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Rural America has long been perceived as "left behind" by policies that leave it struggling while benefiting bustling urban cities and suburbs. That narrative holds some truth: rural America has a declining population, along with lower educational attainment and lower workforce participation than urban America. But while rural America certainly faces challenges, it also has its own strengths and assets. "Small Towns, Big Opportunities: Many Workers in Rural Areas Have Good Jobs, but These Areas Need Greater Investment in Education, Training, and Career Counseling" counters some of the negative stereotypes and finds that working adults in rural America are almost as likely (50 percent) as working adults in urban America (54 percent) to have a good job.

Unbiased Learning to Rank (ULTR) aims to leverage biased implicit user feedback (e.g., click) to optimize an unbiased ranking model. The effectiveness of the existing ULTR methods has primarily been validated on synthetic datasets. However, their performance on real-world click data remains unclear. Recently, Baidu released a large publicly available dataset of their web search logs. Subsequently, the NTCIR-17 ULTRE-2 task released a subset dataset extracted from it. We conduct experiments on commonly used or effective ULTR methods on this subset to determine whether they maintain their effectiveness. In this paper, we propose a Contextual Dual Learning Algorithm with Listwise Distillation (CDLA-LD) to simultaneously address both position bias and contextual bias. We utilize a listwise-input ranking model to obtain reconstructed feature vectors incorporating local contextual information and employ the Dual Learning Algorithm (DLA) method to jointly train this ranking model and a propensity model to address position bias. As this ranking model learns the interaction information within the documents list of the training set, to enhance the ranking model's generalization ability, we additionally train a pointwise-input ranking model to learn the listwise-input ranking model's capability for relevance judgment in a listwise manner. Extensive experiments and analysis confirm the effectiveness of our approach., Comment: 12 pages, 2 figures

Large language models (LLMs) have been found to produce hallucinations when the question exceeds their internal knowledge boundaries. A reliable model should have a clear perception of its knowledge boundaries, providing correct answers within its scope and refusing to answer when it lacks knowledge. Existing research on LLMs' perception of their knowledge boundaries typically uses either the probability of the generated tokens or the verbalized confidence as the model's confidence in its response. However, these studies overlook the differences and connections between the two. In this paper, we conduct a comprehensive analysis and comparison of LLMs' probabilistic perception and verbalized perception of their factual knowledge boundaries. First, we investigate the pros and cons of these two perceptions. Then, we study how they change under questions of varying frequencies. Finally, we measure the correlation between LLMs' probabilistic confidence and verbalized confidence. Experimental results show that 1) LLMs' probabilistic perception is generally more accurate than verbalized perception but requires an in-domain validation set to adjust the confidence threshold. 2) Both perceptions perform better on less frequent questions. 3) It is challenging for LLMs to accurately express their internal confidence in natural language.

Effective fraud detection and analysis of government-issued identity documents, such as passports, driver's licenses, and identity cards, are essential in thwarting identity theft and bolstering security on online platforms. The training of accurate fraud detection and analysis tools depends on the availability of extensive identity document datasets. However, current publicly available benchmark datasets for identity document analysis, including MIDV-500, MIDV-2020, and FMIDV, fall short in several respects: they offer a limited number of samples, cover insufficient varieties of fraud patterns, and seldom include alterations in critical personal identifying fields like portrait images, limiting their utility in training models capable of detecting realistic frauds while preserving privacy. In response to these shortcomings, our research introduces a new benchmark dataset, IDNet, designed to advance privacy-preserving fraud detection efforts. The IDNet dataset comprises 837,060 images of synthetically generated identity documents, totaling approximately 490 gigabytes, categorized into 20 types from $10$ U.S. states and 10 European countries. We evaluate the utility and present use cases of the dataset, illustrating how it can aid in training privacy-preserving fraud detection methods, facilitating the generation of camera and video capturing of identity documents, and testing schema unification and other identity document management functionalities., Comment: 40 pages

Literature research, vital for scientific advancement, is overwhelmed by the vast ocean of available information. Addressing this, we propose an automated review generation method based on Large Language Models (LLMs) to streamline literature processing and reduce cognitive load. In case study on propane dehydrogenation (PDH) catalysts, our method swiftly generated comprehensive reviews from 343 articles, averaging seconds per article per LLM account. Extended analysis of 1041 articles provided deep insights into catalysts' composition, structure, and performance. Recognizing LLMs' hallucinations, we employed a multi-layered quality control strategy, ensuring our method's reliability and effective hallucination mitigation. Expert verification confirms the accuracy and citation integrity of generated reviews, demonstrating LLM hallucination risks reduced to below 0.5% with over 95% confidence. Released Windows application enables one-click review generation, aiding researchers in tracking advancements and recommending literature. This approach showcases LLMs' role in enhancing scientific research productivity and sets the stage for further exploration., Comment: 16 pages, 3 figures, 3 tables

