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Mathematical modeling is a powerful tool for describing, predicting, and understanding complex phenomena exhibited by real-world systems. However, identifying the equations that govern a system's dynamics from experimental data remains a significant challenge without a definitive solution. In this study, evolutionary computing techniques are presented to estimate the governing equations of a dynamical system using time-series data. The main approach is to propose polynomial equations with unknown coefficients, and subsequently perform a parametric estimation using genetic algorithms. Some of the main contributions of the present study are an adequate modification of the genetic algorithm to remove terms with minimal contributions, and a mechanism to escape local optima during the search. To evaluate the proposed method, we applied it to three dynamical systems: a linear model, a nonlinear model, and the Lorenz system. Our results demonstrate a reconstruction with an Integral Square Error below 0.22 and a coefficient of determination R-squared of 0.99 for all systems, indicating successful reconstruction of the governing dynamic equations.

We revisit and update the axion-induced pair production process in a nuclear electric field mediated by the axion-electron coupling, $a+{{}^{A}_{Z}X} \rightarrow {{}^{A}_{Z}X} + e^{+} + e^{-}$. This process emerges as one of the most efficient channels for detecting axions with energies above a few MeV in large underground detectors. It is particularly relevant for detecting axions produced in nuclear reactions, such as the $p+d~\rightarrow~{ }^3 \mathrm{He}~+~a(5.5\,\mathrm{MeV})$ reaction in the solar pp-chain, and for axions originating in supernovae. Despite recent interest in detecting high-energy axions, the pair production process has received limited attention, even in scenarios where it is the dominant detection channel. This study fills this gap by demonstrating that pair production is a highly effective detection mechanism for high-energy axions. We apply our results to axions from supernovae and the solar 5.5 MeV line, recasting the current bounds of Borexino and comparing the detection capabilities of the JUNO and Hyper-Kamiokande detectors., Comment: 13 pages, 6 figures

We use density functional theory calculations to characterize the early stages of fluorination of silver's (100) and (110) surfaces. In the Ag(100) surface, the hollow site is the most favorable for F adatoms. In the Ag(110) surface, three adsorption sites, namely hollow, long bridge, and short bridge, exhibit similar energies. These locations are also more favorable than an F adatom occupying a vacancy site irrespectively of whether the vacancy was present or not in the pristine surface. The computed energy as a function of surface coverage is used to compute the equilibrium thermodynamics phase diagram. We argue that for the typical pressure and temperature of fluorination experiments, the state of the surface is not determined by thermodynamics but by kinetics. Combining these results with scanning tunneling microscopy (STM) topographic simulations, we propose assignments to features observed experimentally. We present a minimal model of the apparent topography of adatoms in different locations in terms of hydrogenic orbitals, explaining the observed trends. The model links the STM apparent topography to structural information and the oxidation states of the Ag atoms near the adatom., Comment: 18 pages, 12 Figures

Over the last few years, content verification through reliable sources has become a fundamental need to combat disinformation. Here, we present a language-agnostic model designed to assess the reliability of sources across multiple language editions of Wikipedia. Utilizing editorial activity data, the model evaluates source reliability within different articles of varying controversiality such as Climate Change, COVID-19, History, Media, and Biology topics. Crafting features that express domain usage across articles, the model effectively predicts source reliability, achieving an F1 Macro score of approximately 0.80 for English and other high-resource languages. For mid-resource languages, we achieve 0.65 while the performance of low-resource languages varies; in all cases, the time the domain remains present in the articles (which we dub as permanence) is one of the most predictive features. We highlight the challenge of maintaining consistent model performance across languages of varying resource levels and demonstrate that adapting models from higher-resource languages can improve performance. This work contributes not only to Wikipedia's efforts in ensuring content verifiability but in ensuring reliability across diverse user-generated content in various language communities.

Robots are being created each year with the goal of integrating them into our daily lives. As such, there is an interest in research in evaluating the trust of humans toward robots. In addition, teleoperating robotic arms can be challenging for non-experts. To reduce the strain put on the user, we created TELESIM, a modular and plug-and-play framework that enables direct teleoperation of any robotic arm using a digital twin as the interface between users and the robotic system. We evaluated our framework using a user survey with three robots and control methods and recorded the user's workload and performance at completing a tower stacking task. However, an analysis of the strain on the user and their ability to trust robots was omitted. This paper addresses these omissions by presenting the additional results of our user survey of 37 participants carried out in United Kingdom. In addition, we present the results of an additional user survey, under similar conditions performed in Japan, with the goal of addressing the limitations of our previous approach, by interfacing a VR controller with a UR5e. Our experimental results show that the UR5e has more towers built. Additionally, the UR5e gives the least amount of cognitive stress, while the combination of Senseglove and UR3 provides the user with the highest physical strain and causes the user to feel more frustrated. Finally, the Japanese participants seem more trusting of robots than the British participants.

We use low-temperature scanning tunneling microscopy (LT-STM) to characterize the early stages of silver fluorination. On Ag(100), we observe only one adsorbate species, which shows a bias-dependent STM topography. Notably, at negative bias voltages, $V_{\rm B}<0$, the apparent shape can be described as a round protrusion surrounded by a moat-like depression (sombrero). As the voltage increases, the apparent shape changes, eventually evolving into a round depression. From the STM images, we determine the adsorption site to be the hollow position. On Ag(110) we find adsorbates with three distinct STM topographies. One type exhibits the same shape change with $V_{\rm B}$ as observed on Ag(100), that is, from a sombrero shape to a round depression as the voltage changes from negative to positive values; the other two types are observed as round depressions regardless of $V_{\rm B}$. From the STM images, we find the three adsorbates to be sitting on the short-bridge, hollow and top position on the Ag(110) surface, with a relative abundance of 60\%, 35\% and 5\%., Comment: 13 pages, 7 figures

