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Grading programming assignments is crucial for guiding students to improve their programming skills and coding styles. This study presents an automated grading framework, CodEv, which leverages Large Language Models (LLMs) to provide consistent and constructive feedback. We incorporate Chain of Thought (CoT) prompting techniques to enhance the reasoning capabilities of LLMs and ensure that the grading is aligned with human evaluation. Our framework also integrates LLM ensembles to improve the accuracy and consistency of scores, along with agreement tests to deliver reliable feedback and code review comments. The results demonstrate that the framework can yield grading results comparable to human evaluators, by using smaller LLMs. Evaluation and consistency tests of the LLMs further validate our approach, confirming the reliability of the generated scores and feedback.

Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering the single-harmonic and the dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is $6.4\!\times\!10^{-27}$ for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is $8.8\!\times\!10^{-9}$ for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of non-standard polarizations as predicted by the Brans-Dicke theory., Comment: main paper: 12 pages, 6 figures, 4 tables

This brief is designed for various stakeholders that work with youth with multiple system experiences, specifically for Title I, Part D State coordinators, State Agency and local program staff, Education for Homeless Children and Youth (EHCY) State coordinators and liaisons, and Title I, Part A Foster Care State Education Agencies (SEAs) and Local Education Agency (LEA) points of contact. It provides an overview of the intersection between youth experiences of homelessness, child welfare, and juvenile justice involvement and the federal education programs that support them. The goal of this brief is to encourage collaboration across federal programs and provide key resources to support this intersecting population. [This document was produced by Longevity Consulting.]

We present BreezyVoice, a Text-to-Speech (TTS) system specifically adapted for Taiwanese Mandarin, highlighting phonetic control abilities to address the unique challenges of polyphone disambiguation in the language. Building upon CosyVoice, we incorporate a $S^{3}$ tokenizer, a large language model (LLM), an optimal-transport conditional flow matching model (OT-CFM), and a grapheme to phoneme prediction model, to generate realistic speech that closely mimics human utterances. Our evaluation demonstrates BreezyVoice's superior performance in both general and code-switching contexts, highlighting its robustness and effectiveness in generating high-fidelity speech. Additionally, we address the challenges of generalizability in modeling long-tail speakers and polyphone disambiguation. Our approach significantly enhances performance and offers valuable insights into the workings of neural codec TTS systems.

Semantic communications aim to enhance transmission efficiency by jointly optimizing source coding, channel coding, and modulation. While prior research has demonstrated promising performance in simulations, real-world implementations often face significant challenges, including noise variability and nonlinear distortions, leading to performance gaps. This article investigates these challenges in a multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM)-based semantic communication system, focusing on the practical impacts of power amplifier (PA) nonlinearity and peak-to-average power ratio (PAPR) variations. Our analysis identifies frequency selectivity of the actual channel as a critical factor in performance degradation and demonstrates that targeted mitigation strategies can enable semantic systems to approach theoretical performance. By addressing key limitations in existing designs, we provide actionable insights for advancing semantic communications in practical wireless environments. This work establishes a foundation for bridging the gap between theoretical models and real-world deployment, highlighting essential considerations for system design and optimization., Comment: 7 pages, 5 figures

Multiple-input multiple-output (MIMO) communication has led to immense enhancements in data rates and efficient spectrum management. The evolution of MIMO has been accompanied by increased hardware complexity and array sizes, causing system power consumption to rise as a result. Despite past advances in power-efficient hybrid architectures, new solutions are needed to enable extremely large-scale MIMO deployments for 6G and beyond. In this paper, we introduce a novel architecture that integrates low-power reconfigurable antennas with both digital and analog precoding. This \emph{tri-hybrid} approach addresses key limitations in traditional and hybrid MIMO systems by improving power consumption and adding new layer for signal processing. We provide a comprehensive analysis of the proposed architecture and compare its performance with existing solutions, including fully-digital and hybrid MIMO systems. The results demonstrate significant improvements in energy efficiency, highlighting the potential of the tri-hybrid system to meet the growing demands of future wireless networks. We also discuss several design and implementation challenges, including the need for technological advancements in reconfigurable array hardware and tunable antenna parameters., Comment: IEEE Transactions on Communications (invited)

Entropy stable discontinuous Galerkin (DG) methods improve the robustness of high order DG simulations of nonlinear conservation laws. These methods yield a semi-discrete entropy inequality, and rely on an algebraic flux differencing formulation which involves both summation-by-parts (SBP) discretization matrices and entropy conservative two-point finite volume fluxes. However, explicit expressions for such two-point finite volume fluxes may not be available for all systems, or may be computationally expensive to compute. This paper proposes an alternative approach to constructing entropy stable DG methods using an artificial viscosity coefficient based on the local violation of a cell entropy inequality and the local entropy dissipation. The resulting method recovers the same global semi-discrete entropy inequality that is satisfied by entropy stable flux differencing DG methods. The artificial viscosity coefficients are parameter-free and locally computable over each cell, and the resulting artificial viscosity preserves both high order accuracy and a hyperbolic maximum stable time-step size under explicit time-stepping.

