A Study of Nonlinear Dynamics of EEG Responses to Simulated Unmanned Vehicle Tasks

In the contemporary world, mental workload becomes higher as technology evolves and task demand becomes overwhelming. The operators of a system are usually required to complete tasks with higher complicity within a shorter period of time. Continuous operation under a high level of mental workload can be a major source of risk and human error, thus put the operator in a hazardous working environment. Therefore, it is necessary to monitor and assess mental workload. In this study, an unmanned vehicle operation with visual detection tasks was investigated by means of nonlinear analysis of EEG time series. Nonlinear analysis is considered more advantageous compared with traditional power spectrum analysis of EEG. Besides, nonlinear analysis is more capable to capture the nature of EEG data and human performance, which is a process that subjects to constant changes. By examining the nonlinear dynamics of EEG, it is more likely to obtain a deeper understanding of brain activity. The objective of this study is to investigate the mental workload under different task levels through the examination of brain activity via nonlinear dynamics of EEG time series in simulated unmanned ground vehicle visual detection tasks. The experiment was conducted by the team lead by Dr. Lauren Reinerman Jones at Institute for Simulation & Training, University of Central Florida. One hundred and fifty subjects participated the experiment to complete four visual detection task scenarios (1) change detection, (2) threat detection task, (3) dual task with different change detection task rates, and (4) dual task with different threat detection task rates. Their EEG was recorded during performing the tasks at nine EEG channels. This study develops a massive data processing program to calculate the largest Lyapunov exponent, correlation dimension of the EEG data. This study also develops the program for performing 0-1 test on the EEG data in Python language environment. The result of this study verifies the existence of chaotic dynamics in EEG time series, reveals the change in brain activity as the effect of changing task demand in more detailed level, and obtains new insights from the psychophysiological mental workload measurement used in the preliminary study. The results of this study verified the existence of the chaotic dynamics in the EEG time series. This study also supported the hypothesis that EEG data exhibits change in the level of nonlinearity corresponding to differed task levels. The nonlinear analysis of EEG time series data is able to discriminate the change in brain activity derived from the changes in task load. All nonlinear dynamics analysis techniques used in this study is able to find the difference of nonlinearity in EEG among task levels, as well as between single task scenario and dual task scenario

Can crude oil serve as a hedging asset for underlying securities? - Research on the heterogenous correlation between crude oil and stock index

In the increasingly frequent global financial turmoil, investors prefer to invest in stable assets to hedge risks. Crude oil naturally has dual use value as a general commodity and as a financial asset, which has attracted wide attention. In this paper, we adopt a wavelet coherence analysis to study the standard of crude oil as a hedging asset and analyze the dynamic correlation of crude oil and stock market price fluctuations in the four economies of the United States, Japan, China and Hong Kong at different frequencies. The empirical evidence shows that crude oil can be conditionally used as a hedging asset for underlying securities. From the perspective of space, crude oil is suitable for investors in China's stock market as a hedging asset, while for stock markets in the US, Japan and Hong Kong, the ability of crude oil to hedge risk has been greatly weakened. From the perspective of investment term, although crude oil cannot be regarded as a hedging asset for long-term investment, it can still play a hedging role in the short term. When the market is in a state of panic, the ability of oil to hedge risk is stronger

IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning

In the field of machine reading comprehension (MRC), existing systems have surpassed the average performance of human beings in many tasks like SQuAD. However, there is still a long way to go when it comes to logical reasoning. Although some methods for it have been put forward, they either are designed in a quite complicated way or rely too much on external structures. In this paper, we proposed IDOL (InDicator-Oriented Logic Pre-training), an easy-to-understand but highly effective further pre-training task which logically strengthens the pre-trained models with the help of 6 types of logical indicators and a logically rich dataset LGP (LoGic Pre-training). IDOL achieves state-of-the-art performance on ReClor and LogiQA, the two most representative benchmarks in logical reasoning MRC, and is proven to be capable of generalizing to different pre-trained models and other types of MRC benchmarks like RACE and SQuAD 2.0 while keeping competitive general language understanding ability through testing on tasks in GLUE. Besides, at the beginning of the era of large language models, we take several of them like ChatGPT into comparison and find that IDOL still shows its advantage.Comment: Accepted to the Findings of ACL 202

