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U.S. public schools are engaged in an unprecedented effort to expand tutoring in the wake of the COVID-19 pandemic. Broad-based support for scaling tutoring emerged, in part, because of the large effects on student achievement found in prior meta-analyses. We conduct an expanded meta-analysis of 265 randomized controlled trials and explore how estimates change when we better align our sample with a policy-relevant target of inference: large-scale tutoring programs in the U.S. aiming to improve standardized test performance. Pooled effect sizes from studies with stronger target-equivalence remain meaningful but are only a third to a half as large as those from our full sample. This result is driven by stark declines in pooled effect sizes as program scale increases. We explore four hypotheses for this pattern and document how a bundled package of recommended design features serves to partially inoculate programs from these attenuated effects at scale.

The U.S. has witnessed a resurgence of labor activism, with teachers at the forefront. We examine how teacher strikes affect compensation, working conditions, and productivity with an original dataset of 772 teacher strikes generating 48 million student days idle between 2007 and 2023. Using an event study framework, we find that, on average, strikes increase compensation by 8% and lower pupil-teacher ratios by 0.5 students, driven by new state revenues. We find little evidence of sizable impacts on student achievement up to five years post-strike, though strikes lasting 10 or more days decrease math achievement in the short-term.

Since perturbations are omnipresent in physics, understanding their impact on the dynamics of quantum many-body systems is a vitally important but notoriously difficult question. On the one hand, random-matrix and typicality arguments suggest a rather simple damping in the overwhelming majority of cases, e.g., exponential damping according to Fermi's Golden Rule. On the other hand, counterexamples are known to exist, and it remains unclear how frequent and under which conditions such counterexamples appear. In our work, we consider the spin-1/2 XXZ chain as a paradigmatic example of a quantum many-body system and study the dynamics of the magnetization current in the easy-axis regime. Using numerical simulations based on dynamical quantum typicality, we show that the standard autocorrelation function is damped in a nontrivial way and that only a modified version of this function is damped in a simple manner. Employing projection-operator techniques in addition, we demonstrate that both, the nontrivial and simple damping relation can be understood on perturbative grounds. Our results are in agreement with earlier findings for the particle current in the Hubbard chain., Comment: 12 pages, 10 figures

Simulations on self-propelling active cubes reveal interesting behaviors at both the individual and the collective level, emphasizing the importance of developing experimental analogs that allow to test these theoretical predictions. The majority of experimental realizations of active colloidal cubes rely on light actuation and or magnetic fields to have a persistent active mechanism, and lack material versatility. Here we propose a system of active bimetallic cubes whose propulsion mechanism is based on a catalytic reaction and study their behavior. We realize such a system from synthetic silica cuboids and 3D printed micro cubes, followed by the deposition of gold and platinum layers on their surface. We characterize the colloids dynamics for different thicknesses of the gold layer at low and high hydrogen peroxide concentrations. We show that the thickness of the base gold layer has only a minor effect on the self propulsion speed and in addition induces a gravitational torque which leads to particles with a velocity director pointing out of the plane thus effectively suppressing propulsion. We find that a higher active force can remedy the effects of torque, resulting in particle orientations that are favorable for in plane propulsion. Finally, we use 3D printing to compare our results to cubes made from a different material, size and roundness, and demonstrate that the speed scaling with increasing particle size originates from the size-dependent drag. Our experiments extend fabrication of active cubes to different materials and propulsion mechanisms and highlight that the design of active particles with anisotropic shapes requires consideration of the interplay between the shape and activity to achieve favorable sedimentation and efficient in plane propulsion., Comment: 25 pages 4 figures

How we should design and interact with social artificial intelligence depends on the socio-relational role the AI is meant to emulate or occupy. In human society, relationships such as teacher-student, parent-child, neighbors, siblings, or employer-employee are governed by specific norms that prescribe or proscribe cooperative functions including hierarchy, care, transaction, and mating. These norms shape our judgments of what is appropriate for each partner. For example, workplace norms may allow a boss to give orders to an employee, but not vice versa, reflecting hierarchical and transactional expectations. As AI agents and chatbots powered by large language models are increasingly designed to serve roles analogous to human positions - such as assistant, mental health provider, tutor, or romantic partner - it is imperative to examine whether and how human relational norms should extend to human-AI interactions. Our analysis explores how differences between AI systems and humans, such as the absence of conscious experience and immunity to fatigue, may affect an AI's capacity to fulfill relationship-specific functions and adhere to corresponding norms. This analysis, which is a collaborative effort by philosophers, psychologists, relationship scientists, ethicists, legal experts, and AI researchers, carries important implications for AI systems design, user behavior, and regulation. While we accept that AI systems can offer significant benefits such as increased availability and consistency in certain socio-relational roles, they also risk fostering unhealthy dependencies or unrealistic expectations that could spill over into human-human relationships. We propose that understanding and thoughtfully shaping (or implementing) suitable human-AI relational norms will be crucial for ensuring that human-AI interactions are ethical, trustworthy, and favorable to human well-being., Comment: 76 pages, 2 figures

