Search

This paper is concerned with self-similar solutions of the steady Navier-Stokes system in a two-dimensional sector with the no-slip boundary condition. We give necessary and sufficient conditions in terms of the angle of the sector and the flux to guarantee the existence of self-similar solutions of a given type. We also investigate the uniqueness and non-uniqueness of flows with a given type, which not only give rigorous justifications for some statements in \cite{Rosenhead40} but also show that some numerical computations in \cite{Rosenhead40} may not be precise. The non-uniqueness result is a new phenomenon for these flows. As a consequence of the classification of self-similar solutions in the half-space, we characterize the leading order term of the steady Navier-Stokes system in an aperture domain when the flux is small. The main approach is to study the ODE system governing self-similar solutions, where the detailed properties of both complete and incomplete elliptic functions have been investigated.

This paper proposes a data-driven model predictive control for multirotor collision avoidance considering uncertainty and an unknown model from a payload. To address this challenge, sparse identification of nonlinear dynamics (SINDy) is used to obtain the governing equation of the multirotor system. The SINDy can discover the equations of target systems with low data, assuming that few functions have the dominant characteristic of the system. Model predictive control (MPC) is utilized to obtain accurate trajectory tracking performance by considering state and control input constraints. To avoid a collision during operation, MPC optimization problem is again formulated using inequality constraints about an obstacle. In simulation, SINDy can discover a governing equation of multirotor system including mass parameter uncertainty and aerodynamic effects. In addition, the simulation results show that the proposed method has the capability to avoid an obstacle and track the desired trajectory accurately.

We report on a search for millicharged particles (mCPs) produced in cosmic ray proton atmospheric interactions using data collected during the first science run of the LUX-ZEPLIN experiment. The mCPs produced by two processes -- meson decay and proton bremsstrahlung -- are considered in this study. This search utilized a novel signature unique to liquid xenon (LXe) time projection chambers (TPCs), allowing sensitivity to mCPs with masses ranging from 10 to 1000 MeV/c$^2$ and fractional charges between 0.001 and 0.02 of the electron charge e. With an exposure of 60 live days and a 5.5 tonne fiducial mass, we observed no significant excess over background. This represents the first experimental search for atmospheric mCPs and the first search for mCPs using an underground LXe experiment.

$\beta$ Cephei pulsating variable (BCEP) stars are the most massive pulsating variable stars in the main sequence, exhibiting both p- and g-mode pulsations. In this study, we identified 155 BCEP stars or candidates using data from TESS and Gaia, of which 83 were first confirmed as BCEP stars. They have visual magnitudes ranging from 8 to 12 mag and effective temperatures between approximately 20,000 and 30,000 K, while the parallaxes of most targets are between 0.2 and 0.6 mas. The study indicates that these BCEP stars have pulsation periods ranging from 0.06 to 0.31 days, with amplitudes ranging from 0.1 to 55.8 mmag in the TESS band. Additionally, the number of BCEP stars increases as the pulsation amplitude decreases. These targets align with the distribution region of BCEP stars in the luminosity-period (L-P) and temperature-period (T-P) diagrams. We have updated the L-P relation of BCEP stars. The Hertzsprung-Russell (H-R) diagram indicates that these targets are in the main-sequence evolutionary phase, with masses ranging from 7 to 20 $M_{\odot}$ and luminosities between 2800 and 71,000 $L_{\odot}$. They are almost in the theoretical instability region of BCEP stars but as previously reported, this region at the low-mass end (red) is not filled. The distribution of the pulsation constant indicates that the dominant pulsation periods of BCEP stars consist mainly of low-order p-mode pulsations with a high proportion of radial fundamental modes. These BCEP stars are excellent objects for enhancing our understanding of the structure and evolution of massive stars through asteroseismology.

This paper reports the discovery of new slowly pulsating B-type stars. Based on the photometric, spectral, and astrometric data of TESS, LAMOST, and Gaia surveys, we have found 286 new slowly pulsating B-type stars (SPB stars) and 21 candidates. Among these, 20 are Be stars or candidates with emission line profiles. It is shown that these SPB stars have luminosities between 40 and 2850 $L_{\odot}$ and effective temperatures ranging from 10000K to 21000K. Their pulsation periods are from 0.14 to 6.5 days with amplitude ranges of 0.2-20 mmag in TESS band. It is indicated that these targets follow the distribution of the SPB stars in the period-luminosity (P-L) and the period-temperature (P-T) diagrams. Their positions on the H-R diagram reveal that most of these pulsators are distributed in the instability region of SPB stars, in the main-sequence evolutionary stage, and with mass ranges of 2.5-7 $M_{\odot}$. However, there are some targets beyond the red edge of the theoretical instability region, which should be caused by the rapid rotation reducing the measured effective temperature. The discovery of these new SPB stars increases the total number by over 60\%, which are significant samples for further investigating the structure and evolution of intermediate-mass and even massive stars by asteroseismology.

The O- and B-type (OB-type) pulsating stars are important objects to study the structure and evolution of massive stars through asteroseismology. A large amount of data from various sky surveys provide an unprecedented opportunity to search for and study this kind of variable star. We identify 155 OB-type pulsating stars or candidates, including 38 Oe/Be stars or candidates, from the data observed by TESS, LAMOST, and GAIA, which are almost new. Among the 155 objects, 87 samples are identified as SPB stars including 37 objects with pure low-frequency and 50 objects with both low- and high-frequency pulsation, and 14 samples are identified as BCEP stars with both low- and high-frequency pulsation. The H-R diagram shows that these SPB and BCEP stars are mainly located in their instability regions and in the evolutionary stage of the main-sequence with a mass range of 2.5-20 $M_{\odot}$ and 7-20 $M_{\odot}$. Two special objects show fourier spectra similar to BCEP stars but with different positions in H-R, Period-Temperature (P-T), and Period-Luminosity (P-L) diagrams. Meanwhile, 52 other targets are identified as candidates of OB-type pulsating stars. We also derive the preliminary results of the P-L relation for SPB and BCEP stars, respectively. This work also indicates that in addition to the H-R diagram, P-T and P-L diagrams are also very useful for the classification of SPB and BCEP. Further detailed analysis of these objects can dramatically increase our understanding of theories of evolution and structure for massive OB-type pulsating stars.

