus on the weighted Hardy spaces of polynomial growth, which cover the classical Hardy space, weighted Bergman spaces, weighted Dirichlet spaces and much broader. Our main results are as follows. $(1)$ We obtain the boundedness of the composition operators with symbols of analytic automorphisms of unit open disk acting on weighted Hardy spaces of polynomial growth, which implies the multiplication operator $M_z$ is similar to $M_{\varphi}$ for any analytic automorphism $\varphi$ on the unit open disk. Moreover, we obtain the boundedness of composition operators induced by analytic functions on the unit closed disk on weighted Hardy spaces of polynomial growth. $(2)$ For any Blaschke product $B$ of order $m$, $M_B$ is similar to $\bigoplus_{1}^m M_z$, which is an affirmative answer to a generalized version of a question proposed by R. Douglas in 2007. $(3)$ We also give counterexamples to show that the composition operators with symbols of analytic automorphisms of unit open disk acting on a weighted Hardy space of intermediate growth could be unbounded, which indicates the necessity of the setting of polynomial growth condition. Then, the collection of weighted Hardy spaces of polynomial growth is almost the largest class such that Douglas's question has an affirmative answer. $(4)$ Finally, we give the Jordan representation theorem and similarity classification for the analytic functions on the unit closed disk as multiplication operators on a weighted Hardy space of polynomial growth.
PMU Tracker: A Visualization Platform for Epicentric Event Propagation Analysis in the Power Grid

The electrical power grid is a critical infrastructure, with disruptions in transmission having severe repercussions on daily activities, across multiple sectors. To identify, prevent, and mitigate such events, power grids are being refurbished as ‘smart’ systems that include the widespread deployment of GPS-enabled phasor measurement units (PMUs). PMUs provide fast, precise, and time-synchronized measurements of voltage and current, enabling real-time wide-area monitoring and control. However, the potential benefits of PMUs, for analyzing grid events like abnormal power oscillations and load fluctuations, are hindered by the fact that these sensors produce large, concurrent volumes of noisy data. In this paper, we describe working with power grid engineers to investigate how this problem can be addressed from a visual analytics perspective. As a result, we have developed PMU Tracker, an event localization tool that supports power grid operators in visually analyzing and identifying power grid events and tracking their propagation through the power grid's network. As a part of the PMU Tracker interface, we develop a novel visualization technique which we term an epicentric cluster dendrogram, which allows operators to analyze the effects of an event as it propagates outwards from a source location. We robustly validate PMU Tracker with: (1) a usage scenario demonstrating how PMU Tracker can be used to analyze anomalous grid events, and (2) case studies with power grid operators using a real-world interconnection dataset. Our results indicate that PMU Tracker effectively supports the analysis of power grid events; we also demonstrate and discuss how PMU Tracker's visual analytics approach can be generalized to other domains composed of time-varying networks with epicentric event characteristics.
Counting Subgraphs in Somewhere Dense Graphs

We study the problems of counting copies and induced copies of a small pattern graph $H$ in a large host graph $G$. Recent work fully classified the complexity of those problems according to structural restrictions on the patterns $H$. In this work, we address the more challenging task of analysing the complexity for restricted patterns and restricted hosts. Specifically we ask which families of allowed patterns and hosts imply fixed-parameter tractability, i.e., the existence of an algorithm running in time $f(H)\cdot |G|^{O(1)}$ for some computable function $f$. Our main results present exhaustive and explicit complexity classifications for families that satisfy natural closure properties. Among others, we identify the problems of counting small matchings and independent sets in subgraph-closed graph classes $\mathcal{G}$ as our central objects of study and establish the following crisp dichotomies as consequences of the Exponential Time Hypothesis: (1) Counting $k$-matchings in a graph $G\in\mathcal{G}$ is fixed-parameter tractable if and only if $\mathcal{G}$ is nowhere dense. (2) Counting $k$-independent sets in a graph $G\in\mathcal{G}$ is fixed-parameter tractable if and only if $\mathcal{G}$ is nowhere dense. Moreover, we obtain almost tight conditional lower bounds if $\mathcal{G}$ is somewhere dense, i.e., not nowhere dense. These base cases of our classifications subsume a wide variety of previous results on the matching and independent set problem, such as counting $k$-matchings in bipartite graphs (Curticapean, Marx; FOCS 14), in $F$-colourable graphs (Roth, Wellnitz; SODA 20), and in degenerate graphs (Bressan, Roth; FOCS 21), as well as counting $k$-independent sets in bipartite graphs (Curticapean et al.; Algorithmica 19).
Optimal Policy for Inventory Management with Periodic and Controlled Resets