Accurate people localisation using drones is crucial for effective crowd management, not only during massive events and public gatherings but also for monitoring daily urban crowd flow. Traditional methods for tiny object localisation using high-resolution drone imagery often face limitations in precision and efficiency, primarily due to constraints in image scaling and sliding window techniques. To address these challenges, a novel approach dedicated to point-oriented object localisation is proposed. Along with this approach, the Pixel Distill module is introduced to enhance the processing of high-definition images by extracting spatial information from individual pixels at once. Additionally, a new dataset named UP-COUNT, tailored to contemporary drone applications, is shared. It addresses a wide range of challenges in drone imagery, such as simultaneous camera and object movement during the image acquisition process, pushing forward the capabilities of crowd management applications. A comprehensive evaluation of the proposed method on the proposed dataset and the commonly used DroneCrowd dataset demonstrates the superiority of our approach over existing methods and highlights its efficacy in drone-based crowd object localisation tasks. These improvements markedly increase the algorithm's applicability to operate in real-world scenarios, enabling more reliable localisation and counting of individuals in dynamic environments., Comment: This is the pre-print. The article is submitted to the Engineering Applications of Artificial Intelligence journal

We prove absence of ground states in the infrared-divergent spin boson model at large coupling. Our key argument reduces the proof to verifying long range order in the dual one-dimensional continuum Ising model, i.e., to showing that the respective two point function is lower bounded by a strictly positive constant. We can then use known results from percolation theory to establish long range order at large coupling. Combined with the known existence of ground states at small coupling, our result proves that the spin boson model undergoes a phase transition with respect to the coupling strength. We also present an expansion for the vacuum overlap of the spin boson ground state in terms of the Ising $n$-point functions, which implies that the phase transition is unique, i.e., that there is a critical coupling constant below which a ground state exists and above which none can exist., Comment: 17 pages

Current evaluations of defenses against prompt attacks in large language model (LLM) applications often overlook two critical factors: the dynamic nature of adversarial behavior and the usability penalties imposed on legitimate users by restrictive defenses. We propose D-SEC (Dynamic Security Utility Threat Model), which explicitly separates attackers from legitimate users, models multi-step interactions, and expresses the security-utility in an optimizable form. We further address the shortcomings in existing evaluations by introducing Gandalf, a crowd-sourced, gamified red-teaming platform designed to generate realistic, adaptive attack. Using Gandalf, we collect and release a dataset of 279k prompt attacks. Complemented by benign user data, our analysis reveals the interplay between security and utility, showing that defenses integrated in the LLM (e.g., system prompts) can degrade usability even without blocking requests. We demonstrate that restricted application domains, defense-in-depth, and adaptive defenses are effective strategies for building secure and useful LLM applications., Comment: Niklas Pfister, V\'aclav Volhejn and Manuel Knott contributed equally

The EdResearch for Action "Design Principles Series" focuses on a single program or practice that has been proven to have positive effects on student outcomes. Authors -- leading experts from across the field of education research -- look across many high-quality studies of similar programs to identify the components and conditions that are key to its effective implementation. The "Design Principles Series" helps practitioners adapt and successfully implement an evidence-based program to meet the needs of their target population. This brief is an update of the previous version published in 2021. It incorporates new research on effective design and implementation components in high-impact tutoring programs. [This brief was prepared in collaboration with the University of Virginia, School of Education and Human Development and National Student Support Accelerator.]

Budget constraints and limited supplies of local tutors have caused many K-12 school districts to pivot from individual tutoring in-person toward small-group tutoring online to expand access to personalized instruction. We conduct a field experiment to explore the effect of increasing student-tutor ratios on middle school students' math achievement and growth during an online tutoring program. We leverage a novel feature of the program where tutors often taught individual and small-group tutoring sessions, allowing them to directly compare their experiences across these settings. Both experimental estimates and tutor survey responses suggest 1:1 tutoring is more effective than 3:1 tutoring online. Tutoring small groups in an online format presents additional challenges for personalizing instruction, developing relationships, fostering participation, and managing student behavior.

