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Children spend most of their time at home in their early years, yet efforts to promote human capital at home in many low- and middle-income settings remain limited. We conduct a randomized controlled trial to evaluate an intervention which encourages parents and caregivers to foster human capital accumulation among their children between ages 3 and 5, with a focus on math and phonics skills. Children gain 0.52 and 0.51 standard deviations relative to the control group on math and phonics tests, respectively (p<0.001). A year later effects persist, but math gains dissipate to 0.15 (p=0.06) and phonics to 0.13 (p=0.12). Effects appear to be mediated largely through instructional support by parents and not other parent investment mechanisms, such as more positive parent-child interactions or additional time spent on education at home beyond the intervention. Our results show that parents can be effective conduits of educational instruction even in low-resource settings. [Funding for this paper was received from the Global Innovation Fund.]

Rapid progress in precision nanofabrication and atomic design over the past 50 years has ushered in a succession of transformative eras for molding the generation and flow of light. The use of nanoscale and atomic features to design light sources and optical elements-encapsulated by the term nanophotonics-has led to new fundamental science and innovative technologies across the entire electromagnetic spectrum, with substantial emphasis on the microwave to visible regimes. In this review, we pay special attention to the impact and potential of nanophotonics in a relatively exotic yet technologically disruptive regime: high-energy particles such as X-ray photons and free electrons-where nanostructures and atomic design open the doors to unprecedented technologies in quantum science and versatile X-ray sources and optics. As the practical generation of X-rays is intrinsically linked to the existence of energetic free or quasi-free-electrons, our review will also capture related phenomena and technologies that combine free electrons with nanophotonics, including free-electron-driven nanophotonics at other photon energies. In particular, we delve into the demonstration and study of quantum recoil in the X-ray regime, the study of nanomaterial design and free-electron wave shaping as means to enhance and control X-ray radiation, examine the free-electron generation enabled by nanophotonics, and analyze the high-harmonic generation by quasi-free electrons. We also discuss applications of quantum nanophotonics for X-rays and free electrons, including nanostructure waveguides for X-rays, photon pair enhanced X-ray imaging, mirrors, and lenses for X-rays, among others.

Deep learning approaches have been widely adopted for precipitation nowcasting in recent years. Previous studies mainly focus on proposing new model architectures to improve pixel-wise metrics. However, they frequently result in blurry predictions which provide limited utility to forecasting operations. In this work, we propose a new Fourier Amplitude and Correlation Loss (FACL) which consists of two novel loss terms: Fourier Amplitude Loss (FAL) and Fourier Correlation Loss (FCL). FAL regularizes the Fourier amplitude of the model prediction and FCL complements the missing phase information. The two loss terms work together to replace the traditional $L_2$ losses such as MSE and weighted MSE for the spatiotemporal prediction problem on signal-based data. Our method is generic, parameter-free and efficient. Extensive experiments using one synthetic dataset and three radar echo datasets demonstrate that our method improves perceptual metrics and meteorology skill scores, with a small trade-off to pixel-wise accuracy and structural similarity. Moreover, to improve the error margin in meteorological skill scores such as Critical Success Index (CSI) and Fractions Skill Score (FSS), we propose and adopt the Regional Histogram Divergence (RHD), a distance metric that considers the patch-wise similarity between signal-based imagery patterns with tolerance to local transforms. Code is available at https://github.com/argenycw/FACL, Comment: Accepted by NeurIPS 2024. Camera-ready submission

Blind spectroscopy of massive lensing galaxy clusters with MUSE has revealed large numbers of gravitationally-lensed Lyman-$ \alpha $ emitters exhibiting asymmetric profiles at $ 2.9 \leq z \leq 6.7 $, suggesting abundant outflows from low-mass star-forming galaxies in the early universe. Are these primaeval galaxies experiencing their first bursts of star formation, or established galaxies experiencing rejuvenation? With JWST rest-frame optical/NIR continuum imaging now available for many of these objects, we can search for older stellar populations. Here, we search for spectroscopic confirmation of outflows from these galaxies, finding a few high-signal-to-noise cases in which blueshifted interstellar absorption lines are detected. Next, we analyse the star formation histories with combined HST + JWST photometry. We find most them to be well characterised by very young, low metallicity stellar populations. However, despite the rest-frame optical/NIR coverage of JWST, we cannot place strict upper bounds on the mass in old stars (age $ > 100\,\text{Myr} $)., Comment: 10 pages, 3 figures. Submitted to Proceedings of the International Astronomical Union 2024 (Symposium 391 - The first chapters of our cosmic history with JWST)

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19th, during the LIGO-Virgo-KAGRA 15th Engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been identified in data when at least two gravitational-wave observatories were operating, which covered $\sim 14\%$ of this five-day window. We report the search detection efficiency for various possible gravitational-wave emission models. Considering the distance to M101 (6.7 Mpc), we derive constraints on the gravitational-wave emission mechanism of core-collapse supernovae across a broad frequency spectrum, ranging from 50 Hz to 2 kHz where we assume the GW emission occurred when coincident data are available in the on-source window. Considering an ellipsoid model for a rotating proto-neutron star, our search is sensitive to gravitational-wave energy $1 \times 10^{-5} M_{\odot} c^2$ and luminosity $4 \times 10^{-5} M_{\odot} c^2/\text{s}$ for a source emitting at 50 Hz. These constraints are around an order of magnitude more stringent than those obtained so far with gravitational-wave data. The constraint on the ellipticity of the proto-neutron star that is formed is as low as $1.04$, at frequencies above $1200$ Hz, surpassing results from SN 2019ejj., Comment: Main paper: 6 pages, 4 figures and 1 table. Total with appendices: 20 pages, 4 figures, and 1 table

