Miracle-dz/UniFAR-FTUs huggingface dataset:
AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices

Federated Learning (FL) has achieved significant achievements recently, enabling collaborative model training on distributed data over edge devices. Iterative gradient or model exchanges between devices and the centralized server in the standard FL paradigm suffer from severe efficiency bottlenecks on the server. While enabling collaborative training without a central server, existing decentralized FL approaches either focus on the synchronous mechanism that deteriorates FL convergence or ignore device staleness with an asynchronous mechanism, resulting in inferior FL accuracy. In this paper, we propose an Asynchronous Efficient Decentralized FL framework, i.e., AEDFL, in heterogeneous environments with three unique contributions. First, we propose an asynchronous FL system model with an efficient model aggregation method for improving the FL convergence. Second, we propose a dynamic staleness-aware model update approach to achieve superior accuracy. Third, we propose an adaptive sparse training method to reduce communication and computation costs without significant accuracy degradation. Extensive experimentation on four public datasets and four models demonstrates the strength of AEDFL in terms of accuracy (up to 16.3% higher), efficiency (up to 92.9% faster), and computation costs (up to 42.3% lower).
An In-depth Look at Gemini's Language Abilities

The recently released Google Gemini class of models are the first to comprehensively report results that rival the OpenAI GPT series across a wide variety of tasks. In this paper, we do an in-depth exploration of Gemini's language abilities, making two contributions. First, we provide a third-party, objective comparison of the abilities of the OpenAI GPT and Google Gemini models with reproducible code and fully transparent results. Second, we take a closer look at the results, identifying areas where one of the two model classes excels. We perform this analysis over 10 datasets testing a variety of language abilities, including reasoning, answering knowledge-based questions, solving math problems, translating between languages, generating code, and acting as instruction-following agents. From this analysis, we find that Gemini Pro achieves accuracy that is close but slightly inferior to the corresponding GPT 3.5 Turbo on all tasks that we benchmarked. We further provide explanations for some of this under-performance, including failures in mathematical reasoning with many digits, sensitivity to multiple-choice answer ordering, aggressive content filtering, and others. We also identify areas where Gemini demonstrates comparably high performance, including generation into non-English languages, and handling longer and more complex reasoning chains. Code and data for reproduction can be found at https://github.com/neulab/gemini-benchmark
Backward stochastic difference equations on lattices with application to market equilibrium analysis

We study backward stochastic difference equations (BS{\Delta}E) driven by a d-dimensional stochastic process on a lattice whose increments have only d + 1 possible values that generates the lattice. Regarding the driving process as a d dimensional asset price process, we give applications to an optimal investment problem and a market equilibrium analysis, where utility functionals are defined through BS{\Delta}E.
Global-Local MAV Detection under Challenging Conditions based on Appearance and Motion

Visual detection of micro aerial vehicles (MAVs) has received increasing research attention in recent years due to its importance in many applications. However, the existing approaches based on either appearance or motion features of MAVs still face challenges when the background is complex, the MAV target is small, or the computation resource is limited. In this paper, we propose a global-local MAV detector that can fuse both motion and appearance features for MAV detection under challenging conditions. This detector first searches MAV target using a global detector and then switches to a local detector which works in an adaptive search region to enhance accuracy and efficiency. Additionally, a detector switcher is applied to coordinate the global and local detectors. A new dataset is created to train and verify the effectiveness of the proposed detector. This dataset contains more challenging scenarios that can occur in practice. Extensive experiments on three challenging datasets show that the proposed detector outperforms the state-of-the-art ones in terms of detection accuracy and computational efficiency. In particular, this detector can run with near real-time frame rate on NVIDIA Jetson NX Xavier, which demonstrates the usefulness of our approach for real-world applications. The dataset is available at https://github.com/WestlakeIntelligentRobotics/GLAD. In addition, A video summarizing this work is available at https://youtu.be/Tv473mAzHbU.
A Unified Framework for Multi-Domain CTR Prediction via Large Language Models