When the ejecta of supernovae interact with the progenitor star's circumstellar environment, a strong shock is driven back into the ejecta, causing the material to become bright optically and in X-rays. Most notably, as the shock traverses the H-rich envelope, it begins to interact with metal rich material. Thus, continued monitoring of bright and nearby supernovae provides valuable clues about both the progenitor structure and its pre-supernova evolution. Here we present late-time, multi-epoch optical and Chandra} X-ray spectra of the core-collapse supernova SN 1996cr. Magellan IMACS optical spectra taken in July 2017 and August 2021 show a very different spectrum from that seen in 2006 with broad, double-peaked optical emission lines of oxygen, argon, and sulfur with expansion velocities of $\pm 4500$ km s$^{-1}$. Red-shifted emission components are considerably fainter compared to the blue-shifted components, presumably due to internal extinction from dust in the supernova ejecta. Broad $\pm 2400$ km s$^{-1}$ H$\alpha$ is also seen which we infer is shocked progenitor pre-SN mass-loss, H-rich material. Chandra data indicate a slow but steady decline in overall X-ray luminosity, suggesting that the forward shock has broken through any circumstellar shell or torus which is inferred from prior deep Chandra ACIS-S/HETG observations. The X-ray properties are consistent with what is expected from a shock breaking out into a lower density environment. Though originally identified as a SN IIn, based upon late time optical emission line spectra, we argue that the SN 1996cr progenitor was partially or highly stripped, suggesting a SN IIb/Ib., Comment: 20 pages, 11 figures

Cosmic shear, galaxy clustering, and the abundance of massive halos each probe the large-scale structure of the universe in complementary ways. We present cosmological constraints from the joint analysis of the three probes, building on the latest analyses of the lensing-informed abundance of clusters identified by the South Pole Telescope (SPT) and of the auto- and cross-correlation of galaxy position and weak lensing measurements (3$\times$2pt) in the Dark Energy Survey (DES). We consider the cosmological correlation between the different tracers and we account for the systematic uncertainties that are shared between the large-scale lensing correlation functions and the small-scale lensing-based cluster mass calibration. Marginalized over the remaining $\Lambda$CDM parameters (including the sum of neutrino masses) and 52 astrophysical modeling parameters, we measure $\Omega_\mathrm{m}=0.300\pm0.017$ and $\sigma_8=0.797\pm0.026$. Compared to constraints from Planck primary CMB anisotropies, our constraints are only 15% wider with a probability to exceed of 0.22 ($1.2\sigma$) for the two-parameter difference. We further obtain $S_8\equiv\sigma_8(\Omega_\mathrm{m}/0.3)^{0.5}=0.796\pm0.013$ which is lower than the Planck measurement at the $1.6\sigma$ level. The combined SPT cluster, DES 3$\times$2pt, and Planck datasets mildly prefer a non-zero positive neutrino mass, with a 95% upper limit $\sum m_\nu<0.25~\mathrm{eV}$ on the sum of neutrino masses. Assuming a $w$CDM model, we constrain the dark energy equation of state parameter $w=-1.15^{+0.23}_{-0.17}$ and when combining with Planck primary CMB anisotropies, we recover $w=-1.20^{+0.15}_{-0.09}$, a $1.7\sigma$ difference with a cosmological constant. The precision of our results highlights the benefits of multiwavelength multiprobe cosmology., Comment: Submitted to Phys. Rev. D

The hot plasma in galaxy clusters, the intracluster medium (ICM), is expected to be shaped by subsonic turbulent motions, which are key for heating, cooling, and transport mechanisms. The turbulent motions contribute to the non-thermal pressure which, if not accounted for, consequently imparts a hydrostatic mass bias. Accessing information about turbulent motions is thus of major astrophysical and cosmological interest. Characteristics of turbulent motions can be indirectly accessed through surface brightness fluctuations. This study expands on our pilot investigations of surface brightness fluctuations in the SZ and X-ray by examining, for the first time, a large sample of 60 clusters using both SPT-SZ and XMM-Newton data and span the redshift range $0.2 < z < 1.5$, thus constraining the respective pressure and density fluctuations within 0.6~$R_{500}$. We deem density fluctuations to be of sufficient quality for 32 clusters, finding mild correlations between the peak of the amplitude spectra of density fluctuations and various dynamical parameters. We infer turbulent velocities from density fluctuations with an average Mach number $\mathcal{M}_{\text{3D}} = 0.52 \pm 0.14$, in agreement with numerical simulations. For clusters with inferred turbulent Mach numbers from both pressure, $\mathcal{M}_{\text{P}}$ and density fluctuations, $\mathcal{M}_{\rho}$, we find broad agreement between $\mathcal{M}_{\text{P}}$ and $\mathcal{M}_{\rho}$. Our results suggest a bimodal Mach number distribution, with the majority of clusters being turbulence-dominated (subsonic) while the remainder are shock-dominated (supersonic)., Comment: 18 pages, 10 figures, 4 tables. Submitted to ApJ; comments welcome

