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Magic-angle twisted bilayer graphene displays a complex phase diagram as a function of flat band filling, featuring compressibility cascade transitions and a variety of competing ground states with broken spin, valley and point group symmetries. Recent THz photocurrent spectroscopy experiments have shown a dependence on the filling which is not consistent with the simplest cascade picture of sequential filling of equivalent flat bands. In this work, we show that when time-reversal symmetry is broken due to valley polarization, a magnetic injection photocurrent develops which can be used to distinguish different spin-valley polarization scenarios. Using the topological heavy fermion model we compute both shift and injection currents as a function of filling and argue that current experiments can be used to determine the spontaneous valley polarization.

Technologies such as robotics, Artificial Intelligence (AI), and Computer Vision (CV) can be applied to crime scene analysis (CSA) to help protect lives, facilitate justice, and deter crime, but an overview of the tasks that can be automated has been lacking. Here we follow a speculate prototyping approach: First, the STAIR tool is used to rapidly review the literature and identify tasks that seem to have not received much attention, like accessing crime sites through a window, mapping/gathering evidence, and analyzing blood smears. Secondly, we present a prototype of a small drone that implements these three tasks with 75%, 85%, and 80% performance, to perform a minimal analysis of an indoor crime scene. Lessons learned are reported, toward guiding next work in the area., Comment: 7 pages, 4 figures, to be submitted to ARSO 2025

Soil is a complex, dynamic material, with physical properties that depend on its biological content. We propose a cellular automaton model for self-organizing soil structure, where soil aggregates and serves as food for microbial species. These, in turn, produce nutrients that facilitate self-amplification, establishing a cyclical dynamic of consumption and regeneration. Our model explores the spatial interactions between these components and their role in sustaining a balanced ecosystem. The main results demonstrate that (1) spatial structure supports a stable living state, preventing population collapse or uncontrolled growth; (2) the spatial model allows for the coexistence of parasitic species, which exploit parts of the system without driving it to extinction; and (3) optimal growth conditions for microbes are associated to diverse length scales in the soil structure, suggesting that heterogeneity is key to ecosystem resilience. These findings highlight the importance of spatio-temporal dynamics of life in soil ecology.

Context. Gaia-Sausage-Enceladus is considered the last major merger that contributed to the formation of the Milky Way. Its remnants dominate the nearby accreted stellar halo of the Milky Way. Aim. We aim to characterise the star formation history of Gaia-Sausage-Enceladus through the age and metallicity of its stellar populations. Methods. From Gaia DR3 data, we dynamically define three Gaia-Sausage-Enceladus samples with different criteria and possible degrees of contamination from other substructures in the halo. Then, we derive the stellar age and metallicity distributions using the CMDfit.Gaia package. Results. We identify three main populations of stars and a fourth smaller one following an almost linear age-[M/H] relation. The three oldest populations correspond to the bulk of the star formation that lasted for, at least, $\sim$3-4 Gyr and ended about 10 Gyr ago, its metallicities ranging from $-$1.7 to $-$0.8. We categorise these populations into two main epochs: the evolution of GSE in isolation and the merger event. This separation finds independent support from the age-metallicty relation of GSE globular clusters (Aguado-Agelet et al., subm.). The fourth population is younger and more metal-rich, at $\sim$8.5 Gyr and [M/H]$\sim-0.4$; its link to GSE is unclear., Comment: See companion paper by Aguado-Agelet et al. 14 pages, 14 figures, 4 appendices. Submitted for publication in A&A

We present the age determination of 13 globular clusters dynamically associated with the Gaia-Sausage-Enceladus (GSE) merger event, as part of the CARMA project effort to trace the Milky Way assembly history. We used deep and homogeneous archival $Hubble$ $Space$ $Telescope$ data, and applied isochrone-fitting to derive homogeneous age estimates. We find that the majority of the selected clusters form a well-defined age-metallicity relation, with a few outliers. Among these, NGC 288 and NGC 6205 are more than 2 Gyr older than the other GSE globular clusters at similar metallicity, and are therefore interpreted as of likely in-situ origin. Moreover, NGC 7099 is somewhat younger than the average GSE trend, this suggesting a possible alternative dwarf galaxy progenitor, while NGC 5286 is mildly older, as if its progenitor was characterised by an higher star-formation efficiency. Another remarkable feature of the resulting age-metallicity relation is the presence of two epochs of globular cluster formation, with a duration of $\sim0.3$ Gyr each and separated by $\sim2$ Gyr. These findings are in excellent agreement with the age-metallicity relation of halo field stars found by Gonz\'alez-Koda et al., clearly hinting at episodic star-formation in GSE. The age of the two formation epochs is similar to the mean age of the two groups of in-situ globular clusters previously studied by CARMA. These epochs might therefore be precisely pinpointing two important dynamical events that GSE had with the Milky Way during its evolutionary history. Finally, we discuss the correlation between the recent determination of Si and Eu with the clusters age and origin., Comment: Main paper: 10 pages and 5 figures. Appendix: 13 pages and 12 figures. See the companion paper by Gonzalez-Koda et al

Adaptive mesh refinement efficiently facilitates the computation of gravitational waveforms in numerical relativity. However, determining precisely when, where, and to what extent to refine when solving the Einstein equations poses challenges; several ad hoc refinement criteria have been explored in the literature. This work introduces an optimized resolution baseline derived in situ from the inspiral trajectory (ORBIT). This method uses the binary's orbital frequency as a proxy for anticipated gravitational waves to dynamically refine the grid, satisfying the Nyquist frequency requirements on grid resolution up to a specified spin weighted spherical harmonic order. ORBIT sustains propagation of gravitational waves while avoiding the more costly alternative of maintaining high resolution across an entire simulation, both spatially and temporally. We find that enabling ORBIT decreases waveform noise by an order of magnitude and better resolves high-order wave amplitudes through merger. Combined with WAMR and other improvements, updates to Dendro-GR decrease waveform noise, decrease constraint violations, and boost refinement efficiency each by factors of $\mathcal{O}(100)$, while reducing computational cost by a factor of four. ORBIT and other recent improvements to Dendro-GR begin to prepare us for gravitational wave science with next-generation detectors., Comment: 13 pages, 8 figures. See https://www.youtube.com/playlist?list=PLCmfEjDzshzOP9rLkN0s9T-MtBk-fmAo6 for videos of runs

