Explaining the DAMPE data with scalar dark matter and gauged U(1)Le−LμU(1)_{L_e-L_\mu} interaction

Inspired by the peak structure observed by recent DAMPE experiment in $e^+e^-$ cosmic-ray spectrum, we consider a scalar dark matter (DM) model with gauged $U(1)_{L_e-L_\mu}$ symmetry, which is the most economical anomaly-free theory to potentially explain the peak by DM annihilation in nearby subhalo. We utilize the process $\chi \chi \to Z^\prime Z^\prime \to l \bar{l} l^\prime \bar{l}^\prime$, where $\chi$, $Z^\prime$, $l^{(\prime)}$ denote the scalar DM, the new gauge boson and $l^{(\prime)} =e, \mu$, respectively, to generate the $e^+e^-$ spectrum. By fitting the predicted spectrum to the experimental data, we obtain the favored DM mass range $m_\chi \simeq 3060^{+80}_{-100} \, {\rm GeV}$ and $\Delta m \equiv m_\chi - m_{Z^\prime} \lesssim 14 \, {\rm GeV}$ at $68\%$ Confidence Level (C.L.). Furthermore, we determine the parameter space of the model which can explain the peak and meanwhile satisfy the constraints from DM relic abundance, DM direct detection and the collider bounds. We conclude that the model we consider can account for the peak, although there exists a tension with the constraints from the LEP-II bound on $m_{Z^\prime}$ arising from the cross section measurement of $e^+e^- \to Z^{\prime\ast} \to e^+ e^-$.Comment: 15 pages, 4 figure

BatchEval: Towards Human-like Text Evaluation

Significant progress has been made in automatic text evaluation with the introduction of large language models (LLMs) as evaluators. However, current sample-wise evaluation paradigm suffers from the following issues: (1) Sensitive to prompt design; (2) Poor resistance to noise; (3) Inferior ensemble performance with static reference. Inspired by the fact that humans treat both criterion definition and inter sample comparison as references for evaluation, we propose BatchEval, a paradigm that conducts batch-wise evaluation iteratively to alleviate the above problems. We explore variants under this paradigm and confirm the optimal settings are two stage procedure with heterogeneous batch composition strategy and decimal scoring format. Comprehensive experiments across 3 LLMs on 4 text evaluation tasks demonstrate that BatchEval outperforms state-of-the-art methods by 10.5% on Pearson correlations with only 64% API cost on average. Further analyses have been conducted to verify the robustness, generalization, and working mechanism of BatchEval.Comment: 19 pages, 9 figure

Turning Dust into Gold: Distilling Complex Reasoning Capabilities from LLMs by Leveraging Negative Data

Large Language Models (LLMs) have performed well on various reasoning tasks, but their inaccessibility and numerous parameters hinder wide application in practice. One promising way is distilling the reasoning ability from LLMs to small models by the generated chain-of-thought reasoning paths. In some cases, however, LLMs may produce incorrect reasoning chains, especially when facing complex mathematical problems. Previous studies only transfer knowledge from positive samples and drop the synthesized data with wrong answers. In this work, we illustrate the merit of negative data and propose a model specialization framework to distill LLMs with negative samples besides positive ones. The framework consists of three progressive steps, covering from training to inference stages, to absorb knowledge from negative data. We conduct extensive experiments across arithmetic reasoning tasks to demonstrate the role of negative data in distillation from LLM.Comment: AAAI 202

Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning

Self-consistency (SC) has been a widely used decoding strategy for chain-of-thought reasoning. Despite bringing significant performance improvements across a variety of multi-step reasoning tasks, it is a high-cost method that requires multiple sampling with the preset size. In this paper, we propose a simple and scalable sampling process, \textbf{E}arly-Stopping \textbf{S}elf-\textbf{C}onsistency (ESC), to greatly reduce the cost of SC without sacrificing performance. On this basis, one control scheme for ESC is further derivated to dynamically choose the performance-cost balance for different tasks and models. To demonstrate ESC's effectiveness, we conducted extensive experiments on three popular categories of reasoning tasks: arithmetic, commonsense and symbolic reasoning over language models with varying scales. The empirical results show that ESC reduces the average number of sampling of chain-of-thought reasoning by a significant margin on six benchmarks, including MATH (-33.8%), GSM8K (-80.1%), StrategyQA (-76.8%), CommonsenseQA (-78.5%), Coin Flip (-84.2%) and Last Letters (-67.4%), while attaining comparable performances.Comment: ICLR 202