Lipid membrane deformations have been predicted to lead to indirect forces between the objects that induce these deformations. Recent experimental measurements have found an attractive interaction between spherical particles that all induce a deformation towards the inside of a giant unilamellar vesicle. Here, we complement these experimental observations by investigating the interactions between deformations pointing in opposite directions with respect to the membrane normal vector. This is experimentally realized by a particle deforming the membrane towards the inside of the GUV and pulling a membrane tube towards the outside of the membrane. Particles completely wrapped by the membrane are repelled from the tube with a strength of 3~k$_B$T at a distance of 0.5~$\mu$m. However, particles that strongly curve the membrane by adhering only to a patch of about 50~\% of its surface area are attracted to the center of the tube with a strength of -5.3~k$_B$T at a minimum distance of about 1~$\mu$m. We find that such Janus particles also experience attractive interactions when both deforming the membrane in the same way. These quantitative experimental observations provide new insights into interactions between oppositely membrane deforming objects, important for cooperative protein assembly at or interactions of microplastics with cell membranes.

We present NeuralMag, a flexible and high-performance open-source Python library for micromagnetic simulations. NeuralMag leverages modern machine learning frameworks, such as PyTorch and JAX, to perform efficient tensor operations on various parallel hardware, including CPUs, GPUs, and TPUs. The library implements a novel nodal finite-difference discretization scheme that provides improved accuracy over traditional finite-difference methods without increasing computational complexity. NeuralMag is particularly well-suited for solving inverse problems, especially those with time-dependent objectives, thanks to its automatic differentiation capabilities. Performance benchmarks show that NeuralMag is competitive with state-of-the-art simulation codes while offering enhanced flexibility through its Python interface and integration with high-level computational backends.

Catalytic microswimmers typically swim close to walls due to hydrodynamic and/or phoretic effects. The walls in turn are known to affect their propulsion, making it difficult to single out the contributions that stem from particle-based catalytic propulsion only, thereby preventing an understanding of the propulsion mechanism. Here, we use acoustic tweezers to lift catalytically active Janus spheres away from the wall to study their motion in bulk and when approaching a wall. Mean-squared displacement analysis shows that diffusion constants at different heights match with Fax\'en's prediction for the near-wall hydrodynamic mobility. Both particles close to a substrate and in bulk show a decrease in velocity with increasing salt concentration, suggesting that the dominant factor for the decrease in speed is a reduction of the swimmer-based propulsion. The velocity-height profile follows a hydrodynamic scaling relation as well, implying a coupling between the wall and the swimming speed. The observed speed reduction upon addition of salt matches expectations from a electrokinetic theory, except for experiments in 0.1 wt% hydrogen peroxide in bulk, which could indicate contributions from a different propulsion mechanism. Our results help with the understanding of ionic effects on microswimmers in 3D and point to a coupling between the wall and the particle that affects its self-propulsion speed.

We show simplified DPS-QKD using a SiN micro-ring resonator operated at 852 nm. A raw-key rate of up to 25.3 kb/s is reached at a QBER suitable for secure-key extraction. Short-reach QKD operation is maintained for zero-touch link layouts with C-band telecom fiber.

This study introduces a novel data-centric approach to improve real-time surgical guidance using fiber-based fluorescence lifetime imaging (FLIm). A key aspect of the methodology is the accurate detection of the aiming beam, which is essential for localizing points used to map FLIm measurements onto the tissue region within the surgical field. The primary challenge arises from the complex and variable conditions encountered in the surgical environment, particularly in Transoral Robotic Surgery (TORS). Uneven illumination in the surgical field can cause reflections, reduce contrast, and results in inconsistent color representation, further complicating aiming beam detection. To overcome these challenges, an instance segmentation model was developed using a data-centric training strategy that improves accuracy by minimizing label noise and enhancing detection robustness. The model was evaluated on a dataset comprising 40 in vivo surgical videos, demonstrating a median detection rate of 85%. This performance was maintained when the model was integrated in a clinical system, achieving a similar detection rate of 85% during TORS procedures conducted in patients. The system's computational efficiency, measured at approximately 24 frames per second (FPS), was sufficient for real-time surgical guidance. This study enhances the reliability of FLIm-based aiming beam detection in complex surgical environments, advancing the feasibility of real-time, image-guided interventions for improved surgical precision

