Search

Do residential neighbors affect each others' schooling choices? We exploit oversubscription lotteries in Chile's centralized school admission system to identify the effect of close neighbors on application and enrollment decisions. A student is 5-7% more likely to rank a high school as their first preference and to attend that school if their closest neighbor attended it the prior year. These effects are stronger among boys and applicants with lower parents' education and prior academic achievement, measured by previous scores in national standardized tests. Lower-achieving applicants are more likely to follow neighbors when their closest neighbor's test scores are higher. A neighbor enrolling in a school with one s.d. higher school effectiveness, peer composition, or school climate induces increases of 0.02-0.04 s.d. in the applicant's attended school. Our findings suggest that targeted policies aimed at increasing information to disadvantaged families have the potential to alleviate these frictions and generate significant multiplier effects.

Intercalation is a robust approach for modulating the properties of epitaxial graphene on SiC and stabilizing two-dimensional (2D) intercalant layers at the graphene/SiC interface. In this work, we present synchrotron-based angle resolved photoelectron spectroscopy (ARPES) measurements focussing on the band structure of intercalated Pb under a single layer of epitaxial graphene. The interlayer Pb exhibits a metallic character, a $(1 \times 1)$ registry with respect to SiC, and free electron-like bands to a first order. Divergences from the free electron approximation include various band splittings and gaps throughout the Pb Brillouin zone. Light polarization dependent ARPES measurements indicate a predominant out-of-plane orbital character for the Pb bands, suggesting potential interactions between the interlayer Pb and graphene's $\pi$ orbitals that may induce proximity effects in graphene. Density functional theory (DFT) calculations for a $(1 \times 1)$ Pb monolayer on SiC show a reasonable qualitative agreement with the experimentally observed interlayer bands as well as the polarization dependent measurements. Finally, temperature dependent ARPES measurements reveal that the nearly charge-neutral graphene layer involves charge transfer from both the interlayer Pb and the substrate SiC., Comment: 11 pages and 6 figures (and 3 pages of supplementary information with 6 figures)

The analysis of spatio-temporal data has been the object of research in several areas of knowledge. One of the main objectives of such research is the need to evaluate the behavior of climate effects in certain regions across a period of time. When certain climate patterns appear for several days or even weeks, causing the areas affected by them to have the same kind of weather for an extended period of time, the use of blocks for these phenomena may be a good strategy. Additionally, having repeated measures for observations within blocks helps to control for differences between observations, thus gaining more statistical power. In view of these perspectives, this study presents a spatio-temporal regression model with block structure with repeated measures incorporating as predictors functional variables of fixed and random nature. To accommodate complex spatial, temporal and block structures, functional components based on random effects were considered in addition to the class Mat\'ern covariance structure, which was responsible to account for spatial covariance. This work is motivated by a precipitation dataset collected monthly from various meteorological stations in Goi\'as State, Brazil, covering the years 1980 to 2001 (21 years). In this framework, spatial effects are represented by individual meteorological stations, temporal effects by months, block effects by climate patterns, and repeated measures by the years within those patterns. The proposed model demonstrated promising results in simulation studies and effectively estimated precipitation using the available data.

Novel view synthesis has been greatly enhanced by the development of radiance field methods. The introduction of 3D Gaussian Splatting (3DGS) has effectively addressed key challenges, such as long training times and slow rendering speeds, typically associated with Neural Radiance Fields (NeRF), while maintaining high-quality reconstructions. In this work (BeSplat), we demonstrate the recovery of sharp radiance field (Gaussian splats) from a single motion-blurred image and its corresponding event stream. Our method jointly learns the scene representation via Gaussian Splatting and recovers the camera motion through Bezier SE(3) formulation effectively, minimizing discrepancies between synthesized and real-world measurements of both blurry image and corresponding event stream. We evaluate our approach on both synthetic and real datasets, showcasing its ability to render view-consistent, sharp images from the learned radiance field and the estimated camera trajectory. To the best of our knowledge, ours is the first work to address this highly challenging ill-posed problem in a Gaussian Splatting framework with the effective incorporation of temporal information captured using the event stream., Comment: Accepted for publication at EVGEN2025, WACV-25 Workshop