Inventory management problems with periodic and controllable resets occur in the context of managing water storage in the developing world and retailing limited-time availability products. In this paper, we consider a set of sequential decision problems in which the decision-maker must not only balance holding and shortage costs but discard all inventory before a ﬁxed number of decision epochs, with the option for an early inventory reset. Finding optimal policies using dynamic programming for these problems is particularly challenging since the resulting value functions are non-convex. Moreover, this structure cannot be easily analyzed using existing extended deﬁnitions, such as K -convexity. Our key contribution is to present suﬃcient conditions that ensure the optimal policy has an easily interpretable structure that generalizes the well-known ( s,S ) policy from the operations literature. Furthermore, we demonstrate that the optimal policy has a four-threshold structure under these rather mild conditions. We then conclude with computational experiments, thereby illustrating the policy structures that can be extracted in several inventory management scenarios.
Application of image-to-image translation in improving pedestrian detection

The lack of effective target regions makes it difficult to perform several visual functions in low intensity light, including pedestrian recognition, and image-to-image translation. In this situation, with the accumulation of high-quality information by the combined use of infrared and visible images it is possible to detect pedestrians even in low light. In this study we are going to use advanced deep learning models like pix2pixGAN and YOLOv7 on LLVIP dataset, containing visible-infrared image pairs for low light vision. This dataset contains 33672 images and most of the images were captured in dark scenes, tightly synchronized with time and location.
Magnetic Effects Promote Supermassive Star Formation in Metal-enriched Atomic-cooling Halos

Intermediate-mass black holes (with ≥105 M ⊙) are promising candidates for the origin of supermassive black holes (with ∼109 M ⊙) in the early universe (redshift z ∼ 6). Chon & Omukai first pointed out direct collapse black hole (DCBH) formation in metal-enriched atomic-cooling halos (ACHs), which relaxes the DCBH formation criterion. On the other hand, Hirano et al. showed that magnetic effects promote DCBH formation in metal-free ACHs. We perform a set of magnetohydrodynamical simulations to investigate star formation in magnetized ACHs with metallicities Z/Z ⊙ = 0, 10−5, and 10−4. Our simulations show that the mass accretion rate onto the protostars becomes lower in metal-enriched ACHs than in metal-free ACHs. However, many protostars form from gravitationally and thermally unstable metal-enriched gas clouds. Under such circumstances, the magnetic field rapidly increases as magnetic field lines wind up due to the spin of protostars. The region with the amplified magnetic field expands outwards due to the orbital motion of protostars and the rotation of the accreting gas. The amplified magnetic field extracts angular momentum from the accreting gas, promotes the coalescence of low-mass protostars, and increases the mass growth rate of the primary protostar. We conclude that the magnetic field amplification is always realized in metal-enriched ACHs regardless of the initial magnetic field strength, which affects the DCBH formation criterion. In addition, we find a qualitatively different trend from the previous unmagnetized simulations in that the mass growth rate is maximal for extremely metal-poor ACHs with Z/Z ⊙ = 10−5.
NeuralFMU: Presenting a workflow for integrating hybrid NeuralODEs into real world applications

The term NeuralODE describes the structural combination of an Artificial Neural Network (ANN) and a numerical solver for Ordinary Differential Equations (ODE), the former acts as the right-hand side of the ODE to be solved. This concept was further extended by a black-box model in the form of a Functional Mock-up Unit (FMU) to obtain a subclass of NeuralODEs, named NeuralFMUs. The resulting structure features the advantages of the first-principle and data-driven modeling approaches in one single simulation model: a higher prediction accuracy compared to conventional First-Principle Models (FPMs) and also a lower training effort compared to purely data-driven models. We present an intuitive workflow to set up and use NeuralFMUs, enabling the encapsulation and reuse of existing conventional models exported from common modeling tools. Moreover, we exemplify this concept by deploying a NeuralFMU for a consumption simulation based on a Vehicle Longitudinal Dynamics Model (VLDM), which is a typical use case in the automotive industry. Related challenges that are often neglected in scientific use cases, such as real measurements (e.g., noise), an unknown system state or high-frequency discontinuities, are handled in this contribution. To build a hybrid model with a higher prediction quality than the original FPM, we briefly highlight two open-source libraries: FMI.jl, which allows for the import of FMUs into the Julia programming language, as well as the library FMIFlux.jl, which enables the integration of FMUs into neural network topologies to obtain a NeuralFMU.
Light Curves and Event Rates of Axion Instability Supernovae