We propose to analyze CLAS12 RG-C data to study the tensor transverse-momentum-dependent parton distribution functions (TMDs) on deuteron data. The deuteron is the lightest nucleus with spin-1, in essence a weakly bound system of two spin-1/2 nucleons. However, one of the most intriguing characteristics of the deuteron is that the tensor polarized structure provides direct access to the quark and gluon distribution of light nuclear system, which cannot be naively constructed from the proton and neutron. We will study the tensor polarized structure functions with the Semi-inclusive Deep Inelastic Scattering (SIDIS) $eD \arrow eP_{h}X$ and Inclusive processes in the available data on deuterated ammonia (ND3) target. We will perform the first ever SIDIS analysis extraction of the tensor structure functions, which can be interpreted in term of completely unexplored tensor polarized TMDs. Our analysis will focus on the extraction of the tensor structure functions b1 from inclusive process, and $F_{U(LL),T}$ and $F^{cos 2\phi_{h}}_{U(LL)}$ from SIDIS. These last two structure functions carry information related to two tensor-polarized TMDs, $f_{1LL}$ and $h^{\perp}_{1LL}$. These initial exploratory measurements of tensor-polarized structure functions will enable the first extraction of spin-1 TMDs and motivate more precise future measurements.

Statistics and Optimization are foundational to modern Machine Learning. Here, we propose an alternative foundation based on Abstract Algebra, with mathematics that facilitates the analysis of learning. In this approach, the goal of the task and the data are encoded as axioms of an algebra, and a model is obtained where only these axioms and their logical consequences hold. Although this is not a generalizing model, we show that selecting specific subsets of its breakdown into algebraic atoms obtained via subdirect decomposition gives a model that generalizes. We validate this new learning principle on standard datasets such as MNIST, FashionMNIST, CIFAR-10, and medical images, achieving performance comparable to optimized multilayer perceptrons. Beyond data-driven tasks, the new learning principle extends to formal problems, such as finding Hamiltonian cycles from their specifications and without relying on search. This algebraic foundation offers a fresh perspective on machine intelligence, featuring direct learning from training data without the need for validation dataset, scaling through model additivity, and asymptotic convergence to the underlying rule in the data.

Based on bond percolation theory, a method is presented here to calculate the relationship between capillary pressure and saturation in porous media from first principles. The governing equations are formulated on the undirected graph of the pore network. The graph is a simplified mathematical object that accounts for the topology of the pore structure. Thus, the calculation is extremely computationally efficient since it is mesh-free and voxel-free. Two topological invariants are identified: The bond percolation threshold and the residual saturation. Bond percolation theory is used to obtain a closed-form pressure-saturation relation in terms of the geometry of the pores (pore throat distribution) and material parameters (contact angle and interfacial tension), universal exponents, and topological invariants, based on scaling relations., Comment: 5 pages, 4 figures

Parallel Data Curation (PDC) techniques aim to filter out noisy parallel sentences from the web-mined corpora. Prior research has demonstrated that ranking sentence pairs using similarity scores on sentence embeddings derived from Pre-trained Multilingual Language Models (multiPLMs) and training the NMT systems with the top-ranked samples, produces superior NMT performance than when trained using the full dataset. However, previous research has shown that the choice of multiPLM significantly impacts the ranking quality. This paper investigates the reasons behind this disparity across multiPLMs. Using the web-mined corpora CCMatrix and CCAligned for En$\rightarrow$Si, En$\rightarrow$Ta and Si$\rightarrow$Ta, we show that different multiPLMs (LASER3, XLM-R, and LaBSE) are biased towards certain types of sentences, which allows noisy sentences to creep into the top-ranked samples. We show that by employing a series of heuristics, this noise can be removed to a certain extent. This results in improving the results of NMT systems trained with web-mined corpora and reduces the disparity across multiPLMs.

Hyperspectral object tracking using snapshot mosaic cameras is emerging as it provides enhanced spectral information alongside spatial data, contributing to a more comprehensive understanding of material properties. Using transformers, which have consistently outperformed convolutional neural networks (CNNs) in learning better feature representations, would be expected to be effective for Hyperspectral object tracking. However, training large transformers necessitates extensive datasets and prolonged training periods. This is particularly critical for complex tasks like object tracking, and the scarcity of large datasets in the hyperspectral domain acts as a bottleneck in achieving the full potential of powerful transformer models. This paper proposes an effective methodology that adapts large pretrained transformer-based foundation models for hyperspectral object tracking. We propose an adaptive, learnable spatial-spectral token fusion module that can be extended to any transformer-based backbone for learning inherent spatial-spectral features in hyperspectral data. Furthermore, our model incorporates a cross-modality training pipeline that facilitates effective learning across hyperspectral datasets collected with different sensor modalities. This enables the extraction of complementary knowledge from additional modalities, whether or not they are present during testing. Our proposed model also achieves superior performance with minimal training iterations., Comment: Accepted to 14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)

We study the ergodicity of partially hyperbolic endomorphisms, focusing on skew products where the base dynamics are governed by Anosov endomorphisms. For this family, we establish ergodicity and prove that accessibility holds for an open and dense subset. By analyzing the topological implications of accessibility, we demonstrate that conservative accessible partially hyperbolic endomorphisms are topologically transitive. Leveraging accessibility, we further show ergodicity for skew products with $\mathbb{S}^1$-fibers. Finally, although out the context of rotation extensions, we prove ergodic stability results for partially hyperbolic endomorphisms with $\dim(E^c) = 1.$, Comment: 21 pages, 4 pictures

We study systems of two and three mesons composed of pions and kaons at maximal isospin using four CLS ensembles with $a\approx 0.063\;$fm, including one with approximately physical quark masses. Using the stochastic Laplacian-Heaviside method, we determine the energy spectrum of these systems including many levels in different momentum frames and irreducible representations. Using the relativistic two- and three-body finite-volume formalism, we constrain the two and three-meson K matrices, including not only the leading $s$ wave, but also $p$ and $d$ waves. By solving the three-body integral equations, we determine, for the first time, the physical-point scattering amplitudes for $3\pi^+$, $3K^+$, $\pi^+\pi^+ K^+$ and $K^+ K^+ \pi^+$ systems. These are determined for total angular momentum $J^P=0^-$, $1^+$, and $2^-$. We also obtain accurate results for $2\pi^+$, $\pi^+ K^+$, and $2K^+$ phase shifts. We compare our results to Chiral Perturbation Theory, and to phenomenological fits., Comment: 61 pages, 33 figures, 37 tables