Cryptocurrency introduces usability challenges by requiring users to manage signing keys. Popular signing key management services (e.g., custodial wallets), however, either introduce a trusted party or burden users with managing signing key shares, posing the same usability challenges. TEEs (Trusted Execution Environments) are a promising technology to avoid both, but practical implementations of TEEs suffer from various side-channel attacks that have proven hard to eliminate. This paper explores a new approach to side-channel mitigation through economic incentives for TEE-based cryptocurrency wallet solutions. By taking the cost and profit of side-channel attacks into consideration, we designed a Stick-and-Carrot-based cryptocurrency wallet, CrudiTEE, that leverages penalties (the stick) and rewards (the carrot) to disincentivize attackers from exfiltrating signing keys in the first place. We model the attacker's behavior using a Markov Decision Process (MDP) to evaluate the effectiveness of the bounty and enable the service provider to adjust the parameters of the bounty's reward function accordingly.

Hot dust-obscured galaxies (Hot DOGs) are a population of hyperluminous, heavily obscured quasars discovered by the \emph{Wide-field Infrared Survey Explorer} (\emph{WISE}) all-sky survey at high redshift. Observations suggested the growth of these galaxies may be driven by mergers. Previous environmental studies have statistically shown Hot DOGs may reside in dense regions. Here we use the Very Large Telescope (VLT) narrowband and broadband imaging to search for Ly$\alpha$ emitters (LAEs) in the 6.8' * 6.8' field of the Hot DOG W2246$-$0526 at $z=4.6$. W2246$-$0526 is the most distant Hot DOG. We find that there is an overdensity of LAEs in W2246$-$0526 field compared with the blank fields. This is the direct evidence that this most distant Hot DOG is in an overdense environment on the Mpc scale, and the result relates to the merger origin of Hot DOGs., Comment: 11 pages, 8 figures, Accepted for publication in The Astrophysical Journal

3D surface reconstruction from images is essential for numerous applications. Recently, Neural Radiance Fields (NeRFs) have emerged as a promising framework for 3D modeling. However, NeRFs require accurate camera poses as input, and existing methods struggle to handle significantly noisy pose estimates (i.e., outliers), which are commonly encountered in real-world scenarios. To tackle this challenge, we present a novel approach that optimizes radiance fields with scene graphs to mitigate the influence of outlier poses. Our method incorporates an adaptive inlier-outlier confidence estimation scheme based on scene graphs, emphasizing images of high compatibility with the neighborhood and consistency in the rendering quality. We also introduce an effective intersection-over-union (IoU) loss to optimize the camera pose and surface geometry, together with a coarse-to-fine strategy to facilitate the training. Furthermore, we propose a new dataset containing typical outlier poses for a detailed evaluation. Experimental results on various datasets consistently demonstrate the effectiveness and superiority of our method over existing approaches, showcasing its robustness in handling outliers and producing high-quality 3D reconstructions. Our code and data are available at: \url{https://github.com/Iris-cyy/SG-NeRF}., Comment: ECCV 2024

Embodied artificial intelligence (AI) represents an artificial intelligence system that interacts with the physical world through sensors and actuators, seamlessly integrating perception and action. This design enables AI to learn from and operate within complex, real-world environments. Large Language Models (LLMs) deeply explore language instructions, playing a crucial role in devising plans for complex tasks. Consequently, they have progressively shown immense potential in empowering embodied AI, with LLM-based embodied AI emerging as a focal point of research within the community. It is foreseeable that, over the next decade, LLM-based embodied AI robots are expected to proliferate widely, becoming commonplace in homes and industries. However, a critical safety issue that has long been hiding in plain sight is: could LLM-based embodied AI perpetrate harmful behaviors? Our research investigates for the first time how to induce threatening actions in embodied AI, confirming the severe risks posed by these soon-to-be-marketed robots, which starkly contravene Asimov's Three Laws of Robotics and threaten human safety. Specifically, we formulate the concept of embodied AI jailbreaking and expose three critical security vulnerabilities: first, jailbreaking robotics through compromised LLM; second, safety misalignment between action and language spaces; and third, deceptive prompts leading to unaware hazardous behaviors. We also analyze potential mitigation measures and advocate for community awareness regarding the safety of embodied AI applications in the physical world., Comment: Preliminary version (17 pages, 4 figures). Work in progress, revisions ongoing. Appreciate understanding and welcome any feedback

With the development of autonomous driving technology, there are increasing demands for vehicle control, and MPC has become a widely researched topic in both industry and academia. Existing MPC control methods based on vehicle kinematics or dynamics have challenges such as difficult modeling, numerous parameters, strong nonlinearity, and high computational cost. To address these issues, this paper adapts an existing Data-driven MPC control method and applies it to autonomous vehicle steering control. This method avoids the need for complex vehicle system modeling and achieves trajectory tracking with relatively low computational time and small errors. We validate the control effectiveness of the algorithm in specific scenario through CarSim-Simulink simulation and perform comparative analysis with PID and vehicle kinematics MPC, confirming the feasibility and superiority of it for vehicle steering control.

