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Using a combination of HST, JWST, and ALMA data, we perform spatially resolved spectral energy distributions (SED) fitting of fourteen 4

We investigate the cosmological dynamics of a homogeneous scalar field non-minimally coupled to torsion gravity, which also interacts with cold dark matter through energy and momentum transfer. The matter and radiation perfect fluids are modeled using the Sorkin-Schutz formalism. We identify scaling regimes of the field during both the radiation and matter eras. Additionally, we discovered a field-dominated scaling attractor; however, it does not exhibit accelerated expansion, making it unsuitable for describing dark energy. Nevertheless, we find two attractor solutions that do exhibit accelerated expansion: one is a quintessence-like fixed point, and the other is a de Sitter fixed point., Comment: 16 pages, 8 figures

We investigate the spatial extent of the [CII] line emission in a sample of 34 galaxies at $z=4-6$ from the ALMA-CRISTAL Survey. By modeling the [CII] line emission in the visibility data directly, we derive the effective radius of [CII] line emission assuming exponential distribution. These measurements comprise not only isolated galaxies but also interacting systems, identified thanks to the high spatial resolution of the data. The [CII] line radius ranges from 0.5 to 3.5 kpc with an average value of 1.9 kpc. We compare the [CII] sizes with the sizes of UV and FIR continua, which were measured from the HST F160W and ALMA Band-7 continuum images, respectively. We confirm that the [CII] line emission is more spatially extended than the continuum emission, with average size ratios of $R_{e,[CII]}/R_{e,UV}=2.90$ and $R_{e,[CII]}/R_{e,FIR}=1.54$, although about half of the FIR-detected sample show comparable spatial extent between [CII] line and FIR continuum emission ($R_{e,[CII]}\approx R_{e, FIR}$). The residual visibility data of the best-fit model do not show evidence of flux excesses either individually or in stacking analysis. This indicates that the [CII] line emission in star-forming galaxies can be characterized by an extended exponential disk profile. Overall, our results suggest that the spatial extent of [CII] line emission can primarily be explained by photodissociation regions associated with star formation activity, while the contribution from diffuse neutral medium (atomic gas) and the effects of mergers may further expand the [CII] line distributions, causing their variations among our sample. We report the correlations between the [CII] line, dust, and Lya line properties, which may be in line with our scenario. Future 3D-analysis of Lya and Ha lines will shed light on the association of the extended [CII] line emission with atomic gas and outflows., Comment: Submitted to A&A, 21 pages, 14 figures, 4 tables

This paper presents the first comprehensive interpretability analysis of a Transformer-based Sign Language Translation (SLT) model, focusing on the translation from video-based Greek Sign Language to glosses and text. Leveraging the Greek Sign Language Dataset, we examine the attention mechanisms within the model to understand how it processes and aligns visual input with sequential glosses. Our analysis reveals that the model pays attention to clusters of frames rather than individual ones, with a diagonal alignment pattern emerging between poses and glosses, which becomes less distinct as the number of glosses increases. We also explore the relative contributions of cross-attention and self-attention at each decoding step, finding that the model initially relies on video frames but shifts its focus to previously predicted tokens as the translation progresses. This work contributes to a deeper understanding of SLT models, paving the way for the development of more transparent and reliable translation systems essential for real-world applications., Comment: Accepted at IAI Workshop @ NeurIPS 2024

Dynamical systems (DS) theory is fundamental for many areas of science and engineering. It can provide deep insights into the behavior of systems evolving in time, as typically described by differential or recursive equations. A common approach to facilitate mathematical tractability and interpretability of DS models involves decomposing nonlinear DS into multiple linear DS separated by switching manifolds, i.e. piecewise linear (PWL) systems. PWL models are popular in engineering and a frequent choice in mathematics for analyzing the topological properties of DS. However, hand-crafting such models is tedious and only possible for very low-dimensional scenarios, while inferring them from data usually gives rise to unnecessarily complex representations with very many linear subregions. Here we introduce Almost-Linear Recurrent Neural Networks (AL-RNNs) which automatically and robustly produce most parsimonious PWL representations of DS from time series data, using as few PWL nonlinearities as possible. AL-RNNs can be efficiently trained with any SOTA algorithm for dynamical systems reconstruction (DSR), and naturally give rise to a symbolic encoding of the underlying DS that provably preserves important topological properties. We show that for the Lorenz and R\"ossler systems, AL-RNNs discover, in a purely data-driven way, the known topologically minimal PWL representations of the corresponding chaotic attractors. We further illustrate on two challenging empirical datasets that interpretable symbolic encodings of the dynamics can be achieved, tremendously facilitating mathematical and computational analysis of the underlying systems., Comment: 38th Conference on Neural Information Processing Systems (NeurIPS 2024)

We propose a simple cellular automaton model of a self-healing system and investigate its properties. In the model, the substrate is a two-dimensional checkerboard configuration which can be damaged by changing values of a finite number of sites. The cellular automaton we consider is a checkerboard voting rule, a binary rule with Moore neighbourhood which is topologically conjugate to majority voting rule. For a single color damage (when only cells in the same state are modified), the rule always fixes the damage. For a general damage, when it is localized inside a $3 \times 3$ square, the rule also fixes it always. When the damage is inside of a larger $n \times n$ square, the efficiency of the rule in fixing the damage becomes smaller than $100\%$, but it remains better than $98\%$ for $n \leq 5$ and better than $75 \%$ for $n\leq 7$. We show that in the limit of infinite $n$ the efficiency tends to zero., Comment: 12 pages, 5 figures

