From attention to profit: quantitative trading strategy based on transformer

In traditional quantitative trading practice, navigating the complicated and dynamic financial market presents a persistent challenge. Former machine learning approaches have struggled to fully capture various market variables, often ignore long-term information and fail to catch up with essential signals that may lead the profit. This paper introduces an enhanced transformer architecture and designs a novel factor based on the model. By transfer learning from sentiment analysis, the proposed model not only exploits its original inherent advantages in capturing long-range dependencies and modelling complex data relationships but is also able to solve tasks with numerical inputs and accurately forecast future returns over a period. This work collects more than 5,000,000 rolling data of 4,601 stocks in the Chinese capital market from 2010 to 2019. The results of this study demonstrated the model's superior performance in predicting stock trends compared with other 100 factor-based quantitative strategies with lower turnover rates and a more robust half-life period. Notably, the model's innovative use transformer to establish factors, in conjunction with market sentiment information, has been shown to enhance the accuracy of trading signals significantly, thereby offering promising implications for the future of quantitative trading strategies

Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review

This paper delves into the pivotal role of prompt engineering in unleashing the capabilities of Large Language Models (LLMs). Prompt engineering is the process of structuring input text for LLMs and is a technique integral to optimizing the efficacy of LLMs. This survey elucidates foundational principles of prompt engineering, such as role-prompting, one-shot, and few-shot prompting, as well as more advanced methodologies such as the chain-of-thought and tree-of-thoughts prompting. The paper sheds light on how external assistance in the form of plugins can assist in this task, and reduce machine hallucination by retrieving external knowledge. We subsequently delineate prospective directions in prompt engineering research, emphasizing the need for a deeper understanding of structures and the role of agents in Artificial Intelligence-Generated Content (AIGC) tools. We discuss how to assess the efficacy of prompt methods from different perspectives and using different methods. Finally, we gather information about the application of prompt engineering in such fields as education and programming, showing its transformative potential. This comprehensive survey aims to serve as a friendly guide for anyone venturing through the big world of LLMs and prompt engineering

Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models

Recently, growing interest has been aroused in extending the multimodal capability of large language models (LLMs), e.g., vision-language (VL) learning, which is regarded as the next milestone of artificial general intelligence. However, existing solutions are prohibitively expensive, which not only need to optimize excessive parameters, but also require another large-scale pre-training before VL instruction tuning. In this paper, we propose a novel and affordable solution for the effective VL adaption of LLMs, called Mixture-of-Modality Adaptation (MMA). Instead of using large neural networks to connect the image encoder and LLM, MMA adopts lightweight modules, i.e., adapters, to bridge the gap between LLMs and VL tasks, which also enables the joint optimization of the image and language models. Meanwhile, MMA is also equipped with a routing algorithm to help LLMs achieve an automatic shift between single- and multi-modal instructions without compromising their ability of natural language understanding. To validate MMA, we apply it to a recent LLM called LLaMA and term this formed large vision-language instructed model as LaVIN. To validate MMA and LaVIN, we conduct extensive experiments under two setups, namely multimodal science question answering and multimodal dialogue. The experimental results not only demonstrate the competitive performance and the superior training efficiency of LaVIN than existing multimodal LLMs, but also confirm its great potential as a general-purpose chatbot. More importantly, the actual expenditure of LaVIN is extremely cheap, e.g., only 1.4 training hours with 3.8M trainable parameters, greatly confirming the effectiveness of MMA. Our project is released at https://luogen1996.github.io/lavin

Substoichiometrically Different Mitotypes Coexist in Mitochondrial Genomes of Brassica napus L

Cytoplasmic male sterility (CMS) has been identified in numerous plant species. Brassica napus CMS plants, such as Polima (pol), MI, and Shaan 2A, have been identified independently by different researchers with different materials in conventional breeding processes. How this kind of CMS emerges is unclear. Here, we report the mitochondrial genome sequence of the prevalent mitotype in the most widely used pol-CMS line, which has a length of 223,412 bp and encodes 34 proteins, 3 ribosomal RNAs, and 18 tRNAs, including two near identical copies of trnH. Of these 55 genes, 48 were found to be identical to their equivalents in the “nap” cytoplasm. The nap mitotype carries only one copy of trnH, and the sequences of five of the six remaining genes are highly similar to their equivalents in the pol mitotype. Forty-four open reading frames (ORFs) with unknown function were detected, including two unique to the pol mitotype (orf122 and orf132). At least five rearrangement events are required to account for the structural differences between the pol and nap sequences. The CMS-related orf224 neighboring region (∼5 kb) rearranged twice. PCR profiling based on mitotype-specific primer pairs showed that both mitotypes are present in B. napus cultivars. Quantitative PCR showed that the pol cytoplasm consists mainly of the pol mitotype, and the nap mitotype is the main genome of nap cytoplasm. Large variation in the copy number ratio of mitotypes was found, even among cultivars sharing the same cytoplasm. The coexistence of mitochondrial mitotypes and substoichiometric shifting can explain the emergence of CMS in B. napus

