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Strong first-order phase transitions in a dark sector offer a compelling explanation for the stochastic gravitational wave background in the nano-Hertz range recently detected by pulsar timing arrays (PTAs). We explore the possibility that such a phase transition at the same time gives mass to a stable fermion that accounts for the observed dark matter abundance and leads to testable effects in laboratory experiments. Concretely, we consider a classically conformal dark sector with a hidden $U(1)^\prime$ gauge symmetry that couples to the Standard Model via kinetic mixing. Since the PTA signal requires a phase transition in the MeV temperature range, spontaneous symmetry breaking gives rise to a sub-GeV dark matter candidate that couples to the Standard Model via a dark photon mediator and obtains its relic abundance via annihilations into electrons and dark Higgs bosons. Such a scenario is tightly constrained by laboratory searches for dark photons and cosmological constraints on the decays of dark Higgs bosons after the phase transition. We show that viable parameter regions can be found both for the case that the dark Higgs bosons remain in equilibrium with the Standard Model and that they decouple and only decay much later. In the latter case, the parameter regions preferred by the PTA signal and the dark matter relic abundance can be fully explored by future beam-dump experiments searching for missing energy., Comment: 33 pages, 12 figures + appendices

Tensor products are ubiquitous in algebra, topology, logic and category theory. The present paper explores the monoidal structure of the category $\mathcal{V}\hspace{0pt}\mbox{-}\hspace{.5pt}\mathbf{Sup}$ of separated cocomplete enriched categories over a commutative quantale $\mathcal{V}$, the many-valued analogue of complete sup-lattices. We recover the known result that $\mathcal{V}\hspace{0pt}\mbox{-}\hspace{.5pt}\mathbf{Sup}$ is $*$-autonomous and we show that the nuclear/dualizable objects in $\mathcal{V}\hspace{0pt}\mbox{-}\hspace{.5pt}\mathbf{Sup}$ are precisely the completely distributive cocomplete $\mathcal{V}$-categories., Comment: 19 pages

Consider a group $G$ of order $M$ acting unitarily on a real inner product space $V$. We show that the sorting based embedding obtained by applying a general linear map $\alpha : \mathbb{R}^{M \times N} \to \mathbb{R}^D$ to the invariant map $\beta_\Phi : V \to \mathbb{R}^{M \times N}$ given by sorting the coorbits $(\langle v, g \phi_i \rangle_V)_{g \in G}$, where $(\phi_i)_{i=1}^N \in V$, satisfies a bi-Lipschitz condition if and only if it separates orbits. Additionally, we note that any invariant Lipschitz continuous map (into a Hilbert space) factors through the sorting based embedding, and that any invariant continuous map (into a locally convex space) factors through the sorting based embedding as well., Comment: 25 pages

A problem by Feichtinger, Heil, and Larson asks whether a positive-definite integral operator with $M_1$ kernel admits a symmetric rank-one decomposition which is strongly square-summable with respect to the $M_1$ norm. In conjunction with a concurrent result by Bandeira-Mixon-Steinerberger, we provide a negative answer to the problem and study special cases where such factorization is possible. We also discuss an application of the results to the semidefinite relaxation of a quadratic program., Comment: 20 pages

This study experimentally examines the effect and changes in the delivered fields, using water-equivalent phantoms with and without titanium (Ti) dental implants positioned along the primary beam path. We measure in detail the composition and spectral-tracking characterization of particles generated in the entrance region of the Bragg curve using high-spatial resolution, spectral and time-sensitive imaging detectors with a pixelated array provided by the ASIC chip Timepix3. A 170 MeV proton beam was collimated and modulated in a polymethyl methacrylate (PMMA). Placing two dental implants at the end of the protons range in the phantom, the radiation was measured using two pixeled detectors with Si sensors. The Timepix3 (TPX3) detectors equipped with silicon sensors measure in detail particle fluxes, dose rates (DR) and linear energy transfer (LET) spectra for resolved particle types. Artificial intelligence (AI) based-trained neural networks (NN) calibrated in well-defined radiation fields were used to analyze and identify particles based on morphology and characteristic spectral-tracking response. The beam was characterized and single-particle tracks were registered and decomposed into particle-type groups. The resulting particle fluxes in both setups are resolved into three main classes of particles: i) protons, ii) electrons and photons iii) ions. Protons are the main particle component responsible for dose deposition. High-energy transfer particles (HETP), namely ions exhibited differences in both dosimetric aspects that were investigated: DR and particle fluxes, when the Ti implants were placed in the setup. The detailed multi-parametric information of the secondary radiation field provides a comprehensive understanding of the impact of Ti materials in proton therapy., Comment: 8 pages, 4 figures, IWORID Conference