Click-Through Rate (CTR) prediction is a crucial task in online recommendation platforms as it involves estimating the probability of user engagement with advertisements or items by clicking on them. Given the availability of various services like online shopping, ride-sharing, food delivery, and professional services on commercial platforms, recommendation systems in these platforms are required to make CTR predictions across multiple domains rather than just a single domain. However, multi-domain click-through rate (MDCTR) prediction remains a challenging task in online recommendation due to the complex mutual influence between domains. Traditional MDCTR models typically encode domains as discrete identifiers, ignoring rich semantic information underlying. Consequently, they can hardly generalize to new domains. Besides, existing models can be easily dominated by some specific domains, which results in significant performance drops in the other domains (i.e. the"seesaw phenomenon"). In this paper, we propose a novel solution Uni-CTR to address the above challenges. Uni-CTR leverages a backbone Large Language Model (LLM) to learn layer-wise semantic representations that capture commonalities between domains. Uni-CTR also uses several domain-specific networks to capture the characteristics of each domain. Note that we design a masked loss strategy so that these domain-specific networks are decoupled from backbone LLM. This allows domain-specific networks to remain unchanged when incorporating new or removing domains, thereby enhancing the flexibility and scalability of the system significantly. Experimental results on three public datasets show that Uni-CTR outperforms the state-of-the-art (SOTA) MDCTR models significantly. Furthermore, Uni-CTR demonstrates remarkable effectiveness in zero-shot prediction. We have applied Uni-CTR in industrial scenarios, confirming its efficiency.
Probing Shocked Ejecta in SN 1987A: A Novel Diagnostic Approach Using XRISM-Resolve

Supernova (SN) 1987A is one of the best candidates to exploit the capabilities of the freshly launched XRISM satellite. This celestial object offers the unique opportunity to study the evolution of an SN into a young supernova remnant. To date, the X-ray emission has been dominated by the shocked circumstellar medium, with no shocked ejecta firmly detected. However, recent studies provide compelling evidence that in the forthcoming years, the X-ray emission from SN 1987A will increasingly stem from the ejecta. Our aim is to assess the proficiency of the XRISM-Resolve high-resolution spectrometer in pinpointing signatures of the shocked ejecta in SN 1987A. Taking advantage of a self-consistent state-of-the-art magneto-hydrodynamic simulation that describes the evolution from SN 1987A to its remnant, we synthesized the XRISM-Resolve spectrum of SN 1987A, as it would be collected in the allocated observation during the performance verification phase, which is foreseen for 2024. Our predictions clearly show the leading role of shocked ejecta in shaping the profile of the emission lines. The Doppler broadening associated with the bulk motion along the line of sight of the rapidly expanding ejecta is shown to increase the line widths well above the values observed so far. The quantitative comparison between our synthetic spectra and the XRISM spectra will enable us to establish a strong connection between the broadened line emission and the freshly shocked ejecta. This, in turn, will allow us to retrieve the ejecta dynamics and chemical composition from the X-ray emission.
Time-periodic solutions to the Navier--Stokes equations on the whole space including the two-dimensional case

Let us consider the incompressible Navier--Stokes equations with the time-periodic external forces in the whole space $\mathbb{R}^n$ with $n\geq 2$ and investigate the existence and non-existence of time-periodic solutions. In the higher dimensional case $n \geq 3$, we construct a unique small solution for given small time-periodic force in the scaling critical spaces of Besov type and prove its stability under small perturbations. In contrast, for the two-dimensional case $n=2$, the time-periodic solvability of the Navier--Stokes equations has been long standing open. It is the central work of this paper that we have now succeeded in solving this issue negatively by providing examples of small external forces such that each of them does not generate time-periodic solutions.
GIARPS High-resolution Observations of T Tauri stars (GHOST) V. New insights into disk winds from 3 km/s resolution observations