We investigate coding schemes that map source symbols into multisets of an alphabet set. Such a formulation of source coding is an alternative approach to the traditional framework and is inspired by an object tracking problem over proximity sensor networks. We define a \textit{multiset combinatorial Gray code} as a mulitset code with fixed multiset cardinality that possesses combinatorial Gray code characteristic. For source codes that are organized as a grid, namely an integer lattice, we propose a solution by first constructing a mapping from the grid to the alphabet set, the codes are then defined as the images of rectangular blocks in the grid of fixed dimensions. We refer to the mapping as a \textit{color mapping} and the code as a \textit{color multiset code}. We propose the idea of product multiset code that enables us to construct codes for high dimensional grids based on 1-dimensional (1D) grids. We provide a detailed analysis of color multiset codes on 1D grids, focusing on codes that require the minimal number of colors. To illustrate the application of such a coding scheme, we consider an object tracking problem on 2D grids and show its efficiency, which comes from exploiting transmission parallelism. Some numerical results are presented to conclude the paper., Comment: 30 pages, 4 figures

The magnetar SGR 1935+2154 is the only known Galactic source of fast radio bursts (FRBs). FRBs from SGR 1935+2154 were first detected by CHIME/FRB and STARE2 in 2020 April, after the conclusion of the LIGO, Virgo, and KAGRA Collaborations' O3 observing run. Here we analyze four periods of gravitational wave (GW) data from the GEO600 detector coincident with four periods of FRB activity detected by CHIME/FRB, as well as X-ray glitches and X-ray bursts detected by NICER and NuSTAR close to the time of one of the FRBs. We do not detect any significant GW emission from any of the events. Instead, using a short-duration GW search (for bursts $\leq$ 1 s) we derive 50\% (90\%) upper limits of $10^{48}$ ($10^{49}$) erg for GWs at 300 Hz and $10^{49}$ ($10^{50}$) erg at 2 kHz, and constrain the GW-to-radio energy ratio to $\leq 10^{14} - 10^{16}$. We also derive upper limits from a long-duration search for bursts with durations between 1 and 10 s. These represent the strictest upper limits on concurrent GW emission from FRBs., Comment: 15 pages of text including references, 4 figures, 5 tables

Teaching performance assessments (TPAs) can be used to assess the readiness of potential teachers because they require candidates to provide evidence of their planning and teaching skills through classroom videos accompanied by commentaries Ensuring that teaching candidates are well prepared to enter the classroom is a critical mission for teacher preparation programs and state agencies that approve programs and set teacher licensure standards. Teaching performance assessments (TPAs) can be used to assess the readiness of potential teachers because they require candidates to provide evidence of their teaching knowledge and skills through classroom videos, lesson plans, student work, and analysis of teaching and learning. Multiple studies have found that TPA scores predict effectiveness once candidates enter the classroom as licensed teachers. TPAs have been adopted in at least 16 states as a requirement of either teacher preparation program completion or initial licensure. California, the focus of this study, was one of the first states to adopt a TPA as a licensure requirement for beginning teachers. The report on which this brief is based explored whether certain preparation experiences predicted success on the California Teaching Performance Assessment (CalTPA) or the educative Teaching Performance Assessment (edTPA), the two widely available TPAs used across California preparation programs. Focusing on the 2021-22 and 2022-23 school years, this analysis examined whether TPA success for more than 18,000 California teaching candidates varied by preparation pathway, program, and the nature of preparation experiences as reported by respondents on the annual survey administered to all those completing preparation and applying for their preliminary teaching credential with the California Commission on Teacher Credentialing (CTC).

Letters of recommendation from school counselors are required to apply to many selective colleges and universities. Still, relatively little is known about how this non-standardized component may affect equity in admissions. We use cutting-edge natural language processing techniques to algorithmically analyze a national dataset of over 600,000 student applications and counselor recommendation letters submitted via the Common App platform. We examine how the length and topical content of letters (e.g., sentences about Personal Qualities, Athletics, Intellectual Promise, etc.) relate to student self-identified race/ethnicity, sex, and proxies for socioeconomic status. Paired with regression analyses, we explore whether demographic differences in letter characteristics persist when accounting for additional student, school, and counselor characteristics, as well as among letters written by the same counselor and among students with comparably competitive standardized test scores. We ultimately find large and noteworthy naïve differences in letter length and content across nearly all demographic groups, many in alignment with known inequities (e.g., many more sentences about Athletics among White and higher-SES students, longer letters and more sentences on Personal Qualities for private school students). However, these differences vary drastically based on the exact controls and comparison groups included -- demonstrating that the ultimate implications of these letter differences for equity hinges on exactly how and when letters are used in admissions processes (e.g., among which groups of students are they used to "break ties"?). Findings do not point to a clear recommendation whether institutions should keep or discard letter requirements, but reflect the importance of reading letters and overall applications in the context of structural opportunity. We discuss additional implications and possible recommendations for college access and admissions policy/practice.

Recent advances in large language models (LLMs) have shown significant promise, yet their evaluation raises concerns, particularly regarding data contamination due to the lack of access to proprietary training data. To address this issue, we present C$^2$LEVA, a comprehensive bilingual benchmark featuring systematic contamination prevention. C$^2$LEVA firstly offers a holistic evaluation encompassing 22 tasks, each targeting a specific application or ability of LLMs, and secondly a trustworthy assessment due to our contamination-free tasks, ensured by a systematic contamination prevention strategy that fully automates test data renewal and enforces data protection during benchmark data release. Our large-scale evaluation of 15 open-source and proprietary models demonstrates the effectiveness of C$^2$LEVA.

After decades of experimental efforts, the DAMA/LIBRA(DL) annual modulation (AM) analysis on the ${\chi}$N (WIMP Dark Matter interactions on nucleus) channel remains the only one which can be interpreted as positive signatures. This has been refuted by numerous time-integrated (TI) and AM analysis. It has been shown that ${\chi}$e (WIMP interactions with electrons) alone is not compatible with the DL AM data. We expand the investigations by performing an AM analysis with the addition of ${\chi}$e long-range and short-range interactions to ${\chi}$N, derived using the frozen-core approximation method. Two scenarios are considered, where the ${\chi}$N and ${\chi}$e processes are due to a single ${\chi}$ (${\Gamma}^{1\chi}_{tot}$) or two different ${\chi}$s (${\Gamma}^{2\chi}_{tot}$). The combined fits with ${\chi}$N and ${\chi}$e provide stronger significance to the DL AM data which are compatible with the presence of additional physical effects beyond \c{hi}N alone. This is the first analysis which explores how ${\chi}$e AM can play a role in DL AM. The revised allowed regions as well as the exclusion contours from the other null AM experiments are presented. All DL AM allowed parameter spaces in ${\chi}$N and ${\chi}$e channels under both ${\Gamma}^{1\chi}_{tot}$ and ${\Gamma}^{2\chi}_{tot}$ are excluded at the 90\% confidence level by the combined null AM results. It can be projected that DL-allowed parameter spaces from generic models with interactions induced by two-WIMPs are ruled out., Comment: 6 pages, 5 figures

