Fog Network Task Scheduling for IoT Applications

In the Internet of Things (IoT) networks, the data traffic would be very bursty and unpredictable. It is therefore very difficult to analyze and guarantee the delay performance for delay-sensitive IoT applications in fog networks, such as emergency monitoring, intelligent manufacturing, and autonomous driving. To address this challenging problem, a Bursty Elastic Task Scheduling (BETS) algorithm is developed to best accommodate bursty task arrivals and various requirements in IoT networks, thus optimizing service experience for delay-sensitive applications with only limited communication resources in time-varying and competing environments. To better describe the stability and consistence of Quality of Service (QoS) in realistic scenarios, a new performance metric "Bursty Service Experience Index (BSEI)" is defined and quantified as delay jitter normalized by the average delay. Finally, the numeral results shows that the performance of BETS is fully evaluated, which can achieve 5-10 times lower BSEI than traditional task scheduling algorithms, e.g. Proportional Fair (PF) and the Max Carrier-to-Interference ratio (MCI), under bursty traffic conditions. These results demonstrate that BETS can effectively smooth down the bursty characteristics in IoT networks, and provide much predictable and acceptable QoS for delay-sensitive applications

Set-Based Face Recognition Beyond Disentanglement: Burstiness Suppression With Variance Vocabulary

Set-based face recognition (SFR) aims to recognize the face sets in the unconstrained scenario, where the appearance of same identity may change dramatically with extreme variances (e.g., illumination, pose, expression). We argue that the two crucial issues in SFR, the face quality and burstiness, are both identity-irrelevant and variance-relevant. The quality and burstiness assessment are interfered with by the entanglement of identity, and the face recognition is interfered with by the entanglement of variance. Thus we propose to separate the identity features with the variance features in a light-weighted set-based disentanglement framework. Beyond disentanglement, the variance features are fully utilized to indicate face quality and burstiness in a set, rather than being discarded after training. To suppress face burstiness in the sets, we propose a vocabulary-based burst suppression (VBS) method which quantizes faces with a reference vocabulary. With interword and intra-word normalization operations on the assignment scores, the face burtisness degrees are appropriately estimated. The extensive illustrations and experiments demonstrate the effect of the disentanglement framework with VBS, which gets new state-of-the-art on the SFR benchmarks. The code will be released at https://github.com/Liubinggunzu/set_burstiness.Comment: ACM MM 2022 accepted, code will be release

Ustekinumab treats psoriasis by suppressing RORC and T-box but its suppression of GATA restrains its efficacy

Psoriasis is a T-cell mediated disease that involves IL-23/Th17 and IL-12/Th1 axes. Ustekinumab, a fully human monoclonal antibody targeting the p40 subunit of both IL-12 and IL-23, has proven to be efficient and safe for treating patients with psoriasis. Yet, there have been no reports with human skin/blood samples that would elucidate the molecular mechanisms by which ustekinumab calms psoriasis skin lesions. To investigate the efficacy and molecular pathway (RORC, t-BOX and GATA) of ustekinumab in treating patients with psoriasis skin lesions. A total of 30 patients with psoriasis were randomized into placebo group and treatment group. PASI of each patient was calculated at 0, 12 and 24 weeks post-treatment. The mRNA levels of RORC, t-BOX and GATA in peripheral blood mononuclear cells separated from patients’ whole blood were analyzed using qPCR. Decreased mRNA of RORC, t-BOX and GATA were observed after continuous injections, indicating that ustekinumab exerts its effect by interacting with these molecules; while no significant difference in foxp3 mRNA levels were found between placebo group and treatment group

Severe nausea and vomiting in pregnancy: psychiatric and cognitive problems and brain structure in children