We introduce a model that can accurately simulate radiation from undulator sources for ray tracing applications. It incorporates several key effects relevant to 4$^\text{th}$ generation synchrotron sources, such as electron emittance, energy spread, and diffraction-limited beam size. This code has been developed as part of SHADOW4, the latest version of the widely used SHADOW X-ray optics ray tracing program. The approach relies on calculating the field distribution in the far field, which determines the ray divergences. The integration of existing models for electron energy spread is also addressed. Rays sampled at the source follow a size distribution derived by backpropagating the far field radiation. These models are detailed, and several examples are provided., Comment: 12 page, 12 figures

The increased brightness temperature of young rocky protoplanets during their magma ocean epoch makes them potentially amenable to atmospheric characterization to distances from the solar system far greater than thermally equilibrated terrestrial exoplanets, offering observational opportunities for unique insights into the origin of secondary atmospheres and the near surface conditions of prebiotic environments. The Large Interferometer For Exoplanets (LIFE) mission will employ a space-based mid-infrared nulling interferometer to directly measure the thermal emission of terrestrial exoplanets. Here, we seek to assess the capabilities of various instrumental design choices of the LIFE mission concept for the detection of cooling protoplanets with transient high-temperature magma ocean atmospheres, in young stellar associations in particular. Using the LIFE mission instrument simulator (LIFEsim) we assess how specific instrumental parameters and design choices, such as wavelength coverage, aperture diameter, and photon throughput, facilitate or disadvantage the detection of protoplanets. We focus on the observational sensitivities of distance to the observed planetary system, protoplanet brightness temperature using a blackbody assumption, and orbital distance of the potential protoplanets around both G- and M-dwarf stars. Our simulations suggest that LIFE will be able to detect (S/N $\geq$ 7) hot protoplanets in young stellar associations up to distances of $\approx$100 pc from the solar system for reasonable integration times (up to $\sim$hours). Detection of an Earth-sized protoplanet orbiting a solar-sized host star at 1 AU requires less than 30 minutes of integration time. M-dwarfs generally need shorter integration times. The contribution from wavelength regions $<$6 $\mu$m is important for decreasing the detection threshold and discriminating emission temperatures., Comment: 18 pages, 19 figures; accepted for publication in A&A

Stimulated by the keen interest of possible collective behavior in high-energy proton-proton and proton-nucleus collisions, we study two-particle angular correlations in pseudorapidity and azimuthal differences in simulated p + p interactions by the Pythia 8 event generator. Multi-parton interactions and color connection are included in these simulations which have been perceived to produce collectivity in final-state particles. Meanwhile, contributions from genuine few-body nonflow correlations, not of collective flow behavior, are known to be severe in those small-system collisions. We present our Pythia correlation studies in a pedagogical way and report azimuthal harmonic anisotropies analyzed by several methods. We observe anisotropies in those Pythia simulated events qualitatively and semi-quantitatively similar to experimental data. Our findings highlight the delicate nature of azimuthal anisotropies in small-system collisions, and provide a benchmark helping improve data analysis and interpret experimental measurements in small-system collisions., Comment: 11 pages, 13 figures

Given a bipartite graph $G$, the \textsc{Bicluster Editing} problem asks for the minimum number of edges to insert or delete in $G$ so that every connected component is a bicluster, i.e. a complete bipartite graph. This has several applications, including in bioinformatics and social network analysis. In this work, we study the parameterized complexity under the natural parameter $k$, which is the number of allowed modified edges. We first show that one can obtain a kernel with $4.5k$ vertices, an improvement over the previously known quadratic kernel. We then propose an algorithm that runs in time $O^*(2.581^k)$. Our algorithm has the advantage of being conceptually simple and should be easy to implement., Comment: The author acknowledges the contributions of Beno\^it Charbonneau and Pierre-Luc Parent for their help in finding the $4.5k$ lower bound on the kernel size

In this paper, we address the vision-based autonomous landing problem in complex urban environments using deep neural networks for semantic segmentation and risk assessment. We propose employing the SegFormer, a state-of-the-art visual transformer network, for the semantic segmentation of complex, unstructured urban environments. This approach yields valuable information that can be utilized in smart autonomous landing missions, particularly in emergency landing scenarios resulting from system failures or human errors. The assessment is done in real-time flight, when images of an RGB camera at the Unmanned Aerial Vehicle (UAV) are segmented with the SegFormer into the most common classes found in urban environments. These classes are then mapped into a level of risk, considering in general, potential material damage, damaging the drone itself and endanger people. The proposed strategy is validated through several case studies, demonstrating the huge potential of semantic segmentation-based strategies to determining the safest landing areas for autonomous emergency landing, which we believe will help unleash the full potential of UAVs on civil applications within urban areas.