We present Flat'n'Fold, a novel large-scale dataset for garment manipulation that addresses critical gaps in existing datasets. Comprising 1,212 human and 887 robot demonstrations of flattening and folding 44 unique garments across 8 categories, Flat'n'Fold surpasses prior datasets in size, scope, and diversity. Our dataset uniquely captures the entire manipulation process from crumpled to folded states, providing synchronized multi-view RGB-D images, point clouds, and action data, including hand or gripper positions and rotations. We quantify the dataset's diversity and complexity compared to existing benchmarks and show that our dataset features natural and diverse manipulations of real-world demonstrations of human and robot demonstrations in terms of visual and action information. To showcase Flat'n'Fold's utility, we establish new benchmarks for grasping point prediction and subtask decomposition. Our evaluation of state-of-the-art models on these tasks reveals significant room for improvement. This underscores Flat'n'Fold's potential to drive advances in robotic perception and manipulation of deformable objects. Our dataset can be downloaded at https://cvas-ug.github.io/flat-n-fold

A fundamental issue about installation of photovoltaic solar power stations is the optimization of the energy generation and the fault detection, for which different techniques and methodologies have already been developed considering meteorological conditions. This fact implies the use of unstable and difficult predictable variables which may give rise to a possible problem for the plausibility of the proposed techniques and methodologies in particular conditions. In this line, our goal is to provide a decision support system for photovoltaic fault detection avoiding meteorological conditions. This paper has developed a mathematical mechanism based on fuzzy sets in order to optimize the energy production in the photovoltaic facilities, detecting anomalous behaviors in the energy generated by the facilities over time. Specifically, the incorrect and correct behaviors of the photovoltaic facilities have been modeled through the use of different membership mappings. From these mappings, a decision support system based on OWA operators informs of the performances of the facilities per day, by using natural language. Moreover, a state machine is also designed to determine the stage of each facility based on the stages and the performances from previous days. The main advantage of the designed system is that it solves the problem of "constant loss of energy production", without the consideration of meteorological conditions and being able to be more profitable. Moreover, the system is also scalable and portable, and complements previous works in energy production optimization. Finally, the proposed mechanism has been tested with real data, provided by Grupo Energ\'etico de Puerto Real S.A. which is an enterprise in charge of the management of six photovoltaic facilities in Puerto Real, C\'adiz, Spain, and good results have been obtained for faulting detection.

Formal concept analysis (FCA) is a useful mathematical tool for obtaining information from relational datasets. One of the most interesting research goals in FCA is the selection of the most representative variables of the dataset, which is called attribute reduction. Recently, the attribute reduction mechanism has been complemented with the use of local congruences in order to obtain robust clusters of concepts, which form convex sublattices of the original concept lattice. Since the application of such local congruences modifies the quotient set associated with the attribute reduction, it is fundamental to know how the original context (attributes, objects and relationship) has been modified in order to understand the impact of the application of the local congruence in the attribute reduction.

Attribute and size reductions are key issues in formal concept analysis. In this paper, we consider a special kind of equivalence relation to reduce concept lattices, which will be called local congruence. This equivalence relation is based on the notion of congruence on lattices, with the goal of losing as less information as possible and being suitable for the reduction of concept lattices. We analyze how the equivalence classes obtained from a local congruence can be ordered. Moreover, different properties related to the algebraic structure of the whole set of local congruences are also presented. Finally, a procedure to reduce concept lattices by the new weaker notion of congruence is introduced. This procedure can be applied to the classical and fuzzy formal concept analysis frameworks.

Galaxy properties are known to be affected by their environment. This is well established for the extremes of the density scales, between the high-density cluster environment and the low-density field. It is however not fully understood how the intermediate-density regime of cosmic web filaments affects galaxy evolution. We investigate this environmental effect using a mass complete sample of 23,441 galaxies in the Sloan Digital Sky Survey DR8 Main Galaxy Sample (${M}_{\text{Stellar}} > 10^{9.91} \text{M}_{\odot}$). We define 6 environments, probing different density regimes and representing unique stages in the structure formation process, comparing the differences in star formation activity and morphology between them. We find that galaxies in filaments tend to be less star forming and favour more early-type morphologies than those in the field. These differences persist when considering stellar mass-matched samples, suggesting that this is a consequence of the environment. We further investigate whether these trends are a result of the large scale or local environment through constructing samples matched both in stellar mass and local galaxy density. We find that when also matching in local galaxy density, the differences observed between the filament and field population vanishes, concluding that the environmental effect of filaments can be entirely parameterised by a local galaxy density index. We find that differences can still be seen in comparisons with the interiors of clusters, suggesting these are unique environments which can impart additional physical processes not characterised by local galaxy density., Comment: 19 pages, 10 figures, Accepted for publication in MNRAS

We present IMMERTWIN, a mixed reality framework for enhance robotic arm teleoperation using a closed-loop digital twin as a bridge for interaction between the user and the robotic system. We evaluated IMMERTWIN by performing a medium-scale user survey with 26 participants on two robots. Users were asked to teleoperate with both robots inside the virtual environment to pick and place 3 cubes in a tower and to repeat this task as many times as possible in 10 minutes, with only 5 minutes of training beforehand. Our experimental results show that most users were able to succeed by building at least a tower of 3 cubes regardless of the robot used and a maximum of 10 towers (1 tower per minute). In addition, users preferred to use IMMERTWIN over our previous work, TELESIM, as it caused them less mental workload. The project website and source code can be found at: https://cvas-ug.github.io/immertwin

Let $C\subset\mathbb{N}^p$ be an integer polyhedral cone. An affine semigroup $S\subset C$ is a $ C$-semigroup if $| C\setminus S|<+\infty$. This structure has always been studied using a monomial order. The main issue is that the choice of these orders is arbitrary. In the present work we choose the order given by the semigroup itself, which is a more natural order. This allows us to generalise some of the definitions and results known from numerical semigroup theory to $C$-semigroups., Comment: 10 pages