The defect chemistry and thermal oxidation of lanthanide (Ln) incorporated-UO2 are critical for understanding and predicting their behavior as enhanced fuels, mixed oxide (MOX) fuels, spent nuclear fuels (SNF), and particles for safeguard purposes. In this study, we independently controlled the Ln type (Ce4+, Nd3+, and Gd3+) and the preparation condition (reduced and nonreduced) to investigate their correlations to the generated non-equilibrated defects correspondingly. From early to late lanthanides: Ce and U formed close-to-ideal solid solutions in Fm-3m and oxidized to (Ce, U)4O9, Nd and U mixing under the reducing condition formed solid solutions with oxygen vacancies aggregating near Nd, and the mixing of smaller Gd with U resulted in short-range subnano-domain segregations with Ia-3 region embedded in the global Fm-3m matrix. Both trivalent Ln-incorporated UO2 oxidized to a mixture of (Ln, U)4O9 and (Ln, U)3O8. From these signature defect structures resulting from both Ln type and preparation condition, we proposed kinetic model and thermodynamic hypothesis for explaining the oxidation resistance of (Ln, U)O2. Although originated from f-block oxides, the discovery of long-range disorder short-range ordering may be not uncommon in other metal oxide systems, which can strongly influence their functionalities and properties.

The alignment of large language models (LLMs) is critical for developing effective and safe language models. Traditional approaches focus on aligning models during the instruction tuning or reinforcement learning stages, referred to in this paper as `post alignment'. We argue that alignment during the pre-training phase, which we term `native alignment', warrants investigation. Native alignment aims to prevent unaligned content from the beginning, rather than relying on post-hoc processing. This approach leverages extensively aligned pre-training data to enhance the effectiveness and usability of pre-trained models. Our study specifically explores the application of native alignment in the context of Arabic LLMs. We conduct comprehensive experiments and ablation studies to evaluate the impact of native alignment on model performance and alignment stability. Additionally, we release open-source Arabic LLMs that demonstrate state-of-the-art performance on various benchmarks, providing significant benefits to the Arabic LLM community., Comment: Accepted to NeurIPS 2024 main conference. see https://github.com/FreedomIntelligence/AceGPT-v2

Deep learning models are widely used to process Computed Tomography (CT) data in the automated screening of pulmonary diseases, significantly reducing the workload of physicians. However, the three-dimensional nature of CT volumes involves an excessive number of voxels, which significantly increases the complexity of model processing. Previous screening approaches often overlook this issue, which undoubtedly reduces screening efficiency. Towards efficient and effective screening, we design a hierarchical approach to reduce the computational cost of pulmonary disease screening. The new approach re-organizes the screening workflows into three steps. First, we propose a Computed Tomography Volume Compression (CTVC) method to select a small slice subset that comprehensively represents the whole CT volume. Second, the selected CT slices are used to detect pulmonary diseases coarsely via a lightweight classification model. Third, an uncertainty measurement strategy is applied to identify samples with low diagnostic confidence, which are re-detected by radiologists. Experiments on two public pulmonary disease datasets demonstrate that our approach achieves comparable accuracy and recall while reducing the time by 50%-70% compared with the counterparts using full CT volumes. Besides, we also found that our approach outperforms previous cutting-edge CTVC methods in retaining important indications after compression., Comment: Under Review

We study the Wasserstein barycenter problem in the setting of non-compact, non-smooth extended metric measure spaces. We introduce a couple of new concepts and obtain the existence, uniqueness, absolute continuity of the Wasserstein barycenter, and prove Jensen's inequality in an abstract framework. This generalized several results on Euclidean space, Riemannian manifolds and Alexandrov spaces, to metric measure spaces satisfying Riemannian Curvature-Dimension condition \`a la Lott--Sturm--Villani, and some extended metric measure spaces including abstract Wiener spaces and configuration spaces over Riemannian manifolds. We also introduce a new curvature-dimesion condition, we call Barycenter-Curvature-Dimension condition {\rm BCD}. We prove its stability under measured-Gromov--Hausdorff convergence and prove the existence of the Wasserstein barycenter under this new condition. In addition, we get some geometric inequalities including a multi-marginal Brunn--Minkowski inequality and a functional Blaschke--Santal\'o type inequality.