It was recently proposed that exotic particles can trigger a new stellar instability that is analogous to the e − e + pair instability if they are produced and reach equilibrium in the stellar plasma. In this study, we construct axion instability supernova (AISN) models caused by the new instability to predict their observational signatures. We focus on heavy axion-like particles (ALPs) with masses of ∼400 keV–2 MeV and coupling with photons of g a γ ∼ 10−5 GeV−1. It is found that the 56Ni mass and the explosion energy are significantly increased by ALPs for a fixed stellar mass. As a result, the peak times of the light curves of AISNe occur earlier than those of standard pair-instability supernovae by 10–20 days when the ALP mass is equal to the electron mass. Also, the event rate of AISNe is 1.7–2.6 times higher than that of pair-instability supernovae, depending on the high mass cutoff of the initial mass function.
Type II universal spacetimes

We study type II universal metrics of the Lorentzian signature. These metrics simultaneously solve vacuum field equations of all theories of gravitation with the Lagrangian being a polynomial curvature invariant constructed from the metric, the Riemann tensor and its covariant derivatives of an arbitrary order. We provide examples of type II universal metrics for all composite number dimensions. On the other hand, we have no examples for prime number dimensions and we prove the non-existence of type II universal spacetimes in five dimensions. We also present type II vacuum solutions of selected classes of gravitational theories, such as Lovelock, quadratic and L ( Riemann ) ? > gravities.
An efficient approach for nonconvex semidefinite optimization via customized alternating direction method of multipliers

We investigate a class of general combinatorial graph problems, including MAX-CUT and community detection, reformulated as quadratic objectives over nonconvex constraints and solved via the alternating direction method of multipliers (ADMM). We propose two reformulations: one using vector variables and a binary constraint, and the other further reformulating the Burer-Monteiro form for simpler subproblems. Despite the nonconvex constraint, we prove the ADMM iterates converge to a stationary point in both formulations, under mild assumptions. Additionally, recent work suggests that in this latter form, when the matrix factors are wide enough, local optimum with high probability is also the global optimum. To demonstrate the scalability of our algorithm, we include results for MAX-CUT, community detection, and image segmentation benchmark and simulated examples.
A penalized criterion for selecting the number of clusters for K-medians

Clustering is a usual unsupervised machine learning technique for grouping the data points into groups based upon similar features. We focus here on unsupervised clustering for contaminated data, i.e in the case where K-medians should be preferred to K-means because of its robustness. More precisely, we concentrate on a common question in clustering: how to chose the number of clusters? The answer proposed here is to consider the choice of the optimal number of clusters as the minimization of a risk function via penalization. In this paper, we obtain a suitable penalty shape for our criterion and derive an associated oracle-type inequality. Finally, the performance of this approach with different types of K-medians algorithms is compared on a simulation study with other popular techniques. All studied algorithms are available in the R package Kmedians on CRAN.
Visual Firewall Log Analysis - At the Border Between Analytical and Appealing

In this paper, we present our design study on developing an interactive visual firewall log analysis system in collaboration with an IT service provider. We describe the human-centered design process, in which we additionally considered hedonic qualities by including the usage of personas, psychological need cards and interaction vocabulary. For the problem characterization we especially focus on the demands of the two main clusters of requirements: high-level overview and low-level analysis, represented by the two defined personas, namely information security officer and network analyst. This resulted in the prototype of a visual analysis system consisting of two interlinked parts. One part addresses the needs for rather strategical tasks while also fulfilling the need for an appealing appearance and interaction. The other part rather addresses the requirements for operational tasks and aims to provide a high level of flexibility. We describe our design journey, the derived domain tasks and task abstractions as well as our visual design decisions, and present our final prototypes based on a usage scenario. We also report on our capstone event, where we conducted an observed experiment and collected feedback from the information security officer. Finally, as a reflection, we propose the extension of a widely used design study process with a track for an additional focus on hedonic qualities.