The incompressibility of infinite nuclear matter (K_\infty) is a parameter in the description of the nuclear equation of state that governs the energy cost associated with density oscillations near the saturation density. The most direct experimental method for studying this property of infinite nuclear matter is to probe the isoscalar giant monopole resonance (ISGMR) in finite nuclei. This work explores the use of 6Li as a probe to study the ISGMR in several stable nuclei: 58Ni, 90Zr, 116Sn, and 208Pb, as complementary to using inelastic scattering of {\alpha}-particles, which has been used to great effect over the last several decades. Elastic and inelastic scattering data for these targets were collected with 343-MeV 6Li beams. In all nuclei studied in this work, the ISGMR strength distributions extracted from multipole decomposition analyses of the inelastic scattering spectra agree very well with the previously measured ISGMR responses from {\alpha}-particle scattering, establishing the feasibility of employing 6Li inelastic scattering in investigations of the ISGMR., Comment: Accepted for publication in Phys. Rev. C

Flare, an optical phenomenon resulting from unwanted scattering and reflections within a lens system, presents a significant challenge in imaging. The diverse patterns of flares, such as halos, streaks, color bleeding, and haze, complicate the flare removal process. Existing traditional and learning-based methods have exhibited limited efficacy due to their reliance on single-image approaches, where flare removal is highly ill-posed. We address this by framing flare removal as a multi-view image problem, taking advantage of the view-dependent nature of flare artifacts. This approach leverages information from neighboring views to recover details obscured by flare in individual images. Our proposed framework, GN-FR (Generalizable Neural Radiance Fields for Flare Removal), can render flare-free views from a sparse set of input images affected by lens flare and generalizes across different scenes in an unsupervised manner. GN-FR incorporates several modules within the Generalizable NeRF Transformer (GNT) framework: Flare-occupancy Mask Generation (FMG), View Sampler (VS), and Point Sampler (PS). To overcome the impracticality of capturing both flare-corrupted and flare-free data, we introduce a masking loss function that utilizes mask information in an unsupervised setting. Additionally, we present a 3D multi-view flare dataset, comprising 17 real flare scenes with 782 images, 80 real flare patterns, and their corresponding annotated flare-occupancy masks. To our knowledge, this is the first work to address flare removal within a Neural Radiance Fields (NeRF) framework., Comment: Accepted for publication at BMVC-24

Recent studies have demonstrated that few-shot learning allows LLMs to generate training data for supervised models at a low cost. However, the quality of LLM-generated data may not entirely match that of human-labeled data. This raises a crucial question: how should one balance the trade-off between the higher quality but more expensive human data and the lower quality yet substantially cheaper LLM-generated data? In this paper, we synthesized training data for conversational semantic frame analysis using GPT-4 and examined how to allocate budgets optimally to achieve the best performance. Our experiments, conducted across various budget levels, reveal that optimal cost-efficiency is achieved by combining both human and LLM-generated data across a wide range of budget levels. Notably, as the budget decreases, a higher proportion of LLM-generated data becomes more preferable., Comment: 12 pages including 4 pages of references and appendix. 7 figures

We present the first measurement of the $^{47}$K($d,p\gamma$)$^{48}$K transfer reaction, performed in inverse kinematics using a reaccelerated beam of $^{47}$K. The level scheme of $^{48}$K has been greatly extended with nine new bound excited states identified and spectroscopic factors deduced. Detailed comparisons with SDPF-U and SDPF-MU shell-model calculations reveal a number of discrepancies with these results, and a preference for SDPF-MU is found. Intriguingly, an apparent systematic overestimation of spectroscopic factors and a poor reproduction of the energies for 1$^-$ states suggests that the mixing between the $\pi s^{\,\,\,1}_{1/2} d^{\,\,\,4}_{3/2}$ and $\pi s^{\,\,\,2}_{1/2} d^{\,\,\,3}_{3/2}$ proton configurations in $^{48}$K is not correctly described using current interactions, challenging our descriptions of light $N=28$ nuclei., Comment: 7 pages, 3 figures, 1 table

Angular distributions of the elastic and inelastic deuteron-nucleus scattering off $^{24}$Mg, $^{28}$Si, $^{58}$Ni, $^{90}$Zr, $^{116}$Sn, and $^{208}$Pb have been measured at a beam energy of 98 MeV/nucleon, with the goal of constraining the deuteron optical potential in this kinematical regime, and to extract the reduced transition probabilities for the ground-state transitions to low-lying excited states of these nuclei. Two potential models were used in the analysis of the measured $(d,d)$ and $(d,d')$ data within the optical model and the distorted-wave Born approximation: the phenomenological optical model potential associated with the collective model of nuclear scattering, and the semi-microscopic double-folding model of the deuteron-nucleus potential based on a realistic density-dependent M3Y interaction. The deuteron optical potential and inelastic $(d,d')$ scattering form factors were calculated using these two potential models, allowing for a direct comparison between the potential models as well as the validation of the deduced $E\lambda$ transition rates., Comment: Accepted for publication in Phys. Rev. C