The integral identity conjecture of Kontsevich and Soibelman plays an important role in proving the existence of motivic Donaldson-Thomas invariants for three-dimensional noncommutative Calabi-Yau manifolds. There are a number of different formulations of this conjecture in different contexts and accordingly, there are corresponding solutions to them. The methods devoted to solving this conjecture are diverse, ranging from $\ell$-adic cohomology of rigid analytic varieties to Hrushovski-Kazhdan motivic integration and motivic Fubini theorem for tropicalization maps,... In a recent work, Ivorra deduces a functorial version of the integral identity in the stable homotopy of schemes, from the Braden hyperbolic localization theorem. This functorial version concerns Ayoub's nearby cycles functor associated with a $\mathbb{G}_m$-equivariant function $f \colon X \longrightarrow \mathbb{A}^1$ on vector bundles with a linear action of $\mathbb{G}_m$ on algebraic varieties over a field of characteristic zero. In the present work, we follow the functorial approach by Ivorra and extend the scope of the original conjecture by Kontsevich and Soibelman by studying more generally the case of $\mathbb{G}_m$-equivariant functions on algebraic $S$-spaces with a $\tau$-locally linearizable action of $\mathbb{G}_m$ over a noetherian scheme $S$ and extend the functorial integral identity to these cases. Our result is functorial, it allows more flexibity on the characteristic of the base scheme and the formulation exists in the world of motives which can realizes to other settings, e.g., \'etale realization, Betti realization, virtual motives... We also prove a result which is independent of interest and already appears in the work of others, namely, the motivic version of the functorial compatibility of the Bernstein isomorphisms with constant term functors.

Pulsating stars in eclipsing binaries are very important to understand stellar interior structures through astroseismology because their absolute parameters such as the masses and radii can be determined in high precision based on photometric and spectroscopic data. The high-precision and continuously time-series photometric data of the Transiting Exoplanet Survey Satellite (TESS) provides an unprecedented opportunity to search for and study this kind of variable stars in the whole sky. About 1626 Algol type (EA-type) eclipsing binary systems were observed by TESS in the 1-45 sectors with 2-minutes short cadence. By analyzing those TESS data, we found 57 new pulsating stars in EA-type binary stars. The preliminary results show that those binary systems have orbital periods in the range from 0.4 to 27 days, while the periods of pulsating components are in the range from 0.02 to 5 days. It is detected that 43 targets follow the correlation between pulsation and orbital periods of oscillating eclipsing binaries of Algol type (oEA), which may indicate that they are typical oEA stars. The other 14 targets may be other types of variable stars in eclipsing binary systems. These objects are a very interesting source to investigate the binary structures and evolutions as well as to understand the influences of tidal forces and mass transfer on stellar pulsations.

We investigate through first-principles calculations rutile CoF$_2$ as an altermagnetic material. It is confirmed that there are two antiparallel magnetic moments of Co ions in the unit cell and the magnetic easy axis is in the z axis. A giant nonrelativstic spin splitting is observed near the valence band edge (valence band maximum) and there are ideal flat bands at the valence band edge along the [110] directions in the Brillouin zone. The giant nonrelativstic spin splitting and the band-edge flat bands are both robust against the spin-orbits coupling. More importantly, these band-edge flat bands can induce two-dimensional hole gases in two different planes. Applying a magnetic field in the xy plane, there will appear Landau levels and quantum Hall effects can be achieved for both of the spin channels. When the magnetic field is along the diagonal directions, quantum Hall effects can be induced for one spin and there is a longitudinal conductivity for the other spin., Comment: 7 pages, 5 figures

In anomaly detection, the scarcity of anomalous data compared to normal data poses a challenge in effectively utilizing deep neural network representations to identify anomalous features. From a data-centric perspective, generative models can solve this data imbalance issue by synthesizing anomaly datasets. Although previous research tried to enhance the controllability and quality of generating defects, they do not consider the relation between background and defect. Since the defect depends on the object's background (i.e., the normal part of an object), training only the defect area cannot utilize the background information, and even generation can be biased depending on the mask information. In addition, controlling logical anomalies should consider the dependency between background and defect areas (e.g., orange colored defect on a orange juice bottle). In this paper, our paper proposes modeling a relationship between the background and defect, where background affects denoising defects; however, the reverse is not. We introduce the regularizing term to disentangle denoising background from defects. From the disentanglement loss, we rethink defect generation with DDIM Inversion, where we generate the defect on the target normal image. Additionally, we theoretically prove that our methodology can generate a defect on the target normal image with an invariant background. We demonstrate our synthetic data is realistic and effective in several experiments., Comment: 10 pages

A signed distance function (SDF) is a useful representation for continuous-space geometry and many related operations, including rendering, collision checking, and mesh generation. Hence, reconstructing SDF from image observations accurately and efficiently is a fundamental problem. Recently, neural implicit SDF (SDF-NeRF) techniques, trained using volumetric rendering, have gained a lot of attention. Compared to earlier truncated SDF (TSDF) fusion algorithms that rely on depth maps and voxelize continuous space, SDF-NeRF enables continuous-space SDF reconstruction with better geometric and photometric accuracy. However, the accuracy and convergence speed of scene-level SDF reconstruction require further improvements for many applications. With the advent of 3D Gaussian Splatting (3DGS) as an explicit representation with excellent rendering quality and speed, several works have focused on improving SDF-NeRF by introducing consistency losses on depth and surface normals between 3DGS and SDF-NeRF. However, loss-level connections alone lead to incremental improvements. We propose a novel neural implicit SDF called "SplatSDF" to fuse 3DGSandSDF-NeRF at an architecture level with significant boosts to geometric and photometric accuracy and convergence speed. Our SplatSDF relies on 3DGS as input only during training, and keeps the same complexity and efficiency as the original SDF-NeRF during inference. Our method outperforms state-of-the-art SDF-NeRF models on geometric and photometric evaluation by the time of submission.