Memecoins, driven by social media engagement and cultural narratives, have rapidly grown within the Web3 ecosystem. Unlike traditional cryptocurrencies, they are shaped by humor, memes, and community sentiment. This paper introduces the Coin-Meme dataset, an open-source collection of visual, textual, community, and financial data from the Pump.fun platform on the Solana blockchain. We also propose a multimodal framework to analyze memecoins, uncovering patterns in cultural themes, community interaction, and financial behavior. Through clustering, sentiment analysis, and word cloud visualizations, we identify distinct thematic groups centered on humor, animals, and political satire. Additionally, we provide financial insights by analyzing metrics such as Market Entry Time and Market Capitalization, offering a comprehensive view of memecoins as both cultural artifacts and financial instruments within Web3. The Coin-Meme dataset is publicly available at https://github.com/hwlongCUHK/Coin-Meme.git., Comment: 4 pages, 2 figures, conference

Large language models (LLMs) are enabling designers to give life to exciting new user experiences for information access. In this work, we present a system that generates LLM personas to debate a topic of interest from different perspectives. How might information seekers use and benefit from such a system? Can centering information access around diverse viewpoints help to mitigate thorny challenges like confirmation bias in which information seekers over-trust search results matching existing beliefs? How do potential biases and hallucinations in LLMs play out alongside human users who are also fallible and possibly biased? Our study exposes participants to multiple viewpoints on controversial issues via a mixed-methods, within-subjects study. We use eye-tracking metrics to quantitatively assess cognitive engagement alongside qualitative feedback. Compared to a baseline search system, we see more creative interactions and diverse information-seeking with our multi-persona debate system, which more effectively reduces user confirmation bias and conviction toward their initial beliefs. Overall, our study contributes to the emerging design space of LLM-based information access systems, specifically investigating the potential of simulated personas to promote greater exposure to information diversity, emulate collective intelligence, and mitigate bias in information seeking.

Fluid antenna system (FAS) as a new version of reconfigurable antenna technologies promoting shape and position flexibility, has emerged as an exciting and possibly transformative technology for wireless communications systems. FAS represents any software-controlled fluidic, conductive or dielectric structure that can dynamically alter antenna's shape and position to change the gain, the radiation pattern, the operating frequency, and other critical radiation characteristics. With its capability, it is highly anticipated that FAS can contribute greatly to the upcoming sixth generation (6G) wireless networks. This article substantiates this thought by addressing four major questions: 1) Is FAS crucial to 6G? 2) How to characterize FAS? 3) What are the applications of FAS? 4) What are the relevant challenges and future research directions? In particular, five promising research directions that underscore the potential of FAS are discussed. We conclude this article by showcasing the impressive performance of FAS.

Automatic detection of prohibited items in X-ray images plays a crucial role in public security. However, existing methods rely heavily on labor-intensive box annotations. To address this, we investigate X-ray prohibited item detection under labor-efficient point supervision and develop an intra-inter objectness learning network (I$^2$OL-Net). I$^2$OL-Net consists of two key modules: an intra-modality objectness learning (intra-OL) module and an inter-modality objectness learning (inter-OL) module. The intra-OL module designs a local focus Gaussian masking block and a global random Gaussian masking block to collaboratively learn the objectness in X-ray images. Meanwhile, the inter-OL module introduces the wavelet decomposition-based adversarial learning block and the objectness block, effectively reducing the modality discrepancy and transferring the objectness knowledge learned from natural images with box annotations to X-ray images. Based on the above, I$^2$OL-Net greatly alleviates the problem of part domination caused by severe intra-class variations in X-ray images. Experimental results on four X-ray datasets show that I$^2$OL-Net can achieve superior performance with a significant reduction of annotation cost, thus enhancing its accessibility and practicality.

The broad-band radio spectral energy distribution (SED) of star-forming galaxies (SFGs) contains a wealth of complex physics. We aim to determine the physical emission and loss processes causing radio SED curvature and steepening to see which observed global astrophysical properties are correlated with radio SED complexity. We have acquired radio continuum data between 70 MHz and 17 GHz for a sample of 19 southern local (z < 0.04) SFGs. Of this sample 11 are selected to contain low-frequency (< 300 MHz) turnovers (LFTOs) in their SEDs and eight are control galaxies with similar global properties. We model the radio SEDs for our sample using a Bayesian framework whereby radio emission (synchrotron and free-free) and absorption or loss processes are included modularly. We find that without the inclusion of higher frequency data, single synchrotron power-law based models are always preferred for our sample; however, additional processes including free-free absorption (FFA) and synchrotron losses are often required to accurately model radio SED complexity in SFGs. The fitted synchrotron spectral indices range from -0.45 to -1.07 and are strongly anticorrelated with stellar mass suggesting that synchrotron losses are the dominant mechanism acting to steepen the spectral index in larger nearby SFGs. We find that LFTOs in the radio SED are independent from the inclination. The merging systems in our SFG sample have elevated specific star formation rates and flatter fitted spectral indices with unconstrained LFTOs. Lastly, we find no significant separation in global properties between SFGs with or without modelled LFTOs. Overall LFTOs are likely caused by a combination of FFA and ionisation losses in individual recent starburst regions with specific orientations and interstellar medium properties that, when averaged over the entire galaxy, do not correlate with global astrophysical properties., Comment: 34 pages, 18 figures, 12 tables. Accepted for publication in PASA

Unsupervised 3D representation learning via masked-and-reconstruction with differentiable rendering is promising to reduce the labeling burden for fusion 3D perception. However, previous literature conduct pre-training for different modalities separately because of the hight GPU memory consumption. Consequently, the interaction between the two modalities (images and point clouds) is neglected during pre-training. In this paper, we explore joint unsupervised pre-training for fusion 3D perception via differentiable rendering and propose CLAP, short for Curvature sampLing and swApping Prototype assignment prediction. The contributions are three-fold. 1) To overcome the GPU memory consumption problem, we propose Curvature Sampling to sample the more informative points/pixels for pre-training. 2) We propose to use learnable prototypes to represent parts of the scenes in a common feature space and bring the idea of swapping prototype assignment prediction to learn the interaction between the two modalities. 3) To further optimize learnable prototypes, we propose an Expectation-Maximization training scheme to maximize the similarity between embeddings and prototypes, followed by a Gram Matrix Regularization Loss to avoid collapse. Experiment results on NuScenes show that CLAP achieves 300% more performance gain as compared to previous SOTA 3D pre-training method via differentiable rendering. Codes and models will be released.