Tip-of-the-tongue (TOT) search occurs when a user struggles to recall a specific identifier, such as a document title. While common, existing search systems often fail to effectively support TOT scenarios. Research on TOT retrieval is further constrained by the challenge of collecting queries, as current approaches rely heavily on community question-answering (CQA) websites, leading to labor-intensive evaluation and domain bias. To overcome these limitations, we introduce two methods for eliciting TOT queries - leveraging large language models (LLMs) and human participants - to facilitate simulated evaluations of TOT retrieval systems. Our LLM-based TOT user simulator generates synthetic TOT queries at scale, achieving high correlations with how CQA-based TOT queries rank TOT retrieval systems when tested in the Movie domain. Additionally, these synthetic queries exhibit high linguistic similarity to CQA-derived queries. For human-elicited queries, we developed an interface that uses visual stimuli to place participants in a TOT state, enabling the collection of natural queries. In the Movie domain, system rank correlation and linguistic similarity analyses confirm that human-elicited queries are both effective and closely resemble CQA-based queries. These approaches reduce reliance on CQA-based data collection while expanding coverage to underrepresented domains, such as Landmark and Person. LLM-elicited queries for the Movie, Landmark, and Person domains have been released as test queries in the TREC 2024 TOT track, with human-elicited queries scheduled for inclusion in the TREC 2025 TOT track. Additionally, we provide source code for synthetic query generation and the human query collection interface, along with curated visual stimuli used for eliciting TOT queries.

Subhalo abundance matching (SHAM) is widely used for connecting galaxies to dark matter haloes. In SHAM, galaxies and (sub-)haloes are sorted according to their mass (or mass proxy) and matched by their rank order. In this work, we show that SHAM is the solution of the optimal transport (OT) problem on empirical distributions (samples or catalogues) for any metric transport cost function. In the limit of large number of samples, it converges to the solution of the OT problem between continuous distributions. We propose SHAM-OT: a formulation of abundance matching where the halo-galaxy relation is obtained as the optimal transport plan between galaxy and halo mass functions. By working directly on these (discretized) functions, SHAM-OT eliminates the need for sampling or sorting and is solved using efficient OT algorithms at negligible compute and memory cost. Scatter in the galaxy-halo relation can be naturally incorporated through regularization of the transport plan. SHAM-OT can easily be generalized to multiple marginal distributions. We validate our method using analytical tests with varying cosmology and luminosity function parameters, and on simulated halo catalogues. The efficiency of SHAM-OT makes it particularly advantageous for Bayesian inference that requires marginalization over stellar mass or luminosity function uncertainties., Comment: 7 pages, 6 figures, submitted

A sequence of distinct closed surfaces in a hyperbolic 3-manifold M is asymptotically geodesic if their principal curvatures tend uniformly to zero. When M has finite volume, we show such sequences are always asymptotically dense in the 2-plane Grassmann bundle of M. When M has infinite volume and is geometrically finite, we show such sequences do not exist. As an application of the former, we obtain partial answers to the question of whether a negatively curved Riemannian 3-manifold that contains a sequence of asymptotically totally geodesic or totally umbilic surfaces must be hyperbolic. Finally, we give examples to show that if the dimension of M is greater than 3, the possible limiting behavior of asymptotically geodesic surfaces is less constrained than for totally geodesic surfaces., Comment: 25+2 pages

We present an algorithm for solving the one-dimensional space collisional Boltzmann transport equation (BTE) for electrons in low-temperature plasmas (LTPs). Modeling LTPs is useful in many applications, including advanced manufacturing, material processing, and hypersonic flows, to name a few. The proposed BTE solver is based on an Eulerian formulation. It uses Chebyshev collocation method in physical space and a combination of Galerkin and discrete ordinates in velocity space. We present self-convergence results and cross-code verification studies compared to an in-house particle-in-cell (PIC) direct simulation Monte Carlo (DSMC) code. Boltzsim is our open source implementation of the solver. Furthermore, we use Boltzsim to simulate radio-frequency glow discharge plasmas (RF-GDPs) and compare with an existing methodology that approximates the electron BTE. We compare these two approaches and quantify their differences as a function of the discharge pressure. The two approaches show an 80x, 3x, 1.6x, and 0.98x difference between cycle-averaged time periodic electron number density profiles at 0.1 Torr, 0.5 Torr, 1 Torr, and 2 Torr discharge pressures, respectively. As expected, these differences are significant at low pressures, for example less than 1 Torr.

In this work, we characterize the radiation-induced damage in two thick, p-channel skipper-CCDs irradiated unbiased and at room temperature with 217-MeV protons. We evaluate the overall performance of the sensors and demonstrate their single-electron/single-photon sensitivity after receiving a fluence on the order of 10$^{10}$~protons/cm$^2$. Using the pocket-pumping technique, we quantify and characterize the proton-induced defects from displacement damage. We report an overall trap density of 0.134~traps/pixel for a displacement damage dose of $2.3\times10^7$~MeV/g. Three main proton-induced trap species were identified, V$_2$, C$_i$O$_i$ and V$_n$O$_m$, and their characteristic trap energies and cross sections were extracted. We found that while divacancies are the most common proton-induced defects, C$_i$O$_i$ defects have a greater impact on charge integrity at typical operating temperatures because their emission-time constants are comparable or larger than typical readout times. To estimate ionization damage, we measure the characteristic output transistor curves. We found no threshold voltage shifts after irradiation. Our results highlight the potential of skipper-CCDs for applications requiring high-radiation tolerance and can be used to find the operating conditions in which effects of radiation-induced damage are mitigated.