We contribute to the theory of (homotopy) colimits inside homotopy type theory. The heart of our work characterizes the connection between colimits in coslices of a universe, called coslice colimits, and colimits in the universe (i.e., ordinary colimits). To derive this characterization, we find an explicit construction of colimits in coslices that is tailored to reveal the connection. We use the construction to derive properties of colimits. Notably, we prove that the forgetful functor from a coslice creates colimits over trees. We also use the construction to examine how colimits interact with orthogonal factorization systems and with cohomology theories. As a consequence of their interaction with orthogonal factorization systems, all pointed colimits (special kinds of coslice colimits) preserve $n$-connectedness, which implies that higher groups are closed under colimits on directed graphs. We have formalized our main construction of the coslice colimit functor in Agda. The code for this paper is available at https://github.com/PHart3/colimits-agda .

Asteroseismology has emerged as a powerful tool to unravel the intricate relationships between evolved stars and their planetary systems. In this study, we leverage this technique to investigate the evolutionary stages of five exoplanet host stars, each exhibiting solar-like oscillations. Building on our previous work that identified two host stars as red clump and red giant branch (RGB) stars, this study focuses on a new and broader sample. By precisely measuring asteroseismic parameters such as the period spacing of dipole gravity modes ($\Delta\Pi_{1}$), we provide definitive confirmation of these stars' evolutionary states as subgiants or RGB stars. These results are not only crucial for understanding the internal structures of evolved stars but also for predicting the eventual fate of their planetary companions, which may face engulfment as their host stars expand. This research highlights the profound role of asteroseismology in advancing our knowledge of planetary system evolution and opens new pathways for exploring how stellar evolution impacts planetary survival. Our findings set the stage for future studies on the dynamic fates of exoplanets, providing key insights into the intricate processes of stellar and planetary evolution.

The field of visual and physiological optics is undergoing continuous significant advancements, driven by a deeper understanding of the human visual system and the development of cutting-edge optical technologies. This Roadmap, authored by leading experts, delves into critical areas such as corneal biomechanical properties, keratoconus, and advancements in corneal imaging and elastography. It explores the intricate structure-function relationship within the eye lens, offering new perspectives through lens models and ray tracing techniques. The document also covers advancements in retinal imaging, highlighting the current state and future directions, and the role of adaptive optics in evaluating retinal structure and function in both healthy and diseased eyes. Furthermore, it addresses the modelling of ocular surfaces using different mathematical functions and examines the factors affecting peripheral image quality in the human eye, emphasizing the importance of these aspects in visual performance. Additional topics include schematic and functional models of the human eye, the impact of optical and chromatic aberrations, and the design of contact, and intraocular lenses. Finally, the Roadmap addresses the intersection of neurosciences with vision health, presenting a comprehensive overview of current research and future trends aimed at improving visual health and optical performance. Ultimately, this Roadmap aims to serve as a valuable resource for ophthalmologists, optometrists, vision scientists, and engineers dedicated to advancing the field of visual and physiological optics., Comment: This is the pre-peer-review and pre-editing version of the article, as submitted by the author to Journal of Optics

Facial aging is a complex process, highly dependent on multiple factors like gender, ethnicity, lifestyle, etc., making it extremely challenging to learn a global aging prior to predict aging for any individual accurately. Existing techniques often produce realistic and plausible aging results, but the re-aged images often do not resemble the person's appearance at the target age and thus need personalization. In many practical applications of virtual aging, e.g. VFX in movies and TV shows, access to a personal photo collection of the user depicting aging in a small time interval (20$\sim$40 years) is often available. However, naive attempts to personalize global aging techniques on personal photo collections often fail. Thus, we propose MyTimeMachine (MyTM), which combines a global aging prior with a personal photo collection (using as few as 50 images) to learn a personalized age transformation. We introduce a novel Adapter Network that combines personalized aging features with global aging features and generates a re-aged image with StyleGAN2. We also introduce three loss functions to personalize the Adapter Network with personalized aging loss, extrapolation regularization, and adaptive w-norm regularization. Our approach can also be extended to videos, achieving high-quality, identity-preserving, and temporally consistent aging effects that resemble actual appearances at target ages, demonstrating its superiority over state-of-the-art approaches., Comment: Project page: https://mytimemachine.github.io/

The remarkable capabilities of Large Language Models (LLMs) make them increasingly compelling for adoption in real-world healthcare applications. However, the risks associated with using LLMs in medical applications have not been systematically characterized. We propose using five key principles for safe and trustworthy medical AI: Truthfulness, Resilience, Fairness, Robustness, and Privacy, along with ten specific aspects. Under this comprehensive framework, we introduce a novel MedGuard benchmark with 1,000 expert-verified questions. Our evaluation of 11 commonly used LLMs shows that the current language models, regardless of their safety alignment mechanisms, generally perform poorly on most of our benchmarks, particularly when compared to the high performance of human physicians. Despite recent reports indicate that advanced LLMs like ChatGPT can match or even exceed human performance in various medical tasks, this study underscores a significant safety gap, highlighting the crucial need for human oversight and the implementation of AI safety guardrails.

Current image-to-3D approaches suffer from high computational costs and lack scalability for high-resolution outputs. In contrast, we introduce a novel framework to directly generate explicit surface geometry and texture using multi-view 2D depth and RGB images along with 3D Gaussian features using a repurposed Stable Diffusion model. We introduce a depth branch into U-Net for efficient and high quality multi-view, cross-domain generation and incorporate epipolar attention into the latent-to-pixel decoder for pixel-level multi-view consistency. By back-projecting the generated depth pixels into 3D space, we create a structured 3D representation that can be either rendered via Gaussian splatting or extracted to high-quality meshes, thereby leveraging additional novel view synthesis loss to further improve our performance. Extensive experiments demonstrate that our method surpasses existing baselines in geometry and texture quality while achieving significantly faster generation time., Comment: 3DV 2025, Project page: https://hao-yu-wu.github.io/gen3d/

Samaras, a distinct category of fruit, are composed of heavier seeds and lighter wings. Diversity in morphologies and structures subtly contributes to the flight patterns of various seeds, thereby serving as a key factor in the reproductive strategies of plants. To explore the mechanisms underlying various samara flight behaviors, we proposed an effective scheme by manipulating the mass distribution on a plate to mimic various three-dimensional descent behaviors of samaras. Through this framework, we experimentally identified and characterized four distinct flight modes. The three-dimensional vortical structures were then numerically analyzed to gain insights into the samara-inspired flight behaviors. Our study demonstrates how strategic mass distribution in samaras leads to diverse flight behaviors that leverage vortices to enhance seed dispersal, offering a fresh perspective for the design of biomimetic fliers.