Understanding the physics of the integrable spin-1/2 XXZ chain has witnessed substantial progress, due to the development and application of sophisticated analytical and numerical techniques. In particular, infinite-temperature magnetization transport has turned out to range from ballistic, over superdiffusive, to diffusive behavior in different parameter regimes of the anisotropy. Since integrability is rather the exception than the rule, a crucial question is the change of transport under integrability-breaking perturbations. This question includes the stability of superdiffusion at the isotropic point and the change of diffusion constants in the easy-axis regime. In our work, we study this change of diffusion constants by a variety of methods and cover both, linear response theory in the closed system and the Lindblad equation in the open system, where we throughout focus on periodic boundary conditions. In the closed system, we compare results from the recursion method to calculations for finite systems and find evidence for a continuous change of diffusion constants over the full range of perturbation strengths. In the open system weakly coupled to baths, we find diffusion constants in quantitative agreement with the ones in the closed system in a range of nonweak perturbations, but disagreement in the limit of weak perturbations. Using a simple model in this limit, we point out the possibility of a diverging diffusion constant in such an open system., Comment: 6 pages, 4 figures (+ 5 pages, 7 figures)

Recent progress in large language models (LLMs) offers promising new approaches for recommendation system tasks. While the current state-of-the-art methods rely on fine-tuning LLMs to achieve optimal results, this process is costly and introduces significant engineering complexities. Conversely, methods that directly use LLMs without additional fine-tuning result in a large drop in recommendation quality, often due to the inability to capture collaborative information. In this paper, we propose a Simple Training-free Approach for Recommendation (STAR), a framework that utilizes LLMs and can be applied to various recommendation tasks without the need for fine-tuning, while maintaining high quality recommendation performance. Our approach involves a retrieval stage that uses semantic embeddings from LLMs combined with collaborative user information to retrieve candidate items. We then apply an LLM for pairwise ranking to enhance next-item prediction. Experimental results on the Amazon Review dataset show competitive performance for next item prediction, even with our retrieval stage alone. Our full method achieves Hits@10 performance of +23.8% on Beauty, +37.5% on Toys & Games, and -1.8% on Sports & Outdoors relative to the best supervised models. This framework offers an effective alternative to traditional supervised models, highlighting the potential of LLMs in recommendation systems without extensive training or custom architectures.

Analyses of major group mergers are key to understanding the evolution of large-scale structure in the Universe and the microphysical properties of the hot gas in these systems. We present imaging and spectral analyses of deep Chandra observations of hot gas structures formed in the major merger of the NGC 7618 and UGC 12491 galaxy groups and compare the observed hot gas morphology, temperatures, and abundances with recent simulations. The morphology of the observed multiple cold front edges and boxy wings are consistent with those expected to be formed by Kelvin-Helmholtz instabilities and gas sloshing in inviscid gas. The arc-shaped slingshot tail morphologies seen in each galaxy suggest that the dominant galaxies are near their orbital apogee after having experienced at least one core passage at a large impact parameter., Comment: 30 pages, 8 figures

Detecting arousals in sleep is essential for diagnosing sleep disorders. However, using Machine Learning (ML) in clinical practice is impeded by fundamental issues, primarily due to mismatches between clinical protocols and ML methods. Clinicians typically annotate only the onset of arousals, while ML methods rely on annotations for both the beginning and end. Additionally, there is no standardized evaluation methodology tailored to clinical needs for arousal detection models. This work addresses these issues by introducing a novel post-processing and evaluation framework emphasizing approximate localization and precise event count (ALPEC) of arousals. We recommend that ML practitioners focus on detecting arousal onsets, aligning with clinical practice. We examine the impact of this shift on current training and evaluation schemes, addressing simplifications and challenges. We utilize a novel comprehensive polysomnographic dataset (CPS) that reflects the aforementioned clinical annotation constraints and includes modalities not present in existing polysomnographic datasets. We release the dataset alongside this paper, demonstrating the benefits of leveraging multimodal data for arousal onset detection. Our findings significantly contribute to integrating ML-based arousal detection in clinical settings, reducing the gap between technological advancements and clinical needs.

Recent advances in machine learning algorithms have unlocked new insights in observational astronomy by allowing astronomers to probe new frontiers. In this article, we present a methodology to disentangle the intrinsic X-ray spectrum of galaxy clusters from the instrumental response function. Employing state-of-the-art modeling software and data mining techniques of the Chandra data archive, we construct a set of 100,000 mock Chandra spectra. We train a recurrent inference machine (RIM) to take in the instrumental response and mock observation and output the intrinsic X-ray spectrum. The RIM can recover the mock intrinsic spectrum below the 1-$\sigma$ error threshold; moreover, the RIM reconstruction of the mock observations are indistinguishable from the observations themselves. To further test the algorithm, we deconvolve extracted spectra from the central regions of the galaxy group NGC 1550, known to have a rich X-ray spectrum, and the massive galaxy clusters Abell 1795. Despite the RIM reconstructions consistently remaining below the 1-$\sigma$ noise level, the recovered intrinsic spectra did not align with modeled expectations. This discrepancy is likely attributable to the RIM's method of implicitly encoding prior information within the neural network. This approach holds promise for unlocking new possibilities in accurate spectral reconstructions and advancing our understanding of complex X-ray cosmic phenomena., Comment: Submitted to AJ