Systematic literature review is essential for evidence-based medicine, requiring comprehensive analysis of clinical trial publications. However, the application of artificial intelligence (AI) models for medical literature mining has been limited by insufficient training and evaluation across broad therapeutic areas and diverse tasks. Here, we present LEADS, an AI foundation model for study search, screening, and data extraction from medical literature. The model is trained on 633,759 instruction data points in LEADSInstruct, curated from 21,335 systematic reviews, 453,625 clinical trial publications, and 27,015 clinical trial registries. We showed that LEADS demonstrates consistent improvements over four cutting-edge generic large language models (LLMs) on six tasks. Furthermore, LEADS enhances expert workflows by providing supportive references following expert requests, streamlining processes while maintaining high-quality results. A study with 16 clinicians and medical researchers from 14 different institutions revealed that experts collaborating with LEADS achieved a recall of 0.81 compared to 0.77 experts working alone in study selection, with a time savings of 22.6%. In data extraction tasks, experts using LEADS achieved an accuracy of 0.85 versus 0.80 without using LEADS, alongside a 26.9% time savings. These findings highlight the potential of specialized medical literature foundation models to outperform generic models, delivering significant quality and efficiency benefits when integrated into expert workflows for medical literature mining.

We study the existence, uniqueness, and regularity of weak solutions to a class of obstacle problems, where the obstacle condition can be imposed on a subset of the domain. In particular, we establish the optimal H\"older regularity for Signorini-type problems, that is, the obstacle condition is imposed only on a subset of codimension one. For this purpose, we employ capacities, Alt--Caffarelli--Friedman-type and Almgren-type monotonicity formulae, and investigate an associated mixed boundary value problem. Further, we apply this problem to study classical obstacle problems for irregular obstacles., Comment: 35 pages, 1 figure

We introduce the problem of formally verifying properties of Markov processes where the parameters are the output of machine learning models. Our formulation is general and solves a wide range of problems, including verifying properties of probabilistic programs that use machine learning, and subgroup analysis in healthcare modeling. We show that for a broad class of machine learning models, including linear models, tree-based models, and neural networks, verifying properties of Markov chains like reachability, hitting time, and total reward can be formulated as a bilinear program. We develop a decomposition and bound propagation scheme for solving the bilinear program and show through computational experiments that our method solves the problem to global optimality up to 100x faster than state-of-the-art solvers. We also release $\texttt{markovml}$, an open-source tool for building Markov processes, integrating pretrained machine learning models, and verifying their properties, available at https://github.com/mmaaz-git/markovml., Comment: 8 pages (main manuscript), 3 figures, 1 table

The rapid increase in the parameter counts of Large Language Models (LLMs), reaching billions or even trillions, presents significant challenges for their practical deployment, particularly in resource-constrained environments. To ease this issue, we propose PIP (Perturbation-based Iterative Pruning), a novel double-view structured pruning method to optimize LLMs, which combines information from two different views: the unperturbed view and the perturbed view. With the calculation of gradient differences, PIP iteratively prunes those that struggle to distinguish between these two views. Our experiments show that PIP reduces the parameter count by approximately 20% while retaining over 85% of the original model's accuracy across varied benchmarks. In some cases, the performance of the pruned model is within 5% of the unpruned version, demonstrating PIP's ability to preserve key aspects of model effectiveness. Moreover, PIP consistently outperforms existing state-of-the-art (SOTA) structured pruning methods, establishing it as a leading technique for optimizing LLMs in environments with constrained resources. Our code is available at: https://github.com/caoyiiiiii/PIP.

We present a 5 month NICER X-ray monitoring campaign for two low luminosity active galactic nuclei (LLAGNs) -- NGC 4594 and IC 1459 -- with complementary Swift and NuSTAR observations. Utilizing an absorbed power law and thermal source model combined with NICER's SCORPEON background model, we demonstrate the effectiveness of joint source/background modeling for constraining emission from faint, background-dominated targets. Both sources are dominated by nuclear power law emission with photon indices $\Gamma \sim 1.5 - 2$, with NGC 4594 being slightly harder than IC 1459. The thermal contribution in both sources is fainter, but constant, with $kT \sim 0.5$ keV ($\sim 5 \times 10^6$ K). The power law flux and $\Gamma$ are strongly anti-correlated in both sources, as has been seen for other LLAGNs with radiatively inefficient accretion flows. NGC 4594 is the brighter source and exhibits significant aperiodic variability. Its variability timescale with an upper limit of $5 - 7$ days indicates emission originating from $< 100 R_{g}$, at the scale of the inner accretion flow. A spectral break found at $\sim 6$ keV, while tentative, could arise from synchrotron/inverse compton emission. This high-cadence LLAGN X-ray monitoring campaign underlines the importance of multi-wavelength variability studies for a sample of LLAGNs to truly understand their accretion and outflow physics., Comment: 18 pages, 5 figures, 5 tables. Accepted to ApJ

Automated grading has become an essential tool in education technology due to its ability to efficiently assess large volumes of student work, provide consistent and unbiased evaluations, and deliver immediate feedback to enhance learning. However, current systems face significant limitations, including the need for large datasets in few-shot learning methods, a lack of personalized and actionable feedback, and an overemphasis on benchmark performance rather than student experience. To address these challenges, we propose a Zero-Shot Large Language Model (LLM)-Based Automated Assignment Grading (AAG) system. This framework leverages prompt engineering to evaluate both computational and explanatory student responses without requiring additional training or fine-tuning. The AAG system delivers tailored feedback that highlights individual strengths and areas for improvement, thereby enhancing student learning outcomes. Our study demonstrates the system's effectiveness through comprehensive evaluations, including survey responses from higher education students that indicate significant improvements in motivation, understanding, and preparedness compared to traditional grading methods. The results validate the AAG system's potential to transform educational assessment by prioritizing learning experiences and providing scalable, high-quality feedback.

