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The ongoing discovery of exoplanets has sparked significant interest in finding suitable worlds that could potentially support life. Stellar ultraviolet (UV; 100-3000 \AA) radiation may play a crucial role in determining the habitability of their planets. In this paper, we conducted a detailed analysis of the UV photometry of over 2700 host stars with confirmed planets, using observational data from the GALEX and Swift UVOT missions. We performed aperture photometry on single-exposure images, and provided photometric catalogs that can be used to explore a wide range of scientific questions, such as stellar UV activity and planet habitability. By calculating the circumstellar habitable zone (CHZ) and UV habitable zone (UHZ), we found that fewer than 100 exoplanets fall within both of these zones, with the majority being gas giants. We also examined stellar activity based on their far-UV (FUV) and near-UV (NUV) emissions. We found the FUV$-$NUV color more effectively represents stellar activity compared to the $R^{\prime}_{\rm FUV}$ and $R^{\prime}_{\rm NUV}$ indices. The Sun's low FUV emission and moderate NUV emission highlight its uniqueness among (solar-like) stars., Comment: 22 pages, 14 figures, 6 tables. Accepted by APJS. Comments welcome!

Large language models (LLMs) have achieved remarkable performance and are widely deployed in various applications, while the serving of LLM inference has raised concerns about user experience and serving throughput. Accordingly, service level objectives (SLOs) and goodput-the number of requests that meet SLOs per second-are introduced to evaluate the performance of LLM serving. However, existing metrics fail to capture the nature of user experience. We observe two ridiculous phenomena in existing metrics: 1) delaying token delivery can smooth the tail time between tokens (tail TBT) of a request and 2) dropping the request that fails to meet the SLOs midway can improve goodput. In this paper, we revisit SLO and goodput metrics in LLM serving and propose a unified metric framework smooth goodput including SLOs and goodput to reflect the nature of user experience in LLM serving. The framework can adapt to specific goals of different tasks by setting parameters. We re-evaluate the performance of different LLM serving systems under multiple workloads based on this unified framework and provide possible directions for future optimization of existing strategies. We hope that this framework can provide a unified standard for evaluating LLM serving and foster researches in the field of LLM serving optimization to move in a cohesive direction.

Stellar ultraviolet (UV) emission serves as a crucial indicator for estimating magnetic activity and evaluating the habitability of exoplanets orbiting stars. In this paper, we present a straightforward method to derive stellar photospheric UV emission for F to M main-sequence stars. By using PARSEC models, we establish relations between near-UV (NUV) and far-UV (FUV) magnitudes from the Galaxy Evolution Explorer (GALEX), NUV magnitudes from the China Space Station Telescope, and stellar effective temperatures and Gaia BP$-$RP color for different metallicities. Together with the observed sample, we find that for NUV emission, the photospheric contribution to the observed flux is less than 20% for M stars, around 10% to 70% for G stars, and ranges from 30% to 85% for G and F stars. For FUV emission, the photospheric contribution is less than $10^{-6}$ for M stars, below $10^{-4}$ for K stars, around $10^{-4}$ to 10% for G stars, and between 6% and 50% for F stars. Our work enables the simple and effective determination of stellar excess UV emission and the exploration of magnetic activity., Comment: 10 pages, 6 figures, 3 tables. Accepted for publication in ApJ

We performed a suit of three-dimensional hydrodynamical simulations with a resolution of $\sim10$ parsecs to investigate the development of multiphase galactic wind in M82. The star formation and related feedback processes are solved self-consistently using a sink particle method, rather than relying on various assumptions that were used in previous studies. Our simulations produce a starburst event lasting around 25 Myr, which has a total stellar mass of 1.62 - 3.34 $\times 10^8\, \rm{M_{\odot}}$, consistent with observational estimates. The total injected supernova energy is between $1.14\times 10^{57}$ and $2.4\times 10^{57} \rm{erg}$. Supernova (SN) feedback heats portions of the cool gas in the central disc to warm and hot phases, and then drives the gas in all three phases out, eventually forming multiphase outflows. These outflows can replicate key properties of the winds observed in M82, such as morphology, mass outflow rate, and X-ray emission flux, provided the gas return from star-forming clumps to the interstellar medium is implemented appropriately. The maximum mass outflow rate of all gas (hot) is about 6-12 (2-3)$\rm{M_{\odot}/yr}$ at $r\sim4.0\,$ kpc, corresponding to a mass loading factor of 2-4. However, the outflow velocities in our simulations are slower than observational estimates by $\sim 20\%-60\%$. The gas return process significantly influences the outflow properties, while the initial gas distribution in the nuclear region has a moderate effect. However, our results face some challenges in achieving convergence as the resolution increases. We discuss potential improvements to address these issues in future work., Comment: 30 pages, 19 figures, to be submitted. Comments welcome