This paper aims at revisit the physical and dynamical properties of the warm atomic gas in the inner disk region of classical T Tauri stars (CTTs) and relate them to the properties of the outer dusty disk. We used the high resolution (R=115,000) spectra of 36 CTTs observed as part of the GHOsT project and analysed the profile and luminosity of the brightest optical forbidden lines, namely [OI]630 and 557nm, [SII]406 and 673nm, and [NII]658nm. We find that in about 40% of sources the so-called narrow low-velocity component (NLVC) display a peak velocity compatible with the stellar velocity. In these sources, that typically show lower mass accretion rates and the absence of a jet, the [OI]630nm profiles are well fitted by a simple Keplerian disk model, indicating that the emission from the disk is dominant with respect to the wind contribution. For transitional disks (TD), no correlation is found between $R_{kep}$, derived from the line HWHM, and the size of the dust cavity. We also see an anti-correlation between the [OI] 557/630 nm ratio and $R_{kep}$, which suggests that the [OI] emitting region expands as the gas cools and becomes less dense. We confirmed previous findings on the density and temperature ranges implied by the line ratios, and additionally constrained the ionisation fraction in the NLVC to be<0.1. We however discuss the limits of applying this diagnostic to winds that are not spatially resolved. For the outflow component, we estimated the mass-loss for both the disk winds and jets and compared the results with X-ray photoevaporative models. We conclude that without better knowledge of the wind geometry, and given the limitation of the diagnostics, the mass-loss in the wind traced by the LVC cannot be constrained better than a factor of 100, with a mass-loss/mass-accretion ratio spanning between ~ 0.01 and more than 1.
Analytical and numerical calculation of the effect of edge states of the Kane-Mele model on the RKKY interaction

In this paper, we investigate the Kane-Mele model and endeavor to demonstrate, through analytical calculations, how the presence of topological edge states influences the RKKY interaction. We illustrate that the effect diminishes as one moves away from the edges. To facilitate our analytical approach, we initially utilize a one-dimensional wire exhibiting linear dispersion for each spin as an approximation to the Kane-Mele model. We examine its impact on the RKKY interaction. Subsequently, we establish a correspondence between the edge states of the Kane-Mele model and a one-dimensional quantum wire model, wherein the coupling strength diminishes with increasing distance from the edges. Finally, we compare the analytical results with numerical findings obtained using the Landauer-Buttiker formulation.
Code Ownership in Open-Source AI Software Security

As open-source AI software projects become an integral component in the AI software development, it is critical to develop a novel methods to ensure and measure the security of the open-source projects for developers. Code ownership, pivotal in the evolution of such projects, offers insights into developer engagement and potential vulnerabilities. In this paper, we leverage the code ownership metrics to empirically investigate the correlation with the latent vulnerabilities across five prominent open-source AI software projects. The findings from the large-scale empirical study suggest a positive relationship between high-level ownership (characterised by a limited number of minor contributors) and a decrease in vulnerabilities. Furthermore, we innovatively introduce the time metrics, anchored on the project's duration, individual source code file timelines, and the count of impacted releases. These metrics adeptly categorise distinct phases of open-source AI software projects and their respective vulnerability intensities. With these novel code ownership metrics, we have implemented a Python-based command-line application to aid project curators and quality assurance professionals in evaluating and benchmarking their on-site projects. We anticipate this work will embark a continuous research development for securing and measuring open-source AI project security.
Fractional factors and component factors in graphs with isolated toughness smaller than 1

Let $G$ be a simple graph and let $n,m$ be two integers with $0<m<n$. We prove that $iso(G-S)\leq \frac{n}{m}|S|$ for every $S \subset V(G)$ if and only if $G$ has a $\{C_{2i+1},T \colon 1 \leq i<\frac{m}{n-m}, T\in\mathcal{T}_{\frac{n}{m}}\}$-factor, where $iso(G-S)$ denotes the number of isolated vertices of $G-S$ and $\mathcal{T}_{\frac{n}{m}}$ is a special family of trees. Furthermore, we characterize the trees in $\mathcal{T}_{\frac{n}{m}}$ in terms of their bipartition.
CLIM: Contrastive Language-Image Mosaic for Region Representation

