DeWave: Discrete EEG Waves Encoding for Brain Dynamics to Text Translation

The translation of brain dynamics into natural language is pivotal for brain-computer interfaces (BCIs), a field that has seen substantial growth in recent years. With the swift advancement of large language models, such as ChatGPT, the need to bridge the gap between the brain and languages becomes increasingly pressing. Current methods, however, require eye-tracking fixations or event markers to segment brain dynamics into word-level features, which can restrict the practical application of these systems. These event markers may not be readily available or could be challenging to acquire during real-time inference, and the sequence of eye fixations may not align with the order of spoken words. To tackle these issues, we introduce a novel framework, DeWave, that integrates discrete encoding sequences into open-vocabulary EEG-to-text translation tasks. DeWave uses a quantized variational encoder to derive discrete codex encoding and align it with pre-trained language models. This discrete codex representation brings forth two advantages: 1) it alleviates the order mismatch between eye fixations and spoken words by introducing text-EEG contrastive alignment training, and 2) it minimizes the interference caused by individual differences in EEG waves through an invariant discrete codex. Our model surpasses the previous baseline (40.1 and 31.7) by 3.06% and 6.34%, respectively, achieving 41.35 BLEU-1 and 33.71 Rouge-F on the ZuCo Dataset. Furthermore, this work is the first to facilitate the translation of entire EEG signal periods without needing word-level order markers (e.g., eye fixations), scoring 20.5 BLEU-1 and 29.5 Rouge-1 on the ZuCo Dataset, respectively. Codes and the final paper will be public soon

Graph-based Facial Affect Analysis: A Review of Methods, Applications and Challenges

Facial affect analysis (FAA) using visual signals is important in human-computer interaction. Early methods focus on extracting appearance and geometry features associated with human affects, while ignoring the latent semantic information among individual facial changes, leading to limited performance and generalization. Recent work attempts to establish a graph-based representation to model these semantic relationships and develop frameworks to leverage them for various FAA tasks. In this paper, we provide a comprehensive review of graph-based FAA, including the evolution of algorithms and their applications. First, the FAA background knowledge is introduced, especially on the role of the graph. We then discuss approaches that are widely used for graph-based affective representation in literature and show a trend towards graph construction. For the relational reasoning in graph-based FAA, existing studies are categorized according to their usage of traditional methods or deep models, with a special emphasis on the latest graph neural networks. Performance comparisons of the state-of-the-art graph-based FAA methods are also summarized. Finally, we discuss the challenges and potential directions. As far as we know, this is the first survey of graph-based FAA methods. Our findings can serve as a reference for future research in this field.Comment: 20 pages, 12 figures, 5 table

Automata-Based Analysis of Stage Suspended Boom Systems

A stage suspended boom system is an automatic steeve system orchestrated by the PLC (programmable logic controller). Security and fault-recovering are two important properties. In this paper, we analyze and verify the boom system formally. We adopt the hybrid automaton to model the boom system. The forward reachability is used to verify the properties with the reachable states. We also present a case study to illustrate the feasibility of the proposed verification

Ganoderma triterpenes Protect Against Hyperhomocysteinemia Induced Endothelial-Mesenchymal Transition via TGF-β Signaling Inhibition

Endothelial dysfunction is one of the most important pathological status in hyperhomocysteinemia (HHcy) related cardiovascular diseases. Whereas, the underlying mechanisms have not been fully elucidated yet, concomitant with the absence of effective treatment. The purpose of this study was to explore the main mechanisms involved in HHcy-induced endothelial injury and identify the protective effect of Ganoderma triterpenes (GT). Bovine aortic endothelial cells (BAECs) were applied as in vitro experimental model. The small molecular inhibitors were used to explore the signalings involved in HHcy-induced endothelial injury. The experimental results provided initial evidence that HHcy led to endothelial-mesenchymal transition (EndMT). Meanwhile, TGF-β/Smad, PI3K/AKT and MAPK pathways were activated in this process, which was demonstrated by pretreatment with TGF-β RI kinase inhibitor VI SB431542, PI3K inhibitor LY294002, p38 inhibitor SB203580, and ERK inhibitor PD98059. Furthermore, it was found that GT restrained the process of HHcy-induced EndMT via reducing oxidative stress and suppressing fore mentioned pathways with further inhibiting the activity of Snail. These results implicate that there is an untapped potential for GT as a novel therapeutic candidate for HHcy-induced EndMT through alleviating oxidative stress and canonical TGF-β/Smad and non-Smad dependent signaling pathways