The hadronic vacuum polarization contribution to $(g-2)_\mu$ can be determined via dispersive methods from $e^+e^-\to\;$hadrons data. We propose a novel approach to measure the hadronic cross section $\sigma_{\mathrm{had}}$ as an alternative to the initial-state radiation and energy scan techniques, which relies on positron annihilation off atomic electrons of a high $Z$ target ($^{238}$U, $Z=92$). We show that by leveraging the relativistic electron velocities of the inner atomic shells, a high-intensity $12\,$GeV positron beam, such as the one foreseen at JLab, can allow to measure $\sigma_{\mathrm{had}}$ with high statistical accuracy from the two-pion threshold up to above $\sqrt{s} \sim 1\,$GeV., Comment: 6 pages, 2 figures. One numerical error corrected. Conclusions unchanged

Neural networks (NNs) hold great promise for advancing inverse design via topology optimization (TO), yet misconceptions about their application persist. This article focuses on neural topology optimization (neural TO), which leverages NNs to reparameterize the decision space and reshape the optimization landscape. While the method is still in its infancy, our analysis tools reveal critical insights into the NNs' impact on the optimization process. We demonstrate that the choice of NN architecture significantly influences the objective landscape and the optimizer's path to an optimum. Notably, NNs introduce non-convexities even in otherwise convex landscapes, potentially delaying convergence in convex problems but enhancing exploration for non-convex problems. This analysis lays the groundwork for future advancements by highlighting: 1) the potential of neural TO for non-convex problems and dedicated GPU hardware (the "good"), 2) the limitations in smooth landscapes (the "bad"), and 3) the complex challenge of selecting optimal NN architectures and hyperparameters for superior performance (the "ugly")., Comment: 33 pages, 19 figures, includes Supporting Information in the same PDF

The Latinx diaspora in the United States is a rapidly growing and complex demographic who face intersectional harms and marginalizations in sociotechnical systems and are currently underserved in CSCW research. While the field understands that algorithms and digital content are experienced differently by marginalized populations, more investigation is needed about how Latinx people experience social media and, in particular, visual media. In this paper, we focus on how Latinx people experience the algorithmic system of the video-sharing platform TikTok. Through a bilingual interview and visual elicitation study of 19 Latinx TikTok users and 59 survey participants, we explore how Latinx individuals experience TikTok and its Latinx content. We find Latinx TikTok users actively use platform affordances to create positive and affirming identity content feeds, but these feeds are interrupted by negative content (i.e. violence, stereotypes, linguistic assumptions) due to platform affordances that have unique consequences for Latinx diaspora users. We discuss these implications on Latinx identity and representation, introduce the concept of \textit{algorithmic identity compression}, where sociotechncial systems simplify, flatten, and conflate intersection identities, resulting in compression via the loss of critical cultural data deemed unnecessary by these systems and designers of them. This study explores how Latinx individuals are particularly vulnerable to this in sociotechnical systems, such as, but not limited to, TikTok.

Nonlinear dynamic simulations of mechanical resonators have been facilitated by the advent of computational techniques that generate nonlinear reduced order models (ROMs) using the finite element (FE) method. However, designing devices with specific nonlinear characteristics remains inefficient since it requires manual adjustment of the design parameters and can result in suboptimal designs. Here, we integrate an FE-based nonlinear ROM technique with a derivative-free optimization algorithm to enable the design of nonlinear mechanical resonators. The resulting methodology is used to optimize the support design of high-stress nanomechanical Si3N4 string resonators, in the presence of conflicting objectives such as simultaneous enhancement of Q-factor and nonlinear Duffing constant. To that end, we generate Pareto frontiers that highlight the trade-offs between optimization objectives and validate the results both numerically and experimentally. To further demonstrate the capability of multi-objective optimization for practical design challenges, we simultaneously optimize the design of nanoresonators for three key figure-of-merits in resonant sensing: power consumption, sensitivity and response time. The presented methodology can facilitate and accelerate designing (nano)mechanical resonators with optimized performance for a wide variety of applications.

Disordered hyperuniform materials are very promising for applications but the successful route for synthesizing them requires to understand the interactions induced by the host media that can switch off this hidden order. With this aim we study the model system of vortices in the $\beta$-Bi$_2$Pd superconductor where correlated defects seem to play a determinant role for the nucleation of a gel vortex phase at low densities. We directly image vortices in extended fields-of-view and show that the disordered vortex structure in this material is anti-hyperuniform, contrasting with the case of vortex structures nucleated in samples with point-like disorder. Based on numerical simulations, we show that this anti-hyperuniform structure arises both, from the interaction of a diluted vortex structure with a fourfold-symmetric correlated disorder quite likely generated when cleaving the samples and from the out of equilibrium nature of the quenched configuration., Comment: 13 pages, 10 figures

Single-step Coulomb excitation of $^{46,48,49,50}$Ti is presented. A complete set of $E2$ matrix elements for the quintuplet of states in $^{49}$Ti, centered on the $2^+$ core excitation, was measured for the first time. A total of nine $E2$ matrix elements are reported, four of which were previously unknown. $^{49}_{22}$Ti$_{27}$ shows a $20\%$ quenching in electric quadrupole transition strength as compared to its semi-magic $^{50}_{22}$Ti$_{28}$ neighbour. This $20\%$ quenching, while empirically unprecedented, can be explained with a remarkably simple two-state mixing model, which is also consistent with other ground-state properties such as the magnetic dipole moment and electric quadrupole moment. A connection to nucleon transfer data and the quenching of single-particle strength is also demonstrated. The simplicity of the $^{49}$Ti-$^{50}$Ti pair (i.e., approximate single-$j$ $0f_{7/2}$ valence space and isolation of yrast states from non-yrast states) provides a unique opportunity to disentangle otherwise competing effects in the ground-state properties of atomic nuclei, the emergence of collectivity, and the role of proton-neutron interactions., Comment: 8 pages, 5 figures, accepted in Physics Letters B