In this work, we present MedImageInsight, an open-source medical imaging embedding model. MedImageInsight is trained on medical images with associated text and labels across a diverse collection of domains, including X-Ray, CT, MRI, dermoscopy, OCT, fundus photography, ultrasound, histopathology, and mammography. Rigorous evaluations demonstrate MedImageInsight's ability to achieve state-of-the-art (SOTA) or human expert level performance across classification, image-image search, and fine-tuning tasks. Specifically, on public datasets, MedImageInsight achieves SOTA in CT 3D medical image retrieval, as well as SOTA in disease classification and search for chest X-ray, dermatology, and OCT imaging. Furthermore, MedImageInsight achieves human expert performance in bone age estimation (on both public and partner data), as well as AUC above 0.9 in most other domains. When paired with a text decoder, MedImageInsight achieves near SOTA level single image report findings generation with less than 10\% the parameters of other models. Compared to fine-tuning GPT-4o with only MIMIC-CXR data for the same task, MedImageInsight outperforms in clinical metrics, but underperforms on lexical metrics where GPT-4o sets a new SOTA. Importantly for regulatory purposes, MedImageInsight can generate ROC curves, adjust sensitivity and specificity based on clinical need, and provide evidence-based decision support through image-image search (which can also enable retrieval augmented generation). In an independent clinical evaluation of image-image search in chest X-ray, MedImageInsight outperformed every other publicly available foundation model evaluated by large margins (over 6 points AUC), and significantly outperformed other models in terms of AI fairness (across age and gender). We hope releasing MedImageInsight will help enhance collective progress in medical imaging AI research and development.

Text-Video Retrieval (TVR) methods typically match query-candidate pairs by aligning text and video features in coarse-grained, fine-grained, or combined (coarse-to-fine) manners. However, these frameworks predominantly employ a one(query)-to-one(candidate) alignment paradigm, which struggles to discern nuanced differences among candidates, leading to frequent mismatches. Inspired by Comparative Judgement in human cognitive science, where decisions are made by directly comparing items rather than evaluating them independently, we propose TokenBinder. This innovative two-stage TVR framework introduces a novel one-to-many coarse-to-fine alignment paradigm, imitating the human cognitive process of identifying specific items within a large collection. Our method employs a Focused-view Fusion Network with a sophisticated cross-attention mechanism, dynamically aligning and comparing features across multiple videos to capture finer nuances and contextual variations. Extensive experiments on six benchmark datasets confirm that TokenBinder substantially outperforms existing state-of-the-art methods. These results demonstrate its robustness and the effectiveness of its fine-grained alignment in bridging intra- and inter-modality information gaps in TVR tasks.

Learning from data silos is a difficult task for organizations that need to obtain knowledge of objects that appeared in multiple independent data silos. Objects in multi-organizations, such as government agents, are referred by different identifiers, such as driver license, passport number, and tax file number. The data distributions in data silos are mostly non-IID (Independently and Identically Distributed), labelless, and vertically partitioned (i.e., having different attributes). Privacy concerns harden the above issues. Conditions inhibit enthusiasm for collaborative work. While Federated Learning (FL) has been proposed to address these issues, the difficulty of labeling, namely, label costliness, often hinders optimal model performance. A potential solution lies in contrastive learning, an unsupervised self-learning technique to represent semantic data by contrasting similar data pairs. However, contrastive learning is currently not designed to handle tabular data silos that existed within multiple organizations where data linkage by quasi identifiers are needed. To address these challenges, we propose using semi-supervised contrastive federated learning, which we refer to as Contrastive Federated Learning with Data Silos (CFL). Our approach tackles the aforementioned issues with an integrated solution. Our experimental results demonstrate that CFL outperforms current methods in addressing these challenges and providing improvements in accuracy. Additionally, we present positive results that showcase the advantages of our contrastive federated learning approach in complex client environments., Comment: 18 Pages. Was submitted on Artificial Intelligence Journal, Jan 29, 2024, ARTINT-D-24-00098

