DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition

This paper presents our pioneering effort for emotion recognition in conversation (ERC) with pre-trained language models. Unlike regular documents, conversational utterances appear alternately from different parties and are usually organized as hierarchical structures in previous work. Such structures are not conducive to the application of pre-trained language models such as XLNet. To address this issue, we propose an all-in-one XLNet model, namely DialogXL, with enhanced memory to store longer historical context and dialog-aware self-attention to deal with the multi-party structures. Specifically, we first modify the recurrence mechanism of XLNet from segment-level to utterance-level in order to better model the conversational data. Second, we introduce dialog-aware self-attention in replacement of the vanilla self-attention in XLNet to capture useful intra- and inter-speaker dependencies. Extensive experiments are conducted on four ERC benchmarks with mainstream models presented for comparison. The experimental results show that the proposed model outperforms the baselines on all the datasets. Several other experiments such as ablation study and error analysis are also conducted and the results confirm the role of the critical modules of DialogXL.Comment: Accepted by AAAI 2021 main conferenc

Improving Safety Service Patrol Performance

Safety Service Patrols (SSPs) provide motorists with assistance free of charge on most freeways and some key primary roads in Virginia. This research project is focused on developing a tool to help the Virginia Department of Transportation (VDOT) optimize SSP routes and schedules (hereafter called SSP-OPT). The computational tool, SSP-OPT, takes readily available data (e.g., corridor and segment lengths, turnaround points, average annual daily traffic) and outputs potential SSP configurations that meet the desired criteria and produce the best possible performance metrics for a given corridor. At a high level, the main components of the developed tool include capabilities to: a) generate alternative feasible SSP beat configurations for a corridor; b)predict incidents and SSP characteristics (e.g., incident frequency, SSP service time) for a given SSP beat configuration; c) estimate performance measures (e.g., SSP response time, number of incidents responded to); and d) identify and present the best SSP configuration(s) through visual aids that facilitate decision making. To generate the incident data needed for the simulation-based SSP-OPT tool, a hierarchical negative binomial model and a hierarchical Weibull model are developed for incident frequencies and incident durations, respectively, based on the historical incident data. These models have been found to be effective in simulating the spatiotemporal distribution of incidents along highway corridors and for generating their attribute data (e.g., incident type, duration). The simulation program employs a discrete event-based approach and requires a few calibration parameters (e.g., SSP vehicle speed). After calibrating the model, the validation results show good agreement with field observations when applied to a sample SSP corridor from I-95. A user interface is created for the SSP-OPT tool in MS Excel to facilitate data entry and visualization of the output metrics for a given corridor. The output includes the list of alternative feasible beat configurations and aggregated performance measures from multiple runs for each individual beat, as well as for each alternative beat configuration spanning the entire corridor. The proposed SSP optimization model could be applied to corridors with or without existing SSP service. The tool will help identify the best beat configurations to minimize SSP response times and maximize SSP response rates for a given number of SSP vehicles on a corridor. Implementing these optimal solutions in the field will result in travel time savings and improve highway safety since the SSP resources will be more efficiently utilized, thus reducing the impacts of incidents on traffic flow

Radio frequency fingerprint identification for Internet of Things: A survey

Radio frequency fingerprint (RFF) identification is a promising technique for identifying Internet of Things (IoT) devices. This paper presents a comprehensive survey on RFF identification, which covers various aspects ranging from related definitions to details of each stage in the identification process, namely signal preprocessing, RFF feature extraction, further processing, and RFF identification. Specifically, three main steps of preprocessing are summarized, including carrier frequency offset estimation, noise elimination, and channel cancellation. Besides, three kinds of RFFs are categorized, comprising I/Q signal-based, parameter-based, and transformation-based features. Meanwhile, feature fusion and feature dimension reduction are elaborated as two main further processing methods. Furthermore, a novel framework is established from the perspective of closed set and open set problems, and the related state-of-the-art methodologies are investigated, including approaches based on traditional machine learning, deep learning, and generative models. Additionally, we highlight the challenges faced by RFF identification and point out future research trends in this field

Never Lost in the Middle: Improving Large Language Models via Attention Strengthening Question Answering

