Structural Breaks in Volatility: The Case of Chinese Stock Returns

This article tests for periodic breaks in the unconditional variance of stock return data on two Chinese stock return market indexes. Using the modified ICSS algorithm, we observe three breaks in the Shanghai Stock Exchange composite index and Shenzhen Stock Exchange composite index series. We document the policy changes related to the Chinese stock market and explain that the Chinese stock market is largely influenced by government policy

Causal association between serum 25-Hydroxyvitamin D levels and cutaneous melanoma: a two-sample Mendelian randomization study

BackgroundDespite numerous observational studies on the association between serum 25-Hydroxyvitamin D levels and cutaneous melanoma, causal inferences remain ambiguous due to confounding and reverse causality. This study aimed to elucidate the causal relationship between serum 25-Hydroxyvitamin D levels and melanoma incidence using Mendelian randomization (MR).MethodsA two-sample MR was conducted using genetic variants associated with serum 25-Hydroxyvitamin D levels as instrumental variables. Summary statistics for these variants were derived from genome-wide association studies, and those for melanoma risk were obtained from a comprehensive melanoma case-control study. Robustness of the results was assessed through sensitivity analyses, including the “leave-one-out” approach and tests for potential pleiotropy.ResultsThe MR analysis provided substantial evidence of a positive causal relationship between serum 25-Hydroxyvitamin D levels and the incidence of cutaneous melanoma, suggesting that each unit increase in serum 25-Hydroxyvitamin D levels corresponds with an increased risk of melanoma. Tests for pleiotropy showed minimal effects, and the sensitivity analysis confirmed no disproportionate influence by any individual single nucleotide polymorphism (SNP).ConclusionThe findings indicated a potentially causal positive association between serum 25-Hydroxyvitamin D levels and melanoma risk, challenging traditional beliefs about vitamin D’s role in melanoma. This emphasizes the need for a balanced and personalized approach to vitamin D supplementation and sun exposure, particularly in high-risk populations. These results should be interpreted with caution due to potential unrecognized pleiotropy and confounding factors. Future research should focus on validating these findings in diverse populations and exploring underlying biological mechanisms

Evaluating and Improving Tool-Augmented Computation-Intensive Math Reasoning

Chain-of-thought prompting~(CoT) and tool augmentation have been validated in recent work as effective practices for improving large language models~(LLMs) to perform step-by-step reasoning on complex math-related tasks. However, most existing math reasoning datasets may be not able to fully evaluate and analyze the ability of LLMs in manipulating tools and performing reasoning, as they may only require very few invocations of tools or miss annotations for evaluating intermediate reasoning steps. To address the issue, we construct \textbf{CARP}, a new Chinese dataset consisting of 4,886 computation-intensive algebra problems with formulated annotations on intermediate steps. In CARP, we test four LLMs with CoT prompting, and find that they are all prone to make mistakes at the early steps of the solution, leading to wrong answers. Based on this finding, we propose a new approach that can deliberate the reasoning steps with tool interfaces, namely \textbf{DELI}. In DELI, we first initialize a step-by-step solution based on retrieved exemplars, then iterate two deliberation procedures that check and refine the intermediate steps of the generated solution, from the perspectives of tool manipulation and natural language reasoning, until obtaining converged solutions or reaching the maximum turn. Experimental results on CARP and six other datasets show that the proposed DELI mostly outperforms competitive baselines, and can further boost the performance of existing CoT methods. Our data and code are available in \url{https://github.com/RUCAIBox/CARP}.Comment: 17 pages, working in progres

Microwave Photonic Imaging Radar with a Millimeter-level Resolution

Microwave photonic radars enable fast or even real-time high-resolution imaging thanks to its broad bandwidth. Nevertheless, the frequency range of the radars usually overlaps with other existed radio-frequency (RF) applications, and only a centimeter-level imaging resolution has been reported, making them insufficient for civilian applications. Here, we propose a microwave photonic imaging radar with a millimeter-level resolution by introducing a frequency-stepped chirp signal based on an optical frequency shifting loop. As compared with the conventional linear-frequency modulated (LFM) signal, the frequency-stepped chirp signal can bring the system excellent capability of anti-interference. In an experiment, a frequency-stepped chirp signal with a total bandwidth of 18.2 GHz (16.9 to 35.1 GHz) is generated. Postprocessing the radar echo, radar imaging with a two-dimensional imaging resolution of ~8.5 mm$\times$~8.3 mm is achieved. An auto-regressive algorithm is used to reconstruct the disturbed signal when a frequency interference exists, and the high-resolution imaging is sustained

Evaluating the predictive value of angiogenesis-related genes for prognosis and immunotherapy response in prostate adenocarcinoma using machine learning and experimental approaches