We examine the quasinormal modes exhibited by a massive scalar test field carrying an electric charge, oscillating in the outer region of a Reissner-Nordstr\"om de Sitter black hole. We examine the quasinormal mode effective potential throughout the black hole mass-charge phase space, finding a single-peaked barrier potential on $r_+ < r < r_c$ for all non-extremised black hole solutions for $\ell \geq 1$. Unlike in the Schwarzschild background, increasing scalar field mass heightens the peak of this barrier potential, while increasing the scalar field charge suppresses it. We compute the corresponding quasinormal frequency spectrum using a WKB-based semi-classical method, where, like the Schwarzschild case, we observe anomalous QNM damping behaviour for small scalar field mass below some critical mass $\mu_{crit}$, which is $\ell$-independent for $q=0$., Comment: 29 pages, 9 figures. This manuscript supersedes arXiv:2310.07311

Llama-Breeze2 (hereinafter referred to as Breeze2) is a suite of advanced multi-modal language models, available in 3B and 8B parameter configurations, specifically designed to enhance Traditional Chinese language representation. Building upon the Llama 3.2 model family, we continue the pre-training of Breeze2 on an extensive corpus to enhance the linguistic and cultural heritage of Traditional Chinese. In addition to language modeling capabilities, we significantly augment the models with function calling and vision understanding capabilities. At the time of this publication, as far as we are aware, absent reasoning-inducing prompts, Breeze2 are the strongest performing models in Traditional Chinese function calling and image understanding in its size class. The effectiveness of Breeze2 is benchmarked across various tasks, including Taiwan general knowledge, instruction-following, long context, function calling, and vision understanding. We are publicly releasing all Breeze2 models under the Llama 3.2 Community License. We also showcase the capabilities of the model running on mobile platform with a mobile application which we also open source.

Penrose's Strong Cosmic Censorship conjecture safeguards determinism in General Relativity. Within the initial value approach to General Relativity, proof of Strong Cosmic Censorship preservation is predicated on the unique evolution of the metric. For the Kerr-Newman family of black hole solutions, this requires the inextendability of the metric past the Cauchy horizon, due to the development of a "blue-shift" instability. Attempts to provide a rigorous mathematical proof of Strong Cosmic Censorship has led to the formulation of several Strong Cosmic Censorship conjectures of varying strengths, which seem to be discussed rarely outside of the mathematical relativity literature. In this note, we review some of the arguments for and against Strong Cosmic Censorship preservation, with a focus on the Reissner-Nordstr\"om de Sitter context, where the positive cosmological constant invites a "red-shift" effect that competes against the "blue-shift". We study the consequent role of quasinormal mode behaviour and illustrate the parameter space for which we consistently observe violations of the Strong Cosmic Censorship conjecture within Reissner-Nordstr\"om de Sitter black holes., Comment: 30 pages, 8 figures. This manuscript has some overlap with arXiv:2310.07311 and an ICHEP2024 proceeding available at https://doi.org/10.22323/1.476.0782

We present measurements of large-scale cosmic microwave background (CMB) E-mode polarization from the Cosmology Large Angular Scale Surveyor (CLASS) 90 GHz data. Using 115 det-yr of observations collected through 2024 with a variable-delay polarization modulator, we achieved a polarization sensitivity of $78\,\mathrm{\mu K\,arcmin}$, comparable to Planck at similar frequencies (100 and 143 GHz). The analysis demonstrates effective mitigation of systematic errors and addresses challenges to large-angular-scale power recovery posed by time-domain filtering in maximum-likelihood map-making. A novel implementation of the pixel-space transfer matrix is introduced, which enables efficient filtering simulations and bias correction in the power spectrum using the quadratic cross-spectrum estimator. Overall, we achieved an unbiased time-domain filtering correction to recover the largest angular scale polarization, with the only power deficit, arising from map-making non-linearity, being characterized as less than $3\%$. Through cross-correlation with Planck, we detected the cosmic reionization at $99.4\%$ significance and measured the reionization optical depth $\tau=0.053^{+0.018}_{-0.019}$, marking the first ground-based attempt at such a measurement. At intermediate angular scales ($\ell>30$), our results, both independently and in cross-correlation with Planck, remain fully consistent with Planck's measurements., Comment: 24 pages, 19 figures, 3 tables; submitted to ApJ

3D Gaussian Splatting (3DGS) has shown remarkable performance in novel view synthesis. However, its rendering quality deteriorates with sparse inphut views, leading to distorted content and reduced details. This limitation hinders its practical application. To address this issue, we propose a sparse-view 3DGS method. Given the inherently ill-posed nature of sparse-view rendering, incorporating prior information is crucial. We propose a semantic regularization technique, using features extracted from the pretrained DINO-ViT model, to ensure multi-view semantic consistency. Additionally, we propose local depth regularization, which constrains depth values to improve generalization on unseen views. Our method outperforms state-of-the-art novel view synthesis approaches, achieving up to 0.4dB improvement in terms of PSNR on the LLFF dataset, with reduced distortion and enhanced visual quality., Comment: 5 pages, 5 figures, has been accepted by the ICASSP 2025