Labeling LiDAR point clouds is notoriously time-and-energy-consuming, which spurs recent unsupervised 3D representation learning methods to alleviate the labeling burden in LiDAR perception via pretrained weights. Almost all existing work focus on a single frame of LiDAR point cloud and neglect the temporal LiDAR sequence, which naturally accounts for object motion (and their semantics). Instead, we propose TREND, namely Temporal REndering with Neural fielD, to learn 3D representation via forecasting the future observation in an unsupervised manner. Unlike existing work that follows conventional contrastive learning or masked auto encoding paradigms, TREND integrates forecasting for 3D pre-training through a Recurrent Embedding scheme to generate 3D embedding across time and a Temporal Neural Field to represent the 3D scene, through which we compute the loss using differentiable rendering. To our best knowledge, TREND is the first work on temporal forecasting for unsupervised 3D representation learning. We evaluate TREND on downstream 3D object detection tasks on popular datasets, including NuScenes, Once and Waymo. Experiment results show that TREND brings up to 90% more improvement as compared to previous SOTA unsupervised 3D pre-training methods and generally improve different downstream models across datasets, demonstrating that indeed temporal forecasting brings improvement for LiDAR perception. Codes and models will be released.

In this article, we study the density conjecture of Katz and Sarnak for $L$-functions of ad\'elic Hilbert modular forms and their convolutions. In particular, under the generalised Riemann hypothesis, we establish several instances supporting the conjecture and extending the works of Iwaniec-Luo-Sarnak and many others. For applications, we obtain an upper bound for the average order of $L$-functions of Hilbert modular forms at $s=\frac{1}{2}$ as well as a positive proportion of non-vanishing of certain Rankin-Selberg $L$-functions., Comment: 29 pages

Learning to forecast the trajectories of intelligent agents like pedestrians has caught more researchers' attention. Despite researchers' efforts, it remains a challenge to accurately account for social interactions among agents when forecasting, and in particular, to simulate such social modifications to future trajectories in an explainable and decoupled way. Inspired by the resonance phenomenon of vibration systems, we propose the Resonance (short for Re) model to forecast pedestrian trajectories as co-vibrations, and regard that social interactions are associated with spectral properties of agents' trajectories. It forecasts future trajectories as three distinct vibration terms to represent agents' future plans from different perspectives in a decoupled way. Also, agents' social interactions and how they modify scheduled trajectories will be considered in a resonance-like manner by learning the similarities of their trajectory spectrums. Experiments on multiple datasets, whether pedestrian or vehicle, have verified the usefulness of our method both quantitatively and qualitatively.

Understanding and anticipating human movement has become more critical and challenging in diverse applications such as autonomous driving and surveillance. The complex interactions brought by different relations between agents are a crucial reason that poses challenges to this task. Researchers have put much effort into designing a system using rule-based or data-based models to extract and validate the patterns between pedestrian trajectories and these interactions, which has not been adequately addressed yet. Inspired by how humans perceive social interactions with different level of relations to themself, this work proposes the GrouP ConCeption (short for GPCC) model composed of the Group method, which categorizes nearby agents into either group members or non-group members based on a long-term distance kernel function, and the Conception module, which perceives both visual and acoustic information surrounding the target agent. Evaluated across multiple datasets, the GPCC model demonstrates significant improvements in trajectory prediction accuracy, validating its effectiveness in modeling both social and individual dynamics. The qualitative analysis also indicates that the GPCC framework successfully leverages grouping and perception cues human-like intuitively to validate the proposed model's explainability in pedestrian trajectory forecasting., Comment: 15 pages, 10 figures, submitted to CVPR 2025

With the emergence of large-scale vision-language models, realistic radiology reports may be generated using only medical images as input guided by simple prompts. However, their practical utility has been limited due to the factual errors in their description of findings. In this paper, we propose a novel model for explainable fact-checking that identifies errors in findings and their locations indicated through the reports. Specifically, we analyze the types of errors made by automated reporting methods and derive a new synthetic dataset of images paired with real and fake descriptions of findings and their locations from a ground truth dataset. A new multi-label cross-modal contrastive regression network is then trained on this datsaset. We evaluate the resulting fact-checking model and its utility in correcting reports generated by several SOTA automated reporting tools on a variety of benchmark datasets with results pointing to over 40\% improvement in report quality through such error detection and correction.

Motivation: The three-dimensional (3D) organization of the genome plays a critical role in regulating gene expression and maintaining cellular homeostasis. Disruptions in this spatial organization can result in abnormal chromatin interactions, contributing to the development of various diseases including cancer. Advances in chromosome conformation capture technologies, such as Hi-C, have enabled researchers to study genome architecture at high resolution. However, the efficient visualization and interpretation of these complex datasets remain a major challenge, particularly when integrating genomic annotations and inter-chromosomal interactions. Results: We present gghic, an R package that extends the ggplot2 framework to enable intuitive and customizable visualization of genomic interaction data. gghic introduces novel layers for generating triangular heatmaps of chromatin interactions and annotating them with features such as chromatin loops, topologically associated domains (TADs), gene/transcript models, and data tracks (e.g., ChIP-seq signals). The package supports data from multiple chromosomes, facilitating the exploration of inter-chromosomal interactions. Built to integrate seamlessly with the R/Bioconductor ecosystem, gghic is compatible with widely used genomic data formats, including HiCExperiment and GInteractions objects. We demonstrate the utility of gghic by replicating a published figure showing a translocation event in T-cell acute lymphoblastic leukemia (T-ALL), highlighting its ability to integrate genomic annotations and generate publication-quality figures. Availability and implementation: The R package can be accessed at https://github.com/jasonwong-lab/gghic and is distributed under the GNU General Public License version 3.0.