A quantum network provides an infrastructure connecting quantum devices with revolutionary computing, sensing, and communication capabilities. As the best-known application of a quantum network, quantum key distribution (QKD) shares secure keys guaranteed by the laws of quantum mechanics. A quantum satellite constellation offers a solution to facilitate the quantum network on a global scale. The Micius satellite has verified the feasibility of satellite quantum communications, however, scaling up quantum satellite constellations is challenging, requiring small lightweight satellites, portable ground stations and real-time secure key exchange. Here we tackle these challenges and report the development of a quantum microsatellite capable of performing space-to-ground QKD using portable ground stations. The quantum microsatellite features a payload weighing approximately 23 kg, while the portable ground station weighs about 100 kg. These weights represent reductions by more than an order and two orders of magnitude, respectively, compared to the Micius satellite. Additionally, we multiplex bidirectional satellite-ground optical communication with quantum communication, enabling key distillation and secure communication in real-time. Using the microsatellite and the portable ground stations, we demonstrate satellite-based QKD with multiple ground stations and achieve the sharing of up to 0.59 million bits of secure keys during a single satellite pass. The compact quantum payload can be readily assembled on existing space stations or small satellites, paving the way for a satellite-constellation-based quantum and classical network for widespread real-life applications., Comment: 40 pages, 8 figures

We study the large scale fluctuations of the KPZ equation in dimensions $d \geq 3$ driven by Gaussian noise that is white in time Gaussian but features non-integrable spatial correlation with decay rate $\kappa \in (2, d)$ and a suitable limiting profile. We show that its scaling limit is described by the corresponding additive stochastic heat equation. In contrast to the case of compactly supported covariance, the noise in the stochastic heat equation retains spatial correlation with covariance $|x|^{-\kappa}$. Surprisingly, the noise driving the limiting equation turns out to be the scaling limit of the noise driving the KPZ equation so that, under a suitable coupling, one has convergence in probability, unlike in the case of integrable correlations where fluctuations are enhanced in the limit and convergence is necessarily weak.

In this paper, we present Reed-Solomon coded single-stranded representation learning (RSRL), a novel end-to-end model for learning representations for multi-modal lossless DNA storage. In contrast to existing learning-based methods, the proposed RSRL is inspired by both error-correction codec and structural biology. Specifically, RSRL first learns the representations for the subsequent storage from the binary data transformed by the Reed-Solomon codec. Then, the representations are masked by an RS-code-informed mask to focus on correcting the burst errors occurring in the learning process. With the decoded representations with error corrections, a novel biologically stabilized loss is formulated to regularize the data representations to possess stable single-stranded structures. By incorporating these novel strategies, the proposed RSRL can learn highly durable, dense, and lossless representations for the subsequent storage tasks into DNA sequences. The proposed RSRL has been compared with a number of strong baselines in real-world tasks of multi-modal data storage. The experimental results obtained demonstrate that RSRL can store diverse types of data with much higher information density and durability but much lower error rates.

Inspired by Y. Ollivier's coarse Ricci curvature, we introduce a novel concept of coarse extrinsic curvature on Riemannian submanifolds. This is defined through Wasserstein distances between test probability measures supported in the tubular neighbourhood of the submanifold. This framework provides an understanding of the geometric properties of embeddings, offering valuable insights into their curvature dynamics and intrinsic structures. Additionally, this coarse curvature can also be extracted from empirical measures supported on random point clouds generated by a Poisson point process, and has the potential to extend to metric embeddings., Comment: 52 pages, 6 figures

Given an input query, a recommendation model is trained using user feedback data (e.g., click data) to output a ranked list of items. In real-world systems, besides accuracy, an important consideration for a new model is novelty of its top-k recommendations w.r.t. an existing deployed model. However, novelty of top-k items is a difficult goal to optimize a model for, since it involves a non-differentiable sorting operation on the model's predictions. Moreover, novel items, by definition, do not have any user feedback data. Given the semantic capabilities of large language models, we address these problems using a reinforcement learning (RL) formulation where large language models provide feedback for the novel items. However, given millions of candidate items, the sample complexity of a standard RL algorithm can be prohibitively high. To reduce sample complexity, we reduce the top-k list reward to a set of item-wise rewards and reformulate the state space to consist of tuples such that the action space is reduced to a binary decision; and show that this reformulation results in a significantly lower complexity when the number of items is large. We evaluate the proposed algorithm on improving novelty for a query-ad recommendation task on a large-scale search engine. Compared to supervised finetuning on recent pairs, the proposed RL-based algorithm leads to significant novelty gains with minimal loss in recall. We obtain similar results on the ORCAS query-webpage matching dataset and a product recommendation dataset based on Amazon reviews., Comment: Accepted at KDD 2024