Introduction. Objective Structured Clinical Examinations (OSCEs) are widely used to assess medical students' communication skills, but scoring interview-based assessments is time-consuming and potentially subject to human bias. This study explored the potential of large language models (LLMs) to automate OSCE evaluations using the Master Interview Rating Scale (MIRS). Methods. We compared the performance of four state-of-the-art LLMs (GPT-4o, Claude 3.5, Llama 3.1, and Gemini 1.5 Pro) in evaluating OSCE transcripts across all 28 items of the MIRS under the conditions of zero-shot, chain-of-thought (CoT), few-shot, and multi-step prompting. The models were benchmarked against a dataset of 10 OSCE cases with 174 expert consensus scores available. Model performance was measured using three accuracy metrics (exact, off-by-one, thresholded). Results. Averaging across all MIRS items and OSCE cases, LLMs performed with low exact accuracy (0.27 to 0.44), and moderate to high off-by-one accuracy (0.67 to 0.87) and thresholded accuracy (0.75 to 0.88). A zero temperature parameter ensured high intra-rater reliability ($\alpha = 0.98$ for GPT-4o). CoT, few-shot, and multi-step techniques proved valuable when tailored to specific assessment items. The performance was consistent across MIRS items independent of encounter phases and communication domains. Conclusion. We demonstrated the feasibility of AI-assisted OSCE evaluation and provided benchmarking of multiple LLMs across multiple prompt techniques. Our work provides a baseline performance assessment for LLMs that lays a foundation for future research in automated assessment of clinical communication skills., Comment: 11 pages, 4 figures (+3 figures in supplementary appendix)

Various benchmarks have been proposed to assess the performance of large language models (LLMs) in different coding scenarios. We refer to them as code-related benchmarks. However, there are no systematic guidelines by which such a benchmark should be developed to ensure its quality, reliability, and reproducibility. We propose How2Bench, which is comprised of a 55- 55-criteria checklist as a set of guidelines to govern the development of code-related benchmarks comprehensively. Using HOW2BENCH, we profiled 274 benchmarks released within the past decade and found concerning issues. Nearly 70% of the benchmarks did not take measures for data quality assurance; over 10% did not even open source or only partially open source. Many highly cited benchmarks have loopholes, including duplicated samples, incorrect reference codes/tests/prompts, and unremoved sensitive/confidential information. Finally, we conducted a human study involving 49 participants, which revealed significant gaps in awareness of the importance of data quality, reproducibility, and transparency., Comment: 42 pages

Increasingly many AI systems can plan and execute interactions in open-ended environments, such as making phone calls or buying online goods. As developers grow the space of tasks that such AI agents can accomplish, we will need tools both to unlock their benefits and manage their risks. Current tools are largely insufficient because they are not designed to shape how agents interact with existing institutions (e.g., legal and economic systems) or actors (e.g., digital service providers, humans, other AI agents). For example, alignment techniques by nature do not assure counterparties that some human will be held accountable when a user instructs an agent to perform an illegal action. To fill this gap, we propose the concept of agent infrastructure: technical systems and shared protocols external to agents that are designed to mediate and influence their interactions with and impacts on their environments. Agent infrastructure comprises both new tools and reconfigurations or extensions of existing tools. For example, to facilitate accountability, protocols that tie users to agents could build upon existing systems for user authentication, such as OpenID. Just as the Internet relies on infrastructure like HTTPS, we argue that agent infrastructure will be similarly indispensable to ecosystems of agents. We identify three functions for agent infrastructure: 1) attributing actions, properties, and other information to specific agents, their users, or other actors; 2) shaping agents' interactions; and 3) detecting and remedying harmful actions from agents. We propose infrastructure that could help achieve each function, explaining use cases, adoption, limitations, and open questions. Making progress on agent infrastructure can prepare society for the adoption of more advanced agents.

The rapid deployment of autonomous AI agents creates urgent challenges around authorization, accountability, and access control in digital spaces. New standards are needed to know whom AI agents act on behalf of and guide their use appropriately, protecting online spaces while unlocking the value of task delegation to autonomous agents. We introduce a novel framework for authenticated, authorized, and auditable delegation of authority to AI agents, where human users can securely delegate and restrict the permissions and scope of agents while maintaining clear chains of accountability. This framework builds on existing identification and access management protocols, extending OAuth 2.0 and OpenID Connect with agent-specific credentials and metadata, maintaining compatibility with established authentication and web infrastructure. Further, we propose a framework for translating flexible, natural language permissions into auditable access control configurations, enabling robust scoping of AI agent capabilities across diverse interaction modalities. Taken together, this practical approach facilitates immediate deployment of AI agents while addressing key security and accountability concerns, working toward ensuring agentic AI systems perform only appropriate actions and providing a tool for digital service providers to enable AI agent interactions without risking harm from scalable interaction.

This paper addresses the challenges translating case law under Hong Kong's bilingual legal system. It highlights the initial success of translating all written statutes into Chinese before the 1997 handover, a task mandated by the Basic Law. The effort involved significant collaboration among legal, linguistic, and translation experts, resulting in a comprehensive and culturally appropriate bilingual legal system. However, translating case law remains a significant challenge due to the sheer volume and continuous growth of judicial decisions. The paper critiques the governments and judiciarys sporadic and uncoordinated efforts to translate case law, contrasting it with the thorough approach previously taken for statute translation. Although the government acknowledges the importance of legal bilingualism, it lacks a sustainable strategy for translating case law. The Judiciarys position that translating all judgments is unnecessary, unrealistic, and not cost-effectiveis analyzed and critiqued for its impact on legal transparency and public trust. A proposed solution involves leveraging machine translation technology through a human-machine interactive translation platform, which undergoes two major transitions. Initially based on a neural model, the platform transitions to using a large language model for improved translation accuracy. Furthermore, it evolves from a single-agent system to a multi-agent system, incorporating Translator, Annotator, and Proofreader agents. This multi-agent approach, supported by a grant, aims to facilitate efficient, high-quality translation of judicial judgments by integrating advanced artificial intelligence and continuous feedback mechanisms, thus better meeting the needs of a bilingual legal system.