Galaxies infalling into clusters undergo both star-formation quenching and morphological transformation due to environmental effects. We investigate these processes and their timescales using a local sample of 20,191 cluster and 11,674 field galaxies from SDSS. By analysing morphology as a function of distance from the star-formation main sequence, we show that environmental influence is especially pronounced for low-mass galaxies, which emerge from the green valley with early-type morphologies before their star formation is fully suppressed. Using the galaxies' positions in the clusters' Projected Phase Space, we examine the evolution of blue cloud, green valley, and red sequence fractions as a function of time since infall. Interestingly, the green valley fraction remains constant with time since infall, suggesting a balanced flow of galaxies in and out of this class. We estimate that galaxies less massive than $10^{10}\rm M_{\odot}$ spend approximately 0.4 Gyr in the green valley. By comparing quenched and early-type populations, we provide further evidence for the ``slow-then-rapid'' quenching model and suggest that it can also be applied to morphological transitions. Our results indicate that morphological transformation occurs at larger radii than complete star-formation quenching. About 75% of galaxies undergoing morphological transition in clusters are spirals evolving into S0s, suggesting that infalling galaxies retain their disks, while massive ellipticals are relics of early merger events. Finally, we show it takes approximately 2.5 and 1.2 Gyr after the delay-time ($\sim 3.8 {\rm Gyr}$) for the population of low mass galaxies in clusters to reach a 50% threshold in quenched and early-type fraction, respectively. These findings suggest morphological transition precedes full star formation quenching, with both processes possibly being causally linked., Comment: 14 pages, 10 figures

In this work, we present a methodology for devising forward-backward methods for finding zeros in the sum of a finite number of maximally monotone operators. We extend the framework and techniques from [SIAM J. Optim., 34 (2024), pp. 1569-1594] to cover the case involving a finite number of cocoercive operators, which should be directly evaluated instead of computing their resolvent. The algorithms are induced by three graphs that determine how the algorithm variables interact with each other and how they are combined to compute each resolvent. The hypotheses on these graphs ensure that the algorithms obtained have minimal lifting and are frugal, meaning that the ambient space of the underlying fixed point operator has minimal dimension and that each resolvent and each cocoercive operator is evaluated only once per iteration. This framework not only allows to recover some known methods, but also to generate new ones, as the forward-backward algorithm induced by a complete graph. We conclude with a numerical experiment showing how the choice of graphs influences the performance of the algorithms.

In many scenarios -- when we bite food or during a crash -- fracture is inevitable. Finding solutions to steer fracture to mitigate its impact or turn it into a purposeful functionality, is therefore crucial. Strategies using composites, changes in chemical composition or crystal orientation, have proven to be very efficient, but the crack path control remains limited and has not been achieved in load-bearing structures. Here, we introduce fracture metamaterials consisting of slender elements whose bending enables large elastic deformation as fracture propagates. This interplay between bending and fracture enables tunable energy dissipation and the design of on-demand crack paths of arbitrary complexity. To this end, we use topology optimisation to create unit cells with anisotropic fracture energy, which we then tile up to realize fracture metamaterials with uniform density that we 3D-print. The thin ligaments that constitute the unit cells confer them a strikingly distinct response in tension and shear, and we show that by controlling the orientation and layout of the unit cells the sequential progress of the crack can be controlled, making the fracture path arbitrarily tortuous. This tortuosity increases the energy dissipation of the metamaterial without changing its stiffness. Using bespoke arrangements of unit cells, metamaterials can have on-demand fracture paths of arbitrary complexity. Our findings bring a new perspective on inelastic deformations in mechanical metamaterials, with potential applications in areas as diverse as the food industry, structural design, and for shock and impact damping.

The McEliece scheme is a generic frame which allows to use any error correcting code of which there exists an efficient decoding algorithm to design an encryption scheme by hiding the generator matrix code. Similarly, the Niederreiter frame is the dual version of the McEliece scheme, and achieves smaller ciphertexts. We propose a generalization of the McEliece frame and the Niederreiter frame to matrix codes and the MinRank problem, that we apply to Gabidulin matrix codes (Gabidulin rank codes considered as matrix codes). The masking we consider consists in starting from a rank code C, to consider a matrix version of C and to concatenate a certain number of rows and columns to the matrix codes version of the rank code C and then apply to an isometry for matric codes. The security of the schemes relies on the MinRank problem to decrypt a ciphertext, and the structural security of the scheme relies on a new problem EGMC-Indistinguishability problem that we introduce and that we study in detail. The main structural attack that we propose consists in trying to recover the masked linearity over the extension field which is lost during the masking process. Overall, starting from Gabidulin codes we obtain a very appealing tradeoff between the size of ciphertext and the size of the public key. For 128b of security we propose parameters ranging from ciphertext of size 65 B (and public keys of size 98 kB) to ciphertext of size 138B (and public key of size 41 kB). Our new approach permits to achieve better trade-off between ciphertexts and public key than the classical McEliece scheme. Our new approach permits to obtain an alternative scheme to the classic McEliece scheme, to obtain very small ciphertexts, with moreover smaller public keys than in the classic McEliece scheme. For 256 bits of security, we can obtain ciphertext as low as 119B, or public key as low as 87kB.