While large language models (LLMs) are equipped with longer text input capabilities than before, they are struggling to seek correct information in long contexts. The "lost in the middle" problem challenges most LLMs, referring to the dramatic decline in accuracy when correct information is located in the middle. To overcome this crucial issue, this paper proposes to enhance the information searching and reflection ability of LLMs in long contexts via specially designed tasks called Attention Strengthening Multi-doc QA (ASM QA). Following these tasks, our model excels in focusing more precisely on the desired information. Experimental results show substantial improvement in Multi-doc QA and other benchmarks, superior to state-of-the-art models by 13.7% absolute gain in shuffled settings, by 21.5% in passage retrieval task. We release our model, Ziya-Reader to promote related research in the community

Genetic risk, adherence to healthy lifestyle and acute cardiovascular and thromboembolic complications following SARS-COV-2 infection

Current understanding of determinants for COVID-19-related cardiovascular and thromboembolic (CVE) complications primarily covers clinical aspects with limited knowledge on genetics and lifestyles. Here, we analysed a prospective cohort of 106,005 participants from UK Biobank with confirmed SARS-CoV-2 infection. We show that higher polygenic risk scores, indicating individual's hereditary risk, were linearly associated with increased risks of post-COVID-19 atrial fibrillation (adjusted HR 1.52 [95% CI 1.44 to 1.60] per standard deviation increase), coronary artery disease (1.57 [1.46 to 1.69]), venous thromboembolism (1.33 [1.18 to 1.50]), and ischaemic stroke (1.27 [1.05 to 1.55]). These genetic associations are robust across genders, key clinical subgroups, and during Omicron waves. However, a prior composite healthier lifestyle was consistently associated with a reduction in all outcomes. Our findings highlight that host genetics and lifestyle independently affect the occurrence of CVE complications in the acute infection phrase, which can guide tailored management of COVID-19 patients and inform population lifestyle interventions to offset the elevated cardiovascular burden post-pandemic.</p

Effectiveness of Multidomain Dormitory Environment and Roommate Intervention for Improving Sleep Quality of Medical College Students: A Cluster Randomised Controlled Trial in China

Medical students are vulnerable to sleep disorders, which could be further exaggerated by poor dormitory environment and roommate behaviour. However, there is little evidence of whether dormitory environment intervention is effective in improving the sleep quality of medical college students in developing countries. The present study aimed to evaluate the effects of a comprehensive multidomain intervention on dormitory environment and roommate behaviour among medical college students in China. In this cluster randomised controlled trial, a total of 106 dormitories (364 students) were randomly allocated into an intervention group (55 dormitories, 193 students) and a control group (51 dormitories, 171 students). The intervention group received a three-month intervention with multiple components to improve or adapt to sleep environments in dormitories; the control group received no intervention. Primary and secondary outcomes were measured at study enrolment and three months later for both groups. The linear mixed-effects models showed that, compared with the control group, the intervention was associated with a significantly decreased Pittsburgh Sleep Quality Index (β = −0.67, p = 0.012), and a marginally significant effect on reducing roommates’ influence on sleep schedule (β = −0.21, p = 0.066). Students in the intervention group rated “making dormitory sleep rules” and “wearing eye masks” as the most effective intervention measures. These findings could contribute to the limited body of scientific evidence about sleep intervention in Chinese medical students and highlight the importance of dormitory sleep environments in maintaining sleep quality

Design of Noise Robust Open-Set Radio Frequency Fingerprint Identification Method

Radio frequency fingerprint (RFF) identification (RFFI) is a promising technique for device authentication at the physical layer of the communication stacks. However, practical challenges, particularly in low signal-to-noise ratio (SNR) scenarios, and the lack of comprehensive studies on open-set recognition hinder the widespread application of RFFI. This paper presents an unsupervised open-set RFF identification algorithm designed to address the robustness challenges associated with low SNR. Our approach integrates the Noise2Noise method for denoising, drawing inspiration from its successful applications in image and speech processing. The proposed framework utilizes an image- based autoencoder (AE) to extract features from the differential constellation trace figure (DCTF) of the signals after Noise2Noise denoising. The open-set recognition task is performed by cosine distance measurement. We carried out extensive experimental evaluation involving 18 ZigBee devices and a USRP software-defined radio platform. Our proposed method can achieve a gain up to 25% under low SNR