With the extensive use of digital devices, online experimental platforms are commonly used to conduct experiments to collect data for evaluating different variations of products, algorithms, and interface designs, a.k.a., A/B tests. In practice, multiple A/B testing experiments are often carried out based on a common user population on the same platform. The same user's responses to different experiments can be correlated to some extent due to the individual effect of the user. In this paper, we propose a novel framework that collaboratively analyzes the data from paired A/B tests, namely, a pair of A/B testing experiments conducted on the same set of experimental subjects. The proposed analysis approach for paired A/B tests can lead to more accurate estimates than the traditional separate analysis of each experiment. We obtain the asymptotic distribution of the proposed estimators and demonstrate that the proposed estimators are asymptotically the best linear unbiased estimators under certain assumptions. Moreover, the proposed analysis approach is computationally efficient, easy to implement, and robust to different types of responses. Both numerical simulations and numerical studies based on a real case are used to examine the performance of the proposed method.

In terms of the variable nature of normal active galaxy nuclei (AGN) and luminous quasars, a so-called flux variation gradient (FVG) method has been widely utilized to estimate the underlying non-variable host galaxy fluxes. The FVG method assumes an invariable AGN color, but this assumption has been questioned by the intrinsic color variation of quasars and local Seyfert galaxies. Here, using an up-to-date thermal fluctuation model to simulate multi-wavelength AGN variability, we theoretically demonstrate that the FVG method generally overestimates the host galaxy flux; that is, it is more significant for brighter AGN/quasars. Furthermore, we observationally confirm that the FVG method indeed overestimates the host galaxy flux by comparing it to that estimated through other independent methods. We thus caution that applying the FVG method should be performed carefully in the era of time-domain astronomy.

By optimal fluctuation method, we study short-time distribution $P(\mathcal{A}=A)$ of the functionals, $\mathcal{A}=\int_{0}^{t_f} x^n(t) dt$, along constrained trajectories of random acceleration process for a given time duration $t_f$, where $n$ is a positive integer. We consider two types of constraints: one is called the total constraint, where the initial position and velocity and the final position and velocity are both fixed, and the other is called the partial constraint, where the initial position and velocity, the final position are fixed, and letting the final velocity be free. Via the variation of constrained action functionals, the resulting Euler-Lagrange equations are analytically solved for $n=1$ and 2, and the optimal path, i.e., the most probable realization of the random acceleration process $x(t)$, conditioned on specified $A$ and $n$, are correspondingly obtained. For $n \geq 3$, a numerical scheme is proposed to find the optimal path. We show that, for $n=1$, $P(A)$ is a Gaussian distribution with the variance proportional to $Dt_f^5$ ($D$ is the particle velocity diffusion constant). For $n \geq 2$, $P(A)$ exhibits the non-Gaussian feature. In the small-$A$ limit, $P(A)$ show a essential singularity, $-\ln P(A) \sim A^{-3}$, and the optimal path localizes around the initial state over a long-time window, and then escapes to the final position sharply at a late time. For $A$ much larger than its typical value, there are multiple optimal paths with the same $A$ but with different actions (or probability densities). Among these degenerate paths, one with the minimum action is dominant, and the others are exponentially unlikely. All the theoretical results are validated by simulating the effective Langevin equations governing the constrained random acceleration process., Comment: 15 pages, 8 figures

In the domain of autonomous vehicles (AVs), decision-making is a critical factor that significantly influences the efficacy of autonomous navigation. As the field progresses, the enhancement of decision-making capabilities in complex environments has become a central area of research within data-driven methodologies. Despite notable advances, existing learning-based decision-making strategies in autonomous vehicles continue to reveal opportunities for further refinement, particularly in the articulation of policies and the assurance of safety. In this study, the decision-making challenges associated with autonomous vehicles are conceptualized through the framework of the Constrained Markov Decision Process (CMDP) and approached as a sequence modeling problem. Utilizing the Generative Pre-trained Transformer (GPT), we introduce a novel decision-making model tailored for AVs, which incorporates entropy regularization techniques to bolster exploration and enhance safety performance. Comprehensive experiments conducted across various scenarios affirm that our approach surpasses several established baseline methods, particularly in terms of safety and overall efficacy.