Massive clusters of galaxies are very rare in the observable Universe. Even rarer are mergers of such clusters observed close to pericenter passage. Here, we report on one such case: a massive (~ $10^{15}\,M_\odot$) and hot (kT ~ 10 keV) cluster CL0238.3+2005 at $z\approx 0.42$. For this cluster, we combine X-ray data from SRG/eROSITA and Chandra, optical images from DESI, and spectroscopy from BTA and RTT-150 telescopes. The X-ray and optical morphologies suggest an ongoing merger with the projected separation of subhalos of $\sim 200$ kpc. The line-of-sight velocity of galaxies tentatively associated with the two merging halos differs by 2000-3000 km/s. We conclude that, most plausibly, the merger axis is neither close to the line of sight nor to the sky plane. We compare CL0238 with two well-known clusters MACS0416 and Bullet, and conclude that CL0238 corresponds to an intermediate phase between the pre-merging MACS0416 cluster and the post-merger Bullet cluster. Namely, this cluster has recently (only $\lesssim 0.1$ Gyr ago) experienced an almost head-on merger. We argue that this "just after" system is a very rare case and an excellent target for lensing, Sunyaev-Zeldovich effect, and X-ray studies that can constrain properties ranging from dynamics of mergers to self-interacting dark matter, and plasma effects in intracluster medium that are associated with shock waves, e.g., electron-ion equilibration efficiency and relativistic particle acceleration., Comment: submitted to A&A; comments are welcome

Virtual field trips (VFTs) have proven to be valuable learning tools. Such applications are mostly based on 360{\deg} technology and are to be characterized as single-user applications in technological terms. In contrast, Social VR applications are characterized by multi-user capability and user-specific avatars. From a learning perspective, the concepts of collaborative learning and embodiment have long been proposed as conducive to learning. Both concepts might be supported using Social VR. However, little is currently known about the use of Social VR for VFTs. Accordingly, the research questions are to what extent VFTs can be implemented in Social VR environments and how these Social VR-based VFTs are perceived by learners. This article presents an evaluation study on the development and evaluation of a VFT environment using the Social VR platform Mozilla Hubs. It describes the design decisions to create the environment and evaluation results from a mixed-method study (N=16) using a questionnaire and focus group discussions. The study highlighted the opportunities offered by Social VR-based VFTs but also revealed several challenges that need to be addressed to embrace the potential of Social VR-based VFTs to be utilized regularly in education., Comment: 9 pages, 7 figures, 1 table, submitted to Games and Learning Alliance Conference

The ability to accurately discern active massive black holes (BHs) in nearby dwarf galaxies is paramount to understanding the origins and processes of "seed" BHs in the early Universe. We present Chandra X-ray Observatory observations of a sample of three local dwarf galaxies (M$_{*}$ $\leqslant 3 \times 10^{9}$ M$_\odot$, z $\leqslant$ 0.15) previously identified as candidates for hosting active galactic nuclei (AGN). The galaxies were selected from the NASA-Sloan Atlas (NSA) with spatially coincident X-ray detections in the eROSITA Final Equatorial Depth Survey (eFEDS). Our new Chandra data reveal three X-ray point sources in two of the target galaxies with luminosities between log(L$_{\rm \text{2-10 keV}}$ [erg s$^{-1}$]) = 39.1 and 40.4. Our results support the presence of an AGN in these two galaxies and a ULX in one of them. For the AGNs, we estimate BH masses of $M_{\rm BH} \sim 10^{5-6} M_\odot$ and Eddington ratios on the order of $\sim 10^{-3}$., Comment: Accepted for publication in The Astrophysical Journal. 7 pages

In [Muhl2019], Peter M\"uhlbacher showed that in the random loop model without loop weights, a loop phase transition (assuming it exists) cannot occur at the same parameter as the percolation phase transition of the occupied edges. In this work, we give a quantitative version of this result, specifying a minimal gap between the percolation phase transition and a possible loop phase transition. A substantial part of our argument also works for weighted loop models.