Atomistic structures of materials offer valuable insights into their functionality. Determining these structures remains a fundamental challenge in materials science, especially for systems with defects. While both experimental and computational methods exist, each has limitations in resolving nanoscale structures. Core-level spectroscopies, such as x-ray absorption (XAS) or electron energy-loss spectroscopies (EELS), have been used to determine the local bonding environment and structure of materials. Recently, machine learning (ML) methods have been applied to extract structural and bonding information from XAS/EELS, but most of these frameworks rely on a single data stream, which is often insufficient. In this work, we address this challenge by integrating multimodal ab initio simulations, experimental data acquisition, and ML techniques for structure characterization. Our goal is to determine local structures and properties using EELS and XAS data from multiple elements and edges. To showcase our approach, we use various lithium nickel manganese cobalt (NMC) oxide compounds which are used for lithium ion batteries, including those with oxygen vacancies and antisite defects, as the sample material system. We successfully inferred local element content, ranging from lithium to transition metals, with quantitative agreement with experimental data. Beyond improving prediction accuracy, we find that ML model based on multimodal spectroscopic data is able to determine whether local defects such as oxygen vacancy and antisites are present, a task which is impossible for single mode spectra or other experimental techniques. Furthermore, our framework is able to provide physical interpretability, bridging spectroscopy with the local atomic and electronic structures.

Knowledge distillation (KD) involves transferring knowledge from a pre-trained heavy teacher model to a lighter student model, thereby reducing the inference cost while maintaining comparable effectiveness. Prior KD techniques typically assume homogeneity between the teacher and student models. However, as technology advances, a wide variety of architectures have emerged, ranging from initial Convolutional Neural Networks (CNNs) to Vision Transformers (ViTs), and Multi-Level Perceptrons (MLPs). Consequently, developing a universal KD framework compatible with any architecture has become an important research topic. In this paper, we introduce a feature-based one-for-all (FOFA) KD framework to enable feature distillation across diverse architecture. Our framework comprises two key components. First, we design prompt tuning blocks that incorporate student feedback, allowing teacher features to adapt to the student model's learning process. Second, we propose region-aware attention to mitigate the view mismatch problem between heterogeneous architecture. By leveraging these two modules, effective distillation of intermediate features can be achieved across heterogeneous architectures. Extensive experiments on CIFAR, ImageNet, and COCO demonstrate the superiority of the proposed method.

Many active galaxies harbor powerful relativistic jets, however, the detailed mechanisms of their formation and acceleration remain poorly understood. To investigate the area of jet acceleration and collimation with the highest available angular resolution, we study the innermost region of the bipolar jet in the nearby low-ionization nuclear emission-line region (LINER) galaxy NGC 1052. We combined observations of NGC 1052 taken with VLBA, GMVA, and EHT over one week in the spring of 2017. For the first time, NGC 1052 was detected with the EHT, providing a size of the central region in-between both jet bases of 250 RS (Schwarzschild radii) perpendicular to the jet axes. This size estimate supports previous studies of the jets expansion profile which suggest two breaks of the profile at around 300 RS and 10000 RS distances to the core. Furthermore, we estimated the magnetic field to be 1.25 Gauss at a distance of 22 {\mu}as from the central engine by fitting a synchrotron-self absorption spectrum to the innermost emission feature, which shows a spectral turn-over at about 130 GHz. Assuming a purely poloidal magnetic field, this implies an upper limit on the magnetic field strength at the event horizon of 26000 Gauss, which is consistent with previous measurements. The complex, low-brightness, double-sided jet structure in NGC 1052 makes it a challenge to detect the source at millimeter (mm) wavelengths. However, our first EHT observations have demonstrated that detection is possible up to at least 230 GHz. This study offers a glimpse through the dense surrounding torus and into the innermost central region, where the jets are formed. This has enabled us to finally resolve this region and provide improved constraints on its expansion and magnetic field strength., Comment: 22 pages, 10 figures, published in A&A

Generative AI presents transformative potential across various domains, from creative arts to scientific visualization. However, the utility of AI-generated imagery is often compromised by visual flaws, including anatomical inaccuracies, improper object placements, and misplaced textual elements. These imperfections pose significant challenges for practical applications. To overcome these limitations, we introduce \textit{Yuan}, a novel framework that autonomously corrects visual imperfections in text-to-image synthesis. \textit{Yuan} uniquely conditions on both the textual prompt and the segmented image, generating precise masks that identify areas in need of refinement without requiring manual intervention -- a common constraint in previous methodologies. Following the automated masking process, an advanced inpainting module seamlessly integrates contextually coherent content into the identified regions, preserving the integrity and fidelity of the original image and associated text prompts. Through extensive experimentation on publicly available datasets such as ImageNet100 and Stanford Dogs, along with a custom-generated dataset, \textit{Yuan} demonstrated superior performance in eliminating visual imperfections. Our approach consistently achieved higher scores in quantitative metrics, including NIQE, BRISQUE, and PI, alongside favorable qualitative evaluations. These results underscore \textit{Yuan}'s potential to significantly enhance the quality and applicability of AI-generated images across diverse fields.