Eclipsing Algol-type systems containing a $\delta$ Scuti (hereafter $\delta$ Sct) star enable precise determination of physical parameters and the investigation of stellar internal structure and evolution. We present the absolute parameters of CZ Aquarius (hereafter CZ Aqr) based on TESS data. CZ Aqr has an orbital period of 0.86275209 d, a mass ratio of 0.489 (6), and the secondary component nearly fills its Roche lobe. $O-C$ analysis reveals a downward parabolic trend and a cyclical variation with a period of 88.2 yr. The downward parabola suggests a long-term decrease in the orbital period with $\dot{P}$ = -3.09$\times$$10^{-8}$ d $\textrm{yr}^{-1}$. The mass loss rate is estimated to be 4.54$\times$$10^{-9}$ M$_{\odot}$ $\textrm{yr}^{-1}$, which possibly due to magnetic stellar wind or hot spot. The cyclical variation might be caused by the light travel time effect via the presence of a third body with a minimum mass of $M_{3min}$ = 0.312 (21) M$_{\odot}$. Additionally, there are two possible celestial bodies in a 2:7 resonance orbit around CZ Aqr. The asymmetric light curve is explained by adding a hot spot on the surface of the primary star. After removing the binary model, 26 frequencies were extracted from TESS data. Two radial modes were newly identified among three possible independent frequencies. Our results show that the eclipsing Algol-type system is composed of a $\delta$ Sct primary star and a subgiant star in a quadruple system.

We develop two novel couplings between general pure-jump L\'evy processes in $\R^d$ and apply them to obtain upper bounds on the rate of convergence in an appropriate Wasserstein distance on the path space for a wide class of L\'evy processes attracted to a multidimensional stable process in the small-time regime. We also establish general lower bounds based on certain universal properties of slowly varying functions and the relationship between the Wasserstein and Toscani--Fourier distances of the marginals. Our upper and lower bounds typically have matching rates. In particular, the rate of convergence is polynomial for the domain of normal attraction and slower than a slowly varying function for the domain of non-normal attraction., Comment: 42 pages, 2 figures, for a short YouTube video describing the results, see https://youtu.be/76eJD6a8Kko?si=5OkdWw4AiNp0P1po

The massive semi-detached binary with a long-term decreasing orbital period may involve a rapid mass-transfer phase in Case A, and thus they are good astrophysical laboratories for investigating the evolution of massive binary stars. In this work, by using the long-term observational light curves from the OGLE project and other data in the low-metallicity LMC, four semi-detached massive binaries with long-term decreases in the orbital periods are detected from 165 EB-type close binaries. It is found that the more massive component in S07798 is filling its Roche lobe where the period decrease is caused by mass transfer from the primary to the secondary. However, the other three (S03065, S12631, S16873) are semi-detached binaries with a lobe-filling secondary where the mass transfer between the components should cause the period to increase if the angular momentum is conservative. The long-term period decreases in these three systems may be caused by the angular momentum loss. Additionally, the orbital periods of three systems (S03065, S07798, S16873) are detected to show cyclic variation with periods shorter than 11 years, which can be plausibly explained by the presence of close-in third bodies in these massive binaries. Based on all of these results, it is suggested that the detected four semi-detached binaries almost have multiplicity. The companion stars are crucial for the origin and evolution of these massive close binaries.

While piano music has become a significant area of study in Music Information Retrieval (MIR), there is a notable lack of datasets for piano solo music with text labels. To address this gap, we present PIAST (PIano dataset with Audio, Symbolic, and Text), a piano music dataset. Utilizing a piano-specific taxonomy of semantic tags, we collected 9,673 tracks from YouTube and added human annotations for 2,023 tracks by music experts, resulting in two subsets: PIAST-YT and PIAST-AT. Both include audio, text, tag annotations, and transcribed MIDI utilizing state-of-the-art piano transcription and beat tracking models. Among many possible tasks with the multi-modal dataset, we conduct music tagging and retrieval using both audio and MIDI data and report baseline performances to demonstrate its potential as a valuable resource for MIR research., Comment: Accepted for publication at the 3rd Workshop on NLP for Music and Audio (NLP4MusA 2024)

Large Language Models (LLMs) are proficient at retrieving single facts from extended contexts, yet they struggle with tasks requiring the simultaneous retrieval of multiple facts, especially during generation. This paper identifies a novel "lost-in-the-middle" phenomenon, where LLMs progressively lose track of critical information throughout the generation process, resulting in incomplete or inaccurate retrieval. To address this challenge, we introduce Find All Crucial Texts (FACT), an iterative retrieval method that refines context through successive rounds of rewriting. This approach enables models to capture essential facts incrementally, which are often overlooked in single-pass retrieval. Experiments demonstrate that FACT substantially enhances multi-fact retrieval performance across various tasks, though improvements are less notable in general-purpose QA scenarios. Our findings shed light on the limitations of LLMs in multi-fact retrieval and underscore the need for more resilient long-context retrieval strategies., Comment: Work in Progress