Super-resolution (SR) is a promising cost-effective downscaling methodology for producing high-resolution climate information from coarser counterparts. A particular application is downscaling regional reanalysis outputs (predictand) from the driving global counterparts (predictor). This study conducts an intercomparison of various SR downscaling methods focusing on temperature and using the CERRA reanalysis (5.5 km resolution, produced with a regional atmospheric model driven by ERA5) as example. The method proposed in this work is the Swin transformer and two alternative methods are used as benchmark (fully convolutional U-Net and convolutional and dense DeepESD) as well as the simple bicubic interpolation. We compare two approaches, the standard one using the full domain as input and a more scalable tiling approach, dividing the full domain into tiles that are used as input. The methods are trained to downscale CERRA surface temperature, based on temperature information from the driving ERA5; in addition, the tiling approach includes static orographic information. We show that the tiling approach, which requires spatial transferability, comes at the cost of a lower performance (although it outperforms some full-domain benchmarks), but provides an efficient scalable solution that allows SR reduction on a pan-European scale and is valuable for real-time applications.

We consider the Ginzburg-Landau equation in the plane linearized around the standard degree-one vortex solution $W(x)=w(r)e^{i\theta}$. Using explicit representation formulae for the Fourier modes in $\theta$, we obtain sharp estimates for the inverse of the linearized operator which hold for a large class of right-hand sides. This theory can be applied, for example, to estimate the inverse after dropping the usual orthogonality conditions., Comment: 21 pages

Spatial Database Management Systems (SDBMSs) aim to store, manipulate, and retrieve spatial data. SDBMSs are employed in various modern applications, such as geographic information systems, computer-aided design tools, and location-based services. However, the presence of logic bugs in SDBMSs can lead to incorrect results, substantially undermining the reliability of these applications. Detecting logic bugs in SDBMSs is challenging due to the lack of ground truth for identifying incorrect results. In this paper, we propose an automated geometry-aware generator to generate high-quality SQL statements for SDBMSs and a novel concept named Affine Equivalent Inputs (AEI) to validate the results of SDBMSs. We implemented them as a tool named Spatter (Spatial DBMSs Tester) for finding logic bugs in four popular SDBMSs: PostGIS, DuckDB Spatial, MySQL, and SQL Server. Our testing campaign detected 34 previously unknown and unique bugs in these SDBMS, of which 30 have been confirmed, and 18 have been already fixed. Our testing efforts have been well appreciated by the developers. Experimental results demonstrate that the geometry-aware generator significantly outperforms a naive random-shape generator in detecting unique bugs, and AEI can identify 14 logic bugs in SDBMSs that were overlooked by previous methodologies.

Hate speech detection deals with many language variants, slang, slurs, expression modalities, and cultural nuances. This outlines the importance of working with specific corpora, when addressing hate speech within the scope of Natural Language Processing, recently revolutionized by the irruption of Large Language Models. This work presents a brief analysis of the performance of large language models in the detection of Hate Speech for Rioplatense Spanish. We performed classification experiments leveraging chain-of-thought reasoning with ChatGPT 3.5, Mixtral, and Aya, comparing their results with those of a state-of-the-art BERT classifier. These experiments outline that, even if large language models show a lower precision compared to the fine-tuned BERT classifier and, in some cases, they find hard-to-get slurs or colloquialisms, they still are sensitive to highly nuanced cases (particularly, homophobic/transphobic hate speech). We make our code and models publicly available for future research.

Magnetoelectric materials displaying strong magnetically induced polarization have attracted considerable interest due to their potential applications in spintronics and various fast electrically controlled magnetic devices. CaMn$_7$O$_{12}$ (CMO) stands out for its giant spin-induced ferroelectric polarization. However, the origin of the induced electric polarization in CMO remains highly controversial and continues to be a subject of ongoing debate. In this paper, through room temperature X-ray powder diffraction (XRPD), temperature-dependent magnetic susceptibility, and thermally stimulated depolarizing current (TSDC) measurements, we provide experimental evidence for a route to tune the magnetically induced polarization by modifying the exchange-striction in CMO via Sr-doping. Our findings demonstrate that the large and broad current peaks observed near the first magnetic phase transition ($T_N1 \sim 90$ K) indicate polarization contributions from both thermally stimulated depolarization current (TSDC) and intrinsic magnetically induced electric polarization. We suggest that this reduction in induced electric polarization in CMO originates from the increase in the Mn$^{3+}$ -- O -- Mn$^{4+}$ bond angle due to Sr$^{2+}$ doping, weakening the exchange-striction interaction. Meanwhile, the Dzyaloshinskii-Moriya (DM) effect determines the direction of the induced electric polarization. Our result sheds light on understanding the intriguing giant-induced polarization in CMO and similar compounds with complex magnetic structures.

Visual Anomaly Detection (VAD) has gained significant research attention for its ability to identify anomalous images and pinpoint the specific areas responsible for the anomaly. A key advantage of VAD is its unsupervised nature, which eliminates the need for costly and time-consuming labeled data collection. However, despite its potential for real-world applications, the literature has given limited focus to resource-efficient VAD, particularly for deployment on edge devices. This work addresses this gap by leveraging lightweight neural networks to reduce memory and computation requirements, enabling VAD deployment on resource-constrained edge devices. We benchmark the major VAD algorithms within this framework and demonstrate the feasibility of edge-based VAD using the well-known MVTec dataset. Furthermore, we introduce a novel algorithm, Partially Shared Teacher-student (PaSTe), designed to address the high resource demands of the existing Student Teacher Feature Pyramid Matching (STFPM) approach. Our results show that PaSTe decreases the inference time by 25%, while reducing the training time by 33% and peak RAM usage during training by 76%. These improvements make the VAD process significantly more efficient, laying a solid foundation for real-world deployment on edge devices., Comment: 13 pages, 6 figures