We measure the void size function (VSF) from the Baryon Oscillation Spectroscopic Survey (BOSS DR16) and perform the cosmological constraints. The BOSS DR16 galaxy sample is selected in the redshift range from $z = 0.2$ to 0.8, considering the selection criteria based on galaxy number density. We identify non-spherical voids from this galaxy catalog using the Voronoi tessellation and watershed algorithm without assuming any void shape. We select the void samples based on the void ellipticity, and derive the VSFs in two redshift bins, i.e. $z=0.2-0.5$ and $0.5-0.8$. The VSF model we use is based on the excursion-set theory, including the void linear underdensity threshold $\delta_{\rm v}$ and the redshift space distortion (RSD) parameter $\beta$. The Markov Chain Monte Carlo (MCMC) method is applied to perform the joint constraints on the cosmological and void parameters. We find that the VSF measurement from BOSS DR16 gives $w = -1.214_{-0.375}^{+0.293}$, $\Omega_{\rm m} = 0.280_{-0.047}^{+0.056}$, and $\sigma_8 = 0.874_{-0.210}^{+0.203}$, which can be a good complementary probe to galaxy clustering measurements. Our method demonstrates the potential of using the VSF to study cosmological models, and it can provide a reference for future VSF analysis in the upcoming galaxy spectroscopic surveys., Comment: 11 pages, 5 figures, 2 tables

We observe the magnetic quantum oscillation in the heat capacity of the Kondo insulator YbB$_{12}$. The frequency of these oscillations $F = 670$ T, aligns with findings from magnetoresistance and torque magnetometry experiments for $\mu_0 H > 35$ T in the Kondo insulating phase. Remarkably, the quantum oscillation amplitudes in the heat capacity are substantial, with $\Delta \tilde{C}/T \approx$ 0.5 $\rm{mJ}$ $\rm{mol^{-1}K^{-2}}$ at 0.8 K, accounting for 13$\%$ of the known linear heat capacity coefficient $\gamma$. Double-peak structures of quantum-oscillation amplitudes due to the distribution function of fermions were identified and used to determine the value of the effective mass from the heat capacity, which agrees well with that from torque magnetometry. These observations support charge-neutral fermions contributing to the quantum oscillations in YbB$_{12}$., Comment: 4 figures in the main text, and 3 more figures

The 2017 observing campaign of the Event Horizon Telescope (EHT) delivered the first very long baseline interferometry (VLBI) images at the observing frequency of 230 GHz, leading to a number of unique studies on black holes and relativistic jets from active galactic nuclei (AGN). In total, eighteen sources were observed: the main science targets, Sgr A* and M87 along with various calibrators. We investigated the morphology of the sixteen AGN in the EHT 2017 data set, focusing on the properties of the VLBI cores: size, flux density, and brightness temperature. We studied their dependence on the observing frequency in order to compare it with the Blandford-K\"onigl (BK) jet model. We modeled the source structure of seven AGN in the EHT 2017 data set using linearly polarized circular Gaussian components and collected results for the other nine AGN from dedicated EHT publications, complemented by lower frequency data in the 2-86 GHz range. Then, we studied the dependences of the VLBI core flux density, size, and brightness temperature on the frequency measured in the AGN host frame. We compared the observations with the BK jet model and estimated the magnetic field strength dependence on the distance from the central black hole. Our results indicate a deviation from the standard BK model, particularly in the decrease of the brightness temperature with the observing frequency. Either bulk acceleration of the jet material, energy transfer from the magnetic field to the particles, or both are required to explain the observations.

In this paper, we push the boundaries of fine-grained 3D generation into truly creative territory. Current methods either lack intricate details or simply mimic existing objects -- we enable both. By lifting 2D fine-grained understanding into 3D through multi-view diffusion and modeling part latents as continuous distributions, we unlock the ability to generate entirely new, yet plausible parts through interpolation and sampling. A self-supervised feature consistency loss further ensures stable generation of these unseen parts. The result is the first system capable of creating novel 3D objects with species-specific details that transcend existing examples. While we demonstrate our approach on birds, the underlying framework extends beyond things that can chirp! Code will be released at https://github.com/kamwoh/chirpy3d., Comment: 20 pages

We study spatially-flat dynamical dark energy parametrizations, $w(z)$CDM, with redshift-dependent dark energy equation of state parameter $w(z)$ expressed using three different quadratic and other polynomial forms (as functions of $1-a$, where $a$ is the scale factor), without and with a varying cosmic microwave background (CMB) lensing consistency parameter $A_L$. We use Planck CMB anisotropy data (P18 and lensing) and a large, mutually-consistent non-CMB data compilation that includes Pantheon+ type Ia supernova, baryon acoustic oscillation (BAO), Hubble parameter ($H(z)$), and growth factor ($f\sigma_8$) measurements, but not recent DESI BAO data. The six $w(z)$CDM ($+A_L$) parametrizations show higher consistency between the CMB and non-CMB data constraints compared to the XCDM ($+A_L$) and $w_0 w_a$CDM ($+A_L$) cases. Constraints from the most-restrictive P18+lensing+non-CMB data compilation on the six $w(z)$CDM ($+A_L$) parametrizations indicate that dark energy dynamics is favored over a cosmological constant by $\gtrsim 2\sigma$ when $A_L = 1$, but only by $\gtrsim 1\sigma$ when $A_L$ is allowed to vary (and $A_L>1$ at $\sim2\sigma$ significance). Non-CMB data dominate the P18+lensing+non-CMB compilation at low $z$ and favor quintessence-like dark energy. At high $z$ P18+lensing data dominate, favoring phantom-like dark energy with significance from $1.5\sigma$ to $2.9 \sigma$ when $A_L = 1$, and from $1.1\sigma$ to $1.8\sigma$ when $A_L$ varies. These results suggest that the observed excess weak lensing smoothing of some of the Planck CMB anistropy multipoles is partially responsible for the $A_L = 1$ cases $\gtrsim 2\sigma$ evidence for dark energy dynamics over a cosmological constant., Comment: 29 pages, 18 figures, 8 tables. Related to the analyses of arXiv:2405.00502 and arXiv:2410.13627