Material discovery is a critical research area with profound implications for various industries. In this work, we introduce MatExpert, a novel framework that leverages Large Language Models (LLMs) and contrastive learning to accelerate the discovery and design of new solid-state materials. Inspired by the workflow of human materials design experts, our approach integrates three key stages: retrieval, transition, and generation. First, in the retrieval stage, MatExpert identifies an existing material that closely matches the desired criteria. Second, in the transition stage, MatExpert outlines the necessary modifications to transform this material formulation to meet specific requirements outlined by the initial user query. Third, in the generation state, MatExpert performs detailed computations and structural generation to create new materials based on the provided information. Our experimental results demonstrate that MatExpert outperforms state-of-the-art methods in material generation tasks, achieving superior performance across various metrics including validity, distribution, and stability. As such, MatExpert represents a meaningful advancement in computational material discovery using langauge-based generative models.

In this work, we study the problem of finding Pareto optimal policies in multi-agent reinforcement learning problems with cooperative reward structures. We show that any algorithm where each agent only optimizes their reward is subject to suboptimal convergence. Therefore, to achieve Pareto optimality, agents have to act altruistically by considering the rewards of others. This observation bridges the multi-objective optimization framework and multi-agent reinforcement learning together. We first propose a framework for applying the Multiple Gradient Descent algorithm (MGDA) for learning in multi-agent settings. We further show that standard MGDA is subjected to weak Pareto convergence, a problem that is often overlooked in other learning settings but is prevalent in multi-agent reinforcement learning. To mitigate this issue, we propose MGDA++, an improvement of the existing algorithm to handle the weakly optimal convergence of MGDA properly. Theoretically, we prove that MGDA++ converges to strong Pareto optimal solutions in convex, smooth bi-objective problems. We further demonstrate the superiority of our MGDA++ in cooperative settings in the Gridworld benchmark. The results highlight that our proposed method can converge efficiently and outperform the other methods in terms of the optimality of the convergent policies. The source code is available at \url{https://github.com/giangbang/Strong-Pareto-MARL}., Comment: Submitted to ACML 2024 Special Issue Journal track

Large language models (LLMs) and large multimodal models (LMMs) have shown great potential in automating complex tasks like web browsing and gaming. However, their ability to generalize across diverse applications remains limited, hindering broader utility. To address this challenge, we present OSCAR: Operating System Control via state-Aware reasoning and Re-planning. OSCAR is a generalist agent designed to autonomously navigate and interact with various desktop and mobile applications through standardized controls, such as mouse and keyboard inputs, while processing screen images to fulfill user commands. OSCAR translates human instructions into executable Python code, enabling precise control over graphical user interfaces (GUIs). To enhance stability and adaptability, OSCAR operates as a state machine, equipped with error-handling mechanisms and dynamic task re-planning, allowing it to efficiently adjust to real-time feedback and exceptions. We demonstrate OSCAR's effectiveness through extensive experiments on diverse benchmarks across desktop and mobile platforms, where it transforms complex workflows into simple natural language commands, significantly boosting user productivity. Our code will be open-source upon publication., Comment: Work in progress

Reinforcement learning has achieved promising results on robotic control tasks but struggles to leverage information effectively from multiple sensory modalities that differ in many characteristics. Recent works construct auxiliary losses based on reconstruction or mutual information to extract joint representations from multiple sensory inputs to improve the sample efficiency and performance of reinforcement learning algorithms. However, the representations learned by these methods could capture information irrelevant to learning a policy and may degrade the performance. We argue that compressing information in the learned joint representations about raw multimodal observations is helpful, and propose a multimodal information bottleneck model to learn task-relevant joint representations from egocentric images and proprioception. Our model compresses and retains the predictive information in multimodal observations for learning a compressed joint representation, which fuses complementary information from visual and proprioceptive feedback and meanwhile filters out task-irrelevant information in raw multimodal observations. We propose to minimize the upper bound of our multimodal information bottleneck objective for computationally tractable optimization. Experimental evaluations on several challenging locomotion tasks with egocentric images and proprioception show that our method achieves better sample efficiency and zero-shot robustness to unseen white noise than leading baselines. We also empirically demonstrate that leveraging information from egocentric images and proprioception is more helpful for learning policies on locomotion tasks than solely using one single modality., Comment: 31 pages

The XLZD collaboration is developing a two-phase xenon time projection chamber with an active mass of 60 to 80 t capable of probing the remaining WIMP-nucleon interaction parameter space down to the so-called neutrino fog. In this work we show that, based on the performance of currently operating detectors using the same technology and a realistic reduction of radioactivity in detector materials, such an experiment will also be able to competitively search for neutrinoless double beta decay in $^{136}$Xe using a natural-abundance xenon target. XLZD can reach a 3$\sigma$ discovery potential half-life of 5.7$\times$10$^{27}$ yr (and a 90% CL exclusion of 1.3$\times$10$^{28}$ yr) with 10 years of data taking, corresponding to a Majorana mass range of 7.3-31.3 meV (4.8-20.5 meV). XLZD will thus exclude the inverted neutrino mass ordering parameter space and will start to probe the normal ordering region for most of the nuclear matrix elements commonly considered by the community., Comment: 29 pages, 7 figures

Lithium-ion batteries (LIBs) are utilized as a major energy source in various fields because of their high energy density and long lifespan. During repeated charging and discharging, the degradation of LIBs, which reduces their maximum power output and operating time, is a pivotal issue. This degradation can affect not only battery performance but also safety of the system. Therefore, it is essential to accurately estimate the state-of-health (SOH) of the battery in real time. To address this problem, we propose a fast SOH estimation method that utilizes the sparse model identification algorithm (SINDy) for nonlinear dynamics. SINDy can discover the governing equations of target systems with low data assuming that few functions have the dominant characteristic of the system. To decide the state of degradation model, correlation analysis is suggested. Using SINDy and correlation analysis, we can obtain the data-driven SOH model to improve the interpretability of the system. To validate the feasibility of the proposed method, the estimation performance of the SOH and the computation time are evaluated by comparing it with various machine learning algorithms.

