Short-Term Volatility Prediction Using Deep CNNs Trained on Order Flow

As a newly emerged asset class, cryptocurrency is evidently more volatile compared to the traditional equity markets. Due to its mostly unregulated nature, and often low liquidity, the price of crypto assets can sustain a significant change within minutes that in turn might result in considerable losses. In this paper, we employ an approach for encoding market information into images and making predictions of short-term realized volatility by employing Convolutional Neural Networks. We then compare the performance of the proposed encoding and corresponding model with other benchmark models. The experimental results demonstrate that this representation of market data with a Convolutional Neural Network as a predictive model has the potential to better capture the market dynamics and a better volatility prediction.Comment: Third International Workshop on Modelling Uncertainty in the Financial World (MUFin'23

Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance

As large language models (LLMs) are continuously being developed, their evaluation becomes increasingly important yet challenging. This work proposes Chain-of-Thought Hub, an open-source evaluation suite on the multi-step reasoning capabilities of large language models. We are interested in this setting for two reasons: (1) from the behavior of GPT and PaLM model family, we observe that complex reasoning is likely to be a key differentiator between weaker and stronger LLMs; (2) we envisage large language models to become the next-generation computational platform and foster an ecosystem of LLM-based new applications, this naturally requires the foundation models to perform complex tasks that often involve the composition of linguistic and logical operations. Our approach is to compile a suite of challenging reasoning benchmarks to track the progress of LLMs. Our current results show that: (1) model scale clearly correlates with reasoning capabilities; (2) As of May 2023, Claude-v1.3 and PaLM-2 are the only two models that are comparable with GPT-4, while open-sourced models still lag behind; (3) LLaMA-65B performs closely to code-davinci-002, indicating that with successful further development such as reinforcement learning from human feedback (RLHF), it has great potential to be close to GPT-3.5-Turbo. Our results also suggest that for the open-source efforts to catch up, the community may focus more on building better base models and exploring RLHF.Comment: Preprint. Code at https://github.com/FranxYao/chain-of-thought-hu

TKwinFormer: Top k Window Attention in Vision Transformers for Feature Matching

Local feature matching remains a challenging task, primarily due to difficulties in matching sparse keypoints and low-texture regions. The key to solving this problem lies in effectively and accurately integrating global and local information. To achieve this goal, we introduce an innovative local feature matching method called TKwinFormer. Our approach employs a multi-stage matching strategy to optimize the efficiency of information interaction. Furthermore, we propose a novel attention mechanism called Top K Window Attention, which facilitates global information interaction through window tokens prior to patch-level matching, resulting in improved matching accuracy. Additionally, we design an attention block to enhance attention between channels. Experimental results demonstrate that TKwinFormer outperforms state-of-the-art methods on various benchmarks. Code is available at: https://github.com/LiaoYun0x0/TKwinFormer.Comment: 11 pages, 7 figure

Pyrolysis gas as a carbon source for biogas production via anaerobic digestion

Carbon is an important resource for anaerobes to enhance biogas production. In this study, the possibility of using simulated pyrolysis gas (SPG) as a carbon source for biogas production was investigated. The effects of stirring speed (SS), gas holding time (GHT), and H2 addition on biomethanation of SPG were evaluated. The diversity and structure of microbial communities were also analyzed under an illumina MiSeq platform. Results indicated that at a GHT of 14 h and an SS at 400 rpm, SPG with up to 64.7% CH4could be bio-upgraded to biogas. Gas–liquid mass transfer is the limitation for SPG biomethanation. For the first time, it has been noticed that the addition of H2 can bioupgrade SPG to high quality biogas (with 91.1% CH4). Methanobacterium was considered as a key factor in all reactors. This study provides an idea and alternative way to convert lignocellulosic biomass and solid organic waste into energy (e.g., pyrolysis was used as a pretreatment to produce pyrolysis gas from biomass, and then, pyrolysis gas was bioupgraded to higher quality biogas via anaerobic digestion)

LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving

Existing learning-based autonomous driving (AD) systems face challenges in comprehending high-level information, generalizing to rare events, and providing interpretability. To address these problems, this work employs Large Language Models (LLMs) as a decision-making component for complex AD scenarios that require human commonsense understanding. We devise cognitive pathways to enable comprehensive reasoning with LLMs, and develop algorithms for translating LLM decisions into actionable driving commands. Through this approach, LLM decisions are seamlessly integrated with low-level controllers by guided parameter matrix adaptation. Extensive experiments demonstrate that our proposed method not only consistently surpasses baseline approaches in single-vehicle tasks, but also helps handle complex driving behaviors even multi-vehicle coordination, thanks to the commonsense reasoning capabilities of LLMs. This paper presents an initial step toward leveraging LLMs as effective decision-makers for intricate AD scenarios in terms of safety, efficiency, generalizability, and interoperability. We aspire for it to serve as inspiration for future research in this field. Project page: https://sites.google.com/view/llm-mp

Ecological strategies of Hyphantria cunea (Lepidoptera: Arctiidae) response to different larval densities

Population density is an essential factor affecting the life history traits of insects and their trade-off relationships, as increasing density intensifies intraspecific competition. It decreases the average resources available to individuals within a population, affecting their morphology, physiology, behavior, and fitness. The fall webworm, Hyphantria cunea (Drury) (Lepidoptera: Arctiidae), has been an invasive pest of forest trees, ornamental plants, and fruit trees in China for many years. The larvae have a typical aggregation habit before the fourth instar and keep spitting silk to gather the damaged leaves into silk webs. However, the fitness of H. cunea in response to population density remains unclear. In this study, the critical biological parameters, food utilization, and population parameters of H. cunea in response to different rearing densities were investigated. The results showed that under high population density, H. cunea larvae showed better performance, with faster development, higher survival rates, and shorter generation time, but pupal weight and female fecundity decreased as population density increased. In contrast, for larvae raised in low density, the developmental period was prolonged, and mortality was increased, while higher food utilization, greater body size, and female fecundity were observed. Both males and females had similar development strategies in response to the density, but females may be more resistant to crowding than males. In conclusion, H. cunea could adopt different ecological strategies against the stress of density. High population densities result in shorter generation cycles and higher survival rates. Conversely, the low-density generation period becomes longer but with greater fecundity. The results may help determine the possible outbreak mechanism and develop effective population monitoring and forecasting measures for H. cunea

Tumor-secreted lactate contributes to an immunosuppressive microenvironment and affects CD8 T-cell infiltration in glioblastoma

IntroductionGlioblastoma is a malignant brain tumor with poor prognosis. Lactate is the main product of tumor cells, and its secretion may relate to immunocytes’ activation. However, its role in glioblastoma is poorly understood. MethodsThis work performed bulk RNA-seq analysis and single cell RNA-seq analysis to explore the role of lactate in glioblastoma progression. Over 1400 glioblastoma samples were grouped into different clusters according to their expression and the results were validated with our own data, the xiangya cohort. Immunocytes infiltration analysis, immunogram and the map of immune checkpoint genes’ expression were applied to analyze the potential connection between the lactate level with tumor immune microenvironment. Furthermore, machine learning algorithms and cell-cell interaction algorithm were introduced to reveal the connection of tumor cells with immunocytes. By co-culturing CD8 T cells with tumor cells, and performing immunohistochemistry on Xiangya cohort samples further validated results from previous analysis.DiscussionIn this work, lactate is proved that contributes to glioblastoma immune suppressive microenvironment. High level of lactate in tumor microenvironment can affect CD8 T cells’ migration and infiltration ratio in glioblastoma. To step further, potential compounds that targets to samples from different groups were also predicted for future exploration