Variation in VEGFA and risk of cardiovascular disease in the UK Biobank

BackgroundCardiovascular disease (CVD) is an escalating global health crisis, contributing significantly to worldwide mortality and morbidity. Dyslipidemia stands as a critical risk factor for CVD. Vascular endothelial growth factor A (VEGFA) is pivotal in angiogenesis and represents a clinical target for CVD intervention. However, the impact of genetic modulation of VEGFA on lipid levels and the subsequent risk of cardiovascular events remains unclear.MethodsWe used LDpred2 to calculate genetic scores for lipid levels based on VEGFA variation, serving as instrumental variables to simulate the effect of VEGFA inhibitors. We then assessed the associations between genetic risk for lipid levels and CVD risk by conducting One-sample Mendelian randomization.ResultsOur results indicated that low-density lipoprotein cholesterol [LDL-C; odds ratio (OR) = 1.09, 95% CI: 1.06–1.11], remnant cholesterol (RC; OR = 1.24, 95% CI: 1.13–1.36), and triglycerides (TG; OR = 1.14, 95% CI: 1.07–1.22) were positively associated with the incidence of CVD. In contrast, high-density lipoprotein cholesterol (HDL-C) was inversely associated with the incidence of CVD (OR = 0.80, 95% CI: 0.76–0.86). When considering the genetic score for LDL-C constructed based on VEGFA, the group with a high genetic score demonstrated an elevated CVD risk (OR = 1.11, 95% CI: 1.04–1.19) compared to those with a low genetic score. Notably, One-sample Mendelian randomization results provided evidence of a causal relationship between LDL-C and CVD (p = 8.4×10−3) when using genetic variation in VEGFA as an instrumental variable.ConclusionsGenetic variation mimicking the effect of VEGFA inhibition, which lowers LDL-C levels, was causally associated with a reduced risk of cardiovascular events. These findings offer insight into the potential therapeutic relevance of modulating VEGFA-mediated lipid changes in the prevention and management of CVD

Algebraic Verification Method for SEREs Properties via Groebner Bases Approaches

This work presents an efficient solution using computer algebra system to perform linear temporal properties verification for synchronous digital systems. The method is essentially based on both Groebner bases approaches and symbolic simulation. A mechanism for constructing canonical polynomial set based symbolic representations for both circuit descriptions and assertions is studied. We then present a complete checking algorithm framework based on these algebraic representations by using Groebner bases. The computational experience result in this work shows that the algebraic approach is a quite competitive checking method and will be a useful supplement to the existent verification methods based on simulation

Track Irregularity Assessment in High-Speed Rail by Incorporating Carriage-Body Acceleration with Transfer Function

The determination of the precise track irregularity with unfavorable wavelength, which shall induce vehicle’s violent vibration in terms of the vehicle’s speeds, still challenges the researchers. This study proposes a feasible study of assessing the track irregularity by using the transfer function and the measured carriage-body acceleration by combining the ARX model with state space model. The ARX model and state space model are constructed using system identification to obtain the transfer relation between the track irregularity and the carriage-body acceleration, respectively. The model’s parameters are estimated by the measured data from the high-speed China Railway Comprehensive Inspection Train (CRCIT). The correlation value between the predicted and measured carriage-body acceleration shows that both models can effectively represent the transfer characteristics between the track irregularity and the carriage-body acceleration. Furthermore, the models can help assess the proportion of the vibration caused by track irregularity with the specific wavelengths and determine the track irregularity with unfavorable wavelength

Approximate Equivalence and Optimization for High-Level Datapath

With equivalence and optimization for high-level datapath become more important, functional equivalence algorithm based on groebner bases has been proposed. It requires the zeros of polynomial sets corresponding to two high-level datapaths are and remain the same. It is too restrictive and not robust. In order to achieve an appropriate relationship, in this paper, a new symbolic algebra computation is chose as our technical method, called Ritt-Wu method. We use it to decompose zeros and achieve approximate zeros.The concept of approximate equivalence is defined by relaxing restrictions and expressed using an error control function. This function represents error between original datapath and its approximate system. The value of error is calculated by numerical computation. What is more, the size of error is controlled with controlling restrictions. The error caused by our method is detected, quantified and controlled. Our approach is used to optimize complex datapath, two applications demonstrate the effectiveness of our framework.Project supported by the National Natural Science Foundation of China (No. 60873118) and (No. 60973147),the Doctoral Fund of Ministry of Education of China (No. 20090009110006), the Natural Science Foundation of Guangxi (No. 2011GXNSFA018154), the Science and Technology Foundation of Guangxi (No.10169-1), and Guangxi Scientific Research Project (No. 201012MS274)

Algebraic Verification Method for SEREs Properties via Groebner Bases Approaches

This work presents an efficient solution using computer algebra system to perform linear temporal properties verification for synchronous digital systems. The method is essentially based on both Groebner bases approaches and symbolic simulation. A mechanism for constructing canonical polynomial set based symbolic representations for both circuit descriptions and assertions is studied. We then present a complete checking algorithm framework based on these algebraic representations by using Groebner bases. The computational experience result in this work shows that the algebraic approach is a quite competitive checking method and will be a useful supplement to the existent verification methods based on simulation