Spiking neural networks (SNNs) are neural networks that enable energy-efficient signal processing due to their event-based nature. This paper proposes a novel decoding algorithm for low-density parity-check (LDPC) codes that integrates SNNs into belief propagation (BP) decoding by approximating the check node update equations using SNNs. For the (273,191) and (1023,781) finite-geometry LDPC code, the proposed decoder outperforms sum-product decoder at high signal-to-noise ratios (SNRs). The decoder achieves a similar bit error rate to normalized sum-product decoding with successive relaxation. Furthermore, the novel decoding operates without requiring knowledge of the SNR, making it robust to SNR mismatch., Comment: Submitted to Communication Letters

In the field of Explainable AI (XAI), counterfactual (CF) explanations are one prominent method to interpret a black-box model by suggesting changes to the input that would alter a prediction. In real-world applications, the input is predominantly in tabular form and comprised of mixed data types and complex feature interdependencies. These unique data characteristics are difficult to model, and we empirically show that they lead to bias towards specific feature types when generating CFs. To overcome this issue, we introduce TABCF, a CF explanation method that leverages a transformer-based Variational Autoencoder (VAE) tailored for modeling tabular data. Our approach uses transformers to learn a continuous latent space and a novel Gumbel-Softmax detokenizer that enables precise categorical reconstruction while preserving end-to-end differentiability. Extensive quantitative evaluation on five financial datasets demonstrates that TABCF does not exhibit bias toward specific feature types, and outperforms existing methods in producing effective CFs that align with common CF desiderata., Comment: Paper accepted at ICAIF '24: 5th ACM International Conference on AI in Finance, Brooklyn, NY, USA, November 2024

Audio-based kinship verification (AKV) is important in many domains, such as home security monitoring, forensic identification, and social network analysis. A key challenge in the task arises from differences in age across samples from different individuals, which can be interpreted as a domain bias in a cross-domain verification task. To address this issue, we design the notion of an "age-standardised domain" wherein we utilise the optimised CycleGAN-VC3 network to perform age-audio conversion to generate the in-domain audio. The generated audio dataset is employed to extract a range of features, which are then fed into a metric learning architecture to verify kinship. Experiments are conducted on the KAN_AV audio dataset, which contains age and kinship labels. The results demonstrate that the method markedly enhances the accuracy of kinship verification, while also offering novel insights for future kinship verification research., Comment: 4 pages, 2 figures, submitted to IEEE Signal Processing Letters

This paper formulates the completion time $\tau$ of a project network as $ \tau =\|\mathbf{R} \mathbf{t} \|_\infty $ where the rows of $\mathbf{R}$ are simple paths of the network and $\mathbf{t}$ is a column vector representing the duration of the activities. Considering this product as a linear transformation leads to interesting findings on the topological relevance of both paths and activities using singular value decomposition. The notion of spectral networks is introduced to condense the fundamental structure of the project network. A definition of project stress is introduced to establish a comparison index between two alternatives in terms of slack. Additionally, the Moore-Penrose inverse of $\mathbf{R}$ is presented to find the configuration of the durations of the activities resulting in a given simple path duration vector. Then, the systematic mapping review process carried out to assess our claims' novelty is reported. Finally, we reflect on the notion of relevance for paths and activities and the relationship of the incidence matrix with the proposed approach., Comment: 20 pages, 2 figures

Large amplitude oscillations commonly occur in solar prominences, triggered by energetic phenomena such as jets and flares. On March 14-15, 2015, a filament partially erupted in two stages, leading to oscillations in different parts. This study explores longitudinal oscillations from the eruption, focusing on the mechanisms behind their initiation, with special attention to the large oscillation on March 15. The oscillations and jets are analyzed using the time-distance technique. For flares and their interaction with the filament, we analyze AIA channels and use the DEM technique. Initially, a jet fragments the filament, splitting it into two segments. One remains in place, while the other detaches and moves. This causes oscillations in both segments: (a) the position change causes the detached segment to oscillate with a period of $69 \pm 3$ minutes; (b) the jet flows cause the remaining filament to oscillate with a period of $62 \pm 2$ minutes. In the second phase, on March 15, another jet seemingly activates the detached filament eruption, followed by a flare. A large longitudinal oscillation occurs in the remnant segment with a period of $72 \pm 2$ minutes and velocity amplitude $73 \pm 1 \, \mathrm{km s^{-1}}$. During the oscillation trigger, bright field lines connect the flare with the filament, appearing only in the AIA 131$\r{A}$ and 94$\r{A}$ channels, indicating the presence of hot plasma. DEM analysis confirms this, showing plasma around 10 MK pushing the prominence from its southeastern side, displacing it along the field lines and starting the oscillation. From this, the flare -- not the preceding jet-triggers the oscillation. The hot plasma flows into the filament channel. We explain how flares trigger large oscillations in filaments by proposing that post-flare loops reconnect with the filament channel's magnetic field., Comment: Accepted for publication in Astronomy and Astrophysics