Birth Apshyxia (BA) is a severe condition characterized by insufficient supply of oxygen to a newborn during the delivery. BA is one of the primary causes of neonatal death in the world. Although there has been a decline in neonatal deaths over the past two decades, the developing world, particularly sub-Saharan Africa, continues to experience the highest under-five (<5) mortality rates. While evidence-based methods are commonly used to detect BA in African healthcare settings, they can be subject to physician errors or delays in diagnosis, preventing timely interventions. Centralized Machine Learning (ML) methods demonstrated good performance in early detection of BA but require sensitive health data to leave their premises before training, which does not guarantee privacy and security. Healthcare institutions are therefore reluctant to adopt such solutions in Africa. To address this challenge, we suggest a federated learning (FL)-based software architecture, a distributed learning method that prioritizes privacy and security by design. We have developed a user-friendly and cost-effective mobile application embedding the FL pipeline for early detection of BA. Our Federated SVM model outperformed centralized SVM pipelines and Neural Networks (NN)-based methods in the existing literature, Comment: Poster at ACM compass 2024

In condensed matter systems, interactions between collective modes offer avenues for nonlinear coherent manipulation of coupled excitations and quantum phases. Antiferromagnets, with their inherently coupled magnon modes, provide a promising platform for nonlinear control of microscopic spin waves and macroscopic magnetization. However, nonlinear magnon-magnon interactions have been only partially elaborated, leaving key gaps in the prospects for potential ultrahigh-bandwidth magnonic signal processing. Here, we use a pair of intense terahertz pulses to sequentially excite two distinct coherent magnon modes in an antiferromagnet and find that the magnon mode with a lower frequency undergoes amplification when the higher-frequency mode is driven. We unveil the nonlinear excitation pathways of this stimulated parametric downconversion process by using polarization-selective two-dimensional terahertz spectroscopy. Our work provides fundamental insights into nonlinear magnonics in antiferromagnets, laying the groundwork for forthcoming spintronic and magnonic devices based on nonlinear magnon-magnon interactions.

In general relativity (GR), gravitational memory describes the lasting change in the separation and relative velocity of freely-falling detectors after the passage of gravitational waves (GWs). The phenomenon is intimately related to Weinberg's soft graviton theorems and the Bondi-Metzner-Sachs (BMS) symmetries at future null infinity. In this paper, we elucidate the relation between BMS transformations and the description of gravitational memory in synchronous coordinates, commonly used in GW detectors like LISA. We show that gravitational memory corresponds to large residual diffeomorphisms in this gauge, such as volume-preserving spatial rescalings. We derive the associated soft theorems, for both scattering amplitudes and equal-time (in-in) correlation functions. The latter are exactly the flat space analogues of the well-known cosmological consistency relations., Comment: 7 pages, 1 figure

This work introduces the first framework for reconstructing surgical dialogue from unstructured real-world recordings, which is crucial for characterizing teaching tasks. In surgical training, the formative verbal feedback that trainers provide to trainees during live surgeries is crucial for ensuring safety, correcting behavior immediately, and facilitating long-term skill acquisition. However, analyzing and quantifying this feedback is challenging due to its unstructured and specialized nature. Automated systems are essential to manage these complexities at scale, allowing for the creation of structured datasets that enhance feedback analysis and improve surgical education. Our framework integrates voice activity detection, speaker diarization, and automated speech recaognition, with a novel enhancement that 1) removes hallucinations (non-existent utterances generated during speech recognition fueled by noise in the operating room) and 2) separates speech from trainers and trainees using few-shot voice samples. These aspects are vital for reconstructing accurate surgical dialogues and understanding the roles of operating room participants. Using data from 33 real-world surgeries, we demonstrated the system's capability to reconstruct surgical teaching dialogues and detect feedback instances effectively (F1 score of 0.79+/-0.07). Moreover, our hallucination removal step improves feedback detection performance by ~14%. Evaluation on downstream clinically relevant tasks of predicting Behavioral Adjustment of trainees and classifying Technical feedback, showed performances comparable to manual annotations with F1 scores of 0.82+/0.03 and 0.81+/0.03 respectively. These results highlight the effectiveness of our framework in supporting clinically relevant tasks and improving over manual methods., Comment: Accepted as a proceedings paper at Machine Learning for Health 2024

Type Ia supernovae, critical for studying cosmic expansion, arise from thermonuclear explosions of white dwarfs, but their precise progenitor pathways remain unclear. Growing evidence supports the ``double-degenerate'' scenario, where two white dwarfs interact. The absence of other companion types capable of explaining the observed Ia rate, along with observations of hyper-velocity white dwarfs interpreted as surviving companions of such systems provide compelling evidence in favor of this scenario. Upcoming millihertz gravitational wave observatories like the Laser Interferometer Space Antenna (LISA) are expected to detect thousands of double-degenerate systems, though the most compact known candidate Ia progenitors produce only marginally detectable gravitational wave signals. Here, we report observations of ATLAS J1138-5139, a binary white dwarf system with an orbital period of 28 minutes. Our analysis reveals a 1 solar mass carbon-oxygen white dwarf accreting from a helium-core white dwarf. Given its mass, the accreting carbon-oxygen white dwarf is poised to trigger a typical-luminosity Type Ia supernova within a few million years, or to evolve into a stably mass-transferring AM CVn system. ATLAS J1138-5139 provides a rare opportunity to calibrate binary evolution models by directly comparing observed orbital parameters and mass transfer rates closer to merger than any previously identified candidate Type Ia progenitor. Its compact orbit ensures detectability by LISA, demonstrating the potential of millihertz gravitational wave observatories to reveal a population of Type Ia progenitors on a Galactic scale, paving the way for multi-messenger studies offering insights into the origins of these cosmologically significant explosions., Comment: 40 pages, 7 figures, 2 tables

In recent years, a wide range of mortality models has been proposed to address the diverse factors influencing mortality rates, which has highlighted the need to perform model selection. Traditional mortality model selection methods, such as AIC and BIC, often require fitting multiple models independently and ranking them based on these criteria. This process can fail to account for uncertainties in model selection, which can lead to overly optimistic prediction interval, and it disregards the potential insights from combining models. To address these limitations, we propose a novel Bayesian model selection framework that integrates model selection and parameter estimation into the same process. This requires creating a model building framework that will give rise to different models by choosing different parametric forms for each term. Inference is performed using the reversible jump Markov chain Monte Carlo algorithm, which is devised to allow for transition between models of different dimensions, as is the case for the models considered here. We develop modelling frameworks for data stratified by age and period and for data stratified by age, period and product. Our results are presented in two case studies.