Efficient Spiking Transformer Enabled By Partial Information

Spiking neural networks (SNNs) have received substantial attention in recent years due to their sparse and asynchronous communication nature, and thus can be deployed in neuromorphic hardware and achieve extremely high energy efficiency. However, SNNs currently can hardly realize a comparable performance to that of artificial neural networks (ANNs) because their limited scalability does not allow for large-scale networks. Especially for Transformer, as a model of ANNs that has accomplished remarkable performance in various machine learning tasks, its implementation in SNNs by conventional methods requires a large number of neurons, notably in the self-attention module. Inspired by the mechanisms in the nervous system, we propose an efficient spiking Transformer (EST) framework enabled by partial information to address the above problem. In this model, we not only implemented the self-attention module with a reasonable number of neurons, but also introduced partial-information self-attention (PSA), which utilizes only partial input signals, further reducing computational resources compared to conventional methods. The experimental results show that our EST can outperform the state-of-the-art SNN model in terms of accuracy and the number of time steps on both Cifar-10/100 and ImageNet datasets. In particular, the proposed EST model achieves 78.48% top-1 accuracy on the ImageNet dataset with only 16 time steps. In addition, our proposed PSA reduces flops by 49.8% with negligible performance loss compared to a self-attention module with full information

Self-partitioning SlipChip for slip-induced droplet formation and human papillomavirus viral load quantification with digital LAMP

Human papillomavirus (HPV) is one of the most common sexually transmitted infections worldwide, and persistent HPV infection can cause warts and even cancer. Nucleic acid analysis of HPV viral DNA can be very informative for the diagnosis and monitoring of HPV. Digital nucleic acid analysis, such as digital PCR and digital isothermal amplification, can provide sensitive detection and precise quantification of target nucleic acids, and its utility has been demonstrated in many biological research and medical diagnostic applications. A variety of methods have been developed for the generation of a large number of individual reaction partitions, a key requirement for digital nucleic acid analysis. However, an easily assembled and operated device for robust droplet formation without preprocessing devices, auxiliary instrumentation or control systems is still highly desired. In this paper, we present a self-partitioning SlipChip (sp-SlipChip) microfluidic device for the slip-induced generation of droplets to perform digital loop-mediated isothermal amplification (LAMP) for the detection and quantification of HPV DNA. In contrast to traditional SlipChip methods, which require the precise alignment of microfeatures, this sp-SlipChip utilized a design of “chain-of-pearls” continuous microfluidic channel that is independent of the overlapping of microfeatures on different plates to establish the fluidic path for reagent loading. Initiated by a simple slipping step, the aqueous solution can robustly self-partition into individual droplets by capillary pressure-driven flow. This advantage makes the sp-SlipChip very appealing for the point-of-care quantitative analysis of viral load. As a proof of concept, we performed digital LAMP on an sp-SlipChip to quantify human papillomaviruses (HPVs) 16 and 18 and tested this method with fifteen anonymous clinical samples

Spiking Denoising Diffusion Probabilistic Models

Spiking neural networks (SNNs) have ultra-low energy consumption and high biological plausibility due to their binary and bio-driven nature compared with artificial neural networks (ANNs). While previous research has primarily focused on enhancing the performance of SNNs in classification tasks, the generative potential of SNNs remains relatively unexplored. In our paper, we put forward Spiking Denoising Diffusion Probabilistic Models (SDDPM), a new class of SNN-based generative models that achieve high sample quality. To fully exploit the energy efficiency of SNNs, we propose a purely Spiking U-Net architecture, which achieves comparable performance to its ANN counterpart using only 4 time steps, resulting in significantly reduced energy consumption. Extensive experimental results reveal that our approach achieves state-of-the-art on the generative tasks and substantially outperforms other SNN-based generative models, achieving up to $12\times$ and $6\times$ improvement on the CIFAR-10 and the CelebA datasets, respectively. Moreover, we propose a threshold-guided strategy that can further improve the performances by 16.7% in a training-free manner. The SDDPM symbolizes a significant advancement in the field of SNN generation, injecting new perspectives and potential avenues of exploration.Comment: Under Revie

