The convergence of PM2.5 concentration in Chinese cities: a distribution dynamic approach

To fill the gap in the research on the convergence trend of air pollutants since 2013 in China and overcome the Galton fallacy caused by the parametric regression method, this study examines the convergence trend of the annual average concentration of fine particulate matter 2.5 (PM2.5) in China’s prefecture-level cities after 2013 using a distribution dynamic approach. The winter PM2.5 pollution in Chinese cities is severe. Hence, the convergence of the average winter PM2.5 concentration of prefecturelevel cities is also explored in this study. The results show that during 2015–2019, the annual average PM2.5 concentration level improved significantly. However, the average PM2.5 winter concentration level in 2015–2018 did not significantly decrease, with some cities showing severe pollution levels. The annual average PM2.5 of China’s prefecture-level cities exhibit club convergence, while the PM2.5 concentration in winter exhibits ‘unikurtosis’. In the long run, the annual average PM2.5 clusters around two levels, at approximately 35 lg/m3 and 60 lg/m3 , while the average PM2.5 in winter is concentrated at 100 lg/m3 . In the long run, in the central region, PM2.5 pollution is more severe than in northern and southern areas, regardless of the annual or winter average PM2.5 concentration

AutoEval-Video: An Automatic Benchmark for Assessing Large Vision Language Models in Open-Ended Video Question Answering

We propose a novel and challenging benchmark, AutoEval-Video, to comprehensively evaluate large vision-language models in open-ended video question answering. The comprehensiveness of AutoEval-Video is demonstrated in two aspects: 1) AutoEval-Video constructs open-ended video-questions across 9 skill dimensions, addressing capabilities of perception, comprehension, and generation. 2) AutoEval-Video contains newly collected videos that cover over 40 distinct themes. To efficiently evaluate responses to the open-ended questions, we employ an LLM-based evaluation approach, but instead of merely providing a reference answer, we annotate unique evaluation rules for every single instance (video-question pair). To maximize the robustness of these rules, we develop a novel adversarial annotation mechanism. By using instance-specific rules as prompt, GPT-4, as an automatic evaluator, can achieve a stable evaluation accuracy of around 97.0\%, comparable to the 94.9\% - 97.5\% accuracy of a human evaluator. Furthermore, we assess the performance of eight large vision-language models on AutoEval-Video. Among them, GPT-4V(ision) significantly outperforms other models, achieving an accuracy of 32.2\%. However, there is still substantial room for improvement compared to human accuracy of 72.8\%. By conducting an extensive case study, we uncover several drawbacks of GPT-4V, such as limited temporal and dynamic comprehension, and overly general responses. Code is available at \href{https://github.com/Xiuyuan-Chen/AutoEval-Video}{\color{magenta}https://github.com/Xiuyuan-Chen/AutoEval-Video}

Nonparametric Discrete Choice Experiments with Machine Learning Guided Adaptive Design

Designing products to meet consumers' preferences is essential for a business's success. We propose the Gradient-based Survey (GBS), a discrete choice experiment for multiattribute product design. The experiment elicits consumer preferences through a sequence of paired comparisons for partial profiles. GBS adaptively constructs paired comparison questions based on the respondents' previous choices. Unlike the traditional random utility maximization paradigm, GBS is robust to model misspecification by not requiring a parametric utility model. Cross-pollinating the machine learning and experiment design, GBS is scalable to products with hundreds of attributes and can design personalized products for heterogeneous consumers. We demonstrate the advantage of GBS in accuracy and sample efficiency compared to the existing parametric and nonparametric methods in simulations

Group-based Robustness: A General Framework for Customized Robustness in the Real World

Machine-learning models are known to be vulnerable to evasion attacks that perturb model inputs to induce misclassifications. In this work, we identify real-world scenarios where the true threat cannot be assessed accurately by existing attacks. Specifically, we find that conventional metrics measuring targeted and untargeted robustness do not appropriately reflect a model's ability to withstand attacks from one set of source classes to another set of target classes. To address the shortcomings of existing methods, we formally define a new metric, termed group-based robustness, that complements existing metrics and is better-suited for evaluating model performance in certain attack scenarios. We show empirically that group-based robustness allows us to distinguish between models' vulnerability against specific threat models in situations where traditional robustness metrics do not apply. Moreover, to measure group-based robustness efficiently and accurately, we 1) propose two loss functions and 2) identify three new attack strategies. We show empirically that with comparable success rates, finding evasive samples using our new loss functions saves computation by a factor as large as the number of targeted classes, and finding evasive samples using our new attack strategies saves time by up to 99\% compared to brute-force search methods. Finally, we propose a defense method that increases group-based robustness by up to 3.52$\times$

A Framework for Real-Time Service-Oriented Architecture

Service-oriented architectures (SOA), though widely accepted in a variety of industries, must be enhanced to support real-time activities in order to gain even greater adoption.We present RT-Llama, a novel architecture for real-time SOA to support predictability in business processes. Based on a user-specified process and deadline, our architecture, containing global resource management and business process composition components, can reserve resources in advance for each service in the process to ensure it meets its end-to-end deadline. This is facilitated by also creating a real-time enterprise middleware that manages utilization of local resources by using efficient data structures and handles service requests via reserved CPU bandwidth. We demonstrate that RT-Llama’s reservation components are both efficient and adaptable to dynamic real-time environments