An Evaluation of Requirements Modeling for Cyber-Physical Systems via LLMs

Cyber-physical systems (CPSs) integrate cyber and physical components and enable them to interact with each other to meet user needs. The needs for CPSs span rich application domains such as healthcare and medicine, smart home, smart building, etc. This indicates that CPSs are all about solving real-world problems. With the increasing abundance of sensing devices and effectors, the problems wanted to solve with CPSs are becoming more and more complex. It is also becoming increasingly difficult to extract and express CPS requirements accurately. Problem frame approach aims to shape real-world problems by capturing the characteristics and interconnections of components, where the problem diagram is central to expressing the requirements. CPSs requirements are generally presented in domain-specific documents that are normally expressed in natural language. There is currently no effective way to extract problem diagrams from natural language documents. CPSs requirements extraction and modeling are generally done manually, which is time-consuming, labor-intensive, and error-prone. Large language models (LLMs) have shown excellent performance in natural language understanding. It can be interesting to explore the abilities of LLMs to understand domain-specific documents and identify modeling elements, which this paper is working on. To achieve this goal, we first formulate two tasks (i.e., entity recognition and interaction extraction) and propose a benchmark called CPSBench. Based on this benchmark, extensive experiments are conducted to evaluate the abilities and limitations of seven advanced LLMs. We find some interesting insights. Finally, we establish a taxonomy of LLMs hallucinations in CPSs requirements modeling using problem diagrams. These results will inspire research on the use of LLMs for automated CPSs requirements modeling.Comment: 12 pages, 8 figure

Mitochondrial genome sequencing helps show the evolutionary mechanism of mitochondrial genome formation in Brassica

Abstract Background Angiosperm mitochondrial genomes are more complex than those of other organisms. Analyses of the mitochondrial genome sequences of at least 11 angiosperm species have showed several common properties; these cannot easily explain, however, how the diverse mitotypes evolved within each genus or species. We analyzed the evolutionary relationships of Brassica mitotypes by sequencing. Results We sequenced the mitotypes of cam (Brassica rapa), ole (B. oleracea), jun (B. juncea), and car (B. carinata) and analyzed them together with two previously sequenced mitotypes of B. napus (pol and nap). The sizes of whole single circular genomes of cam, jun, ole, and car are 219,747 bp, 219,766 bp, 360,271 bp, and 232,241 bp, respectively. The mitochondrial genome of ole is largest as a resulting of the duplication of a 141.8 kb segment. The jun mitotype is the result of an inherited cam mitotype, and pol is also derived from the cam mitotype with evolutionary modifications. Genes with known functions are conserved in all mitotypes, but clear variation in open reading frames (ORFs) with unknown functions among the six mitotypes was observed. Sequence relationship analysis showed that there has been genome compaction and inheritance in the course of Brassica mitotype evolution. Conclusions We have sequenced four Brassica mitotypes, compared six Brassica mitotypes and suggested a mechanism for mitochondrial genome formation in Brassica, including evolutionary events such as inheritance, duplication, rearrangement, genome compaction, and mutation. </jats:sec

Taichi on the brain: an activation likelihood estimated meta-analysis of functional neuroimaging data

IntroductionTai Chi Chuan (TCC) is an exercise regimen renowned for its comprehensive benefits to both physical and mental health. The present research endeavor aims to elucidate the neurocognitive impacts of TCC compared to alternative exercise modalities or therapeutic interventions.MethodsA systematic meta-analysis was undertaken, encompassing a rigorous review of diverse datasets, wherein 422 scholarly articles were examined, with a subset of 18 articles meeting the stringent criteria for inclusion in the analytical framework.ResultsThe study cohort comprised 677 participants, characterized by a mean age of 56.52 ± 14.89 years and an average educational attainment of 11.06 ± 3.32 years. Noteworthy alterations in functional neural activity were identified within the superior frontal gyrus.DiscussionThis comprehensive analysis provides significant insights into the enduring neural modifications and the distinctive contributions of TCC to cognitive health. Nevertheless, it is imperative to acknowledge the potential for bias in smaller functional magnetic resonance imaging studies owing to their inconclusive outcomes. This observation underscores the critical need for collaborative, multicenter research initiatives with expanded sample sizes to enhance the robustness and generalizability of future findings

Self-powered electrotactile textile haptic glove for enhanced human-machine interface.