Detecting objects accurately from a large or open vocabulary necessitates the vision-language alignment on region representations. However, learning such a region-text alignment by obtaining high-quality box annotations with text labels or descriptions is expensive and infeasible. In contrast, collecting image-text pairs is simpler but lacks precise object location information to associate regions with texts. In this paper, we propose a novel approach called Contrastive Language-Image Mosaic (CLIM), which leverages large-scale image-text pairs effectively for aligning region and text representations. CLIM combines multiple images into a mosaicked image and treats each image as a ‘pseudo region’. The feature of each pseudo region is extracted and trained to be similar to the corresponding text embedding while dissimilar from others by a contrastive loss, enabling the model to learn the region-text alignment without costly box annotations. As a generally applicable approach, CLIM consistently improves different open-vocabulary object detection methods that use caption supervision. Furthermore, CLIM can effectively enhance the region representation of vision-language models, thus providing stronger backbones for open-vocabulary object detectors. Our experimental results demonstrate that CLIM improves different baseline open-vocabulary object detectors by a large margin on both OV-COCO and OV-LVIS benchmarks. The code is available at https://github.com/wusize/CLIM.
Aspect-Based Sentiment Analysis with Explicit Sentiment Augmentations

Aspect-based sentiment analysis (ABSA), a fine-grained sentiment classification task, has received much attention recently. Many works investigate sentiment information through opinion words, such as "good'' and "bad''. However, implicit sentiment data widely exists in the ABSA dataset, whose sentiment polarity is hard to determine due to the lack of distinct opinion words. To deal with implicit sentiment, this paper proposes an ABSA method that integrates explicit sentiment augmentations (ABSA-ESA) to add more sentiment clues. We propose an ABSA-specific explicit sentiment generation method to create such augmentations. Specifically, we post-train T5 by rule-based data and employ three strategies to constrain the sentiment polarity and aspect term of the generated augmentations. We employ Syntax Distance Weighting and Unlikelihood Contrastive Regularization in the training procedure to guide the model to generate the explicit opinion words with the same polarity as the input sentence. Meanwhile, we utilize the Constrained Beam Search to ensure the augmentations are aspect-related. We test ABSA-ESA on two ABSA benchmarks. The results show that ABSA-ESA outperforms the SOTA baselines on implicit and explicit sentiment accuracy.
Performance of two-level sampling for the glueball spectrum in pure gauge theory

The computation of the glueball spectrum is particularly challenging due to the rapid decay of the signal-to-noise ratio of the correlation functions. To address this issue, advanced techniques such as gauge link smearing and the variational method are commonly employed to identify the spectrum before the signal diminishes significantly. However, a significant improvement in the signal-to-noise ratio can be achieved by utilising multilevel sampling techniques. In this talk, we present a study of the glueball spectrum in pure gauge theory with a two-level algorithm. Specifically, we explore the relation between noise reduction and the various multilevel parameters, such as the width of the dynamical regions and the number of submeasurements.
Effect of Time of Planting Cowpea (Vigna unguiculata (L.) Walp.) Relative to Maize (Zea mays L.) on Growth and Yield of Cowpea

Summary Field investigations were carried out for three seasons in two locations of Uganda to examine yield benefits when cowpea and maize are planted under intensive farming conditions. Additive mixtures of cowpea were planted into maize thrice at 2 weekly intervals together with sole crops. Time of introducing cowpea into maize significantly affected both the growth and yield of cowpea. Simultaneous planting generally showed a yield advantage (LER> 1) of the cowpea/ maize intercropping systems irrespective of the cowpea varieties used, but LER declined when time of introducing cowpea into maize was delayed being as low as 0.76 when cowpea was planted four weeks after planting maize. The reduction in the growth and yield of cowpea was due to increased shading from the maize plants especially when cowpea was introduced at the fourth week. Therefore, to achieve yield benefit simultaneous planting of maize and cowpea recommended. Resume Influence du decalage du semis du niebe ( Vigna unguiculata (L.) Walp. ) par rapport au mais (Zea mays L.) sur la croissance et le rendement du niebe
Characterizing present and future drought changes over eastern China