The UNESCO (2022) points out that the gap between the existing awareness of a person or a community, and the actual habits of everyday life, is attributed to: low levels of understanding the environmental issues at stake; low levels of knowledge regarding energy and climate issues; and lack of attention to social, emotional or behavioral learning. In this context, as environmental and energetic concerns grow rapidly in our daily lives, RAISE - Raising environmental knowledge & awareness, an ERAMUS+ project with partners from three countries (Greece, Portugal, and Italy), was born. The project's first and main objective is to increase the environmental knowledge and awareness (EK&A) of schoolchildren. To achieve this goal, a survey was first carried out to ascertain the students' knowledge and, based on these results, scenarios and educational material were drawn up to build a pedagogical tool - a 3D Virtual World Learning Environment (3D VWLE). This paper presents the desktop research results and the main features of the game that are intended to match the identified educational needs. The 3D VWLE puts students in situations where they can acquire transferable skills. The project demonstrated that the simulation effect of 3D VWLE on skills provides an excellent online learning environment for students to work on and improve their abilities. In other words, virtual worlds open the door to a new way of learning and teaching.

Based on the analysis of a large number of high-fidelity nonlinear gyrokinetic simulations, we propose a novel strategy to improve confinement in tokamak plasmas by combining up-down asymmetric flux surface shaping with the Low Momentum Diffusivity (LMD) regime. We show that the intrinsic momentum flux driven by up-down asymmetry creates strong flow shear in the LMD regime that can significantly reduce energy transport, increasing the critical gradient by up to $25\%$. In contrast to traditional methods for generating flow shear, such as neutral beam injection, this approach requires no external momentum source and is expected to scale well to large fusion devices. The experimental applicability of this strategy in spherical tokamaks is addressed via simulations by considering actual equilibria from MAST and a preliminary equilibrium from SMART., Comment: 7 pages, 5 figures

Social robots have emerged as valuable contributors to individuals' well-being coaching. Notably, their integration into long-term human coaching trials shows particular promise, emphasizing a complementary role alongside human coaches rather than outright replacement. In this context, robots serve as supportive entities during coaching sessions, offering insights based on their knowledge about users' well-being and activity. Traditionally, such insights have been gathered through methods like written self-reports or wearable data visualizations. However, the disclosure of people's information by a robot raises concerns regarding privacy, appropriateness, and trust. To address this, we conducted an initial study with [n = 22] participants to quantify their perceptions of privacy regarding disclosures made by a robot coaching assistant. The study was conducted online, presenting participants with six prerecorded scenarios illustrating various types of information disclosure and the robot's role, ranging from active on-demand to proactive communication conditions., Comment: IEEE ICRA 2024 Workshop on Robot Trust for Symbiotic Societies (RTSS)

The automotive industry is experiencing a massive paradigm shift. Cars are becoming increasingly autonomous, connected, and computerized. Modern electrical/electronic (E/E) architectures are pushing for an unforeseen functionality integration density, resulting in physically separate Electronic Control Units (ECUs) becoming virtualized and mapped to logical partitions within a single physical microcontroller (MCU). While functional safety (FuSa) has been pivotal for vehicle certification for decades, the increasing connectivity and advances have opened the door for a number of car hacks and attacks. This development drives (cyber-)security requirements in cars, and has paved the way for the release of the new security certification standard ISO21434. RISC-V has great potential to transform automotive computing systems, but we argue that current ISA/extensions are not ready yet. This paper provides our critical perspective on the existing RISC-V limitations, particularly on the upcoming WorldGuard technology, to address virtualized MCU requirements in line with foreseen automotive applications and ISO21434 directives. We then present our proposal for the required ISA extensions to address such limitations, mainly targeting initiator-side protection. Finally, we explain our roadmap towards a full open-source proof-of-concept (PoC), which includes extending QEMU, an open-source RISC-V core, and building a complete software stack.

We implement a simulation environment on top of NetSquid that is specifically designed for estimating the end-to-end fidelity across a path of quantum repeaters or quantum switches. The switch model includes several generalizations which are not currently available in other tools, and are useful for gaining insight into practical and realistic quantum network engineering problems: an arbitrary number of memory registers at the switches, simplicity in including entanglement distillation mechanisms, arbitrary switching topologies, and more accurate models for the depolarization noise. An illustrative case study is presented, namely a comparison in terms of performance between a repeater chain where repeaters can only swap sequentially, and a single switch equipped with multiple memory registers, able to handle multiple swapping requests.

Online platforms have transformed the formal job market but continue to struggle with effectively engaging passive candidates-individuals not actively seeking employment but open to compelling opportunities. We introduce the Independent Halting Cascade (IHC) model, a novel framework that integrates complex network diffusion dynamics with economic game theory to address this challenge. Unlike traditional models that focus solely on information propagation, the IHC model empowers network agents to either disseminate a job posting or halt its spread by applying for the position themselves. By embedding economic incentives into agent decision-making processes, the model creates a dynamic interplay between maximizing information spread and promoting application. Our analysis uncovers distinct behavioral regimes within the IHC model, characterized by critical thresholds in recommendation and application probabilities. Extensive simulations on both synthetic and real-world network topologies demonstrate that the IHC model significantly outperforms traditional direct-recommendation systems in recruiting suitable passive candidates. Specifically, the model achieves up to a 30% higher hiring success rate compared to baseline methods. These findings offer strategic insights into leveraging economic incentives and network structures to enhance recruitment efficiency. The IHC model thus provides a robust framework for modernizing recruitment strategies, particularly in engaging the vast pool of passive candidates in the job market., Comment: 15 pages, 6 figures