The p-type point-contact germanium (pPCGe) detectors have been widely adopted in searches for low energy physics events such as neutrinos and dark matter. This is due to their enhanced capabilities of background rejection, sensitivity at energies as low as the sub-keV range and particularly fine energy resolution. Nonetheless, the pPCGe is subject to irregular behaviour caused by surface effects for events near the passivated surface. These surface events can, in general, be distinguished from events that occur in the germanium crystal bulk by its slower pulse rise time. Unfortunately, the rise-time spectra of bulk and surface events starts to convolve with each other at sub-keV energies. In this work, we propose a novel method based on cross-correlation shape-matching combined with a low-pass filter to constrain the initial parameter estimates of the signal pulse. This improvement at the lowest level leads to a 50% reduction in computation time and refinements in the rise-time resolution, which will, in the end, enhance the overall analysis. To evaluate the performance of the method, we simulate artificial pulses that resembles bulk and surface pulses by using a programmable pulse generator module (pulser). The pulser-generated pulses are then used to examine the pulse behaviours at near-threshold energies, suggesting a roughly 70% background-leakage reduction in the bulk spectrum. Finally, the method is tested on data collected from the TEXONO experiment, where the results are consistent with our observations in pulser and demonstrated the possibility of lowering the analysis threshold by at least 10eV.

Neutrino nucleus elastic scattering ({\nu}Ael) with reactor neutrinos is an interaction under full quantum-mechanical coherence. It has not yet been experimentally observed. We present new results on the studies of {\nu}Ael cross section with an electro-cooled p-type point-contact germanium detector at the Kuo-Sheng Reactor Neutrino laboratory. A total of (242)357 kg-days of Reactor ON(OFF) data at a detector threshold of 200 eVee in electron equivalent unit are analyzed. The Lindhard model parametrized by a single variable k which characterizes the quenching function was used. Limits at 90% confidence level are derived on the ratio {\rho} relative to standard model (SM) cross section of {\rho}<4.7 at the predicted value of k=0.162, while k<0.285 at the SM-value of {\rho}=1. Prospects on future positive measurements are discussed., Comment: 6 pages, 4 figures

Scintillators, which convert high-energy radiation into detectable photons, play a crucial role in medical imaging and security applications. The enhancement of scintillator performance through nanophotonics and nanoplasmonics, specifically using the Purcell effect, has shown promise but has so far been limited to ultrathin scintillator films due to the localized nature of this effect. In this study, we present a method to extend nanoplasmonic scintillators to the bulk regime. By integrating 100-nm-size plasmonic spheroid and cuboid nanoparticles with perovskite scintillator nanocrystals, we enable nanoplasmonic scintillators to function effectively within bulk-scale devices. We experimentally demonstrate power and decay rate enhancements of up to (3.20 $\pm$ 0.20) and (4.20 $\pm$ 0.31) fold for plasmonic spheroid and cuboid nanoparticles, respectively, in a 5-mm thick CsPbBr$_3$ nanocrystal-polymer scintillator at RT. Theoretical modeling further predicts similar enhancements of up to (2.63 $\pm$ 0.79) and (5.62 $\pm$ 1.71) fold for the same nanoparticle shapes and dimensions. These findings provide a viable pathway for using nanoplasmonics to enhance bulk scintillator devices, advancing radiation detection technology., Comment: 42 pages with 14 figures, split between main text and supporting information. This is a full-length research article

Objective: Proton therapy is a precision-focused cancer treatment where accurate proton beam range monitoring is critical to ensure effective dose delivery. This can be achieved by prompt gamma detection with a Compton camera like the SiFi-CC. This study aims to show the feasibility of optimising the geometry of SiFi-CC Compton camera for verification of dose distribution via prompt gamma detection using a genetic algorithm (GA). Approach: The SiFi-CC key geometric parameters for optimisation with the GA are the source-to-scatterer and scatterer-to-absorber distances, and the module thicknesses. The optimisation process was conducted with a software framework based on the Geant4 toolkit, which included detailed and realistic modelling of gamma interactions, detector response, and further steps such as event selection and image reconstruction. The performance of each individual configuration was evaluated using a fitness function incorporating factors related to gamma detection efficiency and image resolution. Results: The GA-optimised SiFi-CC configuration demonstrated the capability to detect a 5 mm proton beam range shift with a 2 mm resolution using 5e8 protons. The best-performing geometry, with 16 fibre layers in the scatterer, 36 layers in the absorber, source-to-scatterer distance 150 mm and scatterer-to-absorber distance 120 mm, has an imaging sensitivity of 5.58(1)e-5. Significance: This study demonstrates that the SiFi-CC setup, optimised through a GA, can reliably detect clinically relevant proton beam range shifts, improving real-time range verification accuracy in proton therapy. The presented implementation of a GA is a systematic and feasible way of searching for a SiFi-CC geometry that shows the best performance., Comment: 10 figures, 3 tables

Software fault localization remains challenging due to limited feature diversity and low precision in traditional methods. This paper proposes a novel approach that integrates multi-objective optimization with deep learning models to improve both accuracy and efficiency in fault localization (FL). By framing feature selection as a multi-objective optimization problem (MOP), we extract and fuse three critical fault-related feature sets: spectrum-based, mutation-based, and text-based features, into a comprehensive feature fusion model. These features are then embedded within a deep learning architecture, comprising a multilayer perceptron (MLP) and gated recurrent network (GRN), which together enhance localization accuracy and generalizability. Experiments on the Defects4J benchmark dataset with 434 faults show that the proposed algorithm reduces processing time by 78.2% compared to single-objective methods. Additionally, our MLP and GRN models achieve a 94.2% improvement in localization accuracy compared to traditional FL methods, outperforming state-of-the-art deep learning-based FL method by 7.67%. Further validation using the PROMISE dataset demonstrates the generalizability of the proposed model, showing a 4.6% accuracy improvement in cross-project tests over state-of-the-art deep learning-based FL method.