BackgroundAngiogenesis, the process of forming new blood vessels from pre-existing ones, plays a crucial role in the development and advancement of cancer. Although blocking angiogenesis has shown success in treating different types of solid tumors, its relevance in prostate adenocarcinoma (PRAD) has not been thoroughly investigated.MethodThis study utilized the WGCNA method to identify angiogenesis-related genes and assessed their diagnostic and prognostic value in patients with PRAD through cluster analysis. A diagnostic model was constructed using multiple machine learning techniques, while a prognostic model was developed employing the LASSO algorithm, underscoring the relevance of angiogenesis-related genes in PRAD. Further analysis identified MAP7D3 as the most significant prognostic gene among angiogenesis-related genes using multivariate Cox regression analysis and various machine learning algorithms. The study also investigated the correlation between MAP7D3 and immune infiltration as well as drug sensitivity in PRAD. Molecular docking analysis was conducted to assess the binding affinity of MAP7D3 to angiogenic drugs. Immunohistochemistry analysis of 60 PRAD tissue samples confirmed the expression and prognostic value of MAP7D3.ResultOverall, the study identified 10 key angiogenesis-related genes through WGCNA and demonstrated their potential prognostic and immune-related implications in PRAD patients. MAP7D3 is found to be closely associated with the prognosis of PRAD and its response to immunotherapy. Through molecular docking studies, it was revealed that MAP7D3 exhibits a high binding affinity to angiogenic drugs. Furthermore, experimental data confirmed the upregulation of MAP7D3 in PRAD, correlating with a poorer prognosis.ConclusionOur study confirmed the important role of angiogenesis-related genes in PRAD and identified a new angiogenesis-related target MAP7D3

Производительность труда на предприятии : оценка и направления повышения (на примере ОАО «СтанкоГомель»)

The role of the novel transcription factor ZBED6 for the adhesion/clustering of insulin-producing mouse MIN6 and βTC6 cells was investigated. Zbed6-silencing in the insulin producing cells resulted in increased three-dimensional cell-cell clustering and decreased adhesion to mouse laminin and human laminin 511. This was paralleled by a weaker focal adhesion kinase phosphorylation at laminin binding sites. Zbed6-silenced cells expressed less E-cadherin and more N-cadherin at cell-to-cell junctions. A strong ZBED6-binding site close to the N-cadherin gene transcription start site was observed. Three-dimensional clustering in Zbed6-silenced cells was prevented by an N-cadherin neutralizing antibody and by N-cadherin knockdown. Co-culture of neural crest stem cells (NCSCs) with Zbed6-silenced cells, but not with control cells, stimulated the outgrowth of NCSC processes. The cell-to-cell junctions between NCSCs and βTC6 cells stained more intensely for N-cadherin when Zbed6-silenced cells were co-cultured with NCSCs. We conclude that ZBED6 decreases the ratio between N- and E-cadherin. A lower N- to E-cadherin ratio may hamper the formation of three-dimensional beta-cell clusters and cell-to-cell junctions with NCSC, and instead promote efficient attachment to a laminin support and monolayer growth. Thus, by controlling beta-cell adhesion and cell-to-cell junctions, ZBED6 might play an important role in beta-cell differentiation, proliferation and survival

Extracting lignin-SiO2 composites from Si-rich biomass to prepare Si/C anode materials for lithium ions batteries

The comprehensive utilization of Si-rich biomass is restrained by macromolecular lignin and a large amount of ash. In this study, rice husks (RHs) are treated as a representative by alkali extraction and acid precipitation, and the obtained lignin-SiO2 composite is modified by carbonazation, ball milling, magnesiothermic reduction and additives. Through these processes, a Si/C composite with excellent electrochemical properties is obtained and performs stable cycling performance with high specific capacity retention of 572 mA h g−1 at 1 A g−1 after 1000 cycles. This introduced method provides a potential for utilizing Si-rich biomass comprehensively and preparing desirable Si/C anode materials from Si-rich biomass derived lignin-SiO2 composites

A Survey of Large Language Models

Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for comprehending and grasping a language. As a major approach, language modeling has been widely studied for language understanding and generation in the past two decades, evolving from statistical language models to neural language models. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora, showing strong capabilities in solving various NLP tasks. Since researchers have found that model scaling can lead to performance improvement, they further study the scaling effect by increasing the model size to an even larger size. Interestingly, when the parameter scale exceeds a certain level, these enlarged language models not only achieve a significant performance improvement but also show some special abilities that are not present in small-scale language models. To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size. Recently, the research on LLMs has been largely advanced by both academia and industry, and a remarkable progress is the launch of ChatGPT, which has attracted widespread attention from society. The technical evolution of LLMs has been making an important impact on the entire AI community, which would revolutionize the way how we develop and use AI algorithms. In this survey, we review the recent advances of LLMs by introducing the background, key findings, and mainstream techniques. In particular, we focus on four major aspects of LLMs, namely pre-training, adaptation tuning, utilization, and capacity evaluation. Besides, we also summarize the available resources for developing LLMs and discuss the remaining issues for future directions.Comment: ongoing work; 51 page

Chinese Antarctic Magnetometer Chain at the Cusp Latitude

A Chinese Antarctic Magnetometer (CAM) chain from Zhongshan Station (ZHS) to Dome-A (DMA) has been established since February 2009. A regular magnetometer is operated at ZHS, and four low power magnetometers are operated along the interior route from ZHS to DMA in the cusp latitude, extending over a distance of 1260 km. These stations fill an important void in the Antarctic magnetometer network. Furthermore, the CAM chain is magnetically conjugated with the Arctic region reaching from the Svalbard archipelago to Daneborg, on the east coast of Greenland. Conjugate measurements using the Arctic and Antarctic magnetometers provide excellent opportunities to investigate phenomena related to the coupling of the solar wind to the magnetosphere and ionosphere, such as magnetic impulse events, flux transfer events, traveling convection vortices and ultra-low frequency waves