Automated Machine Learning (AutoML) approaches encompass traditional methods that optimize fixed pipelines for model selection and ensembling, as well as newer LLM-based frameworks that autonomously build pipelines. While LLM-based agents have shown promise in automating machine learning tasks, they often generate low-diversity and suboptimal code, even after multiple iterations. To overcome these limitations, we introduce Tree-Search Enhanced LLM Agents (SELA), an innovative agent-based system that leverages Monte Carlo Tree Search (MCTS) to optimize the AutoML process. By representing pipeline configurations as trees, our framework enables agents to conduct experiments intelligently and iteratively refine their strategies, facilitating a more effective exploration of the machine learning solution space. This novel approach allows SELA to discover optimal pathways based on experimental feedback, improving the overall quality of the solutions. In an extensive evaluation across 20 machine learning datasets, we compare the performance of traditional and agent-based AutoML methods, demonstrating that SELA achieves a win rate of 65% to 80% against each baseline across all datasets. These results underscore the significant potential of agent-based strategies in AutoML, offering a fresh perspective on tackling complex machine learning challenges., Comment: The code is available at https://github.com/geekan/MetaGPT

This report describes the experimental strategy and technologies for a next-generation xenon observatory sensitive to dark matter and neutrino physics. The detector will have an active liquid xenon target mass of 60-80 tonnes and is proposed by the XENON-LUX-ZEPLIN-DARWIN (XLZD) collaboration. The design is based on the mature liquid xenon time projection chamber technology of the current-generation experiments, LZ and XENONnT. A baseline design and opportunities for further optimization of the individual detector components are discussed. The experiment envisaged here has the capability to explore parameter space for Weakly Interacting Massive Particle (WIMP) dark matter down to the neutrino fog, with a 3$\sigma$ evidence potential for the spin-independent WIMP-nucleon cross sections as low as $3\times10^{-49}\rm cm^2$ (at 40 GeV/c$^2$ WIMP mass). The observatory is also projected to have a 3$\sigma$ observation potential of neutrinoless double-beta decay of $^{136}$Xe at a half-life of up to $5.7\times 10^{27}$ years. Additionally, it is sensitive to astrophysical neutrinos from the atmosphere, sun, and galactic supernovae., Comment: 32 pages, 14 figures

We report results of a search for nuclear recoils induced by weakly interacting massive particle (WIMP) dark matter using the LUX-ZEPLIN (LZ) two-phase xenon time projection chamber. This analysis uses a total exposure of $4.2\pm0.1$ tonne-years from 280 live days of LZ operation, of which $3.3\pm0.1$ tonne-years and 220 live days are new. A technique to actively tag background electronic recoils from $^{214}$Pb $\beta$ decays is featured for the first time. Enhanced electron-ion recombination is observed in two-neutrino double electron capture decays of $^{124}$Xe, representing a noteworthy new background. After removal of artificial signal-like events injected into the data set to mitigate analyzer bias, we find no evidence for an excess over expected backgrounds. World-leading constraints are placed on spin-independent (SI) and spin-dependent WIMP-nucleon cross sections for masses $\geq$9 GeV/$c^2$. The strongest SI exclusion set is $2.1\times10^{-48}$ cm$^{2}$ at the 90% confidence level at a mass of 36 GeV/$c^2$, and the best SI median sensitivity achieved is $5.0\times10^{-48}$ cm$^{2}$ for a mass of 40 GeV/$c^2$., Comment: 9 pages, 7 figures. See https://www.hepdata.net/record/155182 for a data release related to this paper

The integration of Artificial Intelligence (AI) in the financial domain has opened new avenues for quantitative trading, particularly through the use of Large Language Models (LLMs). However, the challenge of effectively synthesizing insights from diverse data sources and integrating both structured and unstructured data persists. This paper presents TradeExpert, a novel framework that employs a mix of experts (MoE) approach, using four specialized LLMs, each analyzing distinct sources of financial data, including news articles, market data, alpha factors, and fundamental data. The insights of these expert LLMs are further synthesized by a General Expert LLM to make a final prediction or decision. With specific prompts, TradeExpert can be switched between the prediction mode and the ranking mode for stock movement prediction and quantitative stock trading, respectively. In addition to existing benchmarks, we also release a large-scale financial dataset to comprehensively evaluate TradeExpert's effectiveness. Our experimental results demonstrate TradeExpert's superior performance across all trading scenarios.

Large language models (LLMs) have demonstrated remarkable potential in solving complex tasks across diverse domains, typically by employing agentic workflows that follow detailed instructions and operational sequences. However, constructing these workflows requires significant human effort, limiting scalability and generalizability. Recent research has sought to automate the generation and optimization of these workflows, but existing methods still rely on initial manual setup and fall short of achieving fully automated and effective workflow generation. To address this challenge, we reformulate workflow optimization as a search problem over code-represented workflows, where LLM-invoking nodes are connected by edges. We introduce AFlow, an automated framework that efficiently explores this space using Monte Carlo Tree Search, iteratively refining workflows through code modification, tree-structured experience, and execution feedback. Empirical evaluations across six benchmark datasets demonstrate AFlow's efficacy, yielding a 5.7% average improvement over state-of-the-art baselines. Furthermore, AFlow enables smaller models to outperform GPT-4o on specific tasks at 4.55% of its inference cost in dollars. The code will be available at https://github.com/geekan/MetaGPT.