The existence of superconductivity (SC) in graphene appears to be established in both twisted and non-twisted multilayers. However, whether their building block, single-layer graphene (SLG), can also host SC remains an open question. Earlier theoretical works predicted that SLG could become a chiral d-wave superconductor driven by electronic interactions when doped to its van Hove singularity, but questions such as whether the d-wave SC survives the strong band renormalizations seen in experiments, its robustness against the source of doping, or if it will occur at any reasonable critical temperature (Tc) have remained difficult to answer, in part due to uncertainties in model parameters. In this study, we adopt a random-phase approximation framework based on a Kohn-Luttinger-like mechanism to investigate SC in heavily-doped SLG. We predict that robust d+id topological SC could arise in SLG doped by Tb, with a Tc up to 600 mK. We also investigate the possibility of realizing d-wave SC by employing other dopants, such as Li or Cs. The structural models have been derived from angle-resolved photoemission spectroscopy measurements on Tb-doped graphene and first-principles calculations for Cs and Li doping. We find that dopants that change the lattice symmetry of SLG are detrimental to the d-wave state. The stability of the d-wave SC predicted here in Tb-doped SLG could provide a valuable insight for guiding future experimental efforts aimed at exploring topological superconductivity in monolayer graphene., Comment: 14 pages, 6 figures. Comments are very welcome

Communication-constrained algorithms for decentralized learning and optimization rely on local updates coupled with the exchange of compressed signals. In this context, differential quantization is an effective technique to mitigate the negative impact of compression by leveraging correlations between successive iterates. In addition, the use of error feedback, which consists of incorporating the compression error into subsequent steps, is a powerful mechanism to compensate for the bias caused by the compression. Under error feedback, performance guarantees in the literature have so far focused on algorithms employing a fusion center or a special class of contractive compressors that cannot be implemented with a finite number of bits. In this work, we propose a new decentralized communication-efficient learning approach that blends differential quantization with error feedback. The approach is specifically tailored for decentralized learning problems where agents have individual risk functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces. This constrained formulation includes consensus or single-task optimization as special cases, and allows for more general task relatedness models such as multitask smoothness and coupled optimization. We show that, under some general conditions on the compression noise, and for sufficiently small step-sizes $\mu$, the resulting communication-efficient strategy is stable both in terms of mean-square error and average bit rate: by reducing $\mu$, it is possible to keep the estimation errors small (on the order of $\mu$) without increasing indefinitely the bit rate as $\mu\rightarrow 0$. The results establish that, in the small step-size regime and with a finite number of bits, it is possible to attain the performance achievable in the absence of compression., Comment: arXiv admin note: text overlap with arXiv:2209.07821

We investigate the application of active inference in developing energy-efficient control agents for manufacturing systems. Active inference, rooted in neuroscience, provides a unified probabilistic framework integrating perception, learning, and action, with inherent uncertainty quantification elements. Our study explores deep active inference, an emerging field that combines deep learning with the active inference decision-making framework. Leveraging a deep active inference agent, we focus on controlling parallel and identical machine workstations to enhance energy efficiency. We address challenges posed by the problem's stochastic nature and delayed policy response by introducing tailored enhancements to existing agent architectures. Specifically, we introduce multi-step transition and hybrid horizon methods to mitigate the need for complex planning. Our experimental results demonstrate the effectiveness of these enhancements and highlight the potential of the active inference-based approach., Comment: Accepted at the 10th International Conference on Machine Learning, Optimization, and Data Science

We theoretically propose and experimentally demonstrate the use of a configurable four-qubit photonic system to generate a publicly verifiable quantum random numbers, to perform entanglement verification, and to generate secure public and private key. Quantum circuits, to generate the desired four-qubit states and its experimental realization in the photonic architecture is carried out using photon pairs entangled in polarization and path degree of freedom. By performing measurements on the four-qubit system and accessing partial information of the four-qubit state for public verification, we generate publicly verified and purely secured random bits at the rate of 185 kbps from collective data of 370 kbps. When the system is used for generating public and private keys, an equal number of public and private keys are generated simultaneously. We also record about 97.9% of sampled bits from four-qubit states passing entanglement verification and demonstrate the use of public and private key generated for image encryption-decryption. The theoretical model of noise on the four-qubit state and its effect on the generation rate of verified and secured bits are in perfect agreement with the experimental results. This demonstrates the practical use of the small-scale multi-qubit photonic system for quantum-safe applications by providing the option for real-time verification of the security feature of the quantum system., Comment: 12 pages, 9 figures