Human-machine interface (HMI) plays an important role in various fields, where haptic technologies provide crucial tactile feedback that greatly enhances user experience, especially in virtual reality/augmented reality, prosthetic control, and therapeutic applications. Through tactile feedback, users can interact with devices in a more realistic way, thereby improving the overall effectiveness of the experience. However, existing haptic devices are often bulky due to cumbersome instruments and power modules, limiting comfort and portability. Here, we introduce a concept of wearable haptic technology: a thin, soft, self-powered electrotactile textile haptic (SPETH) glove that uses the triboelectric effect and gas breakdown discharge for localized electrical stimulation. Daily hand movements generate sufficient mechanical energy to power the SPETH glove. Its features-softness, lightweight, self-sustainability, portability, and affordability-enable it to provide tactile feedback anytime and anywhere without external equipment. This makes the SPETH glove an enhanced, battery-free HMI suitable for a wide range of applications

Impact of the Naples Prognostic Score at admission on long-term prognosis among patients with coronary artery disease

BackgroundThe Naples Prognostic Score (NPS) is innovatively constructed to comprehensively evaluate the inflammatory and nutritional status according to several basic blood examinations. This study aimed to investigate the correlation between NPS and long-term prognosis in patients with coronary artery disease (CAD).MethodsThe analysis data of this retrospective cohort study were collected from electronic health records in the People’s Hospital of Guangxi Zhuang Autonomous Region. All adult patients who underwent coronary angiology (CAG) and were diagnosed as having CAD at the People’s Hospital of Guangxi Zhuang Autonomous Region from March 2013 to December 2023 were enrolled. The primary endpoint was all-cause death during follow-up.ResultsThe 28,799 patients were divided into three groups according to the NPS value, with 803 (2.79%) in group 0, 12,130 (42.12%) in group 1, and 15,866 (55.09%) in group 2. Over the median follow-up period of 6.12 years, 3,630 patients (12.60%) died. Long-term all-cause mortality was significantly higher in group 2 and group 1 compared with group 0 (log-rank p &lt; 0.001). Cox regression analysis showed that both continuous NPS and categorical NPS groups were significantly associated with the risk of all-cause mortality in patients with CAD [per 1-point decrement: full adjusted HR = 1.15; 95% CI, 1.11–1.19; compared with group 0 (NPS of 0), group 1 (NPS of 1 or 2), full adjusted HR = 1.38, 95% CI: 1.03–1.85, and group 2 (NPS of 3 or 4), full adjusted HR = 1.70, 95% CI: 1.27–2.28]. Restricted cubic spline analyses showed a linear relationship between NPS and risk of long-term all-cause death.ConclusionsThe present study demonstrates that the NPS was independently associated with long-term all-cause mortality among patients with CAD

Genomic sequencing combined with marker-assisted breeding effectively eliminates potential linkage drag of a target gene: a case study in tobacco

Linkage drag frequently impedes the utilization of beneficial genes from wild species in crop improvement. The N gene from Nicotiana glutinosa confers strong resistance to tobacco mosaic virus (TMV) but introduces linkage drag when introgressed into cultivated tobacco (Nicotiana tabacum). To address this issue, we sequenced the TMV-resistant flue-cured tobacco line 0970A and carried out comparative genomic analysis. Additionally, we used molecular markers to screen a BC4F1 population derived from the cross between 0970A and an elite flue-cured tobacco variety CB-1 (recurrent parent). As a result of sequencing 0970A, the N gene was located at the end of chromosome Nt11. The comparative genomic analysis showed that 0970A inherited approximately 3.74 Mb of N. glutinosa DNA (N-fragment) from its donor, Coker 176. From screening the BC4F1 population with molecular markers, a recombinant was identified. This recombinant had a significantly reduced N-fragment (~270 kb), which minimized the linkage drag while still maintaining resistance to TMV. This research indicates that the combination of genome sequencing and marker-assisted breeding can be successfully applied to reduce linkage drag. The findings offer valuable resources for breeding tobacco with resistance to TMV