Semiconducting transition metal dichalcogenides (MX$_2$) occur in 2H and rhombohedral (3R) polytypes, respectively distinguished by anti-parallel and parallel orientation of consecutive monolayer lattices. In its bulk form, 3R-MX$_2$ is ferroelectric, hosting an out-of-plane electric polarisation, the direction of which is dictated by stacking. Here, we predict that twin boundaries, separating adjacent polarization domains with reversed built-in electric fields, are able to host two-dimensional electrons and holes with an areal density reaching $\sim 10^{13} {\rm cm}^{-2}$. Our modelling suggests that n-doped twin boundaries have a more promising binding energy than p-doped ones, whereas hole accumulation is stable at external surfaces of a twinned film. We also propose that assembling pairs of mono-twin films with a `magic' twist angle $\theta^*$ that provides commensurability between the moir\'e pattern at the interface and the accumulated carrier density, should promote a regime of strongly correlated states of electrons, such as Wigner crystals, and we specify the values of $\theta^*$ for homo- and heterostructures of various TMDs., Comment: 12 pages, 8 Figures

Legal case retrieval (LCR) is a specialised information retrieval task that aims to find relevant cases to a given query case. LCR holds pivotal significance in facilitating legal practitioners in finding precedents. Most of existing LCR methods are based on traditional lexical models and language models, which have gained promising performance in retrieval. However, the domain-specific structural information inherent in legal documents is yet to be exploited to further improve the performance. Our previous work CaseGNN successfully harnesses text-attributed graphs and graph neural networks to address the problem of legal structural information neglect. Nonetheless, there remain two aspects for further investigation: (1) The underutilization of rich edge information within text-attributed case graphs limits CaseGNN to generate informative case representation. (2) The inadequacy of labelled data in legal datasets hinders the training of CaseGNN model. In this paper, CaseGNN++, which is extended from CaseGNN, is proposed to simultaneously leverage the edge information and additional label data to discover the latent potential of LCR models. Specifically, an edge feature-based graph attention layer (EUGAT) is proposed to comprehensively update node and edge features during graph modelling, resulting in a full utilisation of structural information of legal cases. Moreover, a novel graph contrastive learning objective with graph augmentation is developed in CaseGNN++ to provide additional training signals, thereby enhancing the legal comprehension capabilities of CaseGNN++ model. Extensive experiments on two benchmark datasets from COLIEE 2022 and COLIEE 2023 demonstrate that CaseGNN++ not only significantly improves CaseGNN but also achieves supreme performance compared to state-of-the-art LCR methods. Code has been released on https://github.com/yanran-tang/CaseGNN., Comment: arXiv admin note: substantial text overlap with arXiv:2312.11229

Recent advances in Tiny Machine Learning (TinyML) empower low-footprint embedded devices for real-time on-device Machine Learning. While many acknowledge the potential benefits of TinyML, its practical implementation presents unique challenges. This study aims to bridge the gap between prototyping single TinyML models and developing reliable TinyML systems in production: (1) Embedded devices operate in dynamically changing conditions. Existing TinyML solutions primarily focus on inference, with models trained offline on powerful machines and deployed as static objects. However, static models may underperform in the real world due to evolving input data distributions. We propose online learning to enable training on constrained devices, adapting local models towards the latest field conditions. (2) Nevertheless, current on-device learning methods struggle with heterogeneous deployment conditions and the scarcity of labeled data when applied across numerous devices. We introduce federated meta-learning incorporating online learning to enhance model generalization, facilitating rapid learning. This approach ensures optimal performance among distributed devices by knowledge sharing. (3) Moreover, TinyML's pivotal advantage is widespread adoption. Embedded devices and TinyML models prioritize extreme efficiency, leading to diverse characteristics ranging from memory and sensors to model architectures. Given their diversity and non-standardized representations, managing these resources becomes challenging as TinyML systems scale up. We present semantic management for the joint management of models and devices at scale. We demonstrate our methods through a basic regression example and then assess them in three real-world TinyML applications: handwritten character image classification, keyword audio classification, and smart building presence detection, confirming our approaches' effectiveness., Comment: Accepted by Journal Transactions on Embedded Computing Systems (TECS)