We present PDFed, a decentralized, aggregator-free, and asynchronous federated learning protocol for training image diffusion models using a public blockchain. In general, diffusion models are prone to memorization of training data, raising privacy and ethical concerns (e.g., regurgitation of private training data in generated images). Federated learning (FL) offers a partial solution via collaborative model training across distributed nodes that safeguard local data privacy. PDFed proposes a novel sample-based score that measures the novelty and quality of generated samples, incorporating these into a blockchain-based federated learning protocol that we show reduces private data memorization in the collaboratively trained model. In addition, PDFed enables asynchronous collaboration among participants with varying hardware capabilities, facilitating broader participation. The protocol records the provenance of AI models, improving transparency and auditability, while also considering automated incentive and reward mechanisms for participants. PDFed aims to empower artists and creators by protecting the privacy of creative works and enabling decentralized, peer-to-peer collaboration. The protocol positively impacts the creative economy by opening up novel revenue streams and fostering innovative ways for artists to benefit from their contributions to the AI space., Comment: Accepted to CM SIGGRAPH European Conference on Visual Media Production 2024

Success in todays data-driven corporate climate requires a deep understanding of employee behavior. Companies aim to improve employee satisfaction, boost output, and optimize workflow. This research study delves into creating synthetic data, a powerful tool that allows us to comprehensively understand employee performance, flexibility, cooperation, and team dynamics. Synthetic data provides a detailed and accurate picture of employee activities while protecting individual privacy thanks to cutting-edge methods like agent-based models (ABMs), Generative Adversarial Networks (GANs), and statistical models. Through the creation of multiple situations, this method offers insightful viewpoints regarding increasing teamwork, improving adaptability, and accelerating overall productivity. We examine how synthetic data has evolved from a specialized field to an essential resource for researching employee behavior and enhancing management efficiency. Keywords: Agent-Based Model, Generative Adversarial Network, workflow optimization, organizational success, Comment: 8 Pages, 5 figures, 1 github link

In this article, we examine a stochastic partial differential equation (SPDE) driven by a symmetric $\alpha$-stable (S$\alpha$S) L\'evy noise, that is multiplied by a linear function $\sigma(u)=u$ of the solution. The solution is interpreted in the mild sense. For this models, in the case of the Gaussian noise, the solution has an explicit Wiener chaos expansion, and is studied using tools from Malliavin calculus. These tools cannot be used for an infinite-variance L\'evy noise. In this article, we provide sufficient conditions for the existence of a solution, and we give an explicit series expansion of this solution. To achieve this, we use the multiple stable integrals, which were developed in Samorodnitsky and Taqqu (1990, 1991), and originate from the LePage series representation of the noise. To give a meaning to the stochastic integral which appears in the definition of solution, we embed the space-time L\'evy noise into a L\'evy basis, and use the stochastic integration theory (Bichteler and Jacod 1983, Bichteler 2002) with respect to this object, as in other studies of SPDEs with heavy-tailed noise: Chong (2017a), Chong (2017b), Chong, Dalang and Humeau (2019). As applications, we consider the heat and wave equations with linear multiplicative noise, also called the parabolic/hyperbolic Anderson models., Comment: 56 pages, 2 figures