In the domain of continuous control, deep reinforcement learning (DRL) demonstrates promising results. However, the dependence of DRL on deep neural networks (DNNs) results in the demand for extensive data and increased computational complexity. To address this issue, a novel hybrid architecture for actor-critic reinforcement learning (RL) algorithms is introduced. The proposed architecture integrates the broad learning system (BLS) with DNN, aiming to merge the strengths of both distinct architectural paradigms. Specifically, the critic network is implemented using BLS, while the actor network is constructed with a DNN. For the estimations of the critic network parameters, ridge regression is employed, and the parameters of the actor network are optimized through gradient descent. The effectiveness of the proposed algorithm is evaluated by applying it to two classic continuous control tasks, and its performance is compared with the widely recognized deep deterministic policy gradient (DDPG) algorithm. Numerical results show that the proposed algorithm is superior to the DDPG algorithm in terms of computational efficiency, along with an accelerated learning trajectory. Application of the proposed algorithm in other actor-critic RL algorithms is suggested for investigation in future studies., Comment: 7 pages

This paper explores the potential of leveraging language priors learned by text-to-image diffusion models to address ambiguity and visual nuisance in monocular depth estimation. Particularly, traditional monocular depth estimation suffers from inherent ambiguity due to the absence of stereo or multi-view depth cues, and nuisance due to lack of robustness of vision. We argue that language prior in diffusion models can enhance monocular depth estimation by leveraging the geometric prior aligned with the language description, which is learned during text-to-image pre-training. To generate images that reflect the text properly, the model must comprehend the size and shape of specified objects, their spatial relationship, and the scale of the scene. Thus, we propose PriorDiffusion, using a pre-trained text-to-image diffusion model that takes both image and text description that aligned with the scene to infer affine-invariant depth through a denoising process. We also show that language priors can guide the model's attention to specific regions and help it perceive the 3D scene in alignment with user intent. Simultaneously, it acts as a constraint to accelerate the convergence of the diffusion trajectory, since learning 3D properties from a condensed, low-dimensional language feature is more efficient compared with learning from a redundant, high-dimensional image feature. By training on HyperSim and Virtual KITTI, we achieve state-of-the-art zero-shot performance and a faster convergence speed, compared with other diffusion-based depth estimators, across NYUv2, KITTI, ETH3D, and ScanNet.

Gravitational Wave (GW) detectors routinely encounter transient noise bursts, known as glitches, which are caused by either instrumental or environmental factors. Due to their high occurrence rate, glitches can overlap with GW signals, as in the notable case of GW170817, the first detection of a binary neutron star merger. Accurate reconstruction and subtraction of these glitches is a challenging problem that must be addressed to ensure that scientific conclusions drawn from the data are reliable. This problem will intensify with third-generation observatories like the Einstein Telescope (ET) due to their higher detection rates of GWs and the longer duration of signals within the sensitivity band of the detectors. Robust glitch mitigation algorithms are, therefore, crucial for maximizing the scientific output of next-generation GW observatories. For the first time, we demonstrate how the null stream inherent in ET's unique triangular configuration can be leveraged by state-of-the-art glitch characterization methodology to essentially undo the effect of glitches for the purpose of estimating the parameters of the source. The null stream based approach enables characterization and subtraction of glitches that occur arbitrarily close to the peak of the signal without any significant effect on the quality of parameter measurements, and achieves an order of magnitude computational speed-up compared to when the null stream is not available. By contrast, without the null stream, significant biases can occur in the glitch reconstruction, which deteriorate the quality of subsequent measurements of the source parameters. This demonstrates a clear edge which the null stream can offer for precision GW science in the ET era.

PbSe is a narrow bandgap IV-VI compound semiconductor with application in mid-wave infrared optoelectronics, thermoelectrics, and quantum devices. Alkaline earth or rare earth elements such as Sr and Eu can substitute Pb to widen the bandgap of PbSe in heterostructure devices, but they come with challenges such as deteriorating optical and electronic properties, even in dilute concentrations due to their dissimilar atomic nature. We substitute Pb instead with column-IV Ge and assess the potential of rocksalt phase PbGeSe as a wider bandgap semiconductor in thin films grown by molecular beam epitaxy on GaAs substrates. Low sticking of GeSe adatoms requires synthesis temperatures below 260 {\deg}C to incorporate Ge, but this yields poor structural and compositional uniformity as determined by X-ray diffraction. Consequently, as-grown films in the range Pb0.94Ge0.06Se to Pb0.83Ge0.17Se (6-17% Ge) show much less bandgap widening in photoluminescence than prior work on bulk crystals using absorption. We observe that post-growth rapid thermal annealing at temperatures of 375-450 {\deg}C improves the crystal quality and recovers bandgap widening. Rapid interdiffusion of Ge during annealing, however, remains a challenge in harnessing such PbGeSe materials for compositionally sharp heterostructures. Annealed 17%-Ge films emit light at 3-3.1 um with minimal shift in wavelength versus temperature. These samples are wider in bandgap than PbSe films by 55 meV at room temperature and the widening increases to 160 meV at 80 K, thanks to sharply different dependence of bandgap on temperature in PbSe and PbGeSe.

Efforts to reduce platelet wastage in hospital blood banks have focused on ordering policies, but the predominant practice of issuing the oldest unit first may not be optimal when some units are returned unused. We propose a novel, machine learning (ML)-guided issuing policy to increase the likelihood of returned units being reissued before expiration. Our ML model trained to predict returns on 17,297 requests for platelets gave AUROC 0.74 on 9,353 held-out requests. Prior to ML model development we built a simulation of the blood bank operation that incorporated returns to understand the scale of benefits of such a model. Using our trained model in the simulation gave an estimated reduction in wastage of 14%. Our partner hospital is considering adopting our approach, which would be particularly beneficial for hospitals with higher return rates and where units have a shorter remaining useful life on arrival.