Although existing Sparsely Annotated Object Detection (SAOD) approches have made progress in handling sparsely annotated environments in multispectral domain, where only some pedestrians are annotated, they still have the following limitations: (i) they lack considerations for improving the quality of pseudo-labels for missing annotations, and (ii) they rely on fixed ground truth annotations, which leads to learning only a limited range of pedestrian visual appearances in the multispectral domain. To address these issues, we propose a novel framework called Sparsely Annotated Multispectral Pedestrian Detection (SAMPD). For limitation (i), we introduce Multispectral Pedestrian-aware Adaptive Weight (MPAW) and Positive Pseudo-label Enhancement (PPE) module. Utilizing multispectral knowledge, these modules ensure the generation of high-quality pseudo-labels and enable effective learning by increasing weights for high-quality pseudo-labels based on modality characteristics. To address limitation (ii), we propose an Adaptive Pedestrian Retrieval Augmentation (APRA) module, which adaptively incorporates pedestrian patches from ground-truth and dynamically integrates high-quality pseudo-labels with the ground-truth, facilitating a more diverse learning pool of pedestrians. Extensive experimental results demonstrate that our SAMPD significantly enhances performance in sparsely annotated environments within the multispectral domain., Comment: Accepted at AAAI 2025

Strong gravitational lenses come in many forms, but are typically divided into two populations: galaxies, and groups and clusters of galaxies. When calculating the properties of the images we expect to see from these lenses, it is typically assumed that each lens is roughly a singular isothermal sphere. In reality, the largest objects in the Universe (i.e. galaxy clusters) are highly irregular and composed of many components due to a history of (or active) hierarchical mergers. In this work, we analyze the discrepancies in the observables of strongly lensed transients in both scenarios, namely relative magnifications, time delays, and image multiplicities. Focusing on gravitational waves, we compare the detection rates between the single spherical dark matter halo models found in the literature, and publicly available state-of-the-art cluster lens models. We find there to be approximately an order of magnitude fewer detection of strongly lensed transients in the realistic model case, likely caused by their loss of overall strong lensing optical depth. We also report detection rates in the weak lensing or single-image regime. Additionally, we find a systemic shift towards lower time delays between the brightest image pairs in the cases of the realistic models, as well as higher fractions of positive versus negative parity images, as seen elsewhere in the literature. This significant deviation in the joint relative magnification factor-time delay distribution will hinder the feasibility of the reconstruction of lenses through time domain transients alone, but can still provide a lower limit on the lens mass., Comment: 13 pages, 10 figures

Background: The MAJORANA DEMONSTRATOR , a modular array of isotopically enriched high-purity germanium (HPGe) detectors, was constructed to demonstrate backgrounds low enough to justify building a tonne-scale experiment to search for the neutrinoless double-beta decay ($\beta\beta(0\nu)$) of $^{76}\mathrm{Ge}$. Purpose: This paper presents a description of the instrument, its commissioning, and operations. It covers the electroforming, underground infrastructure, enrichment, detector fabrication, low-background and construction techniques, electronics, data acquisition, databases, and data processing of the MAJORANA DEMONSTRATOR. Method: The MAJORANA DEMONSTRATOR operated inside an ultra-low radioactivity passive shield at the 4850-foot~level of the Sanford Underground Research Facility (SURF) from 2015-2021. Results and Conclusions: The MAJORANA DEMONSTRATOR achieved the best energy resolution and second-best background level of any $\beta\beta(0\nu)$ search. This enabled it to achieve an ultimate half-life limit on $\beta\beta(0\nu)$ in $^{76}\mathrm{Ge}$ of $8.3\times 10^{25}$~yr (90\% C.L.) and perform a rich set of searches for other physics beyond the Standard Model., Comment: 72 pages

The representations of conditional entropy and conditional mutual information are significant in explaining the unique effects among variables. While previous studies based on conditional contrastive sampling have effectively removed information regarding discrete sensitive variables, they have not yet extended their scope to continuous cases. This paper introduces Information Subtraction, a framework designed to generate representations that preserve desired information while eliminating the undesired. We implement a generative-based architecture that outputs these representations by simultaneously maximizing an information term and minimizing another. With its flexibility in disentangling information, we can iteratively apply Information Subtraction to represent arbitrary information components between continuous variables, thereby explaining the various relationships that exist between them. Our results highlight the representations' ability to provide semantic features of conditional entropy. By subtracting sensitive and domain-specific information, our framework demonstrates effective performance in fair learning and domain generalization. The code for this paper is available at https://github.com/jh-liang/Information-Subtraction

Out-of-hospital cardiac arrest (OHCA) survival rates remain extremely low due to challenges in the timely accessibility of medical devices. Therefore, effective deployment of automated external defibrillators (AED) can significantly increase survival rates. Precise and interpretable predictions of OHCA occurrences provide a solid foundation for efficient and robust AED deployment optimization. This study develops a novel learn-then-optimize approach, integrating three key components: a machine learning prediction model, SHAP-based interpretable analytics, and a SHAP-guided integer programming (SIP) model. The machine learning model is trained utilizing only geographic data as inputs to overcome data availability obstacles, and its strong predictive performance validates the feasibility of interpretation. Furthermore, the SHAP model elaborates on the contribution of each geographic feature to the OHCA occurrences. Finally, an integer programming model is formulated for optimizing AED deployment, incorporating SHAP-weighted OHCA densities. Various numerical experiments are conducted across different settings. Based on comparative and sensitive analysis, the optimization effect of our approach is verified and valuable insights are derived to provide substantial support for theoretical extension and practical implementation.