In this paper we propose an approach for solving systems of nonlinear equations without computing function derivatives. Motivated by the application area of tomographic absorption spectroscopy, which is a highly-nonlinear problem with variables coupling, we consider a situation where straightforward translation to a fixed point problem is not possible because the operators that represent the relevant systems of nonlinear equations are not self-mappings, i.e., they operate between spaces of different dimensions. To overcome this difficulty we suggest an "alternating common fixed points algorithm" that acts alternatingly on the different vector variables. This approach translates the original problem to a common fixed point problem for which iterative algorithms are abound and exhibits a viable alternative to translation to an optimization problem, which usually requires derivatives information. However, to apply any of these iterative algorithms requires to ascertain the conditions that appear in their convergence theorems. To circumvent the need to verify conditions for convergence, we propose and motivate a derivative-free algorithm that better suits the tomographic absorption spectroscopy problem at hand and is even further improved by applying to it the superiorization approach. This is presented along with experimental results that demonstrate our approach., Comment: Accepted for publication in the journal: Communications in Optimization Theory

We have learnt in the last decades that the majority of galaxies belong to high density regions interconnected in a sponge-like fashion. This large-scale structure is characterised by clusters, filaments, walls, where most galaxies concentrate, but also under-dense regions, called voids. The void regions and the galaxies within represent an ideal place for the study of galaxy formation and evolution as they are largely unaffected by the complex physical processes that transform galaxies in high-density environments. These void galaxies can hold the key as well to answer current challenges to the $\Lambda$CDM paradigm. The Calar Alto Void Integral-field Treasury surveY (CAVITY) is a Legacy project approved by the Calar Alto Observatory to obtain spatially resolved spectroscopic information of $\sim300$ void galaxies in the Local Universe (0.005 < z < 0.050) covering from -17.0 to -21.5 in $\rm r$ band absolute magnitude. It officially started in January 2021 and has been awarded 110 useful dark observing nights at the 3.5 m telescope using the PMAS spectrograph. Complementary follow-up projects including deep optical imaging, integrated, as well as resolved CO data, and integrated HI spectra, have joint the PMAS observations and naturally complete the scientific aim of characterising galaxies in cosmic voids. The extension data has been denominated CAVITY+. The data will be available to the whole community in different data releases, the first of which is planned for July 2024, and it will provide the community with PMAS data cubes for around 100 void galaxies through a user friendly, and well documented, database platform. We present here the survey, sample selection, data reduction, quality control schemes, science goals, and some examples of the scientific power of the CAVITY and CAVITY+ data., Comment: 19 pages, 11 figures. Accepted for publication in A&A - Replacement after A&A minor language edition

We analyse a sample of massive disk galaxies selected from the SDSS-IV/MaNGA survey to investigate how the evolution of these galaxies depends on their stellar and halo masses. We applied a semi-analytic spectral fitting approach to the data from different regions in the galaxies to derive several of their key physical properties. From the best-fit model results, together with direct observables such as morphology, colour, and the Mgb/$\langle$Fe$\rangle$ index ratio measured within $1 R_{\rm e}$, we find that for central galaxies both their stellar and halo masses have a significant influence in their evolution. For a given halo mass, galaxies with higher stellar mass accumulate their stellar mass and become chemically enriched earlier than those with smaller stellar mass. Furthermore, at a given stellar mass, galaxies living in more massive halos have longer star-formation timescales and are delayed in becoming chemically enriched. In contrast, the evolution of massive satellite galaxies is mostly determined by their stellar mass. The results indicate that both the assembled halo mass and the halo assembly history impact the evolution of central galaxies. Our spatially resolved analysis indicates that only the galaxy properties in the central region ($0.0$--$0.5 R_{\rm e}$) show the dependencies described above. This fact supports a halo-driven formation scenario since the galaxies' central regions are more likely to contain old stars formed along with the halo itself, keeping a memory of the halo formation process., Comment: 13 pages, 7 figures, MNRAS accepted

In the early stages of the COVID-19 pandemic, many events and conferences hastily converted to a virtual format, and many commercial ventures promptly developed tools promising seamless transitions to virtual spaces. In particular, efforts to expand and monetize augmented and virtual reality environments increased. While these spaces increased accessibility for some, others were left behind. In 2024, many events returned to on-site venues, yet virtual spaces remain central in academic and research communities, particularly for disabled scholars. As such, in this paper, we advocate for continued virtual access and improved virtual spaces; we also identify some potentially overlooked harms in immersive and embodied virtual spaces., Comment: This is an accepted position statement of CHI 2024 Workshop (Novel Approaches for Understanding and Mitigating Emerging New Harms in Immersive and Embodied Virtual Spaces: A Workshop at CHI 2024)

Wikipedia is the largest web repository of free knowledge. Volunteer editors devote time and effort to creating and expanding articles in more than 300 language editions. As content quality varies from article to article, editors also spend substantial time rating articles with specific criteria. However, keeping these assessments complete and up-to-date is largely impossible given the ever-changing nature of Wikipedia. To overcome this limitation, we propose a novel computational framework for modeling the quality of Wikipedia articles. State-of-the-art approaches to model Wikipedia article quality have leveraged machine learning techniques with language-specific features. In contrast, our framework is based on language-agnostic structural features extracted from the articles, a set of universal weights, and a language version-specific normalization criterion. Therefore, we ensure that all language editions of Wikipedia can benefit from our framework, even those that do not have their own quality assessment scheme. Using this framework, we have built datasets with the feature values and quality scores of all revisions of all articles in the existing language versions of Wikipedia. We provide a descriptive analysis of these resources and a benchmark of our framework. In addition, we discuss possible downstream tasks to be addressed with these datasets, which are released for public use., Comment: Accepted at ICWSM-24

Contemporary GPUs are designed to handle long-latency operations effectively; however, challenges such as core occupancy (number of warps in a core) and pipeline width can impede their latency management. This is particularly evident in Tile-Based Rendering (TBR) GPUs, where core occupancy remains low for extended durations. To address this challenge, we introduce WaSP, a lightweight warp scheduler tailored for GPUs in graphics applications. WaSP strategically mimics prefetching by initiating a select subset of warps, termed priority warps, early in execution to reduce memory latency for subsequent warps. This optimization taps into the inherent but underutilized memory parallelism within the GPU core. This underutilization is a consequence of a baseline scheduler that evenly spaces misses throughout execution to exploit the inherent spatial locality in graphics workloads. WaSP improves on this by reducing average memory latency while maintaining locality for the majority of warps. While maximizing memory parallelism utilization, WaSP prevents saturating the caches with misses to avoid filling up the MSHRs (Miss Status Holding Registers). This approach reduces cache stalls that halt further accesses to the cache. Overall, WaSP yields a significant 3.9% performance speedup. Importantly, WaSP accomplishes these enhancements with a negligible overhead, positioning it as a promising solution for enhancing the efficiency of GPUs in managing latency challenges.