Frequency combs show various applications in molecular fingerprinting, imaging, communications, and so on. In the terahertz frequency range, semiconductor-based quantum cascade lasers (QCLs) are ideal platforms for realizing the frequency comb operation. Although self-started frequency comb operation can be obtained in free-running terahertz QCLs due to the four-wave mixing locking effects, resonant/off-resonant microwave injection, phase locking, and femtosecond laser based locking techniques have been widely used to broaden and stabilize terahertz QCL combs. These active locking methods indeed show significant effects on the frequency stabilization of terahertz QCL combs, but they simultaneously have drawbacks, such as introducing large phase noise and requiring complex optical coupling and/or electrical circuits. Here, we demonstrate Farey tree locking of terahertz QCL frequency combs under microwave injection. The frequency competition between the Farey fraction frequency and the cavity round-trip frequency results in the frequency locking of terahertz QCL combs, and the Farey fraction frequencies can be accurately anticipated based on the downward trend of the Farey tree hierarchy. Furthermore, dual-comb experimental results show that the phase noise of the dual-comb spectral lines is significantly reduced by employing the Farey tree locking method. These results pave the way to deploying compact and low phase noise terahertz frequency comb sources., Comment: 22 page, 7 figures

In recent years, with the rapidly aging population, alleviating the pressure on medical staff has become a critical issue. To improve the work efficiency of medical staff and reduce the risk of infection, we consider the multi-trip autonomous mobile robot (AMR) routing problem with the stochastic environment to find the solution to minimizing the total expected operating cost and maximizing the total service quality of patients so that each route violates the vehicle capacity and the time window with only a very small probability. The travel time of AMRs is stochastic affected by the surrounding environment, the demand for each ward is unknown until the AMR reaches the ward, and the service time is linearly related to the actual demand. We develop a population-based tabu search algorithm (PTS) that combines the genetic algorithm with the tabu search algorithm to solve the problem. Extensive numerical experiments were conducted on the modified Solomon instances to show that the PTS algorithm the efficient and reveals the impacts of the confidence level on the optimal solution, providing insights for the decision-maker to devise delivery schemes that trade-off the operating cost for patient satisfaction.

Recently, both closed-source LLMs and open-source communities have made significant strides, outperforming humans in various general domains. However, their performance in specific professional fields such as medicine, especially within the open-source community, remains suboptimal due to the complexity of medical knowledge. We propose Aquila-Med, a bilingual medical LLM based on Aquila, addressing these challenges through continue pre-training, supervised fine-tuning (SFT), and reinforcement learning from human feedback (RLHF). We construct a large-scale Chinese and English medical dataset for continue pre-training and a high-quality SFT dataset, covering extensive medical specialties. Additionally, we develop a high-quality Direct Preference Optimization (DPO) dataset for further alignment. Aquila-Med achieves notable results across single-turn, multi-turn dialogues, and medical multiple-choice questions, demonstrating the effectiveness of our approach. We open-source the datasets and the entire training process, contributing valuable resources to the research community. Our models and datasets will released at https://huggingface.co/BAAI/AquilaMed-RL.

The potential of ChatGPT to transform the education landscape is drawing increasing attention. With its translation-related capabilities being tested and examined, ChatGPT presents both opportunities and challenges for translation training. The effective integration of ChatGPT into translation training necessitates an understanding of students' reactions to and acceptance of ChatGPT-assisted translation. Against this backdrop, this study draws on the Unified Theory of Acceptance and Use of Technology (UTAUT) to examine the potential determinants of students' adoption of ChatGPT for translation and investigates the moderating effects of use experience and translation training on those relationships. An online survey targeting university students in Hong Kong collected 308 valid responses, including 148 from translation students and 160 from non-translation students. Respondents were divided into two groups based on their ChatGPT use experience. Data were analyzed using structural equation modeling. A multigroup analysis revealed different structural relationships between the influencing factors of students' intention to use ChatGPT across groups. Notably, less-experienced users' behavioral intention to use ChatGPT for translation was more strongly correlated with social influence compared with experienced users. Non-translation students' use intention was more strongly driven by facilitating conditions compared to translation majors. These results are discussed with the different primary purposes of translation and non-translation students' translation practices. The findings of this study contribute to the growing body of research on AI-powered translation training and provide insights for the ongoing adaptation of translation training programs., Comment: 38 pages, 5 figures

This work presents a compact, cumulative and coalescible probabilistic voxel mapping method to enhance performance, accuracy and memory efficiency in LiDAR odometry. Probabilistic voxel mapping requires storing past point clouds and re-iterating on them to update the uncertainty every iteration, which consumes large memory space and CPU cycles. To solve this problem, we propose a two-folded strategy. First, we introduce a compact point-free representation for probabilistic voxels and derive a cumulative update of the planar uncertainty without caching original point clouds. Our voxel structure only keeps track of a predetermined set of statistics for points that lie inside it. This method reduces the runtime complexity from $O(MN)$ to $O(N)$ and the space complexity from $O(N)$ to $O(1)$ where $M$ is the number of iterations and $N$ is the number of points. Second, to further minimize memory usage and enhance mapping accuracy, we provide a strategy to dynamically merge voxels associated with the same physical planes by taking advantage of the geometric features in the real world. Rather than scanning for these coalescible voxels constantly at every iteration, our merging strategy accumulates voxels in a locality-sensitive hash and triggers merging lazily. On-demand merging not only reduces memory footprint with minimal computational overhead but also improves localization accuracy thanks to cross-voxel denoising. Experiments exhibit 20% higher accuracy, 20% faster performance and 70% lower memory consumption than the state-of-the-art.