Laser-plasma accelerators represent a promising technology for future compact accelerating systems, enabling the acceleration of tens of pC to above $1\,$GeV over just a few centimeters. Nonetheless, these devices currently lack the stability, beam quality and average current of conventional systems. While many efforts have focused on improving acceleration stability and quality, little progress has been made in increasing the beam's average current, which is essential for future laser-plasma-based applications. In this paper, we investigate a laser-plasma acceleration regime aimed at increasing the beam average current with energies up to few-MeVs, efficiently enhancing the beam charge. We present experimental results on configurations that allow reaching charges of $5-30\,$nC and a maximum conversion efficiency of around $14\,$%. Through comprehensive Particle-In-Cell simulations, we interpret the experimental results and present a detailed study on electron dynamics. From our analysis, we show that most electrons are not trapped in a plasma wave; rather, they experience ponderomotive acceleration. Thus, we prove the laser pulse as the main driver of the particles' energy gain process.

Traditional cosmic ray filtering algorithms used in X-ray imaging detectors aboard space telescopes perform event reconstruction based on the properties of activated pixels above a certain energy threshold, within 3x3 or 5x5 pixel sliding windows. This approach can reject up to 98% of the cosmic ray background. However, the remaining unrejected background constitutes a significant impediment to studies of low surface brightness objects, which are especially prevalent in the high-redshift universe. The main limitation of the traditional filtering algorithms is their ignorance of the long-range contextual information present in image frames. This becomes particularly problematic when analyzing signals created by secondary particles produced during interactions of cosmic rays with body of the detector. Such signals may look identical to the energy deposition left by X-ray photons, when one considers only the properties within the small sliding window. Additional information is present, however, in the spatial and energy correlations between signals in different parts of the frame, which can be accessed by modern machine learning (ML) techniques. In this work, we continue the development of an ML-based pipeline for cosmic ray background mitigation. Our latest method consist of two stages: first, a frame classification neural network is used to create class activation maps (CAM), localizing all events within the frame; second, after event reconstruction, a random forest classifier, using features obtained from CAMs, is used to separate X-ray and cosmic ray features. The method delivers >40% relative improvement over traditional filtering in background rejection in standard 0.3-10keV energy range, at the expense of only a small (<2%) level of lost X-ray signal. Our method also provides a convenient way to tune the cosmic ray rejection threshold to adapt to a user's specific scientific needs., Comment: To appear in SPIE Astronomical Telescopes + Instrumentation proceedings 2024

Bringing artificial intelligence (AI) alongside next-generation X-ray imaging detectors, including CCDs and DEPFET sensors, enhances their sensitivity to achieve many of the flagship science cases targeted by future X-ray observatories, based upon low surface brightness and high redshift sources. Machine learning algorithms operating on the raw frame-level data provide enhanced identification of background vs. astrophysical X-ray events, by considering all of the signals in the context within which they appear within each frame. We have developed prototype machine learning algorithms to identify valid X-ray and cosmic-ray induced background events, trained and tested upon a suite of realistic end-to-end simulations that trace the interaction of cosmic ray particles and their secondaries through the spacecraft and detector. These algorithms demonstrate that AI can reduce the unrejected instrumental background by up to 41.5 per cent compared with traditional filtering methods. Alongside AI algorithms to reduce the instrumental background, next-generation event reconstruction methods, based upon fitting physically-motivated Gaussian models of the charge clouds produced by events within the detector, promise increased accuracy and spectral resolution of the lowest energy photon events., Comment: Proceedings of the SPIE, Astronomical Telescopes and Instrumentation, Space Telescopes and Instrumentation 2024: Ultraviolet to Gamma Ray

Recent advancements have showcased the potential of Large Language Models (LLMs) in executing reasoning tasks, particularly facilitated by Chain-of-Thought (CoT) prompting. While tasks like arithmetic reasoning involve clear, definitive answers and logical chains of thought, the application of LLM reasoning in recommendation systems (RecSys) presents a distinct challenge. RecSys tasks revolve around subjectivity and personalized preferences, an under-explored domain in utilizing LLMs' reasoning capabilities. Our study explores several aspects to better understand reasoning for RecSys and demonstrate how task quality improves by utilizing LLM reasoning in both zero-shot and finetuning settings. Additionally, we propose RecSAVER (Recommender Systems Automatic Verification and Evaluation of Reasoning) to automatically assess the quality of LLM reasoning responses without the requirement of curated gold references or human raters. We show that our framework aligns with real human judgment on the coherence and faithfulness of reasoning responses. Overall, our work shows that incorporating reasoning into RecSys can improve personalized tasks, paving the way for further advancements in recommender system methodologies., Comment: To be published at ACL 2024