Dynamical systems can be analyzed as computational devices capable of performing information processing. In coupled oscillators, enlarged capabilities are expected when the set of units is formed by subsets with collective behaviour within them but weak correlation between the subsets. A system of non-linear oscillators weakly coupled in the phase approximation is studied. The informational distance maps show different regimes of collective behaviour, ranging from independent, local, and global, as the control parameters of the system are changed. This rich set of behaviours happens despite the simple nature of the model used. Complex hierarchies between oscillators can be identified near the edge of chaos region in the informational distance dendrograms. We identify the emergence of local collective regimes, further corroborated by the spatiotemporal maps. Correlation diagrams also exhibit non-trivial dependence for the system at the edge of chaos.

Resonant positron annihilation on atomic electrons provides a powerful method to search for light new particles coupled to $e^+e^-$. Reliable estimates of production rates require a detailed characterization of electron momentum distributions. We describe a general method that harnesses the target material Compton profile to properly include electron velocity effects in resonant annihilation cross-sections. We additionally find that high $Z$ atoms can efficiently act as particle physics accelerators, providing a density of relativistic electrons that allows to extend by several times the experimental mass reach., Comment: 11 pages, 2 figures. One error corrected, mainly affecting only Fig. 2 right (erratum to appear in PRL). Conclusions unchanged

Human-annotated vision datasets inevitably contain a fraction of human mislabelled examples. While the detrimental effects of such mislabelling on supervised learning are well-researched, their influence on Supervised Contrastive Learning (SCL) remains largely unexplored. In this paper, we show that human-labelling errors not only differ significantly from synthetic label errors, but also pose unique challenges in SCL, different to those in traditional supervised learning methods. Specifically, our results indicate they adversely impact the learning process in the ~99% of cases when they occur as false positive samples. Existing noise-mitigating methods primarily focus on synthetic label errors and tackle the unrealistic setting of very high synthetic noise rates (40-80%), but they often underperform on common image datasets due to overfitting. To address this issue, we introduce a novel SCL objective with robustness to human-labelling errors, SCL-RHE. SCL-RHE is designed to mitigate the effects of real-world mislabelled examples, typically characterized by much lower noise rates (<5%). We demonstrate that SCL-RHE consistently outperforms state-of-the-art representation learning and noise-mitigating methods across various vision benchmarks, by offering improved resilience against human-labelling errors., Comment: arXiv admin note: substantial text overlap with arXiv:2311.16481

Novel hyperuniform materials are emerging as an active field of applied and basic research since they can be designed to have exceptional physical properties. This ubiquitous state of matter presents a hidden order that is characterized by the density of constituents of the system being uniform at large scales, as in a perfect crystal, although they can be isotropic and disordered like a liquid. In the quest for synthesizing hyperuniform materials in experimental conditions, the impact of finite-size effects remains as an open question to be addressed. We use vortex matter in type-II superconductors as a toy model system to study this issue. We previously reported that vortex matter nucleated in samples with point disorder is effectively hyperuniform and thus presents the interesting physical properties inherent to hyperuniform systems. In this work we present experimental evidence that on decreasing the thickness of the vortex system its hyperuniform order is depleted. By means of hydrodynamic arguments we show that the experimentally observed depletion can be associated to two crossovers that we describe within a hydrodynamic approximation. The first crossover length is thickness-dependent and separates a class-II hyperuniform regime at intermediate lengthscales from a regime that can become asymptotically non-hyperuniform for large wavelengths in very thin samples. The second crossover takes place at smaller lengthscales and marks the onset of a faster increase of density fluctuations due to the dispersivity of the elastic constants. Our work points to a novel mechanism of emerging hyperuniformity controlled by the thickness of the host sample, an issue that has to be taken into account when growing hyperuniform structures for technological applications., Comment: 5 figures

Cosmological simulations play a key role in the prediction and understanding of large scale structure formation from initial conditions. We make use of GAN-based Autoencoders (AEs) in an attempt to predict structure evolution within simulations. The AEs are trained on images and cubes issued from respectively 2D and 3D N-body simulations describing the evolution of the dark matter (DM) field. We find that while the AEs can predict structure evolution for 2D simulations of DM fields well, using only the density fields as input, they perform significantly more poorly in similar conditions for 3D simulations. However, additionally providing velocity fields as inputs greatly improves results, with similar predictions regardless of time-difference between input and target., Comment: 11 pages, 11 figures

State-of-the-art pre-trained image models predominantly adopt a two-stage approach: initial unsupervised pre-training on large-scale datasets followed by task-specific fine-tuning using Cross-Entropy loss~(CE). However, it has been demonstrated that CE can compromise model generalization and stability. While recent works employing contrastive learning address some of these limitations by enhancing the quality of embeddings and producing better decision boundaries, they often overlook the importance of hard negative mining and rely on resource intensive and slow training using large sample batches. To counter these issues, we introduce a novel approach named CLCE, which integrates Label-Aware Contrastive Learning with CE. Our approach not only maintains the strengths of both loss functions but also leverages hard negative mining in a synergistic way to enhance performance. Experimental results demonstrate that CLCE significantly outperforms CE in Top-1 accuracy across twelve benchmarks, achieving gains of up to 3.52% in few-shot learning scenarios and 3.41% in transfer learning settings with the BEiT-3 model. Importantly, our proposed CLCE approach effectively mitigates the dependency of contrastive learning on large batch sizes such as 4096 samples per batch, a limitation that has previously constrained the application of contrastive learning in budget-limited hardware environments.