This study aimed to characterize present and future drought changes over eastern China using observations and Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations. We used the standardized precipitation evapotranspiration index (SPEI) to characterize droughts at the timescales of 3‐ and 12‐month. We distinguished the spatial patterns of drought regimes (DRs) using the rotated empirical orthogonal function (EOF) on the SPEI. Our results identified three DRs centered in northern China (DR1), northeastern China (DR2), and southern China (DR3). Droughts in these regimes have increased in recent decades. Most CMIP5 models reproduce at least two of the DRs. The rotated EOF results indicate that the three DRs can jointly explain 46–68% of the SPEI variance (compared with approximately 60% in the observations). Among the 33 CMIP5 models analysed in this study, 13 simulated all three DRs well and showed a strong correlation pattern (>0.5). Further analysis indicated that both the external natural and greenhouse gas forcing experiments in CMIP5 reproduced the DRs, implying that natural variability and anthropogenic activity play important roles in the formation of these DRs. With continued climate warming, the three DRs over eastern China will persist. Despite the uncertainties in drought changes that mainly depend on the potential evapotranspiration methods, climate model results suggest that droughts would be aggravated under warming scenarios. The probability of severe droughts increase by the end of the century: 33% in DR1, 25% in DR2, 34% in DR3 in RCP4.5 and almost double in RCP8.5 scenario in Thornthwaite method based SPEI estimates.
A Random Effects Model-based Method of Moments Estimation of Causal Effect in Mendelian Randomization Studies

Recent advances in genotyping technology have delivered a wealth of genetic data, which is rapidly advancing our understanding of the underlying genetic architecture of complex diseases. Mendelian Randomization (MR) leverages such genetic data to estimate the causal effect of an exposure factor on an outcome from observational studies. In this paper, we utilize genetic correlations to summarize information on a large set of genetic variants associated with the exposure factor. Our proposed approach is a generalization of the MR-inverse variance weighting (IVW) approach where we can accommodate many weak and pleiotropic effects. Our approach quantifies the variation explained by all valid instrumental variables (IVs) instead of estimating the individual effects and thus could accommodate weak IVs. This is particularly useful for performing MR estimation in small studies, or minority populations where the selection of valid IVs is unreliable and thus has a large influence on the MR estimation. Through simulation and real data analysis, we demonstrate that our approach provides a robust alternative to the existing MR methods. We illustrate the robustness of our proposed approach under the violation of MR assumptions and compare the performance with several existing approaches.
Random surfaces with large systoles

We present two constructions, both inspired by ideas from graph theory, of sequences random surfaces of growing area, whose systoles grow logarithmically as a function of their area. This also allows us to prove a new lower bound on the maximal systole of a closed orientable hyperbolic surface of a given genus.
Endogenous preference for non-market goods in carbon abatement decision

Carbon abatement decisions are usually based on the implausible assumption of constant social preference. This paper focuses on a specific case of market and non-market goods, and investigates the optimal climate policy when social preference for them is also changed by climate policy in the DICE model. The relative price of non-market goods grows over time due to increases in both relative scarcity and appreciation of it. Therefore, climbing relative price brings upward the social cost of carbon denominated in terms of market goods. Because abatement decision affects the valuation of non-market goods in the utility function, unlike previous climate-economy models, we solve the model iteratively by taking the obtained abatement rates from the last run as inputs in the current run. The results in baseline calibration advocate a more stringent climate policy, where endogenous social preference to climate policy raises the social cost of carbon further by roughly 12%-18% this century. Moreover, neglecting changing social preference leads to an underestimate of non-market goods damages by 15%. Our results support that climate policy is self-reinforced if it favors more expensive consumption type.