This paper presents a novel approach to improving autonomous vehicle control in environments lacking clear road markings by integrating a diffusion-based motion predictor within an Active Inference Framework (AIF). Using a simulated parking lot environment as a parallel to unmarked roads, we develop and test our model to predict and guide vehicle movements effectively. The diffusion-based motion predictor forecasts vehicle actions by leveraging probabilistic dynamics, while AIF aids in decision-making under uncertainty. Unlike traditional methods such as Model Predictive Control (MPC) and Reinforcement Learning (RL), our approach reduces computational demands and requires less extensive training, enhancing navigation safety and efficiency. Our results demonstrate the model's capability to navigate complex scenarios, marking significant progress in autonomous driving technology.

The excited states of unstable $^{20}$O were investigated via $\gamma$-ray spectroscopy following the $^{19}$O$(d,p)^{20}$O reaction at 8 $A$MeV. By exploiting the Doppler Shift Attenuation Method, the lifetime of the 2$^+_2$ and 3$^+_1$ states were firmly established. From the $\gamma$-ray branching and E2/M1 mixing ratios for transitions deexciting the 2$^+_2$ and 3$^+_1$ states, the B(E2) and B(M1) were determined. Various chiral effective field theory Hamiltonians, describing the nuclear properties beyond ground states, along with a standard USDB interaction, were compared with the experimentally obtained data. Such a comparison for a large set of $\gamma$-ray transition probabilities with the valence space in medium similarity renormalization group ab-initio calculations was performed for the first time in a nucleus far from stability. It was shown that the ab-initio approaches using chiral EFT forces are challenged by detailed high-precision spectroscopic properties of nuclei. The reduced transition probabilities were found to be a very constraining test of the performance of the ab-initio models., Comment: Supplemental Material available

Despite its amazing quantitative successes and contributions to revolutionary technologies, physics currently faces many unsolved mysteries ranging from the meaning of quantum mechanics to the nature of the dark energy that will determine the future of the Universe. It is clearly prohibitive for the general reader, and even the best informed physicists, to follow the vast number of technical papers published in the thousands of specialized journals. For this reason, we have asked the leading experts across many of the most important areas of physics to summarise their global assessment of some of the most important issues. In lieu of an extremely long abstract summarising the contents, we invite the reader to look at the section headings and their authors, and then to indulge in a feast of stimulating topics spanning the current frontiers of fundamental physics from The Future of Physics by William D Phillips and What characterises topological effects in physics? by Gerard t Hooft through the contributions of the widest imaginable range of world leaders in their respective areas. This paper is presented as a preface to exciting developments by senior and young scientists in the years that lie ahead, and a complement to the less authoritative popular accounts by journalists., Comment: 54 pages, 13 figures

Numerous Deep Learning (DL) classification models have been developed for a large spectrum of medical image analysis applications, which promises to reshape various facets of medical practice. Despite early advances in DL model validation and implementation, which encourage healthcare institutions to adopt them, a fundamental questions remain: how can these models effectively handle domain shift? This question is crucial to limit DL models performance degradation. Medical data are dynamic and prone to domain shift, due to multiple factors. Two main shift types can occur over time: 1) covariate shift mainly arising due to updates to medical equipment and 2) concept shift caused by inter-grader variability. To mitigate the problem of domain shift, existing surveys mainly focus on domain adaptation techniques, with an emphasis on covariate shift. More generally, no work has reviewed the state-of-the-art solutions while focusing on the shift types. This paper aims to explore existing domain generalization methods for DL-based classification models through a systematic review of literature. It proposes a taxonomy based on the shift type they aim to solve. Papers were searched and gathered on Scopus till 10 April 2023, and after the eligibility screening and quality evaluation, 77 articles were identified. Exclusion criteria included: lack of methodological novelty (e.g., reviews, benchmarks), experiments conducted on a single mono-center dataset, or articles not written in English. The results of this paper show that learning based methods are emerging, for both shift types. Finally, we discuss future challenges, including the need for improved evaluation protocols and benchmarks, and envisioned future developments to achieve robust, generalized models for medical image classification.