Linear Private Set Union from Multi-Query Reverse Private Membership Test

Private set union (PSU) protocol enables two parties, each holding a set, to compute the union of their sets without revealing anything else to either party. So far, there are two known approaches for constructing PSU protocols. The first mainly depends on additively homomorphic encryption (AHE), which is generally inefficient since it needs to perform a non-constant number of homomorphic computations on each item. The second is mainly based on oblivious transfer and symmetric-key operations, which is recently proposed by Kolesnikov et al. (ASIACRYPT 2019). It features good practical performance, which is several orders of magnitude faster than the first one. However, neither of these two approaches is optimal in the sense that their computation and communication complexity are not both $O(n)$, where $n$ is the size of the set. Therefore, the problem of constructing the optimal PSU protocol remains open. In this work, we resolve this open problem by proposing a generic framework of PSU from oblivious transfer and a newly introduced protocol called multi-query reverse private membership test (mq-RPMT). We present two generic constructions of mq-RPMT. The first is based on symmetric-key encryption and general 2PC techniques. The second is based on re-randomizable public-key encryption. Both constructions lead to PSU with linear computation and communication complexity. We implement our two PSU protocols and compare them with the state-of-the-art PSU. Experiments show that our PKE-based protocol has the lowest communication of all schemes, which is $3.7-14.8\times$ lower depending on set size. The running time of our PSU scheme is $1.2-12\times$ faster than that of state-of-the-art depending on network environments

Encode and Permute that Database! Single-Server Private Information Retrieval with Constant Online Time, Communication, and Client-Side Storage

Private Information Retrieval (PIR) facilitates the retrieval of database entries by a client from a remote server without revealing which specific entry is being queried. The preprocessing model has emerged as a significant technique for constructing efficient PIR systems, allowing parties to execute a one-time, query-independent offline phase, and then a fast online retrieval phase. In particular, Corrigan-Gibbs and Kogan (EUROCRYPT 2020) presented a new framework for constructing PIR with sublinear online time. Nevertheless, their protocol is deemed impractical in the single-server setting due to the heavy use of Fully Homomorphic Encryption (FHE). More recently, two state-of-the-art (SOTA) single-server PIR protocols (Zhou et al., S&P 2024 and Mughees-Ren, ePrint 2023) have eliminated FHE, at the price of linear offline communication. However, the client-side storage is still relatively large ($\tilde{O}(\sqrt{n})$), which poses challenges to practical deployment, especially when the client has limited computation and storage capabilities. To address such limitation, we propose a novel PIR protocol Pai, which only requires constant online time, communication, and client-side storage. The price we pay is only a $1$ - $5\times$ increase in offline communication, which would be acceptable since it is a one-time cost.Building upon our Pai, we also present a Symmetric KPIR (KSPIR) PaiKSPIR and a Chargeable KSPIR (CKSPIR) PaiCKSPIR. These two variants of PIR offer enhanced functionalities while maintaining computational complexities similar to the original Pai. In addition to providing rigorous theoretical proofs of correctness and privacy for Pai, we have undertaken comprehensive protocol implementations and conducted extensive experiments to validate their high efficiency. Our empirical findings demonstrate that our protocols achieve notably higher online efficiency than SOTA protocols, e.g., Pai exhibits $8.8$ - $91.8\times$ better online communication cost and $2.5$ - $8.8\times$ better online time. Given the superior online time and storage, our protocol is well-suited for practical deployment

Muon Flux Measurement at China Jinping Underground Laboratory

China Jinping Underground Laboratory (CJPL) is ideal for studying solar-, geo-, and supernova neutrinos. A precise measurement of the cosmic-ray background would play an essential role in proceeding with the R\&D research for these MeV-scale neutrino experiments. Using a 1-ton prototype detector for the Jinping Neutrino Experiment (JNE), we detected 264 high-energy muon events from a 645.2-day dataset at the first phase of CJPL (CJPL-I), reconstructed their directions, and measured the cosmic-ray muon flux to be $(3.53\pm0.22_{\text{stat.}}\pm0.07_{\text{sys.}})\times10^{-10}$ cm$^{-2}$s$^{-1}$. The observed angular distributions indicate the leakage of cosmic-ray muon background and agree with the simulation accounting for Jinping mountain's terrain. A survey of muon fluxes at different laboratory locations situated under mountains and below mine shaft indicated that the former is generally a factor of $(4\pm2)$ larger than the latter with the same vertical overburden. This study provides a convenient back-of-the-envelope estimation for muon flux of an underground experiment

Performance of the 1-ton Prototype Neutrino Detector at CJPL-I

China Jinping Underground Laboratory (CJPL) provides an ideal site for solar, geo-, and supernova neutrino studies. With a prototype neutrino detector running since 2017, containing 1-ton liquid scintillator (LS), we tested its experimental hardware, performed the physics calibration, and measured its radioactive backgrounds, as an early stage of the Jinping Neutrino Experiment (JNE). We investigated the radon background and implemented the nitrogen sealing technology to control it. This paper presents the details of these studies and will serve as a key reference for the construction and optimization of the future large detector at JNE