Genotype imputation enhances genetic data by predicting missing SNPs using reference haplotype information. Traditional methods leverage linkage disequilibrium (LD) to infer untyped SNP genotypes, relying on the similarity of LD structures between genotyped target sets and fully sequenced reference panels. Recently, reference-free deep learning-based methods have emerged, offering a promising alternative by predicting missing genotypes without external databases, thereby enhancing privacy and accessibility. However, these methods often produce models with tens of millions of parameters, leading to challenges such as the need for substantial computational resources to train and inefficiency for client-sided deployment. Our study addresses these limitations by introducing a baseline for a novel genotype imputation pipeline that supports client-sided imputation models generalizable across any genotyping chip and genomic region. This approach enhances patient privacy by performing imputation directly on edge devices. As a case study, we focus on PRS313, a polygenic risk score comprising 313 SNPs used for breast cancer risk prediction. Utilizing consumer genetic panels such as 23andMe, our model democratizes access to personalized genetic insights by allowing 23andMe users to obtain their PRS313 score. We demonstrate that simple linear regression can significantly improve the accuracy of PRS313 scores when calculated using SNPs imputed from consumer gene panels, such as 23andMe. Our linear regression model achieved an R^2 of 0.86, compared to 0.33 without imputation and 0.28 with simple imputation (substituting missing SNPs with the minor allele frequency). These findings suggest that popular SNP analysis libraries could benefit from integrating linear regression models for genotype imputation, providing a viable and light-weight alternative to reference based imputation., Comment: This paper is 16 pages long and contains 7 figures. For more information and to access related resources: * Web application: https://aaronge-2020.github.io/DeepImpute/ * Code repository: https://github.com/aaronge-2020/DeepImpute

Based on a proposal to observe 18 bright radio sources from the SMS4 catalog with the Neil Gehrels Swift Observatory (hereafter Swift), we obtained X-ray observations of 17 targets (one target was not observed). Following up our first paper that discussed 31 sources (see Maselli et al. 2022; 20 sources detected as point sources and one very extended source), we present results for this final sample of 17 radio sources, that previously lacked dedicated, pointed narrow FOV X-ray observations. One of these 17 sources, undetected by Swift due to a very short exposure, was instead detected by eROSITA, and given in the Data Release 1 (DR1) Catalog. No 1eRASS source was found in the DR1 for the remaining source, unobserved by Swift. The new Swift observations led to eleven X-ray source detections in the 0.3-10 keV band and six upper limits. We investigated the extent of the X-ray emission, the hardness ratio, and when statistics allowed we carried out a spectral analysis. The X-ray emission of eight sources is consistent with point-like emission, while three sources show clear evidence of extent, each with peculiar properties. We used the X-ray determined positions and uncertainties of the twelve detected sources to establish associations with infrared and optical sources from the AllWISE and the GSC 2.4.2 catalogs. Requiring a detection in both the infrared and the optical bands to establish a candidate counterpart for our X-ray detections, we identify counterparts for all twelve sources. We discuss the interesting structure of MRC B0344-345 and PKS B2148-555, two of the six extended X-ray sources that we detected in both our Swift campaigns, and suggest they are very promising for further X-ray and radio investigations. For the 38 SMS4 sources that lack pointed, narrow FOV X-ray telescope observations, after our Swift campaigns, we list 18 likely counterparts from the eROSITA DR1 catalog., Comment: 25 pages, 7 figures, 11 tables; accepted for publication in the Astrophysical Journal Supplement Series. arXiv admin note: text overlap with arXiv:2208.04763

We conducted a scoping review for active learning in the domain of natural language processing (NLP), which we summarize in accordance with the PRISMA-ScR guidelines as follows: Objective: Identify active learning strategies that were proposed for entity recognition and their evaluation environments (datasets, metrics, hardware, execution time). Design: We used Scopus and ACM as our search engines. We compared the results with two literature surveys to assess the search quality. We included peer-reviewed English publications introducing or comparing active learning strategies for entity recognition. Results: We analyzed 62 relevant papers and identified 106 active learning strategies. We grouped them into three categories: exploitation-based (60x), exploration-based (14x), and hybrid strategies (32x). We found that all studies used the F1-score as an evaluation metric. Information about hardware (6x) and execution time (13x) was only occasionally included. The 62 papers used 57 different datasets to evaluate their respective strategies. Most datasets contained newspaper articles or biomedical/medical data. Our analysis revealed that 26 out of 57 datasets are publicly accessible. Conclusion: Numerous active learning strategies have been identified, along with significant open questions that still need to be addressed. Researchers and practitioners face difficulties when making data-driven decisions about which active learning strategy to adopt. Conducting comprehensive empirical comparisons using the evaluation environment proposed in this study could help establish best practices in the domain., Comment: The Version of Record of this contribution is published in Deep Learning Theory and Applications 5th International Conference, DeLTA 2024 Proceedings, and will be available after the conference