Self-partitioning SlipChip for slip-induced droplet formation and human papillomavirus viral load quantification with digital LAMP

Human papillomavirus (HPV) is one of the most common sexually transmitted infections worldwide, and persistent HPV infection can cause warts and even cancer. Nucleic acid analysis of HPV viral DNA can be very informative for the diagnosis and monitoring of HPV. Digital nucleic acid analysis, such as digital PCR and digital isothermal amplification, can provide sensitive detection and precise quantification of target nucleic acids, and its utility has been demonstrated in many biological research and medical diagnostic applications. A variety of methods have been developed for the generation of a large number of individual reaction partitions, a key requirement for digital nucleic acid analysis. However, an easily assembled and operated device for robust droplet formation without preprocessing devices, auxiliary instrumentation or control systems is still highly desired. In this paper, we present a self-partitioning SlipChip (sp-SlipChip) microfluidic device for the slip-induced generation of droplets to perform digital loop-mediated isothermal amplification (LAMP) for the detection and quantification of HPV DNA. In contrast to traditional SlipChip methods, which require the precise alignment of microfeatures, this sp-SlipChip utilized a design of “chain-of-pearls” continuous microfluidic channel that is independent of the overlapping of microfeatures on different plates to establish the fluidic path for reagent loading. Initiated by a simple slipping step, the aqueous solution can robustly self-partition into individual droplets by capillary pressure-driven flow. This advantage makes the sp-SlipChip very appealing for the point-of-care quantitative analysis of viral load. As a proof of concept, we performed digital LAMP on an sp-SlipChip to quantify human papillomaviruses (HPVs) 16 and 18 and tested this method with fifteen anonymous clinical samples

Tenascin-C predicts IVIG non-responsiveness and coronary artery lesions in kawasaki disease in a Chinese cohort

ObjectivesTo assess the predictive value of tenascin-C (TN-C) for intravenous immunoglobulin (IVIG) non-responsiveness and coronary artery lesions (CALs) development at the acute stage of Kawasaki disease, and to build novel scoring systems for identifying IVIG non-responsiveness and CALs.MethodsA total of 261 patients in acute-stage Kawasaki disease were included. Serum samples before IVIG initiation were collected and TN-C expression levels were measured using an enzyme-linked immunosorbent assay. In addition to TN-C, another fifteen clinical and laboratory parameters collected before treatment were compared between IVIG responsive and non-responsive groups, and between groups with and without CALs. Multiple logistic regression analyses were performed to construct new scoring systems for the prediction of IVIG non-responsiveness and CALs development.ResultsIVIG non-responsive group (n = 51) had significantly higher TN-C level compared to IVIG responsive group (n = 210) (15.44 vs. 12.38 IU/L, P < 0.001). A novel scoring system composed of TN-C, total bilirubin, serum sodium and albumin was established to predict IVIG non-responsiveness. Patients with a total score ≥ 2 points were classified as high-risk cases. With the sensitivity of 78.4% and specificity of 73.8%, the efficiency of our scoring system for predicting IVIG non-responsiveness was comparable to the Kobayashi system. Consistently, the group developing CALs at the acute stage (n = 42) had significantly higher TN-C level compared to the group without CALs (n = 219) (19.76 vs. 12.10 IU/L, P < 0.001). A new scoring system showed that patients with elevated TN-C, platelet count ≥ 450 × 109/L, and delayed initial infusion of IVIG had a higher risk of developing CALs. Individuals with a total score ≥ 3 points were classified as high-risk cases. The sensitivity and specificity of the novel simple system for predicting CALs development were 83.3% and 74.0%, respectively, yielding a better efficiency than the Harada score.ConclusionElevated TN-C appeared to be an independent risk factor for both IVIG non-responsiveness and CALs in Chinese children with KD. Our scoring systems containing TN-C is simple and efficient in the early identification of high-risk KD cases that could benefit from more individualized medications