The physics of spin-orbit entangled magnetic moments of $4d$ and $5d$ transition metal ions on a honeycomb lattice has been much explored in search for unconventional magnetic orders or quantum spin liquids expected for compass spin models, where different bonds in the lattice favour different orientations for the magnetic moments. Realizing such physics with rare-earth ions is a promising route to achieve exotic ground states in the extreme spin orbit limit, however this regime has remained experimentally largely unexplored due to major challenges in materials synthesis. Here we report successful synthesis of powders and single crystals of $\beta$-Na$_2$PrO$_3$, with $4f^{1}$ Pr$^{4+}$ $j_\mathrm{eff}\!=\!1/2$ magnetic moments arranged on a hyperhoneycomb lattice with the same threefold coordination as the planar honeycomb. We find a strongly noncollinear magnetic order with highly dispersive gapped excitations that we argue arise from frustration between bond-dependent, anisotropic off-diagonal exchanges, a compass quantum spin model not explored experimentally so far. Our results show that rare-earth ions on threefold coordinated lattices offer a platform for the exploration of quantum compass spin models in the extreme spin orbit regime, with qualitatively distinct physics from that of $4d$ and $5d$ Kitaev materials., Comment: 22 pages in total, main text (9 pages) + supplementary information

Extreme Mass Ratio Inspirals (EMRIs) are key observational targets for the Laser Interferometer Space Antenna (LISA) mission. Unresolvable EMRI signals contribute to forming a gravitational wave background (GWB). Characterizing the statistical features of the GWB from EMRIs is of great importance, as EMRIs will ubiquitously affect large segments of the inference scheme. In this work, we apply a frequentist test for GWB Gaussianity and stationarity, exploring three astrophysically-motivated EMRI populations. We construct the resulting signal by combining state-of-the-art EMRI waveforms and a detailed description of the LISA response with time-delay interferometric variables. Depending on the brightness of the GWB, our analysis demonstrates that the resultant EMRI foregrounds show varying degrees of departure from the usual statistical assumptions that the GWBs are both Gaussian and Stationary. If the GWB is non-stationary with non-Gaussian features, this will challenge the robustness of Gaussian-likelihood model, when applied to global inference results, e.g. foreground estimation, background detection, and individual-source parameters reconstruction., Comment: 12 pages, 5 figures, 3 tables

The metric dimension of a graph measures how uniquely vertices may be identified using a set of landmark vertices. This concept is frequently used in the study of network architecture, location-based problems and communication. Given a graph $G$, the metric dimension, denoted as $\dim(G)$, is the minimum size of a resolving set, a subset of vertices such that for every pair of vertices in $G$, there exists a vertex in the resolving set whose shortest path distance to the two vertices is different. This subset of vertices helps to uniquely determine the location of other vertices in the graph. A basis is a resolving set with a least cardinality. Finding a basis is a problem with practical applications in network design, where it is important to efficiently locate and identify nodes based on a limited set of reference points. The Cartesian product of $P_m$ and $P_n$ is the grid network in network science. In this paper, we investigate two novel types of grids in network science: the Villarceau grid Type I and Type II. For each of these grid types, we find the precise metric dimension.

In this manuscript, we deal with some particular type of homogeneous first order linear systems with variable coefficients, in which we provide qualitative properties of the solution. When the coefficients of the indeterminate functions are periodic with the same period, $T$, we obtain a simple method so as to obtain the Floquet coefficients. We give a new interpretation of the averages on the interval $(0,T)$ of the matrix of the coefficients. We discuss some examples and made some comparison with previous results., Comment: 10 pages

Few primitives are as intertwined with the foundations of cryptography as Oblivious Transfer (OT). Not surprisingly, with the advent of the use of quantum resources in information processing, OT played a central role in establishing new possibilities (and defining impossibilities) pertaining to the use of these novel assets. A major research path is minimizing the required assumptions to achieve OT, and studying their consequences. Regarding its computation, it is impossible to construct unconditionally-secure OT without extra assumptions; and, regarding communication complexity, achieving 1-shot (and even non-interactive) OT has proved to be an elusive task, widely known to be impossible classically. Moreover, this has strong consequencesfor realizing round-optimal secure computation, in particular 1-shot 2-Party Computation (2PC). In this work, three main contributions are evidenced by leveraging quantum resources: 1. Unconditionally-secure 2-message non-interactive OT protocol constructed in the Noisy-Quantum-Storage Model. 2. 1-shot OT in the Noisy-Quantum-Storage Model -- proving that this construction is possible assuming the existence of one-way functions and sequential functions. 3. 1-shot 2PC protocol compiled from a semi-honest 1-shot OT to semi-honest 1-shot Yao's Garbled Circuits protocol., Comment: 32 pages, 2 figures