NLP has recently made exciting progress toward training language models (LMs) with strong scientific problem-solving skills. However, model development has not focused on real-life use-cases of LMs for science, including applications in education that require processing long scientific documents. To address this, we introduce TutorEval and TutorChat. TutorEval is a diverse question-answering benchmark consisting of questions about long chapters from STEM textbooks, written by experts. TutorEval helps measure real-life usability of LMs as scientific assistants, and it is the first benchmark combining long contexts, free-form generation, and multi-disciplinary scientific knowledge. Moreover, we show that fine-tuning base models with existing dialogue datasets leads to poor performance on TutorEval. Therefore, we create TutorChat, a dataset of 80,000 long synthetic dialogues about textbooks. We use TutorChat to fine-tune Llemma models with 7B and 34B parameters. These LM tutors specialized in math have a 32K-token context window, and they excel at TutorEval while performing strongly on GSM8K and MATH. Our datasets build on open-source materials, and we release our models, data, and evaluations., Comment: 8 pages without bibliography and appendix, 26 pages total

We present evidence for strong radiative cooling in a pulsed-power-driven magnetic reconnection experiment. Two aluminum exploding wire arrays, driven by a 20 MA peak current, 300 ns rise time pulse from the Z machine (Sandia National Laboratories), generate strongly-driven plasma flows ($M_A \approx 7$) with anti-parallel magnetic fields, which form a reconnection layer ($S_L \approx 120$) at the mid-plane. The net cooling rate far exceeds the Alfv\'enic transit rate ($\tau_{\text{cool}}^{-1}/\tau_{\text{A}}^{-1} > 100$), leading to strong cooling of the reconnection layer. We determine the advected magnetic field and flow velocity using inductive probes positioned in the inflow to the layer, and inflow ion density and temperature from analysis of visible emission spectroscopy. A sharp decrease in X-ray emission from the reconnection layer, measured using filtered diodes and time-gated X-ray imaging, provides evidence for strong cooling of the reconnection layer after its initial formation. X-ray images also show localized hotspots, regions of strong X-ray emission, with velocities comparable to the expected outflow velocity from the reconnection layer. These hotspots are consistent with plasmoids observed in 3D radiative resistive magnetohydrodynamic simulations of the experiment. X-ray spectroscopy further indicates that the hotspots have a temperature (170 eV) much higher than the bulk layer ($\leq$ 75 eV) and inflow temperatures (about 2 eV), and that these hotspots generate the majority of the high-energy (> 1 keV) emission.

We present results from the first experimental study of strongly radiatively-cooled magnetic reconnection. Two exploding aluminum wire arrays, driven simultaneously by the Z machine ($I_{max} = 20 \, \text{MA}$, $t_{\text{rise}} = 300 \, \text{ns}$), generate a radiatively-cooled reconnection layer ($S_L \approx 120$) in which the total cooling rate exceeds the hydrodynamic transit rate ($\tau_{\text{hydro}}/\tau_{\text{cool}} > 100$). Measurements of X-ray emission from the reconnection layer using a filtered diode ($>1$ keV) show a narrow (50 ns FWHM) burst of emission at 220 ns after current start, consistent with the formation and subsequent rapid cooling of the reconnection layer. Time-gated X-ray images of the reconnection layer show fast-moving (up to 50 km/s) hotspots inside the layer, consistent with the presence of plasmoids observed in 3D resistive magnetohydrodynamic simulations. X-ray spectroscopy shows that these hotspots generate the majority of Al K-shell emission (at around 1.6 keV) prior to the onset of cooling, and exhibit temperatures of 170 eV, much greater than the temperature of the plasma inflows and the rest of the reconnection layer.

In this paper, we tackle the challenge of actively attending to visual scenes using a foveated sensor. We introduce an end-to-end differentiable foveated active vision architecture that leverages a graph convolutional network to process foveated images, and a simple yet effective formulation for foveated image sampling. Our model learns to iteratively attend to regions of the image relevant for classification. We conduct detailed experiments on a variety of image datasets, comparing the performance of our method with previous approaches to foveated vision while measuring how the impact of different choices, such as the degree of foveation, and the number of fixations the network performs, affect object recognition performance. We find that our model outperforms a state-of-the-art CNN and foveated vision architectures of comparable parameters and a given pixel or computation budget, Comment: Accepted at BMVC2023

Even over relatively recent epochs, galaxies have evolved significantly in their location in the mass-metallicity plane, which must be telling us something about the latter stages of galaxy evolution. In this paper, we analyse data from the LEGA-C survey using semi-analytic spectral and photometric fitting to determine these galaxies' evolution up to their observed epoch at $z \sim 0.7$. We confirm that, at $z \sim 0.7$, many objects already lie on the present-day mass-metallicity relation, but with a significant tail of high-mass low-metallicity galaxies that is not seen in the nearby Universe. Similar modelling of the evolution of galaxies in the nearby MaNGA survey allows us to reconstruct their properties at $z \sim 0.7$. Once selection criteria similar to those of LEGA-C are applied, we reassuringly find that the MaNGA galaxies populate the mass-metallicity plane in the same way at $z \sim 0.7$. Matching the LEGA-C sample to their mass-metallicity "twins" in MaNGA at this redshift, we can explore the likely subsequent evolution of individual LEGA-C galaxies. Galaxies already on the present-day mass--metallicity relation form few more stars and their disks fade, so they become smaller and more bulge-like. By contrast, the high-mass low-metallicity galaxies grow their disks through late star formation, and evolve rapidly to higher metallicities due to a cut-off in their wind-driven mass loss. There are significant indications that this late cut-off is associated with the belated end of strong AGN activity in these objects., Comment: 17 pages, 13 figures, MNRAS accepted

