Conflict Adaptation in 5-Year-Old Preschool Children: Evidence From Emotional Contexts

This research investigated the individual behavioral and electrophysiological differences during emotional conflict adaptation processes in preschool children. Thirty children (16 girls, mean age 5.44 ± 0.28 years) completed an emotional Flanker task (stimulus-stimulus cognitive control, S-S) and an emotional Simon task (stimulus-response cognitive control, S-R). Behaviorally, the 5-year-old preschool children exhibited reliable congruency sequence effects (CSEs) in the emotional contexts, with faster response times (RTs) and lower error rates in the incongruent trials preceded by an incongruent trial (iI trial) than in the incongruent trials preceded by a congruent trial (cI trial). Regarding electrophysiology, the children demonstrated longer N2 and P3 latencies in the incongruent trials than in the congruent trials during emotional conflict control processes. Importantly, the boys showed a reliable CSE of N2 amplitude when faced with fearful target expression. Moreover, 5-year-old children showed better emotional CSEs in response to happy targets than to fearful targets as demonstrated by the magnitude of CSEs in terms of the RT, error rate, N2 amplitude and P3 latency. In addition, the results demonstrated that 5-year-old children processed S-S emotional conflicts and S-R emotional conflicts differently and performed better on S-S emotional conflicts than on S-R emotional conflicts according to the comparison of the RT-CSE and P3 latency-CSE values. The current study provides insight into how emotionally salient stimuli affect cognitive processes among preschool children

Non-invasive detection of moving and stationary human with WiFi

Non-invasive human sensing based on radio signals has attracted a great deal of research interest and fostered a broad range of innovative applications of localization, gesture recognition, smart health-care, etc., for which a primary primitive is to detect human presence. Previous works have studied the detection of moving humans via signal variations caused by human movements. For stationary people, however, existing approaches often employ a prerequisite scenario-tailored calibration of channel profile in human-free environments. Based on in-depth understanding of human motion induced signal attenuation reflected by PHY layer channel state information (CSI), we propose DeMan, a unified scheme for non-invasive detection of moving and stationary human on commodity WiFi devices. DeMan takes advantage of both amplitude and phase information of CSI to detect moving targets. In addition, DeMan considers human breathing as an intrinsic indicator of stationary human presence and adopts sophisticated mechanisms to detect particular signal patterns caused by minute chest motions, which could be destroyed by significant whole-body motion or hidden by environmental noises. By doing this, DeMan is capable of simultaneously detecting moving and stationary people with only a small number of prior measurements for model parameter determination, yet without the cumbersome scenario-specific calibration. Extensive experimental evaluation in typical indoor environments validates the great performance of DeMan in various human poses and locations and diverse channel conditions. Particularly, DeMan provides a detection rate of around 95% for both moving and stationary people, while identifies human-free scenarios by 96%, all of which outperforms existing methods by about 30%.Department of Computin

Low-Resource Court Judgment Summarization for Common Law Systems

Common law courts need to refer to similar precedents' judgments to inform their current decisions. Generating high-quality summaries of court judgment documents can facilitate legal practitioners to efficiently review previous cases and assist the general public in accessing how the courts operate and how the law is applied. Previous court judgment summarization research focuses on civil law or a particular jurisdiction's judgments. However, judges can refer to the judgments from all common law jurisdictions. Current summarization datasets are insufficient to satisfy the demands of summarizing precedents across multiple jurisdictions, especially when labeled data are scarce for many jurisdictions. To address the lack of datasets, we present CLSum, the first dataset for summarizing multi-jurisdictional common law court judgment documents. Besides, this is the first court judgment summarization work adopting large language models (LLMs) in data augmentation, summary generation, and evaluation. Specifically, we design an LLM-based data augmentation method incorporating legal knowledge. We also propose a legal knowledge enhanced evaluation metric based on LLM to assess the quality of generated judgment summaries. Our experimental results verify that the LLM-based summarization methods can perform well in the few-shot and zero-shot settings. Our LLM-based data augmentation method can mitigate the impact of low data resources. Furthermore, we carry out comprehensive comparative experiments to find essential model components and settings that are capable of enhancing summarization performance.Comment: First submitted to Information Processing and Management on Oct. 29, 2023. Major Revision submitted on Mar.6, 202