In the field of environmental toxicology, rapid and precise assessment of the inflammatory response to pollutants in biological models is critical. This study leverages the power of deep learning to enable automated assessments of zebrafish, a model organism widely used for its translational relevance to human disease pathways. We present an innovative approach to assessing inflammatory responses in zebrafish exposed to various pollutants through an end-to-end deep learning model. The model employs a Unet-based architecture to automatically process high-throughput lateral zebrafish images, segmenting specific regions and quantifying neutrophils as inflammation markers. Alongside imaging, qPCR analysis offers gene expression insights, revealing the molecular impact of exposure on inflammatory pathways. Moreover, the deep learning model was packaged as a user-friendly executable file (.exe), facilitating widespread application by enabling use on virtually any computer without the need for specialized software or training.

Federated learning (FL) has been demonstrated to be susceptible to backdoor attacks. However, existing academic studies on FL backdoor attacks rely on a high proportion of real clients with main task-related data, which is impractical. In the context of real-world industrial scenarios, even the simplest defense suffices to defend against the state-of-the-art attack, 3DFed. A practical FL backdoor attack remains in a nascent stage of development. To bridge this gap, we present DarkFed. Initially, we emulate a series of fake clients, thereby achieving the attacker proportion typical of academic research scenarios. Given that these emulated fake clients lack genuine training data, we further propose a data-free approach to backdoor FL. Specifically, we delve into the feasibility of injecting a backdoor using a shadow dataset. Our exploration reveals that impressive attack performance can be achieved, even when there is a substantial gap between the shadow dataset and the main task dataset. This holds true even when employing synthetic data devoid of any semantic information as the shadow dataset. Subsequently, we strategically construct a series of covert backdoor updates in an optimized manner, mimicking the properties of benign updates, to evade detection by defenses. A substantial body of empirical evidence validates the tangible effectiveness of DarkFed., Comment: This paper has been accepted by IJCAI 2024

Using neural networks to solve partial differential equations (PDEs) is gaining popularity as an alternative approach in the scientific computing community. Neural networks can integrate different types of information into the loss function. These include observation data, governing equations, and variational forms, etc. These loss functions can be broadly categorized into two types: observation data loss directly constrains and measures the model output, while other loss functions indirectly model the performance of the network, which can be classified as model loss. However, this alternative approach lacks a thorough understanding of its underlying mechanisms, including theoretical foundations and rigorous characterization of various phenomena. This work focuses on investigating how different loss functions impact the training of neural networks for solving PDEs. We discover a stable loss-jump phenomenon: when switching the loss function from the data loss to the model loss, which includes different orders of derivative information, the neural network solution significantly deviates from the exact solution immediately. Further experiments reveal that this phenomenon arises from the different frequency preferences of neural networks under different loss functions. We theoretically analyze the frequency preference of neural networks under model loss. This loss-jump phenomenon provides a valuable perspective for examining the underlying mechanisms of neural networks in solving PDEs.

In this paper, our primary focus is on investigating the extension dimensions of syzygy module categories associated with Artin algebras, particularly under various equivalences. We demonstrate that, for sufficiently large $i$, the $i$-th syzygy module categories of derived equivalent algebras exhibit identical extension dimensions. Furthermore, we establish that the extension dimension of the $i$-th syzygy module category is an invariant under both stable equivalence and separable equivalence for each nonnegative integer $i$.

With the proliferation of large language models (LLMs), the comprehensive alignment of such models across multiple tasks has emerged as a critical area of research. Existing alignment methodologies primarily address single task, such as multi-turn dialogue, coding, mathematical problem-solving, and tool usage. However, AI-driven products that leverage language models usually necessitate a fusion of these abilities to function effectively in real-world scenarios. Moreover, the considerable computational resources required for proper alignment of LLMs underscore the need for a more robust, efficient, and encompassing approach to multi-task alignment, ensuring improved generative performance. In response to these challenges, we introduce a novel technique termed Mixture-of-Instructions (MoI), which employs a strategy of instruction concatenation combined with diverse system prompts to boost the alignment efficiency of language models. We have also compiled a diverse set of seven benchmark datasets to rigorously evaluate the alignment efficacy of the MoI-enhanced language model. Our methodology was applied to the open-source Qwen-7B-chat model, culminating in the development of Qwen-SFT-MoI. This enhanced model demonstrates significant advancements in generative capabilities across coding, mathematics, and tool use tasks.