The increasing demand for energy-efficient solutions in large-scale infrastructure, particularly data centers, requires advanced control strategies to optimize environmental management systems. We propose a multi-agent architecture for distributed control of air-cooled chiller systems in data centers. Our vision employs autonomous agents to monitor and regulate local operational parameters and optimize system-wide efficiency. We demonstrate how this approach improves the responsiveness, operational robustness, and energy efficiency of the system, contributing to the broader goal of sustainable infrastructure management., Comment: 6 pages, 1 figure, 1 table

The Multiple Amplifier Sensing Charge-Coupled Device (MAS-CCD) has emerged as a promising technology for astronomical observation, quantum imaging, and low-energy particle detection due to its ability to reduce the readout noise without increasing the readout time as in its predecessor, the Skipper-CCD, by reading out the same charge packet through multiple inline amplifiers. Previous works identified a new parameter in this sensor, called the Node Removal Inefficiency (NRI), related to inefficiencies in charge transfer and residual charge removal from the output gates after readout. These inefficiencies can lead to distortions in the measured signals similar to those produced by the charge transfer inefficiencies in standard CCDs. This work introduces more details in the mathematical description of the NRI mechanism and provides techniques to quantify its magnitude from the measured data. It also proposes a new operation strategy that significantly reduces its effect with minimal alterations of the timing sequences or voltage settings for the other components of the sensor. The proposed technique is corroborated by experimental results on a sixteen-amplifier MAS-CCD. At the same time, the experimental data demonstrate that this approach minimizes the NRI effect to levels comparable to other sources of distortion the charge transfer inefficiency in scientific devices.

A fundamental requirement in spatial statistics is that covariance functions are positive definite. While many positive definite functions are known for Euclidean spaces, their positive definiteness may not extend to non-Euclidean spaces. We present sufficient conditions to derive valid positive definite functions for spatial statistics on non-Euclidean geometries. Our approach leverages overlooked results due to Schoenberg (1938) to establish conditions under which covariance functions that are valid on Euclidean spaces remain valid on the domain of interest. This approach provides a more accessible and direct framework for spatial statisticians with diverse backgrounds.

Quantum Parametric Circuits are constructed as an alternative to reduce the size of quantum circuits, meaning to decrease the number of quantum gates and, consequently, the depth of these circuits. However, determining the optimal circuit for a given problem remains an open question. Testing various combinations is challenging due to the infinite possibilities. In this work, a quantum circuit recommendation architecture for classification problems is proposed using database complexity measures. A quantum circuit is defined based on a circuit layer and the number of times this layer is iterated. Fourteen databases of varying dimensions and different numbers of classes were used to evaluate six quantum circuits, each with 1, 2, 3, 4, 8, and 16-layer repetitions. Using data complexity measures from the databases, it was possible to identify the optimal circuit capable of solving all problems with up to 100$\%$ accuracy. Furthermore, with a mean absolute error of 0.80 $\pm$ 2.17, one determined the appropriate number of layer repetitions, allowing for an error margin of up to three additional layers. Sixteen distinct machine learning models were employed for the selection of quantum circuits, alongside twelve classical regressor models to dynamically define the number of layers.

NASA's Double Asteroid Redirection Test (DART) spacecraft impacted Dimorphos, the natural satellite of (65803) Didymos, on 2022 September 26, as a first successful test of kinetic impactor technology for deflecting a potentially hazardous object in space. The experiment resulted in a small change to the dynamical state of the Didymos system consistent with expectations and Level 1 mission requirements. In the pre-encounter paper Richardson (2022), predictions were put forward regarding the pre- and post-impact dynamical state of the Didymos system. Here we assess these predictions, update preliminary findings published after the impact, report on new findings related to dynamics, and provide implications for ESA's Hera mission to Didymos, scheduled for launch in 2024 with arrival in late December 2026. Pre-encounter predictions tested to date are largely in line with observations, despite the unexpected, flattened appearance of Didymos compared to the radar model and the apparent pre-impact oblate shape of Dimorphos (with implications for the origin of the system that remain under investigation). New findings include that Dimorphos likely became prolate due to the impact and may have entered a tumbling rotation state. A possible detection of a post-impact transient secular decrease in the binary orbital period suggests possible dynamical coupling with persistent ejecta. Timescales for damping of any tumbling and clearing of any debris are uncertain. The largest uncertainty in the momentum transfer enhancement factor of the DART impact remains the mass of Dimorphos, which will be resolved by the Hera mission., Comment: 24 pages, 11 figures

Remote magnetic sensing can be used to monitor the position of objects in real-time, enabling ground transport monitoring, underground infrastructure mapping and hazardous detection. However, magnetic signals are typically weak and complex, requiring sophisticated physical models to analyze them and a detailed knowledge of the system under study, factors that are frequently unavailable. In this work, we provide a solution to these limitations by demonstrating a Machine Learning (ML) method that can be trained exclusively on experimental data, without the need of any physical model, to predict the position of a magnetic target in real-time. The target can be any object with a magnetic signal above the floor noise, and in this case we use a quantum diamond magnetometer to track variations of few hundreds of nanoteslas produced by an elevator moving along a single axis. The one-dimensional movement is a simple yet challenging scenario, resembling realistic environments such as high buildings, tunnels or train circuits, and is the first step towards building broader applications. Our ML algorithm can be trained in approximately 40 min, achieving over 80% accuracy in predicting the target's position at a rate of 10 Hz, for a positional error tolerance of 30 cm, which is a precise distance compared to the 4-meter spacing between parking levels. Our results open up the possibility to apply this ML method more generally for real-time monitoring of magnetic objects, which will broaden the scope of magnetic detection applications., Comment: Keywords: Machine Learning, Supervised training, Object monitoring, Quantum diamond magnetometry, NV centers. Content: Main text (34 pages) including 6 figures; Supplementary Information (44 pages) including 22 figures

Identifying cliques in dense networks remains a formidable challenge, even with significant advances in computational power and methodologies. To tackle this, numerous algorithms have been developed to optimize time and memory usage, implemented across diverse programming languages. Yet, the inherent NP-completeness of the problem continues to hinder performance on large-scale networks, often resulting in memory leaks and slow computations. In the present study, we critically evaluate classic algorithms to pinpoint computational bottlenecks and introduce novel set-theoretical approaches tailored for network clique computation. Our proposed algorithms are rigorously implemented and benchmarked against existing Python-based solutions, demonstrating superior performance. These findings underscore the potential of set-theoretical techniques to drive substantial performance gains in network analysis.