For a connected graph $G$, we denote by $L(G)$, $m_{G}(\lambda)$, $c(G)$ and $p(G)$ the line graph of $G$, the eigenvalue multiplicity of $\lambda$ in $G$, the cyclomatic number and the number of pendant vertices in $G$, respectively. In 2023, Yang et al. \cite{WL LT} proved that $m_{L(T)}(\lambda)\leq p(T)-1$ for any tree $T$ with $p(T)\geq 3$, and characterized all trees $T$ with $m_{L(T)}(\lambda) = p(T)-1$. In 2024, Chang et al. \cite{-1 LG} proved that, if $G$ is not a cycle, then $m_{L(G)}(\lambda)\leq 2c(G)+p(G)-1$, and characterized all graphs $G$ with $m_{L(G)}(-1) = 2c(G)+p(G)-1$. The remaining ploblem is to characterize all graphs $G$ with $m_{L(G)}(\lambda)= 2c(G)+p(G)-1$ for an arbitrary eigenvalue $\lambda$ of $L(G)$. In this paper, we give this problem a complete solution.

Constructing high-quality Supervised Fine-Tuning (SFT) datasets is critical for the training of large language models (LLMs). Recent studies have shown that using data from a specific source, Ruozhiba, a Chinese website where users ask "silly" questions to better understand certain topics, can lead to better fine-tuning performance. This paper aims to explore some hidden factors: the potential interpretations of its success and a large-scale evaluation of the performance. First, we leverage GPT-4 to analyze the successful cases of Ruozhiba questions from the perspective of education, psychology, and cognitive science, deriving a set of explanatory rules. Then, we construct fine-tuning datasets by applying these rules to the MMLU training set. Surprisingly, our results indicate that rules can significantly improve model performance in certain tasks, while potentially diminishing performance on others. For example, SFT data generated following the "Counterintuitive Thinking" rule can achieve approximately a 5% improvement on the "Global Facts" task, whereas the "Blurring the Conceptual Boundaries" rule leads to a performance drop of 6.14% on the "Econometrics" task. In addition, for specific tasks, different rules tend to have a consistent impact on model performance. This suggests that the differences between the extracted rules are not as significant, and the effectiveness of the rules is relatively consistent across tasks. Our research highlights the importance of considering task diversity and rule applicability when constructing SFT datasets to achieve more comprehensive performance improvements., Comment: 27 pages, 14 figures

We present in this work the first assessment of the previously unexplored effect of positronium formation on the value of the effective number of neutrino species in the Standard Model, $N_{\mathrm{eff}}^{\mathrm{SM}}$. We find that the expected impact on $N_{\mathrm{eff}}^{\mathrm{SM}}$ crucially depends on the temperature at which positronium equilibrates as well as the rate at which this equilibration happens. The dominant factor limiting the formation process is the melting of positronium at high temperatures driven by Debye screening of the electrostatic potential and scatterings with real photons. Using simple physical arguments, we estimate that positronium forms at temperatures around $50-80$ keV. If formation and equilibration were instantaneous, the resulting change in $N_{\mathrm{eff}}^{\mathrm{SM}}$ would be at most $|\Delta N_{\mathrm{eff}}| \sim 10^{-4}$, comparable to other uncertainties in the current benchmark value for $N_{\mathrm{eff}}^{\mathrm{SM}}$. A more gradual formation could however yield a larger change, necessitating in turn a more detailed investigation before we can conclusively neglect the effect of positronium formation on $N_{\mathrm{eff}}^{\mathrm{SM}}$., Comment: 16 pages, 2 figures, 2 tables

With the increasing sophistication of Advanced Persistent Threats (APTs), the demand for effective detection and mitigation strategies and methods has escalated. Program execution leaves traces in the system audit log, which can be analyzed to detect malicious activities. However, collecting and analyzing large volumes of audit logs over extended periods is challenging, further compounded by insufficient labeling that hinders their usability. Addressing these challenges, this paper introduces SAGA (Synthetic Audit log Generation for APT campaigns), a novel approach for generating find-grained labeled synthetic audit logs that mimic real-world system logs while embedding stealthy APT attacks. SAGA generates configurable audit logs for arbitrary duration, blending benign logs from normal operations with malicious logs based on the definitions the MITRE ATT\&CK framework. Malicious audit logs follow an APT lifecycle, incorporating various attack techniques at each stage. These synthetic logs can serve as benchmark datasets for training machine learning models and assessing diverse APT detection methods. To demonstrate the usefulness of synthetic audit logs, we ran established baselines of event-based technique hunting and APT campaign detection using various synthetic audit logs. In addition, we show that a deep learning model trained on synthetic audit logs can detect previously unseen techniques within audit logs.

We report the discovery of three ultracompact binary white dwarf systems hosting accretion disks, with orbital periods of 7.95, 8.68, and 13.15 minutes. This significantly augments the population of mass-transferring binaries at the shortest periods, and provides the first evidence that accretors in ultracompacts can be dense enough to host accretion disks even below 10 minutes (where previously only direct-impact accretors were known). In the two shortest-period systems, we measured changes in the orbital periods driven by the combined effect of gravitational wave emission and mass transfer; we find $\dot{P}$ is negative in one case, and positive in the other. This is only the second system measured with a positive $\dot{P}$, and it the most compact binary known that has survived a period minimum. Using these systems as examples, we show how the measurement of $\dot{P}$ is a powerful tool in constraining the physical properties of binaries, e.g. the mass and mass-radius relation of the donor stars. We find that the chirp masses of ultracompact binaries at these periods seem to cluster around $\mathcal{M}_c \sim 0.3 M_\odot$, perhaps suggesting a common origin for these systems or a selection bias in electromagnetic discoveries. Our new systems are among the highest-amplitude known gravitational wave sources in the millihertz regime, providing exquisite opportunity for multi-messenger study with future space-based observatories such as \textit{LISA} and TianQin; we discuss how such systems provide fascinating laboratories to study the unique regime where the accretion process is mediated by gravitational waves., Comment: Accepted for publication in ApJ