Polar dust has been discovered in a number of local Active Galactic Nuclei (AGN), with radiation-driven torus models predicting a wind to be its main driver. However, little is known about its characteristics, spatial extent, or connection to the larger scale outflows. We present the first JWST/MIRI study aimed at imaging polar dust by zooming onto the centre of ESO 428-G14, part of the Galaxy Activity, Torus, and Outflow Survey (GATOS) survey of local AGN. We detect extended mid-infrared (MIR) emission within 200 pc from the nucleus. This polar structure is co-linear with a radio jet and lies perpendicular to a molecular gas lane that feeds and obscures the nucleus. Its morphology bears a striking resemblance to that of gas ionised by the AGN in the narrow-line region. We demonstrate that part of this spatial correspondence is due to contamination within the JWST filter bands from strong emission lines. Correcting for the contamination, we find the morphology of the dust continuum to be more compact, though still clearly extended out to ~ 100 pc. We estimate the emitting dust has a temperature of ~ 120 K. Using simple models, we find that the heating of small dust grains by the radiation from the central AGN and/or radiative jet-induced shocks is responsible for the extended MIR emission. Radiation-driven dusty winds from the torus is unlikely to be important. This has important implications for scales to which AGN winds can carry dust and dense gas out into their host galaxies., Comment: 17 pages, 8 figures, 2 tables. Published in MNRAS

In ZK-Rollups, provers spend significant computational resources to generate validity proofs. Their costs should be compensated properly, so a sustainable prover market can form over time. Existing transaction fee mechanisms (TFMs) such as EIP-1559, however, do not work in this setting, as EIP-1559 only generates negligible revenue because of burning, while provers often create or purchase specialized hardware in hopes of creating long-term revenue from proving, somewhat reminiscent of proof-of-work miners in the case of chains like Bitcoin. In this paper, we explore the design of transaction fee mechanisms for prover markets. The desiderata for such mechanisms include efficiency (social welfare is maximized), incentive compatibility (it is rational to bid honestly), collusion resistance (no profitable collusion among provers exists), and off-chain agreement proofness (no profitable collusion between users and provers exists). To demonstrate the difficulties of our new setting, we put forward several simple strawman mechanisms, and show they suffer from notable deficiencies.

Mycoplasma pneumoniae pneumonia (MPP) poses significant diagnostic challenges in pediatric healthcare, especially in regions like China where it's prevalent. We introduce PneumoniaAPP, a mobile application leveraging deep learning techniques for rapid MPP detection. Our approach capitalizes on convolutional neural networks (CNNs) trained on a comprehensive dataset comprising 3345 chest X-ray (CXR) images, which includes 833 CXR images revealing MPP and additionally augmented with samples from a public dataset. The CNN model achieved an accuracy of 88.20% and an AUROC of 0.9218 across all classes, with a specific accuracy of 97.64% for the mycoplasma class, as demonstrated on the testing dataset. Furthermore, we integrated explainability techniques into PneumoniaAPP to aid respiratory physicians in lung opacity localization. Our contribution extends beyond existing research by targeting pediatric MPP, emphasizing the age group of 0-12 years, and prioritizing deployment on mobile devices. This work signifies a significant advancement in pediatric pneumonia diagnosis, offering a reliable and accessible tool to alleviate diagnostic burdens in healthcare settings., Comment: 27 Pages,7 figures

DNA regulation and repair processes require direct interactions between proteins and DNA at specific sites. Local fluctuations of the sugar-phosphate backbones and bases of DNA (a form of DNA "breathing") play a central role in such processes. Here we review the development and application of novel spectroscopic methods and analyses - both at the ensemble and single-molecule levels - to study structural and dynamic properties of exciton-coupled cyanine and fluorescent nucleobase analogue dimer-labeled DNA constructs at key positions involved in protein-DNA complex assembly and function. The exciton-coupled dimer probes act as "sensors" of the local conformations adopted by the sugar-phosphate backbones and bases immediately surrounding the dimer probes. These methods can be used to study the mechanisms of protein binding and function at these sites., Comment: Preprint of invited book chapter

With the evolution of self-supervised learning, the pre-training paradigm has emerged as a predominant solution within the deep learning landscape. Model providers furnish pre-trained encoders designed to function as versatile feature extractors, enabling downstream users to harness the benefits of expansive models with minimal effort through fine-tuning. Nevertheless, recent works have exposed a vulnerability in pre-trained encoders, highlighting their susceptibility to downstream-agnostic adversarial examples (DAEs) meticulously crafted by attackers. The lingering question pertains to the feasibility of fortifying the robustness of downstream models against DAEs, particularly in scenarios where the pre-trained encoders are publicly accessible to the attackers. In this paper, we initially delve into existing defensive mechanisms against adversarial examples within the pre-training paradigm. Our findings reveal that the failure of current defenses stems from the domain shift between pre-training data and downstream tasks, as well as the sensitivity of encoder parameters. In response to these challenges, we propose Genetic Evolution-Nurtured Adversarial Fine-tuning (Gen-AF), a two-stage adversarial fine-tuning approach aimed at enhancing the robustness of downstream models. Our extensive experiments, conducted across ten self-supervised training methods and six datasets, demonstrate that Gen-AF attains high testing accuracy and robust testing accuracy against state-of-the-art DAEs.