Despite possessing impressive skills, Large Language Models (LLMs) often fail unpredictably, demonstrating inconsistent success in even basic common sense reasoning tasks. This unpredictability poses a significant challenge to ensuring their safe deployment, as identifying and operating within a reliable "safe zone" is essential for mitigating risks. To address this, we present PredictaBoard, a novel collaborative benchmarking framework designed to evaluate the ability of score predictors (referred to as assessors) to anticipate LLM errors on specific task instances (i.e., prompts) from existing datasets. PredictaBoard evaluates pairs of LLMs and assessors by considering the rejection rate at different tolerance errors. As such, PredictaBoard stimulates research into developing better assessors and making LLMs more predictable, not only with a higher average performance. We conduct illustrative experiments using baseline assessors and state-of-the-art LLMs. PredictaBoard highlights the critical need to evaluate predictability alongside performance, paving the way for safer AI systems where errors are not only minimised but also anticipated and effectively mitigated. Code for our benchmark can be found at https://github.com/Kinds-of-Intelligence-CFI/PredictaBoard

We use lattice QCD calculations of the finite-volume spectra of systems of two and three mesons to determine, for the first time, three-particle scattering amplitudes with physical quark masses. Our results are for combinations of $\pi^+$ and $K^+$, at a lattice spacing $a=0.063\;$fm, and in the isospin-symmetric limit. We also obtain accurate results for maximal-isospin two-meson amplitudes, with those for $\pi^+ K^+$ and $2K^+$ being the first determinations at the physical point. Dense lattice spectra are obtained using the stochastic Laplacian-Heaviside method, and the analysis leading to scattering amplitudes is done using the relativistic finite-volume formalism. Results are compared to chiral perturbation theory and to phenomenological fits to experimental data, finding good agreement., Comment: 7 pages, 4 figures, 2 tables. v2: typos and refs

Deep reinforcement learning agents often face challenges to effectively coordinate perception and decision-making components, particularly in environments with high-dimensional sensory inputs where feature relevance varies. This work introduces SPRIG (Stackelberg Perception-Reinforcement learning with Internal Game dynamics), a framework that models the internal perception-policy interaction within a single agent as a cooperative Stackelberg game. In SPRIG, the perception module acts as a leader, strategically processing raw sensory states, while the policy module follows, making decisions based on extracted features. SPRIG provides theoretical guarantees through a modified Bellman operator while preserving the benefits of modern policy optimization. Experimental results on the Atari BeamRider environment demonstrate SPRIG's effectiveness, achieving around 30% higher returns than standard PPO through its game-theoretical balance of feature extraction and decision-making., Comment: To appear in: AAAI 2025 Workshop on Planning and Reinforcement Learning (PRL) - Bridging the Gap Between AI Planning and Reinforcement Learning

Cone tip resistance and excess water pressure (EWP) measured by piezocone penetration tests (CPTU) may be significantly affected by the partially drained effect in soils with intermediate permeability. To capture this effect, the paper proposes a straightforward, hydro-mechanical coupling solution for cylindrical cavity expansion under partially drained conditions. The mechanical behaviour of soils is modelled using the elastoplastic Tresca model and water flow within porous soils is assumed to obey Darcys law. Two partial differential equations (PDEs) are established in the elastic and plastic zones, respectively, transforming the cavity expansion analysis into a typical Stefan problem with dynamic boundary conditions (i.e. a moving boundary at the elastoplastic interface). An approximate solution for the PDEs is derived by leveraging the variable transformation method. Based on the new solution, a novel normalised penetration rate is defined considering the rigidity index of soils, with which a unique backbone curve for CPTU is found. Finally, the backbone curve is compared with a database comprising 109 in-situ experimental tests, 101 centrifuge modelling tests, and numerical simulation results. The proposed solution may provide a useful theoretical tool for interpreting the consolidation coefficient of fine-grained soils from the penetration stage of multi-rate CPTU, which can enhance the interpretation reliability for CPTU dissipation tests.

We evaluate Kahneman-Tversky Optimization (KTO) as a fine-tuning method for large language models (LLMs) in federated learning (FL) settings, comparing it against Direct Preference Optimization (DPO). Using Alpaca-7B as the base model, we fine-tune on a realistic dataset under both methods and evaluate performance using MT-Bench-1, Vicuna, and AdvBench benchmarks. Additionally, we introduce a redistributed dataset setup, where only KTO is applicable due to its ability to handle single-response feedback, unlike DPO's reliance on paired responses. Our results demonstrate that KTO, in both its original (KTOO) and redistributed (KTOR) configurations, consistently outperforms DPO across all benchmarks. In the redistributed setup, KTO further validates its flexibility and resilience by maintaining superior performance in scenarios where DPO cannot be applied. These findings establish KTO as a robust and scalable fine-tuning method for FL, motivating its adoption for privacy-preserving, decentralized, and heterogeneous environments.

In the current age, users consume multimedia content in very heterogeneous scenarios in terms of network, hardware, and display capabilities. A naive solution to this problem is to encode multiple independent streams, each covering a different possible requirement for the clients, with an obvious negative impact in both storage and computational requirements. These drawbacks can be avoided by using codecs that enable scalability, i.e., the ability to generate a progressive bitstream, containing a base layer followed by multiple enhancement layers, that allow decoding the same bitstream serving multiple reconstructions and visualization specifications. While scalable coding is a well-known and addressed feature in conventional image and video codecs, this paper focuses on a new and very different problem, notably the development of scalable coding solutions for deep learning-based Point Cloud (PC) coding. The peculiarities of this 3D representation make it hard to implement flexible solutions that do not compromise the other functionalities of the codec. This paper proposes a joint quality and resolution scalability scheme, named Scalable Resolution and Quality Hyperprior (SRQH), that, contrary to previous solutions, can model the relationship between latents obtained with models trained for different RD tradeoffs and/or at different resolutions. Experimental results obtained by integrating SRQH in the emerging JPEG Pleno learning-based PC coding standard show that SRQH allows decoding the PC at different qualities and resolutions with a single bitstream while incurring only in a limited RD penalty and increment in complexity w.r.t. non-scalable JPEG PCC that would require one bitstream per coding configuration., Comment: Submitted to IEEE and currently under review

This paper reports results of an investigation about the level of knowledge among undergraduate computer science students in Brazil regarding the topic of simulation. Amid rapid technological evolution, simulation emerges as a crucial resource for training professionals capable of facing complex challenges. The research seeks to analyze the presence and effectiveness of simulation education, exploring students' perceptions, the tools used, the challenges faced, and the prospects for deeper study. This report highlights the importance of academic training in a dynamic technological environment, emphasizing the crucial role of simulation education in undergraduate computer science, while exploring the foundations of the methodologies and educational strategies associated with the topic. A survey research approach is adopted. 108 answers were received from 10 Brazilian states. 19 respondents from 15 different institutions said they had some contact with simulation during their studies. Results reveal that MATLAB/Simulink is the most popular formalism/tool used to teach simulation in Brazil.