Large Language Models (LLMs) exhibit impressive capabilities but require careful alignment with human preferences. Traditional training-time methods finetune LLMs using human preference datasets but incur significant training costs and require repeated training to handle diverse user preferences. Test-time alignment methods address this by using reward models (RMs) to guide frozen LLMs without retraining. However, existing test-time approaches rely on trajectory-level RMs which are designed to evaluate complete responses, making them unsuitable for autoregressive text generation that requires computing next-token rewards from partial responses. To address this, we introduce GenARM, a test-time alignment approach that leverages the Autoregressive Reward Model--a novel reward parametrization designed to predict next-token rewards for efficient and effective autoregressive generation. Theoretically, we demonstrate that this parametrization can provably guide frozen LLMs toward any distribution achievable by traditional RMs within the KL-regularized reinforcement learning framework. Experimental results show that GenARM significantly outperforms prior test-time alignment baselines and matches the performance of training-time methods. Additionally, GenARM enables efficient weak-to-strong guidance, aligning larger LLMs with smaller RMs without the high costs of training larger models. Furthermore, GenARM supports multi-objective alignment, allowing real-time trade-offs between preference dimensions and catering to diverse user preferences without retraining.

Higgs and Goldstone modes manifest as fluctuations in the order parameter of system, offering insights into its phase transitions and symmetry properties. Exploring the dynamics of these collective excitations in a Rydberg atoms system advances various branches of condensed matter, particle physics, and cosmology. Here, we report an experimental signature of Higgs and Goldstone modes in a U(1) symmetry-broken Rydberg atomic gases. By constructing two probe fields to excite atoms, we observe the distinct phase and amplitude fluctuations of Rydberg atoms collective excitations under the particle-hole symmetry. Due to the van der Waals interactions between the Rydberg atoms, we detect a symmetric variance spectrum divided by the divergent regime and phase boundary, capturing the full dynamics of the additional Higgs and Goldstone modes. Studying the Higgs and Goldstone modes in Rydberg atoms allows us to explore fundamental aspects of quantum phase transitions and symmetry breaking phenomena, while leveraging the unique properties of these highly interacting systems to uncover new physics and potential applications in quantum simulation.

A 2-edge-coloured graph $G$ is called {\bf locally complete} if for each vertex $v$, the vertices adjacent to $v$ through edges of the same colour induce a complete subgraph in $G$. Locally complete 2-edge-coloured graphs have nice properties and there exists a polynomial algorithm to decide whether such a graph has an alternating hamiltonian cycle, where alternating means that the colour of two consecutive edges on the cycle are different. In this paper we show that graphs having locally complete 2-edge-colourings can be recognized in polynomial time. We give a forbidden substructure characterization for this class of graphs analogous to Gallai's characterization for cocomparability graphs. Finally, we characterize proper interval graphs and proper circular-arc graphs which have locally complete 2-edge-colourings by forbidden subgraphs.

It is highly desirable to modify and improve the Dirac electron system of graphene for novel electronic properties and promising applications. For this purpose, we study 2D heterostructures consisting of graphene and monolayer TMDs by means of first-principles calculation and effective low-energy hamiltonian model. We determine the model parameters by fitting with the first-prinples bands. MoSe$_2$ and WSe$_2$ are chosen in order to align the Dirac cones of graphene with the intrinsic Fermi levels of the TMDs. It is found that the Dirac energy bands of graphene are modified, but the linear band dispersion near the cones is kept. It is shown that the effective low-energy model hosts Dirac-Rashba feimions in the WSe$_2$/graphene and MoSe$_2$/graphene/WSe$_2$, and there is quantum valley Hall effect in these graphene-based 2D heterostructures. Our further analyses indicate that there are strong interactions between the orbitals and spins especially near the K and K' points. These can be useful in further exploration for novel properties and more functionalities in 2D heterostructures., Comment: 7 pages, 5 figures

Data augmentation, a cornerstone technique in deep learning, is crucial in enhancing model performance, especially with scarce labeled data. While traditional techniques are effective, their reliance on hand-crafted methods limits their applicability across diverse data types and tasks. Although modern learnable augmentation methods offer increased adaptability, they are computationally expensive and challenging to incorporate within prevalent augmentation workflows. In this work, we present a novel, efficient method for data augmentation, effectively bridging the gap between existing augmentation strategies and emerging datasets and learning tasks. We introduce SAFLEX (Self-Adaptive Augmentation via Feature Label EXtrapolation), which learns the sample weights and soft labels of augmented samples provided by any given upstream augmentation pipeline, using a specifically designed efficient bilevel optimization algorithm. Remarkably, SAFLEX effectively reduces the noise and label errors of the upstream augmentation pipeline with a marginal computational cost. As a versatile module, SAFLEX excels across diverse datasets, including natural and medical images and tabular data, showcasing its prowess in few-shot learning and out-of-distribution generalization. SAFLEX seamlessly integrates with common augmentation strategies like RandAug, CutMix, and those from large pre-trained generative models like stable diffusion and is also compatible with frameworks such as CLIP's fine-tuning. Our findings highlight the potential to adapt existing augmentation pipelines for new data types and tasks, signaling a move towards more adaptable and resilient training frameworks., Comment: ICLR 2024