The Calar Alto Void Integral-field Treasury surveY (CAVITY) is a legacy project aimed at characterising the population of galaxies inhabiting voids, which are the most under-dense regions of the cosmic web, located in the Local Universe. This paper describes the first public data release (DR1) of CAVITY, comprising science-grade optical data cubes for the initial 100 out of a total of $\sim$300 galaxies in the Local Universe ($0.005 < z < 0.050$). These data were acquired using the integral-field spectrograph PMAS/PPak mounted on the 3.5m telescope at the Calar Alto observatory. The DR1 galaxy sample encompasses diverse characteristics in the color-magnitude space, morphological type, stellar mass, and gas ionisation conditions, providing a rich resource for addressing key questions in galaxy evolution through spatially resolved spectroscopy. The galaxies in this study were observed with the low-resolution V500 set-up, spanning the wavelength range 3745-7500 \AA, with a spectral resolution of 6.0 \AA (FWHM). Here, we describe the data reduction and characteristics and data structure of the CAVITY datasets essential for their scientific utilisation, highlighting such concerns as vignetting effects, as well as the identification of bad pixels and management of spatially correlated noise. We also provide instructions for accessing the CAVITY datasets and associated ancillary data through the project's dedicated database., Comment: 17 pages, 9 figures, 5 tables. Accepted for publication in A&A

Large language models have demonstrated impressive performance when integrated with vision models even enabling video understanding. However, evaluating these video models presents its own unique challenges, for which several benchmarks have been proposed. In this paper, we show that the currently most used video-language benchmarks can be solved without requiring much temporal reasoning. We identified three main issues in existing datasets: (i) static information from single frames is often sufficient to solve the tasks (ii) the text of the questions and candidate answers is overly informative, allowing models to answer correctly without relying on any visual input (iii) world knowledge alone can answer many of the questions, making the benchmarks a test of knowledge replication rather than visual reasoning. In addition, we found that open-ended question-answering benchmarks for video understanding suffer from similar issues while the automatic evaluation process with LLMs is unreliable, making it an unsuitable alternative. As a solution, we propose TVBench, a novel open-source video multiple-choice question-answering benchmark, and demonstrate through extensive evaluations that it requires a high level of temporal understanding. Surprisingly, we find that most recent state-of-the-art video-language models perform similarly to random performance on TVBench, with only Gemini-Pro and Tarsier clearly surpassing this baseline.

In this paper we introduce two novel models derived from the discrete optimal transport problem. The first model extends the traditional transport problem by adding a quadratic congestion factor directly into the cost function, while the second model replaces conventional constraints with weighted penalization terms. We present theoretical results, for the characterization of interior and corner solution for some specific cases, and we perform smooth comparative statics analysis. We also propose an $O((N+L)(NL)^2)$ algorithm for computing the optimal plan for the penalized model., Comment: Remove comments about infinite types and the stochastic case with independence. Corrected some minor typos. Change the notation, as, for instance, in Proposition 3.2 and Theorem 4.9. Improved abstract and conclusiones (writing). Add details in the proof of Theorem 4.9. Reformulated comparative statics section and corrected statement of Proposition 3.2. Results unchanged

Representation learning is a pivotal area in the field of machine learning, focusing on the development of methods to automatically discover the representations or features needed for a given task from raw data. Unlike traditional feature engineering, which requires manual crafting of features, representation learning aims to learn features that are more useful and relevant for tasks such as classification, prediction, and clustering. We introduce Principal Orthogonal Latent Components Analysis Network (POLCA Net), an approach to mimic and extend PCA and LDA capabilities to non-linear domains. POLCA Net combines an autoencoder framework with a set of specialized loss functions to achieve effective dimensionality reduction, orthogonality, variance-based feature sorting, high-fidelity reconstructions, and additionally, when used with classification labels, a latent representation well suited for linear classifiers and low dimensional visualization of class distribution as well.

We investigate path-wise observables in experiments on driven colloids in a periodic optical field to dissect selected intricate transport features, kinetics, and transition-path time statistics out of thermodynamic equilibrium. These observables directly reflect the properties of individual paths in contrast to the properties of an ensemble of particles, such as radial distribution functions or mean-squared displacements. In particular, we present two distinct albeit equivalent formulations of the underlying stochastic equation of motion, highlight their respective practical relevance, and show how to interchange between them. We discuss conceptually different notions of local velocities and interrogate one- and two-sided first-passage and transition-path-time statistics in and out of equilibrium. We emphasize how path-wise observables may be employed to systematically assess the quality of experimental data and demonstrate that, given sufficient control and sampling, one may quantitatively verify subtle theoretical predictions.

Autonomous systems, like vehicles or robots, require reliable, accurate, fast, resource-efficient, scalable, and low-latency trajectory predictions to get initial knowledge about future locations and movements of surrounding objects for safe human-machine interaction. Furthermore, they need to know the uncertainty of the predictions for risk assessment to provide safe path planning. This paper presents a lightweight method to address these requirements, combining Long Short-Term Memory and Mixture Density Networks. Our method predicts probability distributions, including confidence level estimations for positional uncertainty to support subsequent risk management applications and runs on a low-power embedded platform. We discuss essential requirements for human trajectory prediction in autonomous vehicle applications and demonstrate our method's performance using multiple traffic-related datasets. Furthermore, we explain reliability and sharpness metrics and show how important they are to guarantee the correctness and robustness of a model's predictions and uncertainty assessments. These essential evaluations have so far received little attention for no good reason. Our approach focuses entirely on real-world applicability. Verifying prediction uncertainties and a model's reliability are central to autonomous real-world applications. Our framework and code are available at: https://github.com/kav-institute/mdn_trajectory_forecasting.