Generative Dense Retrieval: Memory Can Be a Burden

Generative Retrieval (GR), autoregressively decoding relevant document identifiers given a query, has been shown to perform well under the setting of small-scale corpora. By memorizing the document corpus with model parameters, GR implicitly achieves deep interaction between query and document. However, such a memorizing mechanism faces three drawbacks: (1) Poor memory accuracy for fine-grained features of documents; (2) Memory confusion gets worse as the corpus size increases; (3) Huge memory update costs for new documents. To alleviate these problems, we propose the Generative Dense Retrieval (GDR) paradigm. Specifically, GDR first uses the limited memory volume to achieve inter-cluster matching from query to relevant document clusters. Memorizing-free matching mechanism from Dense Retrieval (DR) is then introduced to conduct fine-grained intra-cluster matching from clusters to relevant documents. The coarse-to-fine process maximizes the advantages of GR's deep interaction and DR's scalability. Besides, we design a cluster identifier constructing strategy to facilitate corpus memory and a cluster-adaptive negative sampling strategy to enhance the intra-cluster mapping ability. Empirical results show that GDR obtains an average of 3.0 R@100 improvement on NQ dataset under multiple settings and has better scalability.Comment: EACL 2024 mai

In-situ Thermophysical Measurement of Flowing Molten Chloride Salt Using Modulated Photothermal Radiometry

Molten salts are a leading candidate for high-temperature heat transfer fluids (HTFs) for thermal energy storage and conversion systems in concentrated solar power (CSP) and nuclear energy power plants. The ability to probe molten salt thermal transport properties in both stationary and flowing status is important for the evaluation of their heat transfer performance under realistic operational conditions, including the temperature range and potential degradation due to corrosion and contamination. However, accurate thermal transport properties are usually challenging to obtain even for stagnant molten salts due to different sources of errors from convection, radiation, and corrosion, let alone flowing ones. To the best of authors' knowledge, there is no available in-situ technique for measuring flowing molten salt thermal conductivity. Here, we report the first in-situ flowing molten salt thermal conductivity measurement using modulated photothermal radiometry (MPR). We could successfully perform the first in-situ thermal conductivity measurement of flowing molten $NaCl-KCl-MgCl_2$ in the typical operating temperature (520 and 580 $^oC$) with flow velocities ranging from around 0.3 to 1.0 $m$$s^-1$. The relative change of the molten salt thermal conductivity was measured. Gnielinski's correlation was also used to estimate the heat transfer coefficient h of the flowing $NaCl-KCl-MgCl_2$ in the given experimental condition. The work showed the potential of the MPR technique serving as an in-situ diagnostics tool to evaluate the heat transfer performance of flowing molten salts and other high-temperature HTFs

N-Acetylation phenotype and genotype and risk of bladder cancer in benzidine-exposed workers

Several studies in subjects occupationally exposed to arylamine carcinogens have shown increased risks for bladder cancer associated with the slow acetylator phenotype. To follow up these reports, a case-control study of N-acetylation and bladder cancer risk was carried out among subjects occupationally exposed to benzidine, in benzidine dye production and use facilities in China. Thirty-eight bladder cancer cases and 43 controls from these factories were included for study of acetylation phenotype, by dapsone administration, and for polymorphisms in the NAT2 gene, by a polymerase chain reaction (PCR)-based test. In contrast to previous studies, no increase in bladder cancer risk was found for the slow N-acetylation phenotype (OR= 0.3; 95% CI = 0.1-1.3) or for slow N-acetylation-associated double mutations in NAT2 (OR = 0.5; 95% CI = 0.1-1.8). Examination of specific mutations and adjustment for age, weight, city and tobacco use did not alter the results. When examined by level of benzidine exposure in the cases, the bladder cancer risks associated with low (OR = 0.3, 95% CI = 0.0-2.2), medium (OR = 0.7, 95% CI = 0.1-4.5) and high (OR = 0.6, 95% CI = 0.1-3.5) exposure showed no interaction between genotype and benzidine exposure, within the range of exposures experienced by subjects in this study. This study, which is the first to incorporate phenotypic and genotypic analyses, provides evidence that the NAT2-related slow N-acetylation polymorphism is not associated with an increased risk of bladder cancer in workers exposed to benzidine, and may have a protective effec