We investigate the evolutionary stages of four open clusters, Berkeley 39, Collinder 261, NGC 6819, and NGC 7789, of ages ranging from 1.6 -- 6 Gyr. These clusters have previously been classified into dynamically young and intermediate age groups based on the segregation level of BSS with respect to red giant branch stars and main sequence stars, respectively. We identify members of these four clusters using the ML-MOC algorithm on Gaia DR3 data. To examine the relative segregation of cluster members of different evolutionary stages, we utilize cumulative radial distributions, proper motion distributions, and spatial distributions in galactocentric coordinates. Our analysis shows that Berkeley 39 and NGC 6819 exhibit moderate signs of population-wise segregation from evolved to less-evolved members. NGC 7789 shows signs of mass segregation only in the cumulative radial distributions. On the other hand, Collinder 261 exhibits high segregation of BSS in the cumulative radial distribution, while other populations show the same level of segregation., Comment: 15 pages, 6 figures (+1 in Appendix), and 3 tables. Accepted for publication to AJ on 4th September 2024

Computing the proximal operator of the $\ell_\infty$ norm, $\textbf{prox}_{\alpha ||\cdot||_\infty}(\mathbf{x})$, generally requires a sort of the input data, or at least a partial sort similar to quicksort. In order to avoid using a sort, we present an $O(m)$ approximation of $\textbf{prox}_{\alpha ||\cdot||_\infty}(\mathbf{x})$ using a neural network. A novel aspect of the network is that it is able to accept vectors of varying lengths due to a feature selection process that uses moments of the input data. We present results on the accuracy of the approximation, feature importance, and computational efficiency of the approach. We show that the network outperforms a "vanilla neural network" that does not use feature selection. We also present an algorithm with corresponding theory to calculate $\textbf{prox}_{\alpha ||\cdot||_\infty}(\mathbf{x})$ exactly, relate it to the Moreau decomposition, and compare its computational efficiency to that of the approximation., Comment: 30 pages, 5 figures, 6 tables

Automating agricultural processes holds significant promise for enhancing efficiency and sustainability in various farming practices. This paper contributes to the automation of agricultural processes by providing a dedicated mushroom detection dataset related to automated harvesting, growth monitoring, and quality control of the button mushroom produced using Agaricus Bisporus fungus. With over 18,000 mushroom instances in 423 RGB-D image pairs taken with an Intel RealSense D405 camera, it fills the gap in mushroom-specific datasets and serves as a benchmark for detection and instance segmentation algorithms in smart mushroom agriculture. The dataset, featuring realistic growth environment scenarios with comprehensive annotations, is assessed using advanced detection and instance segmentation algorithms. The paper details the dataset's characteristics, evaluates algorithmic performance, and for broader applicability, we have made all resources publicly available including images, codes, and trained models via our GitHub repository https://github.com/abdollahzakeri/m18k

Understanding employee behavior in a workplace is critical for enhancing overall organizational performance. Despite numerous efforts to improve work environments, many organizations still need help with challenges primarily rooted in unaddressed issues or poorly understood behavioral patterns. In this paper, we have focused on recognizing this pattern and the dynamics of complex systems in organizational behavior and studying how factors influence the systems overall behavior using Wolframs Cellular Automata theory. Over the cycle, we observe how leadership influences team dynamics, influences the organization, and drives employee behavior to foster a positive environment. Keywords: Cellular Automata, Employee Behavior, team dynamics, Leadership Influence., Comment: 7 Pages, 5 Figures, Links to Github repository

Sub-GeV dark matter (DM) particles produced via thermal freeze-out evade many of the strong constraints on heavier DM candidates but at the same time face a multitude of new constraints from laboratory experiments, astrophysical observations and cosmological data. In this work we combine all of these constraints in order to perform frequentist and Bayesian global analyses of fermionic and scalar sub-GeV DM coupled to a dark photon with kinetic mixing. For fermionic DM, we find viable parameter regions close to the dark photon resonance, which expand significantly when including a particle-antiparticle asymmetry. For scalar DM, the velocity-dependent annihilation cross section evades the strongest constraints even in the symmetric case. Using Bayesian model comparison, we show that both asymmetric fermionic DM and symmetric scalar DM are preferred over symmetric fermionic DM due to the reduced fine-tuning penalty. Finally, we explore the discovery prospects of near-future experiments both in the full parameter space and for specific benchmark points. We find that the most commonly used benchmark scenarios are already in tension with existing constraints and propose a new benchmark point that can be targeted with future searches., Comment: 59 pages, 5 tables, 24 figures