Motions of visually coupled zebrafish pairs are studied to understand the effects of information exchange on their behavior as a function of their minimal separation ($d$). We find that when $d$ is small, the pair can display a leader-follower relation (LFR) with trajectories of almost synchronized form. However, with larger $d$, although the same LFR is still maintained, the originally similar trajectories turn into different forms. Detailed analysis of their motion trajectories suggests that the pair might be using U-turns (UTs) to exchange information and to maintain a LFR at the same time. A simulation model based on UTs with inferred and proposed rules is able to reproduce prominent features of observed trajectories; indicating that the transition of trajectories can be understood as the result of a change in information exchange between the fish as $d$ increases. Our finding that UTs as important visual signals is consistent with the fact that UTs can induce a large amount of firings in retinas of observing fish.

We present all possible analytical solutions of the Friedmann-Lema\^itre-Robertson-Walker metric in Einstein-aether theory for all values of the cosmological constant and spatial curvature with many reasonable values of the equation-of-state parameter. We analyze the dynamics of each model analytically and also graphically by plotting the geometric radius, Hubble and deceleration parameters along with the effective energy conditions. All our results are compared with the corresponding models in General Relativity. The two key results are (i) the aether does not qualitatively change the dynamics of the cosmological models but merely scales the geometric radius, Hubble, and deceleration parameters, and (ii) we found eight models that are entirely void of any aether, meaning in such a universe aether does not play any role cosmologically, although it affects the solar system dynamics., Comment: 25 pages, 9 figures and 6 tables

We investigate gravitational lensing by \textit{special} Buchdahl inspired metric with the Buchdahl parameter $\tilde{k}$. In strong deflection limit, we derive the deflection angle analytically for the light rays that diverge as photons approach the photon sphere. These are then used in order to compute the angular image positions modeling supermassive black holes, Sgr A* and M87* as lenses. The Einstein rings for the outermost relativistic images are also depicted here alongside observational constraints on $\tilde{k}$ by the Einstein radius and lens mass. Constraints on $\tilde{k}$ are obtained modelling black holes ( Sgr A* and M87*) and Canarias Einstein ring. In weak deflection limit, the analytic expression of deflection angle of the subject asymptotically flat metric in $\mathcal{R}^2$ gravity is determined using the Gauss Bonnet theorem. Considering M87* as a lens, weak deflection angle is used to study the image magnification and image distortion for primary and secondary images. It is shown that image distortion satisfies the hypothesis of Virbhadra. Moreover, it is seen that our general expression of deflection angle reduces, as a special case, to the deflection angle of Schwarzschild metric in both weak and strong deflection limits., Comment: 21 pages, 7 figures. To appear in Annals of Physics

Hirschsprung's disease (HD) is a congenital birth defect diagnosed by identifying the lack of ganglion cells within the colon's muscularis propria, specifically within the myenteric plexus regions. There may be advantages for quantitative assessments of histopathology images of the colon, such as counting the ganglion and assessing their spatial distribution; however, this would be time-intensive for pathologists, costly, and subject to inter- and intra-rater variability. Previous research has demonstrated the potential for deep learning approaches to automate histopathology image analysis, including segmentation of the muscularis propria using convolutional neural networks (CNNs). Recently, Vision Transformers (ViTs) have emerged as a powerful deep learning approach due to their self-attention. This study explores the application of ViTs for muscularis propria segmentation in calretinin-stained histopathology images and compares their performance to CNNs and shallow learning methods. The ViT model achieved a DICE score of 89.9% and Plexus Inclusion Rate (PIR) of 100%, surpassing the CNN (DICE score of 89.2%; PIR of 96.0%) and k-means clustering method (DICE score of 80.7%; PIR 77.4%). Results assert that ViTs are a promising tool for advancing HD-related image analysis., Comment: To be published in the CMBEC47/ACCES26 Joint Conference

Makeup is no longer confined to physical application; people now use mobile apps to digitally apply makeup to their photos, which they then share on social media. However, while this shift has made makeup more accessible, designing diverse makeup styles tailored to individual faces remains a challenge. This challenge currently must still be done manually by humans. Existing systems, such as makeup recommendation engines and makeup transfer techniques, offer limitations in creating innovative makeups for different individuals "intuitively" -- significant user effort and knowledge needed and limited makeup options available in app. Our motivation is to address this challenge by proposing Prot\'eg\'e, a new makeup application, leveraging recent generative model -- GANs to learn and automatically generate makeup styles. This is a task that existing makeup applications (i.e., makeup recommendation systems using expert system and makeup transfer methods) are unable to perform. Extensive experiments has been conducted to demonstrate the capability of Prot\'eg\'e in learning and creating diverse makeups, providing a convenient and intuitive way, marking a significant leap in digital makeup technology!, Comment: 8 pages, 5 figures