The diode rectifier unit-based high voltage direct current (DRU-HVDC) transmission with grid-forming (GFM) wind turbine is becoming a promising scheme for offshore wind farm(OWF) integration due to its high reliability and low cost. In this scheme, the AC network of the OWF and the DRU has completely different synchronization mechanisms and power flow characteristics from the traditional power system. To optimize the power flow and reduce the net loss, this paper carries out the power flow modeling and optimization analysis for the DRU-HVDC transmission system with grid-forming OWFs. The influence of the DRU and the GFM wind turbines on the power flow of the system is analyzed. On this basis, improved constraint conditions are proposed and an optimal power flow (OPF) method is established. This method can minimize the power loss by adjusting the reactive power output of each wind turbine and internal network frequency. Finally, based on MATLAB, this paper uses YALMIP toolkit and CPLEX mathematical solver to realize the programming solution of the OPF model proposed in this paper. The results show that the proposed optimization strategy can effectively reduce the power loss of the entire OWF and the transmission system with an optimization ratio of network losses exceeding 25.3%., Comment: 6 pages, 7 figures, to be published in the 7th IEEE Conference on Energy Internet and Energy System Integration (EI2 2023)

High-cadence, multiwavelength observations have continuously revealed the diversity of tidal disruption events (TDEs), thus greatly advancing our knowledge and understanding of TDEs. In this work, we conducted an intensive optical-UV and X-ray follow-up campaign of TDE AT2023lli, and found a remarkable month-long bump in its UV/optical light curve nearly two months prior to maximum brightness. The bump represents the longest separation time from the main peak among known TDEs to date. The main UV/optical outburst declines as $t^{-4.10}$, making it one of the fastest decaying optically selected TDEs. Furthermore, we detected sporadic X-ray emission 30 days after the UV/optical peak, accompanied by a reduction in the period of inactivity. It is proposed that the UV/optical bump could be caused by the self-intersection of the stream debris, whereas the primary peak is generated by the reprocessed emission of the accretion process. In addition, our results suggest that episodic X-ray radiation during the initial phase of decline may be due to the patched obscurer surrounding the accretion disk, a phenomenon associated with the inhomogeneous reprocessing process. The double TDE scenario, in which two stars are disrupted in sequence, is also a possible explanation for producing the observed early bump and main peak. We anticipate that the multicolor light curves of TDEs, especially in the very early stages, and the underlying physics can be better understood in the near future with the assistance of dedicated surveys such as the deep high-cadence survey of the 2.5-meter Wide Field Survey Telescope (WFST)., Comment: 14 pages, 8 figures,accepted for publication by ApJL

With the rapid proliferation of scientific literature, versatile academic knowledge services increasingly rely on comprehensive academic graph mining. Despite the availability of public academic graphs, benchmarks, and datasets, these resources often fall short in multi-aspect and fine-grained annotations, are constrained to specific task types and domains, or lack underlying real academic graphs. In this paper, we present OAG-Bench, a comprehensive, multi-aspect, and fine-grained human-curated benchmark based on the Open Academic Graph (OAG). OAG-Bench covers 10 tasks, 20 datasets, 70+ baselines, and 120+ experimental results to date. We propose new data annotation strategies for certain tasks and offer a suite of data pre-processing codes, algorithm implementations, and standardized evaluation protocols to facilitate academic graph mining. Extensive experiments reveal that even advanced algorithms like large language models (LLMs) encounter difficulties in addressing key challenges in certain tasks, such as paper source tracing and scholar profiling. We also introduce the Open Academic Graph Challenge (OAG-Challenge) to encourage community input and sharing. We envisage that OAG-Bench can serve as a common ground for the community to evaluate and compare algorithms in academic graph mining, thereby accelerating algorithm development and advancement in this field. OAG-Bench is accessible at https://www.aminer.cn/data/., Comment: KDD'24, 9 pages, 5 appendix pages

Predictive analytics is widely used in various domains, including education, to inform decision-making and improve outcomes. However, many predictive models are proprietary and inaccessible for evaluation or modification by researchers and practitioners, limiting their accountability and ethical design. Moreover, predictive models are often opaque and incomprehensible to the officials who use them, reducing their trust and utility. Furthermore, predictive models may introduce or exacerbate bias and inequity, as they have done in many sectors of society. Therefore, there is a need for transparent, interpretable, and fair predictive models that can be easily adopted and adapted by different stakeholders. In this paper, we propose a fair predictive model based on multivariate adaptive regression splines(MARS) that incorporates fairness measures in the learning process. MARS is a non-parametric regression model that performs feature selection, handles non-linear relationships, generates interpretable decision rules, and derives optimal splitting criteria on the variables. Specifically, we integrate fairness into the knot optimization algorithm and provide theoretical and empirical evidence of how it results in a fair knot placement. We apply our fairMARS model to real-world data and demonstrate its effectiveness in terms of accuracy and equity. Our paper contributes to the advancement of responsible and ethical predictive analytics for social good.