The assumed causal relationships depicted in a DAG are interpreted using a set of rules called D-separation rules. Although these rules can be implemented automatically using standard software, at least a basic understanding of their principles is useful for properly using and interpreting DAGs in practice., Comment: 15 pages, 2 figures 2 boxes

Heteroepitaxial boron carbide (BxC) can be grown on Si face 4H-SiC(0001) using a two-step process involving substrate boridation at 1200$^\circ$C under BCl3 + H2 followed by a chemical vapor deposition (CVD) growth step at 1600$^\circ$C by adding C3H8 precursor. However, in-depth investigation of the early growth stages revealed that complex reactions occur before starting the CVD at high temperature. Indeed, after boridation, the 35 nm BxC buffer layer is covered by an amorphous B-containing layer which evolves and reacts during the temperature ramp up between 1200 to 1600$^\circ$C. Despite the formation of new phases (Si, SiB6), which could be explained by significant solid-state diffusion of Si, C and B elements through the thin BxC layer, the CVD epitaxial re-growth upon reaching 1600$^\circ$C does not seems to be affected by these phases. The resulting single crystalline BxC layers display the epitaxial relationships [1010]BxC(0001)||[1010]4H-SiC(0001). The layers exhibit a B4C composition, e.g. the highest possible C content for the BxC solid solution.

We perform a systematic and comprehensive analysis of sub-leading colour corrections in perturbative QCD processes involving multiple soft gluon emissions. This necessitates going beyond the standard parton shower paradigm in order to incorporate interference effects and is accomplished using the CVolver program, which simulates parton showers at the amplitude level. We can compute cross-sections with full-colour precision and also broken down explicitly in terms of their $N_c$ dependence. In this paper, we focus on the jet cross-section with a veto of additional jets in some fixed region of phase-space, since this is sensitive to wide-angle, soft gluon radiation. We consider $Z \to q \bar{q}$, $H \to gg$, $q\bar{q} \to q\bar{q}$, $qg \to qg$, $gg \to gg$ and $ZZ \to q\bar{q}q\bar{q}$. We find that non-trivial sub-leading colour effects are generally important at the 5 -- 30\% level and much more than this for certain interference contributions. Remarkably, for this observable, we find that for all of the $t$-channel gluon exchange processes that we consider, the strictly leading colour approximation, which includes the replacement $C_F \to C_A/2$, is an excellent approximation to the full colour result (up to an overall colour factor)., Comment: 67 pages, 56 figures

As artificial intelligence models have exploded in scale and capability, understanding of their internal mechanisms remains a critical challenge. Inspired by the success of dynamical systems approaches in neuroscience, here we propose a novel framework for studying computations in deep learning systems. We focus on the residual stream (RS) in transformer models, conceptualizing it as a dynamical system evolving across layers. We find that activations of individual RS units exhibit strong continuity across layers, despite the RS being a non-privileged basis. Activations in the RS accelerate and grow denser over layers, while individual units trace unstable periodic orbits. In reduced-dimensional spaces, the RS follows a curved trajectory with attractor-like dynamics in the lower layers. These insights bridge dynamical systems theory and mechanistic interpretability, establishing a foundation for a "neuroscience of AI" that combines theoretical rigor with large-scale data analysis to advance our understanding of modern neural networks.

Large language models demonstrate remarkable capabilities across various domains, especially mathematics and logic reasoning. However, current evaluations overlook physics-based reasoning - a complex task requiring physics theorems and constraints. We present PhysReason, a 1,200-problem benchmark comprising knowledge-based (25%) and reasoning-based (75%) problems, where the latter are divided into three difficulty levels (easy, medium, hard). Notably, problems require an average of 8.1 solution steps, with hard requiring 15.6, reflecting the complexity of physics-based reasoning. We propose the Physics Solution Auto Scoring Framework, incorporating efficient answer-level and comprehensive step-level evaluations. Top-performing models like Deepseek-R1, Gemini-2.0-Flash-Thinking, and o3-mini-high achieve less than 60% on answer-level evaluation, with performance dropping from knowledge questions (75.11%) to hard problems (31.95%). Through step-level evaluation, we identified four key bottlenecks: Physics Theorem Application, Physics Process Understanding, Calculation, and Physics Condition Analysis. These findings position PhysReason as a novel and comprehensive benchmark for evaluating physics-based reasoning capabilities in large language models. Our code and data will be published at https:/dxzxy12138.github.io/PhysReason.

The Ultraviolet (UV) Type Ia Supernova CubeSat (UVIa) is a CubeSat/SmallSat mission concept that stands to test critical space-borne UV technology for future missions like the Habitable Worlds Observatory (HWO) while elucidating long-standing questions about the explosion mechanisms of Type Ia supernovae (SNe Ia). UVIa will observe whether any SNe Ia emit excess UV light shortly after explosion to test progenitor/explosion models and provide follow-up over many days to characterize their UV and optical flux variations over time, assembling a comprehensive multi-band UV and optical low-redshift anchor sample for upcoming high-redshift SNe Ia surveys (e.g., Euclid, Vera Rubin Observatory, Nancy Roman Space Telescope). UVIa's mission profile requires it to perform rapid and frequent visits to newly discovered SNe Ia, simultaneously observing each SNe Ia in two UV bands (FUV: 1500-1800A and NUV: 1800-2400A) and one optical band (u-band: 3000-4200A). In this study, we describe the UVIa mission concept science motivation, mission design, and key technology development., Comment: submitted to JATIS under the call for papers "Ultraviolet Science & Instrumentation: On the Way to Habitable Worlds Observatory and Beyond"

Recently, it was shown how the primary steps of solvation of a single Na$^+$ ion, instantly created at the surface of a nanometer-sized droplet of liquid helium, can be followed at the atomic level [Albrectsen et al. Nature $\textbf{623}$, 319 (2023)]. This involved measuring, with femtosecond time resolution, the gradual attachment of individual He atoms to the Na$^+$ ion as well as the energy dissipated from the local region of the ion. In the current work, we provide a more comprehensive and detailed description of the experimental findings of the solvation dynamics, and present an improved Poisson-statistical analysis of the time-resovled yields of the solvation complexes, Na$^+$He$_n$. For droplets containing an average of 5200 He atoms, this analysis gives a binding rate of $1.84\pm0.09$ atoms/ps for the binding of the first five He atoms to the Na$^+$ ion. Also, thanks to accurate heoretical values for the evaporation energies of the Na$^+$He$_n$ complexes, obtained by Path Integral Monte Carlo methos using a new potential energy surface presented here for the first time, we improved the determination of the time-dependent removal of the solvation energy from the region around the sodium ion. We find that it follows Newton's law of cooling for the first 6 ps. Measurements were carried out for three different average droplet sizes, $\langle N_D\rangle = $ 9000, 5200 and 3600 helium atoms, and differences between these results are discussed., Comment: Main text: 14 pages, 8 figures. Supplementary material: 1 page, 1 figure