Deep networks are frequently tuned to novel tasks and continue learning from ongoing data streams. Such sequential training requires consolidation of new and past information, a challenge predominantly addressed by retaining the most important data points - formally known as coresets. Traditionally, these coresets consist of entire samples, such as images or sentences. However, recent transformer architectures operate on tokens, leading to the famous assertion that an image is worth 16x16 words. Intuitively, not all of these tokens are equally informative or memorable. Going beyond coresets, we thus propose to construct a deeper-level data summary on the level of tokens. Our respectively named core tokensets both select the most informative data points and leverage feature attribution to store only their most relevant features. We demonstrate that core tokensets yield significant performance retention in incremental image classification, open-ended visual question answering, and continual image captioning with significantly reduced memory. In fact, we empirically find that a core tokenset of 1\% of the data performs comparably to at least a twice as large and up to 10 times larger coreset.

We introduce a novel method for extracting a fragmentation model directly from experimental data without requiring an explicit parametric form, called Histories and Observables for Monte-Carlo Event Reweighting (\homer), consisting of three steps: the training of a classifier between simulation and data, the inference of single fragmentation weights, and the calculation of the weight for the full hadronization chain. We illustrate the use of \homer on a simplified hadronization problem, a $q\bar{q}$ string fragmenting into pions, and extract a modified Lund string fragmentation function $f(z)$. We then demonstrate the use of \homer on three types of experimental data: (i) binned distributions of high level observables, (ii) unbinned event-by-event distributions of these observables, and (iii) full particle cloud information. After demonstrating that $f(z)$ can be extracted from data (the inverse of hadronization), we also show that, at least in this limited setup, the fidelity of the extracted $f(z)$ suffers only limited loss when moving from (i) to (ii) to (iii). Public code is available at https://gitlab.com/uchep/mlhad., Comment: 41 pages, 21 figures. Public code available

Future space observatories dedicated to direct imaging and spectroscopy of extra-solar planets will require ultra-low-noise detectors that are sensitive over a broad range of wavelengths. Silicon charge-coupled devices (CCDs), such as EMCCDs, Skipper CCDs, and Multi-Amplifier Sensing CCDs, have demonstrated the ability to detect and measure single photons from ultra-violet to near-infrared wavelengths, making them candidate technologies for this application. In this context, we study a relatively unexplored source of low-energy background coming from Cherenkov radiation produced by energetic charged particles traversing a silicon detector. In the intense radiation environment of space, energetic cosmic rays produce high-energy tracks and more extended halos of low-energy Cherenkov photons, which are detectable with ultra-low-noise detectors. We present a model of this effect that is calibrated to laboratory data, and we use this model to characterize the residual background rate for ultra-low noise silicon detectors in space. We find that the rate of cosmic-ray-induced Cherenkov photon production is comparable to other detector and astrophysical backgrounds that have previously been considered., Comment: SPIE Proceeding; 9 pages, 6 figures

There have been several attempts in recent years to extend the notions of symplectic and Poisson structures in order to create a suitable geometrical framework for classical field theories, trying to achieve a success similar to the use of these concepts in Hamiltonian mechanics. These notions always have a graded character, since the multisymplectic forms are of a higher degree than two. Another line of work has been to extend the concept of Dirac structures to these new scenarios. In the present paper we review all these notions, relate them and propose and study a generalization that (under some mild regularity conditions) includes them and is of graded nature. We expect this generalization to allow us to advance in the study of classical field theories, their integrability, reduction, numerical approximations and even their quantization.

Despite the advent of touchscreens, typing on physical keyboards remains most efficient for entering text, because users can leverage all fingers across a full-size keyboard for convenient typing. As users increasingly type on the go, text input on mobile and wearable devices has had to compromise on full-size typing. In this paper, we present TapType, a mobile text entry system for full-size typing on passive surfaces--without an actual keyboard. From the inertial sensors inside a band on either wrist, TapType decodes and relates surface taps to a traditional QWERTY keyboard layout. The key novelty of our method is to predict the most likely character sequences by fusing the finger probabilities from our Bayesian neural network classifier with the characters' prior probabilities from an n-gram language model. In our online evaluation, participants on average typed 19 words per minute with a character error rate of 0.6% after 30 minutes of training. Expert typists thereby consistently achieved more than 25 WPM at a similar error rate. We demonstrate applications of TapType in mobile use around smartphones and tablets, as a complement to interaction in situated Mixed Reality outside visual control, and as an eyes-free mobile text input method using an audio feedback-only interface., Comment: In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

The use of Autonomous Surface Vehicles, equipped with water quality sensors and artificial vision systems, allows for a smart and adaptive deployment in water resources environmental monitoring. This paper presents a real implementation of a vehicle prototype that to address the use of Artificial Intelligence algorithms and enhanced sensing techniques for water quality monitoring. The vehicle is fully equipped with high-quality sensors to measure water quality parameters and water depth. Furthermore, by means of a stereo-camera, it also can detect and locate macro-plastics in real environments by means of deep visual models, such as YOLOv5. In this paper, experimental results, carried out in Lago Mayor (Sevilla), has been presented as proof of the capabilities of the proposed architecture. The overall system, and the early results obtained, are expected to provide a solid example of a real platform useful for the water resource monitoring task, and to serve as a real case scenario for deploying Artificial Intelligence algorithms, such as path planning, artificial vision, etc.