Prompt-Based Metric Learning for Few-Shot NER

Few-shot named entity recognition (NER) targets generalizing to unseen labels and/or domains with few labeled examples. Existing metric learning methods compute token-level similarities between query and support sets, but are not able to fully incorporate label semantics into modeling. To address this issue, we propose a simple method to largely improve metric learning for NER: 1) multiple prompt schemas are designed to enhance label semantics; 2) we propose a novel architecture to effectively combine multiple prompt-based representations. Empirically, our method achieves new state-of-the-art (SOTA) results under 16 of the 18 considered settings, substantially outperforming the previous SOTA by an average of 8.84% and a maximum of 34.51% in relative gains of micro F1. Our code is available at https://github.com/AChen-qaq/ProML

Sampled in Pairs and Driven by Text: A New Graph Embedding Framework

In graphs with rich texts, incorporating textual information with structural information would benefit constructing expressive graph embeddings. Among various graph embedding models, random walk (RW)-based is one of the most popular and successful groups. However, it is challenged by two issues when applied on graphs with rich texts: (i) sampling efficiency: deriving from the training objective of RW-based models (e.g., DeepWalk and node2vec), we show that RW-based models are likely to generate large amounts of redundant training samples due to three main drawbacks. (ii) text utilization: these models have difficulty in dealing with zero-shot scenarios where graph embedding models have to infer graph structures directly from texts. To solve these problems, we propose a novel framework, namely Text-driven Graph Embedding with Pairs Sampling (TGE-PS). TGE-PS uses Pairs Sampling (PS) to improve the sampling strategy of RW, being able to reduce ~99% training samples while preserving competitive performance. TGE-PS uses Text-driven Graph Embedding (TGE), an inductive graph embedding approach, to generate node embeddings from texts. Since each node contains rich texts, TGE is able to generate high-quality embeddings and provide reasonable predictions on existence of links to unseen nodes. We evaluate TGE-PS on several real-world datasets, and experiment results demonstrate that TGE-PS produces state-of-the-art results on both traditional and zero-shot link prediction tasks.Comment: Accepted by WWW 2019 (The World Wide Web Conference. ACM, 2019

Self-regulation and Academic Learning in Preschoolers with Autism Spectrum Disorder: Individual Differences and Links to Executive Function, Effortful Control, Reward Sensitivity, School Engagement, and Adaptive Behavior

Children’s self-regulation has shown to be related to the trajectories across various domains of adaptive functioning and school success. Delay in self-regulation development represents an area of major challenge for children with autism spectrum disorder (ASD) (e.g., Jahromi, 2017), a neurodevelopmental disorder characterized by persistent deficits in social communication and interaction as well as restricted and repetitive behaviors (American Psychiatric Association, 2013). Children with ASD are often reported academic difficulties and underachievement compared to their typically developing peers (e.g., Nation et al., 2006). It has been well-documented that typically developing children with greater self-regulation had better academic achievement (e.g., Blair & Razza, 2007). However, few studies have extended the examination of the association between self-regulation and academic learning to the populations with special needs, especially to those with ASD. Moreover, the majority of previous studies solely relied on standardized assessments to reflect children’s temporary learning outcomes rather than their dynamic learning process. Little is known about how children’s self-regulatory skills are related to the way they learn and how various child characteristics moderate this association. Therefore, the goal of this study was to examine how the self-regulatory capacities of children with ASD, including executive function and effortful control, were linked to their dynamic academic learning process and to investigate the moderating effects of various child characteristics on this association, including ASD-related symptoms severity, school engagement, reward sensitivity, and adaptive behavior, all of which represent areas of challenge for children with ASD. Additionally, children with ASD often receive many different types of reinforcement at school. Their ability to wait for delayed reinforcement and their responsiveness to different reinforcers seem crucial for how successful they could adapt to school lives. Thus, another goal of this study was to investigate children with ASD’s responses to delayed reinforcement as well as token and social reinforcers in the natural classroom environment and to identify strategies that can facilitate their tolerance to delayed reinforcement and responsiveness to different types of reinforcers. Thirty-two preschoolers aged 36 to 68 months from two specialized applied behavior analysis schools in the greater New York City area participated in the study. Each participant had an Individualized Education Program with a classification of Preschooler with Disability and had a current diagnosis of autism confirmed with the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2; Lord et al., 2012). Children with ASD received direct measures on their executive function in a laboratory setting and assessments on their responses to delayed reinforcement as well as token and social reinforcers in the natural classroom environment. Parents filled out reports regarding children with ASD’s executive function, effortful control, and reward sensitivity. Teachers completed scales on these children’s school engagement and adaptive behavior. Regarding the participants’ academic learning, instead of using one-time standardized assessments, this study derived school data of multiple literacy and mathematics programs over a period of time to investigate the number of learning opportunities and additional one-to-one educational interventions a child required to achieve an academic objective in the learning process. Findings in this study showed that children with ASD with better self-regulation engaged in school activities to a greater extent, demonstrated better adaptive behavior at school, and were reported to have stronger social communication skills. Children with ASD with better emotional control, attention, and inhibitory control achieved academic objectives in literacy faster, especially in the domains of word recognition and reading comprehension. Also, children with ASD with a better overall EF level learned math concepts and problem-solving skills faster in both trial-based and script-based mathematics curricula, and those with better working memory demonstrated a higher learning rate in the trial-based mathematics programs. Further analyses showed that the relationship between self-regulation and academic learning in children with ASD was influenced by their behavior school engagement and reward sensitivity. These results inform future interventions to focus on the school engagement behaviors and sensitivity to reward in children with ASD when developing their self-regulation and academic learning skills. Moreover, three socially-oriented strategies, including using language, gestures, and eye contact, were found to help children with ASD respond better to delayed reinforcement, above and beyond their self-regulation level. Also, these children responded better in a task that they already mastered under a situation in which tokens could be earned for exchanging preferred items or activities contingent on their performance rather than in a situation where only social attention was available. Overall, self-regulation emerged as a potential protective factor for young children with ASD in their school success in terms of engagement and adaptive level as well as academic learning rates. Self-regulation development is recommended to be included as an essential component in future academic and social-emotional interventions for children with ASD. Meanwhile, developing the ability to use language, gestures, or eye contact to communicate needs and emotions may help children with ASD have a better response to delayed reinforcement in the natural classroom environment. Considering the majority of them demonstrated altered reward sensitivity characterized by nonsocial stimuli hypersensitivity and social rewards hyposensitivity, it is important to enhance their responsiveness and sensitivity to social reinforcers to promote their school adjustment and success

Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets

Short-term load forecasting (STLF) plays a significant role in the operation of electricity trading markets. Considering the growing concern of data privacy, federated learning (FL) is increasingly adopted to train STLF models for utility companies (UCs) in recent research. Inspiringly, in wholesale markets, as it is not realistic for power plants (PPs) to access UCs' data directly, FL is definitely a feasible solution of obtaining an accurate STLF model for PPs. However, due to FL's distributed nature and intense competition among UCs, defects increasingly occur and lead to poor performance of the STLF model, indicating that simply adopting FL is not enough. In this paper, we propose a DRL-assisted FL approach, DEfect-AwaRe federated soft actor-critic (DearFSAC), to robustly train an accurate STLF model for PPs to forecast precise short-term utility electricity demand. Firstly. we design a STLF model based on long short-term memory (LSTM) using just historical load data and time data. Furthermore, considering the uncertainty of defects occurrence, a deep reinforcement learning (DRL) algorithm is adopted to assist FL by alleviating model degradation caused by defects. In addition, for faster convergence of FL training, an auto-encoder is designed for both dimension reduction and quality evaluation of uploaded models. In the simulations, we validate our approach on real data of Helsinki's UCs in 2019. The results show that DearFSAC outperforms all the other approaches no matter if defects occur or not

Energy-delay aware Restricted Access Window with novel retransmission for IEEE 802.11ah networks

Restricted Access Window (RAW) has been introduced to IEEE 802.11ah MAC layer to decrease collision probability. However, the inappropriate application of RAW duration for diverse groups of devices would increase uplink energy consumption, delay and lower down the data rate. In this paper, we study a RAW optimization problem with a novel retransmission scheme that utilizes the next empty slot for retransmission in the uplink. The problem is formulated based on overall energy efficiency and delay of each RAW by applying probability theory and Markov Chain. To jointly optimize energy efficiency and delay, an energy-delay aware window control algorithm is proposed to adapt RAW size by estimating the number of time slots and internal slot duration in one RAW for different groups. The optimal solution is derived by applying Gradient Descent approach. Simulation results show that our proposed algorithm improves up to 113.3% energy efficiency and reduces 53.4% delay compared to the existing RAW

Case Report: A case report and literature review of hemoglobin variation associated with neonatal cyanosis

We will discuss a recent case of unexplained neonatal cyanosis, evaluate its origin, clinical presentation, diagnosis, and treatment, and share with you some of our clinical insights. We report a transient cyanosis in a newborn due to a mutation in the globulin gene (HBG2), as well as diagnosis and treatment. Clinically, the infant was in good overall health, and despite low oxygen saturation, the arterial oxygen partial pressure was always normal. Early respiratory support includes mechanical ventilation, nasal tube oxygen, and eventually stopping oxygen therapy. With the above treatment measures, the blood oxygen saturation of the child always fluctuated at 85%, but the arterial blood oxygen partial pressure was up to 306 mmHg. Further improvement of laboratory tests revealed elevated methemoglobin levels, reticulocytosis, mild anemia, and basically normal on chest x-ray and echocardiography. To clarify the etiology, WES testing was performed. The results showed heterozygous variation in HBG2 gene (c.190C>T. p.H64Y). There is heterozygous variation at this site in the proband father, and no variation at this site in the proband mother. Given the age of the affected infants, we hypothesized that the mutation originated in the gamma peptide chain of the head protein. The baby was discharged from the hospital 10 days after birth, with blood oxygen saturation fluctuating around 90%. The cyanosis disappeared 2 months after discharge, and the blood oxygen saturation level returned to normal

The multi-visit drone-assisted pickup and delivery problem with time windows

We consider a new combined truck-drone routing problem with time windows in the context of last-mile logistics. A fleet of trucks, each equipped with an identical drone, is scheduled to provide both pickup and delivery services to a set of customers with minimum cost. Some customers are paired, in that the goods picked up from one must be delivered to the other on the same route. Drones are launched from and retrieved by trucks at a pool of designated stations, which can be used multiple times. Each drone can serve multiple customers in one flight. We formulate this problem as a large-scale mixed-integer bilinear program, with the bilinear terms used to calculate the load-time-dependent energy consumption of drones. To accelerate the solution process, multiple valid inequalities are proposed. For large-size problems, we develop a customised adaptive large neighbourhood search (ALNS) algorithm, which includes several preprocessing procedures to quickly identify infeasible solutions and accelerate the search process. Moreover, two feasibility test methods are developed for trucks and drones, along with an efficient algorithm to determine vehicles’ optimal waiting time at launch stations, which is important to consider due to the time windows. Extensive numerical experiments demonstrate the effectiveness of the valid inequalities and the strong performance of the proposed ALNS algorithm over two benchmarks in the literature, and highlight the cost-savings of the combined mode over the truck-only mode and the benefits of allowing multiple drone visits