DX Cha (HD 104237) is a spectroscopic binary consisting of a Herbig A7.5Ve-A8Ve primary star and a K3-type companion. Here we report on new $3.55$ micrometer interferometric observations of this source with the Multi Aperture Mid-Infrared Spectroscopic Experiment (MATISSE) at the Very Large Telescope Interferometer (VLTI). To model the four MATISSE observations obtained between 2020 and 2023, we constructed a time-dependent interferometric model of the system, using the oimodeler software. The model consists of an asymmetric ring and two point sources on a Keplerian orbit. Our best-fit model consists of a circumbinary ring with a diameter of $0.86$ au ($8.1$ mas), featuring a strong azimuthal asymmetry. We found that the position angle of the asymmetry changes tens of degrees between the MATISSE epochs. The ring is relatively narrow, with a full width at half maximum (FWHM) of $\sim$$0.13$ au ($1.23$ mas). The presence of circumstellar dust emission so close to the binary is unexpected, as previous hydrodynamic simulations predicted an inner disk cavity with a diameter of $\sim$$4$ au ($\sim$$37.5$ mas). Thus, we argue that the narrow envelope of material we detected is probably not a gravitationally stable circumbinary ring, but may be part of tidal accretion streamers channeling material from the inner edge of the disk toward the stars., Comment: Accepted for publication in ApJ, 11 pages, 5 figures, 5 tables

This study explores an alternative approach to video-based motion analysis using natural reference frames rather than relying on manual alignment. We demonstrate how the motion data itself can reveal optimal reference frames, connecting fundamental physical principles with data analysis. We present three classical mechanics experiments: uniform motion along a track, where the motion direction naturally defines the axis; circular motion of a bicycle wheel, where the center of rotation emerges as the reference origin; and projectile motion from a juggling tutorial, where gravity determines the vertical direction. This approach demonstrates how physical constraints and symmetries can naturally emerge from experimental data, providing a viable approach for introductory physics laboratories., Comment: 11 pages, 3 figures

The non-destructive readout capability of the Skipper Charge Coupled Device (CCD) has been demonstrated to reduce the noise limitation of conventional silicon devices to levels that allow single-photon or single-electron counting. The noise reduction is achieved by taking multiple measurements of the charge in each pixel. These multiple measurements come at the cost of extra readout time, which has been a limitation for the broader adoption of this technology in particle physics, quantum imaging, and astronomy applications. This work presents recent results of a novel sensor architecture that uses multiple non-destructive floating-gate amplifiers in series to achieve sub-electron readout noise in a thick, fully-depleted silicon detector to overcome the readout time overhead of the Skipper-CCD. This sensor is called the Multiple-Amplifier Sensing Charge-Coupled Device (MAS-CCD) can perform multiple independent charge measurements with each amplifier, and the measurements from multiple amplifiers can be combined to further reduce the readout noise. We will show results obtained for sensors with 8 and 16 amplifiers per readout stage in new readout operations modes to optimize its readout speed. The noise reduction capability of the new techniques will be demonstrated in terms of its ability to reduce the noise by combining the information from the different amplifiers, and to resolve signals in the order of a single photon per pixel. The first readout operation explored here avoids the extra readout time needed in the MAS-CCD to read a line of the sensor associated with the extra extent of the serial register. The second technique explore the capability of the MAS-CCD device to perform a region of interest readout increasing the number of multiple samples per amplifier in a targeted region of the active area of the device.

In recent years, remarkable advancements in deep-fake generation technology have led to unprecedented leaps in its realism and capabilities. Despite these advances, we observe a notable lack of structured and deep analysis deepfake technology. The principal aim of this survey is to contribute a thorough theoretical analysis of state-of-the-art face deepfake generation and detection methods. Furthermore, we provide a coherent and systematic evaluation of the implications of deepfakes on face biometric recognition approaches. In addition, we outline key applications of face deepfake technology, elucidating both positive and negative applications of the technology, provide a detailed discussion regarding the gaps in existing research, and propose key research directions for further investigation.

We compute the conservative and radiation-reaction contributions to classical observables in the gravitational scattering between a spinning and a spinless black hole to the fourth order in spin and third order in the gravitational constant. The conservative results are obtained from two-loop amplitudes for the scattering process of a massive scalar with a massive spin-$s$ field $(s=0, 1, 2)$ minimally coupled to gravity, employing the recently introduced spin interpolation method to resolve all spin-Casimir terms. The two-loop amplitude exhibits a spin-shift symmetry in both probe limits, which we conjecture to be a sign of yet unknown integrability of Kerr orbits through the quartic order in spin and to all orders in the gravitational constant. We obtain the radial action from the finite part of the amplitude and use it to compute classical observables, including the impulse and spin kick. This is done using the recently introduced covariant Dirac brackets, which allow for the computation of classical scattering observables for general (non-aligned) spin configurations. Finally, employing the radiation-reaction amplitude proposed by Alessio and Di Vecchia, together with the Dirac brackets, we obtain radiation-reaction contributions to observables at all orders in spin and beyond the aligned-spin limit. We find agreement with known results up to the quadratic order in spin for both conservative and radiation-reaction contributions. Our results advance the state of the art in the understanding of spinning binary dynamics in general relativity and demonstrate the power and simplicity of the Dirac bracket formalism for relating scattering amplitudes to classical observables., Comment: 13 pages; v2: adds references, corrects observables in ancillary files

The Fitzhugh-Nagumo neuronal model is used to explore the influence of the electric field on thermosensitive neurons' dynamics. This study investigates how the electric field affects polarization modulation in cell media induced by changes in ion charge density by adding electrical field as a new variable. Driven by a voltage source acting as an external stimulus current, different firing mode responses of the proposed model are analyzed when an external electrical field is applied. Through computational analysis, the study evaluates the impact of parameters such as cell radius, stimulus voltage source amplitude, frequency, and as well as the presence of an external electric field. The results demonstrate distinct mode transitions of isolated neurons ranging from spiking to bursting, regular and chaotic oscillations. These findings suggest that the firing mode is triggered by periodic external electric fields and cell radius, with the electric field's involvement enhanced to regulate neuron activity and control the dynamics. External electric fields and stimuli play a crucial role in neuronal firing dynamics, affecting the transition between different firing modes. Understanding these effects contributes to the comprehension of neural processes and the potential manipulation of neural activity for various applications in neuroscience and biophysics.