Increased co-expression of TIM-3 with TIGIT or 2B4 on CD8+ T cells is associated with poor prognosis in locally advanced nasopharyngeal carcinoma

The use of immune checkpoint inhibitors in malignant tumors improves patient outcomes. Because single-agent immune checkpoint blockade has a low objective response rate, it is meaningful to explore combined blockade of immune checkpoint receptors. We aimed to investigate the co-expression of TIM-3 with TIGIT or 2B4 on peripheral blood CD8+ T cells from patients with locally advanced nasopharyngeal carcinoma. The correlation between co-expression level and clinical characteristics and prognosis was studied to provide a basis for immunotherapy for nasopharyngeal carcinoma. Flow cytometry was used to detect TIM-3/TIGIT and TIM-3/2B4 co-expression on CD8+ T cells. The differences in co-expression between patients and healthy controls were analyzed. The correlation between co-expression of TIM-3/TIGIT or TIM-3/2B4 and the patient clinical characteristics and prognosis was examined. Also, the correlation between the TIM-3/TIGIT or 2B4 co-expression and other common inhibitory receptors was analyzed. We further validated our results using mRNA data from the Gene Expression Omnibus (GEO) database. TIM-3/TIGIT and TIM-3/2B4 co-expression was upregulated on peripheral blood CD8+ T cells from patients with nasopharyngeal carcinoma. They were both correlated with poor prognosis. There was a correlation between TIM-3/TIGIT co-expression and patient age and pathological stage, whereas TIM-3/2B4 co-expression correlated with age and sex. CD8+ T cells with elevated mRNA levels of TIM3/TIGIT and TIM3/2B4 also showed increased expression of multiple inhibitory receptors, indicating T cell exhaustion in locally advanced nasopharyngeal carcinoma. TIM-3/TIGIT or TIM-3/2B4 can be used as potential targets for combination immunotherapy in locally advanced nasopharyngeal carcinoma

A comprehensive insight into the effects of acidification on varied-sized pores in different rank coals

Elucidating the evolution law of coal pore structure under acidification is crucial for guiding the practical application of acidizing technology and improving the production of coalbed methane. To comprehensively investigate the influence of acidification on varied-sized pores in different rank coals, in this study, fat coal, meagre coal and anthracite coal were collected and acidified with a mixed solution composed of hydrochloric acid (9 wt%) and hydrofluoric acid (3 wt%). An approach integrating low-pressure CO2 adsorption (LPGA-CO2), low-temperature N2 adsorption (LTGA-N2) and Mercury intrusion porosimetry (MIP) was adopted to fully characterize the varied-sized pore structure before and after acidification to eliminate the limitations of single method. The results demonstrated that acid treatment improved the pore opening degree and connectivity in coal, but had essentially no effect on the pore shape. After acidification, all the coal samples showed significant increases in the porosity and total pore volume, which was mainly contributed by the numerous newly formed large mesopores and macropores, especially the macropores (with an average contribution rate of 74.59%). Taken as a whole, acid treatment had the largest impact on macropores, followed by mesopores, and the smallest impact on micropores. In addition, the variation trend of total specific surface area (SSA) under acidification was primarily determined by micropores. For the three different rank coals selected in this study, the total SSA of fat coal (PM) was more easily affected by acidification and had the largest percentage increase after acid treatment, followed by anthracite coal (YM), while that of meagre coal (LA) decreased slightly. This difference was driven primarily by the different variation trend of micropore SSA in different rank coals. After acidification, the SSA of ultra-micropores and super-micropores all increased in fat coal (PM) and anthracite coal (YM), whereas for meagre coal (LA), although ultra-micropores SSA increased, super-micropores SSA decreased, which ultimately led to the slight decrease of its micropore SSA. Moreover, the total pore volume increment of coal was closely related to the macropore volume increment under acidification, but not significantly related to the coal maturity,which might indicate that, compared with coal rank, the mineral content in coal might be a more important consideration when measuring the applicability of acidification technology