The thermal conductivities of crystals and glasses vary strongly and with opposite trends upon heating, decreasing in crystals and increasing in glasses. Here, we show--both with first-principles predictions based on the Wigner transport equation and with thermoreflectance experiments--that the dominant transport mechanisms of crystals (particle-like propagation) and glasses (wave-like tunnelling) can coexist and compensate in materials with crystalline bond order and nearly glassy bond geometry. We demonstrate that ideal compensation emerges in a sample of silica in the form of tridymite, carved from a meteorite found in Steinbach (Germany) in 1724, and yields a "propagation-tunneling-invariant" (PTI) conductivity that is independent from temperature and intermediate between the opposite trends of $\alpha$-quartz crystal and silica glass. We show how such PTI conductivity occurs in the quantum regime below the Debye temperature, and can largely persist at high temperatures in a geometrically amorphous tridymite phase found in refractory bricks fired for years in furnaces for steel smelting. Last, we discuss implications to heat transfer in solids exposed to extreme temperature variations, ranging from planetary cooling to heating protocols to reduce the carbon footprint of industrial furnaces., Comment: 5+11 pages, 3+6 figures

Despite the considerable advancements in English LLMs, the progress in building comparable models for other languages has been hindered due to the scarcity of tailored resources. Our work aims to bridge this divide by introducing an expansive suite of resources specifically designed for the development of Indic LLMs, covering 22 languages, containing a total of 251B tokens and 74.8M instruction-response pairs. Recognizing the importance of both data quality and quantity, our approach combines highly curated manually verified data, unverified yet valuable data, and synthetic data. We build a clean, open-source pipeline for curating pre-training data from diverse sources, including websites, PDFs, and videos, incorporating best practices for crawling, cleaning, flagging, and deduplication. For instruction-fine tuning, we amalgamate existing Indic datasets, translate/transliterate English datasets into Indian languages, and utilize LLaMa2 and Mixtral models to create conversations grounded in articles from Indian Wikipedia and Wikihow. Additionally, we address toxicity alignment by generating toxic prompts for multiple scenarios and then generate non-toxic responses by feeding these toxic prompts to an aligned LLaMa2 model. We hope that the datasets, tools, and resources released as a part of this work will not only propel the research and development of Indic LLMs but also establish an open-source blueprint for extending such efforts to other languages. The data and other artifacts created as part of this work are released with permissive licenses., Comment: ACL-2024 Outstanding Paper

The exchange bias phenomenon, inherent in exchange-coupled ferromagnetic and antiferromagnetic systems, has intrigued researchers for decades. Van der Waals materials, with their layered structure, provide an optimal platform for probing such physical phenomena. However, achieving a facile and effective means to manipulate exchange bias in pristine van der Waals heterostructures remains challenging. In this study, we investigate the origin of exchange bias in MnPS3/Fe3GeTe2 van der Waals heterostructures. Our work demonstrates a method to modulate unidirectional exchange anisotropy, achieving an unprecedented nearly 1000% variation through simple thermal cycling. Despite the compensated interfacial spin configuration of MnPS3, magneto-transport measurements reveal a huge 170 mT exchange bias at 5 K, the largest observed in pristine van der Waals antiferromagnet-ferromagnet interfaces. This substantial magnitude of the exchange bias is linked to an anomalous weak ferromagnetic ordering in MnPS3 below 40 K. On the other hand, the tunability of exchange bias during thermal cycling is ascribed to the modified arrangement of interfacial atoms and changes in the vdW gap during field cooling. Our findings highlight a robust and easily adjustable exchange bias in van der Waals antiferromagnetic/ferromagnetic heterostructures, presenting a straightforward approach to enhance other interface related spintronic phenomena for practical applications.