One of the most challenging problems in cosmology is the Hubble tension, a discrepancy in the predicted expansion rate of the Universe. We leverage the sensitivity of the Dispersion Measure (DM) from Fast Radio Bursts (FRBs) with the Hubble factor to investigate the Hubble tension. We build a catalog of 98 localized FRBs and an independent mock catalog and employ 3 methods to calculate the best value of the $H_0$: i) the mean of $H_0$ values obtained through direct calculation, ii) the maximum likelihood estimate (MLE) and iii) the reconstruction of the cosmic expansion history $H(z)$ using two DM-$z$ relations. When the confirmed FRBs is employed, our predictions are compatible with reports from the Planck+2018, with $H_0=65.13\pm2.52\,\text{km/s/Mpc}$ and $57.67\pm11.99\,\text{km/s/Mpc}$ for MLE and the arithmetic mean, respectively. If we assume a linear and a power-law function for the DM-$z$ relation, our predictions for $H_0$ are $51.27^{+3.80}_{-3.31}\,\text{km/s/Mpc}$ and $77.09^{+8.89}_{-7.64}\,\text{km/s/Mpc}$, respectively. Using 100 mock catalogs of simulated FRBs, we obtain larger values for $H_0$ with all methods considered: $H_{0;\text{ Like}}=67.30\pm0.91\,\text{km/s/Mpc}$, $H_{0;\text{ Mean}}=66.21\pm3.46\,\text{km/s/Mpc}$, $H_{0;\text{ Median}}=66.10\pm1.89\,\text{km/s/Mpc}$, $H_{0;\text{Linear}}=54.34\pm1.57\,\text{km/s/Mpc}$ and $H_{0;\text{Power-law}}=91.84\pm1.82\,\text{km/s/Mpc}$ for the MLE, the arithmetic mean, and linear and power-law $\text{DM}-z$ relations, respectively. Our results for mock FRB catalogs increase the statistical precision, ranging from 1.4\% to 5.2\% for the MLE and arithmetic mean. Our result with the MLE applied to synthetic FRBs is at the same level of precision as reports from SH0ES. The increase in the number of confirmed FRBs will provide us, in combination with other observations, a robust prediction of the value of the Hubble constant., Comment: 9 pages, 4 figures

Change-point models are frequently considered when modeling phenomena where a regime shift occurs at an unknown time. In ageing research, these models are commonly adopted to estimate of the onset of cognitive decline. Yet commonly used models present several limitations. Here, we present a Bayesian non-linear mixed-effects model based on a differential equation designed for longitudinal studies to overcome some limitations of classical change point models used in ageing research. We demonstrate the ability of the proposed model to avoid biases in estimates of the onset of cognitive impairment in a simulated study. Finally, the methodology presented in this work is illustrated by analysing results from memory tests from older adults who participated in the English Longitudinal Study of Ageing.

We investigate the transport of superparamagnetic colloidal particles along self-assembled tracks using a periodically applied magnetic field as a model for ratchet-like mechanisms. Through video microscopy and simulations, we examine how factors such as particle size, track roughness, and the shape, intensity, and frequency of the applied potential influence transport efficiency. The findings reveal that processive motion can be achieved without residual attraction, with optimal transport efficiency governed by the combined effects of particle size ratios, actuation frequency, track roughness, and asymmetry in the applied potential. Additionally, we explore alternative strategies, including weak residual attraction and alternating magnetic fields, to further enhance efficiency. These findings provide valuable insights for the development of synthetic micro/nanomotors with potential applications in drug delivery and environmental remediation.

We construct a new family of explicit codes that are list decodable to capacity and achieve an optimal list size of $O(\frac{1}{\epsilon})$. In contrast to existing explicit constructions of codes achieving list decoding capacity, our arguments do not rely on algebraic structure but utilize simple combinatorial properties of expander graphs. Our construction is based on a celebrated distance amplification procedure due to Alon, Edmonds, and Luby [FOCS'95], which transforms any high-rate code into one with near-optimal rate-distance tradeoff. We generalize it to show that the same procedure can be used to transform any high-rate code into one that achieves list decoding capacity. Our proof can be interpreted as a "local-to-global" phenomenon for (a slight strengthening of) the generalized Singleton bound. Using this construction, for every $R, \epsilon \in (0,1)$ and $k \in \mathbb{N}^+$, we obtain an \emph{explicit} family of codes $\mathcal{C} \subseteq \Sigma^n$, with rate $R$ such that, - They achieve the $\epsilon$-relaxed generalized Singleton bound: for any $g \in \Sigma^n$ and any list $\mathcal{H}$ of at most $k$ codewords, we have, \[ \underset{h \in \mathcal{H}}{\mathbb{E}} [\Delta(g,h)] ~\geq~ \frac{|\mathcal{H}|-1}{|\mathcal{H}|} \cdot (1 - R - \epsilon). \] - The alphabet size is a constant depending only on $\epsilon$ and $k$. - They can be list decoded up to radius $\frac{k-1}{k}(1-R-\epsilon)$, in time $n^{O_{k,\epsilon}(1)}$. As a corollary of our result, we also obtain the first explicit construction of LDPC codes achieving list decoding capacity, and in fact arbitrarily close to the generalized Singleton bound., Comment: STOC 2025

This paper presents a proof-of-concept demonstration of the Skipper-CCD, a sensor with single-electron counting capability, as a promising technology for implementing an electron-pump-based current source. Relying on its single-electron resolution and built-in charge sensing, it allows self-calibration of the charge packets. This article presents an initial discussion of how low ppm and high current realizations can be achieved with this technology. We report experimental results that illustrate the key functionalities in manipulating both small and large electron charge packets, including a comparison of the charge generated, self-measured, and drained by the sensor against measurements from an electrometer. These results were obtained using a standard sensor and readout electronics without specific optimizations for this application. The objective is to explore the potential of Skipper-CCD for realizing an electron-based current source., Comment: 18 pages, 11 figures with 13 image files, 1 table