In time-critical decisions, human decision-makers can interact with AI-enabled situation-aware software to evaluate many imminent and possible scenarios, retrieve billions of facts, and estimate different outcomes based on trillions of parameters in a fraction of a second. In high-order reasoning, "what-if" questions can be used to challenge the assumptions or pre-conditions of the reasoning, "why-not" questions can be used to challenge on the method applied in the reasoning, "so-what" questions can be used to challenge the purpose of the decision, and "how-about" questions can be used to challenge the applicability of the method. When above high-order reasoning questions are applied to assist human decision-making, it can help humans to make time-critical decisions and avoid false-negative or false-positive types of errors. In this paper, we present a model of high-order reasoning to offer recommendations in evidence-based medicine in a time-critical fashion for the applications in ICU. The Large Language Model (LLM) is used in our system. The experiments demonstrated the LLM exhibited optimal performance in the "What-if" scenario, achieving a similarity of 88.52% with the treatment plans of human doctors. In the "Why-not" scenario, the best-performing model tended to opt for alternative treatment plans in 70% of cases for patients who died after being discharged from the ICU. In the "So-what" scenario, the optimal model provided a detailed analysis of the motivation and significance of treatment plans for ICU patients, with its reasoning achieving a similarity of 55.6% with actual diagnostic information. In the "How-about" scenario, the top-performing LLM demonstrated a content similarity of 66.5% in designing treatment plans transferring for similar diseases. Meanwhile, LLMs managed to predict the life status of patients after their discharge from the ICU with an accuracy of 70%., Comment: 13 pages, 15 figures

Using the homogeneous X-ray catalog from ROSAT observations, we conducted a comprehensive investigation into stellar X-ray activity-rotation relations for both single and binary stars. Generally, the relation for single stars consists of two distinct regions: a weak decay region, indicating a continued dependence of the magnetic dynamo on stellar rotation rather than a saturation regime with constant activity, and a rapid decay region, where X-ray activity is strongly correlated with the Rossby number. Detailed analysis reveals more fine structures within the relation: in the extremely fast rotating regime, a decrease in X-ray activity was observed with increasing rotation rate, referred to as super-saturation, while in the extremely slow rotating region, the relation flattens, mainly due to the scattering of F stars. This scattering may result from intrinsic variability in stellar activities over one stellar cycle or the presence of different dynamo mechanisms. Binaries exhibit a similar relation to that of single stars while the limited sample size prevented the identification of fine structures in the relation for binaries. We calculated the mass loss rates of planetary atmosphere triggered by X-ray emissions from host stars. Our findings indicate that for an Earth-like planet within the stellar habitable zone, it would easily lose its entire primordial H/He envelope (equating to about 1% of the planetary mass)., Comment: 17 pages, 12 figures, ApJS accepted

Distributed processing of large-scale graph data has many practical applications and has been widely studied. In recent years, a lot of distributed graph processing frameworks and algorithms have been proposed. While many efforts have been devoted to analyzing these, with most analyzing them based on programming models, less research focuses on understanding their challenges in distributed environments. Applying graph tasks to distributed environments is not easy, often facing numerous challenges through our analysis, including parallelism, load balancing, communication overhead, and bandwidth. In this paper, we provide an extensive overview of the current state-of-the-art in this field by outlining the challenges and solutions of distributed graph algorithms. We first conduct a systematic analysis of the inherent challenges in distributed graph processing, followed by presenting an overview of existing general solutions. Subsequently, we survey the challenges highlighted in recent distributed graph processing papers and the strategies adopted to address them. Finally, we discuss the current research trends and identify potential future opportunities.

In case law, the precedents are the relevant cases that are used to support the decisions made by the judges and the opinions of lawyers towards a given case. This relevance is referred to as the case-to-case reference relation. To efficiently find relevant cases from a large case pool, retrieval tools are widely used by legal practitioners. Existing legal case retrieval models mainly work by comparing the text representations of individual cases. Although they obtain a decent retrieval accuracy, the intrinsic case connectivity relationships among cases have not been well exploited for case encoding, therefore limiting the further improvement of retrieval performance. In a case pool, there are three types of case connectivity relationships: the case reference relationship, the case semantic relationship, and the case legal charge relationship. Due to the inductive manner in the task of legal case retrieval, using case reference as input is not applicable for testing. Thus, in this paper, a CaseLink model based on inductive graph learning is proposed to utilise the intrinsic case connectivity for legal case retrieval, a novel Global Case Graph is incorporated to represent both the case semantic relationship and the case legal charge relationship. A novel contrastive objective with a regularisation on the degree of case nodes is proposed to leverage the information carried by the case reference relationship to optimise the model. Extensive experiments have been conducted on two benchmark datasets, which demonstrate the state-of-the-art performance of CaseLink. The code has been released on https://github.com/yanran-tang/CaseLink.