Impact of gamma-glutamyl carboxylase gene polymorphisms on warfarin dose requirement: A systematic review and meta-analysis

AbstractBackgroundThe Gamma-glutamyl carboxylase (GGCX) gene, as with Vitamin K Epoxide Reductase Complex Subunit 1(VKORC1), CytochromeP450 Complex Subunit 14 F2 (CYP4F2) and CytochromeP450 Complex Subunit2C9 (CYP2C9), is a candidate predictor for appropriate maintenance warfarin dose. However, the association between GGCX gene polymorphisms and warfarin dose requirement is still controversial. To quantify the influence of GGCX polymorphisms on warfarin dose requirements, we performed a systematic review and meta-analysis.MethodsAccording to PRISRM statement (Preferred reporting items for systematic reviews and meta-analyses), a comprehensive literature search was undertaken through August 2014 looking for eligible studies in Embase, Pubmed,Web of Science and the Cochrane Library. The impact of GGCX polymorphisms on mean daily warfarin dose (MDWD) was counted by means of Z test. RevMan 5.2.7 software (developed by the Cochrane Collaboration) was applied to analyze the relationship between GGCX gene polymorphisms and warfarin dose requirements.ResultsNineteen articles including 21 studies with a total of 6957 patients were included in the meta-analysis. Among three investigated single nucleotide polymorphisms (SNPs), rs11676382 showed higher CC genotype frequencies in Asian than those in Caucasian(97.7% vs. 86.9%); patients who were “G carriers” (that is, carried the GGCX rs11676382 CG or GG genotypes) required 27% lower warfarin dose than CC genotype[95%Confidence Interval(CI)=17%-37%, P=0.000, I2%=82.0 and PQ=0.000], moreover, stratified analysis by ethnicity showed similar results in Caucasian(23% lower, 95%CI=12%-33%), but not in Asian. With respect to genetic variation of rs699664 and rs121714145 SNPs, no significant impact on warfarin dose requirements were demonstrated.ConclusionsThis meta-analysis suggested that GGCX rs11676382 polymorphism may be one of factors affecting the dose of warfarin requirement, and the effects are different in different ethnicities. Further studies about this topic in different ethnicities with larger samples are expected to be conducted to validate our results

Clinical manifestations and imaging and pathological features of giant cell angioblastoma: Report of four cases and literature review

Giant cell angioblastoma is a relatively rare vasogenic tumour. To date, studies on its clinical manifestations, imaging characteristics, pathological features, and prognosis are extremely limited and unknown, with only a few cases recorded. In this study, four cases of giant cell angioblastoma confirmed by pathological examination were reported to improve our understanding and deep exploration of the tumour spectrum. All cases in our study were male, including two adults and two boys. The lesions were located in the lower segment of the femur, medial condyle of the femur, knee joint, and popliteal fossa. Regarding the imaging characteristics, two patients with lesions in bone showed bone destruction, while the other two had lesions that invaded soft tissues, showing irregular, abnormal signal shadows and obvious enhancement. Histopathological analysis revealed that the nodular tumour tissue was mainly composed of oval and spindle cells, with varying numbers of osteoclast-like multinucleated giant cells, and the interstitial tissues were often filled with blood vessels of different sizes. The immunophenotype demonstrates that endothelial cells of small vessels in nodules expressed CD31, SMA, and ERG, while osteoclast-like multinucleated giant cells and histiocytes expressed CD68 and CD163, and the surrounding cells expressed SMA. All four patients were treated with surgical resection. One of them relapsed 1 month after surgery and received a second surgical resection. No distant metastasis or death occurred during the follow-up period. This study indicates that giant cell angioblastoma is a local invasive vascular tumour that can develop both in children and adults with skin, mucous membrane, soft tissue, and bone involvement. Imaging characteristics show bone destruction and irregular, abnormal signal shadows; in addition, obvious pathological morphological features can be observed. Currently, the treatment is mainly surgical resection, and interferons may be used as adjuvant chemotherapy