We investigate the efficacy of using the cosmic web nodes identified by the DisPerSE topological filament finder to systematically identify galaxy groups in the infall regions around massive clusters. The large random motions and infall velocities of galaxies in the regions around clusters complicate the detection and characterisation of substructures through normal group-finding algorithms. Yet understanding the co-location of galaxies within filaments and/or groups is a key part of understanding the role of environment on galaxy evolution, particularly in light of next-generation wide-field spectroscopic surveys. Here we use simulated massive clusters from TheThreeHundred collaboration and compare the derived group catalogues, (haloes with $\sigma_{v} > 300 h^{-1}$ km/s) with the critical points from DisPerSE, ran on haloes with more than 100 particles. We find that in 3D, 56\% of DisPerSE nodes are correctly identified as groups (purity) while 68\% of groups are identified as nodes (completeness). The fraction of matches increases with group mass and with distance from the host cluster centre. This rises to a completeness of 100\% for the most massive galaxy groups ($M>10^{14}$ M$_{\odot}$) in 3D, or 63\% when considering the projected 2D galaxy distribution. When a perfect match occurs between a cosmic web node and a galaxy group, the DisPerSE node density ($\delta$) serves as an estimate of the group's mass, albeit with significant scatter. We conclude that the use of a cosmic filament finder shows promise as a useful and straightforward observational tool for disentangling substructure within the infall regions of massive clusters., Comment: Accepted for publication in MNRAS

Robotic vision applications often necessitate a wide range of visual perception tasks, such as object detection, segmentation, and identification. While there have been substantial advances in these individual tasks, integrating specialized models into a unified vision pipeline presents significant engineering challenges and costs. Recently, Multimodal Large Language Models (MLLMs) have emerged as novel backbones for various downstream tasks. We argue that leveraging the pre-training capabilities of MLLMs enables the creation of a simplified framework, thus mitigating the need for task-specific encoders. Specifically, the large-scale pretrained knowledge in MLLMs allows for easier fine-tuning to downstream robotic vision tasks and yields superior performance. We introduce the RoboLLM framework, equipped with a BEiT-3 backbone, to address all visual perception tasks in the ARMBench challenge-a large-scale robotic manipulation dataset about real-world warehouse scenarios. RoboLLM not only outperforms existing baselines but also substantially reduces the engineering burden associated with model selection and tuning. The source code is publicly available at https://github.com/longkukuhi/armbench.

In this work we propose reactoscope, a novel experimental setup for axion-like particle (ALP) searches. Nuclear reactors produce a copious number of photons, a fraction of which could convert into ALPs via Primakoff process in the reactor core. The generated flux of ALPs leaves the nuclear power plant and its passage through a region with a strong magnetic field results in the efficient conversion to photons which can be detected. Such magnetic field is the key component of axion haloscope experiments. Adjacent nuclear reactor and axion haloscope experiment exist in Grenoble, France. There, the Institut Laue-Langevin (ILL) research reactor is situated only $\sim 700$ m from GrAHal, the axion haloscope platform designed to offer several volume and magnetic field (up to 43 T) configurations. We derive sensitivity projections for photophilic ALP searches with ILL and GrAHal, and also scrutinize analogous realizations, such as the one comprising of CAST experiment at CERN and Bugey nuclear power plant. The results that we obtain complement and extend the reach of existing laboratory experiments, e.g. light-shining-through-walls. While the derived sensitivities are not competitive when compared to the astrophysical limits, our analysis is free from the assumptions associated to those., Comment: 7 pages, 4 figures

We present TELESIM, a modular and plug-and-play framework for direct teleoperation of a robotic arm using a digital twin as the interface between the user and the robotic system. We tested TELESIM by performing a user survey with 37 participants on two different robots using two different control modalities: a virtual reality controller and a finger mapping hardware controller using different grasping systems. Users were asked to teleoperate the robot to pick and place 3 cubes in a tower and to repeat this task as many times as possible in 10 minutes, with only 5 minutes of training beforehand. Our experimental results show that most users were able to succeed by building at least a tower of 3 cubes regardless of the control modality or robot used, demonstrating the user-friendliness of TELESIM.

As Multimodal Large Language Models (MLLMs) grow in size, adapting them to specialized tasks becomes increasingly challenging due to high computational and memory demands. Indeed, traditional fine-tuning methods are costly, due to the need for extensive, task-specific training. While efficient adaptation methods exist that aim to reduce these costs, in practice they suffer from shallow inter-modal alignment, which severely hurts model effectiveness. To tackle these computational challenges and improve inter-modal alignment, we introduce the MultiWay-Adapter (MWA), a novel framework featuring an 'Alignment Enhancer'. This enhancer deepens inter-modal alignment, enabling high transferability with minimal tuning effort. Our experiments show that unlike prior efficient tuning approaches, MWA maintains model effectiveness, while reducing training time by up-to 57%. MWA is also lightweight, increasing model size by only 2-3% (in terms of parameters) for state-of-the-art foundation models like BEiT-3 Large. These results demonstrate that MWA provides an efficient and effective adaptation method for MLLMs, significantly broadening their applicability.

Information presented in Wikipedia articles must be attributable to reliable published sources in the form of references. This study examines over 5 million Wikipedia articles to assess the reliability of references in multiple language editions. We quantify the cross-lingual patterns of the perennial sources list, a collection of reliability labels for web domains identified and collaboratively agreed upon by Wikipedia editors. We discover that some sources (or web domains) deemed untrustworthy in one language (i.e., English) continue to appear in articles in other languages. This trend is especially evident with sources tailored for smaller communities. Furthermore, non-authoritative sources found in the English version of a page tend to persist in other language versions of that page. We finally present a case study on the Chinese, Russian, and Swedish Wikipedias to demonstrate a discrepancy in reference reliability across cultures. Our finding highlights future challenges in coordinating global knowledge on source reliability., Comment: Conference on Information & Knowledge Management (CIKM '23)