We present a UV to millimeter spectral energy distribution (SED) analysis of 16 hyperluminous, dust-obscured quasars at z $\sim$ 3, selected by the \textit{Wide-field Infrared Survey Explorer}. We aim to investigate the physical properties of these quasars, with a focus on their molecular gas content. We decompose the SEDs into three components: stellar, cold dust, and active galactic nucleus (AGN). By doing so, we are able to derive and analyze the relevant properties of each component. We determine the molecular gas mass from CO line emission based on Atacama Large Millimeter/submillimeter Array (ALMA) observations. By including ALMA observations in the multiwavelength SED analysis, we derive the molecular gas fractions, gas depletion timescales, and star formation efficiencies (SFEs). Their sample median and 16th-84th quartile ranges are $f_{\rm gas}\,\sim\,0.33_{-0.17}^{+0.33}$, $t_{\rm depl}\,\sim$ 39$_{-28}^{+85}$ Myr, SFE $\sim\,$ 297$_{-195}^{+659}$ $\rm K\,\rm km\,\rm s^{-1}\,\rm pc^{-2}$. Compared to main-sequence galaxies, they have a lower molecular gas content and higher SFEs, similar to quasars in the literature. This suggests that the gas in these quasars is rapidly depleted, likely as the result of intense starburst activity and AGN feedback. The observed correlations between these properties and the AGN luminosities further support this scenario. Additionally, we infer the black hole to stellar mass ratio and black hole mass growth rate, which indicate a significant central black hole mass assembly over short timescales. Our results are consistent with the scenario that our sample represents a short transition phase toward unobscured quasars., Comment: 20 pages, 9 figures, in proof

Collaborative learning (CL) is a distributed learning framework that aims to protect user privacy by allowing users to jointly train a model by sharing their gradient updates only. However, gradient inversion attacks (GIAs), which recover users' training data from shared gradients, impose severe privacy threats to CL. Existing defense methods adopt different techniques, e.g., differential privacy, cryptography, and perturbation defenses, to defend against the GIAs. Nevertheless, all current defense methods suffer from a poor trade-off between privacy, utility, and efficiency. To mitigate the weaknesses of existing solutions, we propose a novel defense method, Dual Gradient Pruning (DGP), based on gradient pruning, which can improve communication efficiency while preserving the utility and privacy of CL. Specifically, DGP slightly changes gradient pruning with a stronger privacy guarantee. And DGP can also significantly improve communication efficiency with a theoretical analysis of its convergence and generalization. Our extensive experiments show that DGP can effectively defend against the most powerful GIAs and reduce the communication cost without sacrificing the model's utility.

Extreme ultraviolet (EUV) waves are thought to be the propagating footprint of the shock on the solar surface. One of the key questions in SEP research is the timing of the SEP release with respect to the time when the EUV wave magnetically connects with an observer. Taking advantage of close-to-the-Sun measurements by Parker Solar Probe (PSP) and Solar Orbiter (SolO), we investigate an SEP event that occurred on 2021 September 28 and was observed at different locations by SolO, PSP, STEREO-A, and near-Earth spacecraft. During this time, SolO, PSP and STEREO-A shared similar nominal magnetic footpoints relative to the SEP source region but were at different heliocentric distances. We find that the SEP release times estimated at these four locations were delayed compared to the times when the EUV wave intercepted the footpoints of the nominal magnetic fields connecting to each spacecraft by around 30 to 60 minutes. Combining observations in multiple wavelengths of radio, white-light, and EUV, with a geometrical shock model, we analyze the associated shock properties, and discuss the acceleration and delayed release processes of SEPs in this event as well as the accuracy and limitations of using EUV waves to determine the SEP acceleration and release times., Comment: Accepted by The Astrophysical Journal

Optical coherence tomography (OCT) is a noninvasive technology that enables real-time imaging of tissue microanatomies. The axial resolution of OCT is intrinsically constrained by the spectral bandwidth of the employed light source while maintaining a fixed center wavelength for a specific application. Physically extending this bandwidth faces strong limitations and requires a substantial cost. We present a novel computational approach, called as O-PRESS, for boosting the axial resolution of OCT with Prior Guidance, a Recurrent mechanism, and Equivariant Self-Supervision. Diverging from conventional superresolution methods that rely on physical models or data-driven techniques, our method seamlessly integrates OCT modeling and deep learning, enabling us to achieve real-time axial-resolution enhancement exclusively from measurements without a need for paired images. Our approach solves two primary tasks of resolution enhancement and noise reduction with one treatment. Both tasks are executed in a self-supervised manner, with equivariance imaging and free space priors guiding their respective processes. Experimental evaluations, encompassing both quantitative metrics and visual assessments, consistently verify the efficacy and superiority of our approach, which exhibits performance on par with fully supervised methods. Importantly, the robustness of our model is affirmed, showcasing its dual capability to enhance axial resolution while concurrently improving the signal-to-noise ratio.