Human social interactions occur in group settings of varying sizes and locations, depending on the type of social activity. The ability to distinguish group formations based on their purposes transforms how group detection mechanisms function. Not only should such tools support the effective detection of serendipitous encounters, but they can derive categories of relation types among users. Determining who is involved, what activity is performed, and when and where the activity occurs are critical to understanding group processes in greater depth, including supporting goal-oriented applications (e.g., performance, productivity, and mental health) that require sensing social factors. In this work, we propose W4-Groups that captures the functional perspective of variability and repeatability when automatically constructing short-term and long-term groups via multiple data sources (e.g., WiFi and location check-in data). We design and implement W4-Groups to detect and extract all four group features who-what-when-where from the user's daily mobility patterns. We empirically evaluate the framework using two real-world WiFi datasets and a location check-in dataset, yielding an average of 92% overall accuracy, 96% precision, and 94% recall. Further, we supplement two case studies to demonstrate the application of W4-Groups for next-group activity prediction and analyzing changes in group behavior at a longitudinal scale, exemplifying short-term and long-term occurrences.

This study aims to assess the composition of scattered particles generated in proton therapy for tumours situated proximal to titanium dental implants. The investigation involves decomposing the mixed field and recording Linear Energy Transfer (LET) spectra to quantify the influence of metallic dental inserts located behind the tumour. A conformal proton beam was used to deliver the treatment plan to an anthropomorphic head phantom with two types of implants (Ti and plastic) inserted in the target volume. The stray radiation resulting during the irradiation was detected by a hybrid semiconductor pixel detector MiniPIX Timepix3 that was placed distal to the Spread-out Bragg peak. Visualization and field decomposition of stray radiation were generated using algorithms trained in particle recognition based on artificial intelligence convolution neural networks (AI CNN). Spectral sensitive aspects of the scattered radiation were collected using two angular positions of the detector relative to the beam direction: 0 and 60{\deg}. Using AI CNN, 3 classes of particles were identified: protons, electrons & photons, ions & fast neutrons. Placing a Ti implant in the beam's path resulted in predominantly electrons and photons, contributing 52.2%, whereas for plastic implants, the contribution was 65.4%. Scattered protons comprised 45.5% and 31.9% with and without Ti inserts, respectively. The LET spectra was derived for each group of particles, with values ranging from 0.01 to 7.5 keV{\mu}m-1 for Ti/plastic implants. The low-LET component was primarily composed of electrons and photons, while the high-LET component corresponded to protons and ions. This method, complemented by directional maps, holds potential for evaluating and validating treatment plans involving stray radiation near organs at risk, offering precise discrimination of the mixt field, enhancing in this way the LET calculation., Comment: 26 pages, 6 figures, 1 table

Consider a real vector space $\mathcal{V}$ and a finite group $G$ acting unitarily on $\mathcal{V}$. We study the general problem of constructing a stable embedding whose domain is the quotient of the vector space modulo the group action, and whose target space is a Euclidean space. We construct an embedding $\Psi$ and we study under which assumptions $\Psi$ is injective in the quotient vector space. The embedding scheme we introduce is based on selecting a fixed subset from the sorted orbit $\downarrow\langle{U_gw_i},{x}\rangle_{g \in G}$, where $w_i$ are appropriate vectors.

In this paper, we present an almost sure central limit theorem (ASCLT) for the hyperbolic Anderson model (HAM) with a L\'evy white noise in a finite-variance setting, complementing a recent work by Balan and Zheng (\emph{Trans.~Amer.~Math.~Soc.}, 2024) on the (quantitative) central limit theorems for the solution to the HAM. We provide two different proofs: one uses the Clark-Ocone formula and takes advantage of the martingale structure of the white-in-time noise, while the other is obtained by combining the second-order Gaussian Poincar\'e inequality with Ibragimov and Lifshits' method of characteristic functions. Both approaches are different from the one developed in the PhD thesis of C. Zheng (2011), allowing us to establish the ASCLT without lengthy computations of star contractions. Moreover, the second approach is expected to be useful for similar studies on SPDEs with colored-in-time noises, whereas the former, based on It\^o calculus, is not applicable., Comment: V2:15pages, minor revision; V1: 15pages