Background: Two studies have suggested that severe prolonged nausea and vomiting during pregnancy is associated with emotional and behavioral problems in offspring, with smaller sample size and short-term follow-up. Moreover, little information is available on the role of the brain structure in the associations. Methods: In a US-based cohort, the association was investigated between severe prolonged nausea and vomiting in pregnancy (extending after the second trimester and termed SNVP), psychiatric and cognitive problems, and brain morphology, from the Adolescent Brain Cognitive Development (ABCD) study, from 10,710 children aged 9–11 years. We validated the emotional including psychiatric findings using the Danish National Cohort Study with 2,092,897 participants. Results: SNVP was significantly associated with emotional and psychiatric problems (t = 8.89, Cohen’s d = 0.172, p = 6.9 × 10−19) and reduced global cognitive performance (t = − 4.34, d = − 0.085, p = 1.4 × 10−5) in children. SNVP was associated with low cortical area and volume, especially in the cingulate cortex, precuneus, and superior medial prefrontal cortex. These lower cortical areas and volumes significantly mediated the relation between SNVP and the psychiatric and cognitive problems in children. In the Danish National Cohort, severe nausea and vomiting in pregnancy were significantly associated with increased risks of behavioral and emotional disorders in children (hazard ratio, 1.24; 95% confidence interval, 1.16–1.33). Conclusions: SNVP is strongly associated with psychiatric and cognitive problems in children, with mediation by brain structure. These associations highlight the clinical importance and potential benefits of the treatment of SNVP, which could reduce the risk of psychiatric disorder in the next generation

Improving Text Matching in E-Commerce Search with A Rationalizable, Intervenable and Fast Entity-Based Relevance Model

Discovering the intended items of user queries from a massive repository of items is one of the main goals of an e-commerce search system. Relevance prediction is essential to the search system since it helps improve performance. When online serving a relevance model, the model is required to perform fast and accurate inference. Currently, the widely used models such as Bi-encoder and Cross-encoder have their limitations in accuracy or inference speed respectively. In this work, we propose a novel model called the Entity-Based Relevance Model (EBRM). We identify the entities contained in an item and decompose the QI (query-item) relevance problem into multiple QE (query-entity) relevance problems; we then aggregate their results to form the QI prediction using a soft logic formulation. The decomposition allows us to use a Cross-encoder QE relevance module for high accuracy as well as cache QE predictions for fast online inference. Utilizing soft logic makes the prediction procedure interpretable and intervenable. We also show that pretraining the QE module with auto-generated QE data from user logs can further improve the overall performance. The proposed method is evaluated on labeled data from e-commerce websites. Empirical results show that it achieves promising improvements with computation efficiency

Maternal hypertensive disorders and neurodevelopmental disorders in offspring: a population-based cohort in two Nordic countries

Maternal hypertensive disorders during pregnancy (HDP) have been associated with neuropsychiatric problems in offspring. We aim to investigate the associations between specific types of maternal HDP and offspring neurodevelopmental disorders and further examine whether the timing of onset and severity of HDP would affect these associations. The study population consisted of 4,489,044 live-born singletons in Denmark during 1978-2012 and Sweden during 1987-2010. Maternal HDP was categorized into chronic hypertension, gestational hypertension, and pre-eclampsia; pre-eclampsia was further stratified according to timing (early-onset, late-onset), or severity (moderate, severe) of the disease. Neurodevelopmental disorders, including attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and intellectual disability (ID), were defined by ICD-coded register diagnosis. Cox regression was used to calculate hazard ratios (HR) while adjusting for potential confounders, and sibling analyses assessed the influence of unmeasured shared familial factors. Maternal HDP was associated with increased risks of ADHD (HR, 1.24; 95% confidence interval [CI], 1.20-1.28), ASD (1.29 [1.24-1.34]), and ID (1.58 [1.50-1.66]) in offspring, respectively, which was mostly driven by pre-eclampsia. The strongest associations were observed for early-onset and severe pre-eclampsia, and the corresponding HRs for ADHD, ASD and ID were 1.93 [1.73-2.16], 1.86 [1.61-2.15], and 3.99 [3.42-4.65], respectively. The results were similar in the sibling analyses. The associations between maternal HDP and offspring neurodevelopmental disorders were consistent across the subgroups of sex, preterm status, parity, maternal age and psychiatric disorders. Maternal HDP, especially early-onset pre-eclampsia, are associated with increased risks of ADHD, ASD, and ID in particular, independent of shared familial factors

SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understanding

Large language models (LLMs) have shown impressive ability for open-domain NLP tasks. However, LLMs are sometimes too footloose for natural language understanding (NLU) tasks which always have restricted output and input format. Their performances on NLU tasks are highly related to prompts or demonstrations and are shown to be poor at performing several representative NLU tasks, such as event extraction and entity typing. To this end, we present SeqGPT, a bilingual (i.e., English and Chinese) open-source autoregressive model specially enhanced for open-domain natural language understanding. We express all NLU tasks with two atomic tasks, which define fixed instructions to restrict the input and output format but still ``open'' for arbitrarily varied label sets. The model is first instruction-tuned with extremely fine-grained labeled data synthesized by ChatGPT and then further fine-tuned by 233 different atomic tasks from 152 datasets across various domains. The experimental results show that SeqGPT has decent classification and extraction ability, and is capable of performing language understanding tasks on unseen domains. We also conduct empirical studies on the scaling of data and model size as well as on the transfer across tasks. Our model is accessible at https://github.com/Alibaba-NLP/SeqGPT.Comment: Initial version of SeqGP

Lower gestational age is associated with lower cortical volume and cognitive and educational performance in adolescence

Background: Gestational age (GA) is associated with later cognition and behavior. However, it is unclear how specific cognitive domains and brain structural development varies with the stepwise change of gestational duration. Methods: This large-scale longitudinal cohort study analyzed 11,878 early adolescents’ brain volume maps at 9–10 years (baseline) and 5685 at 11–12 years (a 2-year follow-up) from the Adolescent Brain Cognitive Development (ABCD) study. According to gestational age, adolescents were divided into five categorical groups: ≤ 33 weeks, 34–35 weeks, 36 weeks, 37–39 weeks, and ≥ 40 weeks. The NIH Toolbox was used to estimate neurocognitive performance, including crystallized and fluid intelligence, which was measured for 11,878 adolescents at baseline with crystallized intelligence and relevant subscales obtained at 2-year follow-up (with participant numbers ranging from 6185 to 6310 depending on the cognitive domain). An additional large population-based cohort of 618,070 middle adolescents at ninth-grade (15–16 years) from the Danish national register was utilized to validate the association between gestational age and academic achievements. A linear mixed model was used to examine the group differences between gestational age and neurocognitive performance, school achievements, and grey matter volume. A mediation analysis was performed to examine whether brain structural volumes mediated the association between GA and neurocognition, followed with a longitudinal analysis to track the changes. Results: Significant group differences were found in all neurocognitive scores, school achievements, and twenty-five cortical regional volumes (P < 0.05, Bonferroni corrected). Specifically, lower gestational ages were associated with graded lower cognition and school achievements and with smaller brain volumes of the fronto-parieto-temporal, fusiform, cingulate, insula, postcentral, hippocampal, thalamic, and pallidal regions. These lower brain volumes mediated the association between gestational age and cognitive function (P = 1 × 10−8, β = 0.017, 95% CI: 0.007–0.028). Longitudinal analysis showed that compared to full term adolescents, preterm adolescents still had smaller brain volumes and crystallized intelligence scores at 11–12 years. Conclusions: These results emphasize the relationships between gestational age at birth and adolescents’ lower brain volume, and lower cognitive and educational performance, measured many years later when 9–10 and 11–12 years old. The study indicates the importance of early screening and close follow-up for neurocognitive and behavioral development for children and adolescents born with gestational ages that are even a little lower than full term