Energy-efficient active tag searching in large scale RFID systems

Radio Frequency Identification (RFID) has attracted much research attention in recent years. RFID can support automatic information tracing and management during the management process in many fields. A typical field that uses RFID is modern warehouse management, where products are attached with tags and the inventory of products is managed by retrieving tag IDs. Many practical applications require searching a group of tags to determine whether they are in the system or not. The existing studies on tag searching mainly focused on improving the time efficiency but paid little attention to energy efficiency which is extremely important for active tags powered by built-in batteries. To fill in this gap, this paper investigates the tag searching problem from the energy efficiency perspective. We first propose an Energy-efficient tag Searching protocol in Multiple reader RFID systems, namely ESiM, which pushes per tag energy consumption to the limit as each tag needs to exchange only one bit data with the reader. We then develop a time efficiency enhanced version of ESiM, namely TESiM, which can dramatically reduce the execution time while only slightly increasing the transmission overhead. Extensive simulation experiments reveal that, compared to state-of-the-art solution in the current literature, TESiM reduces per tag energy consumption by more than one order of magnitude subject to comparable execution time. In most considered scenarios, TESiM even reduces the execution time by more than 50%.This work is partially supported by the National Science Foundation of China (Grant Nos. 61103203, 61332004, 61402056 and 61420106009), NSFC/RGC Joint Research Scheme (Grant No. N_PolyU519/12), and the EU FP7 CLIMBER project (Grant Agreement No. PIRSES-GA-2012-318939)

Negative Magnetoresistance in Dirac Semimetal Cd3As2

A large negative magnetoresistance is anticipated in topological semimetals in the parallel magnetic and electric field configuration as a consequence of the nontrivial topological properties. The negative magnetoresistance is believed to demonstrate the chiral anomaly, a long-sought high-energy physics effect, in solid-state systems. Recent experiments reveal that Cd3As2, a Dirac topological semimetal, has the record-high mobility and exhibits positive linear magnetoresistance in the orthogonal magnetic and electric field configuration. However, the negative magnetoresistance in the parallel magnetic and electric field configuration remains unveiled. Here, we report the observation of the negative magnetoresistance in Cd3As2 microribbons in the parallel magnetic and electric field configuration as large as 66% at 50 K and even visible at room temperatures. The observed negative magnetoresistance is sensitive to the angle between magnetic and electrical field, robust against temperature, and dependent on the carrier density. We have found that carrier densities of our Cd3As2 samples obey an Arrhenius's law, decreasing from 3.0x10^17 cm^-3 at 300 K to 2.2x10^16 cm^-3 below 50 K. The low carrier densities result in the large values of the negative magnetoresistance. We therefore attribute the observed negative magnetoresistance to the chiral anomaly. Furthermore, in the perpendicular magnetic and electric field configuration a positive non-saturating linear magnetoresistance up to 1670% at 14 T and 2 K is also observed. This work demonstrates potential applications of topological semimetals in magnetic devices

Personality-affected Emotion Generation in Dialog Systems

Generating appropriate emotions for responses is essential for dialog systems to provide human-like interaction in various application scenarios. Most previous dialog systems tried to achieve this goal by learning empathetic manners from anonymous conversational data. However, emotional responses generated by those methods may be inconsistent, which will decrease user engagement and service quality. Psychological findings suggest that the emotional expressions of humans are rooted in personality traits. Therefore, we propose a new task, Personality-affected Emotion Generation, to generate emotion based on the personality given to the dialog system and further investigate a solution through the personality-affected mood transition. Specifically, we first construct a daily dialog dataset, Personality EmotionLines Dataset (PELD), with emotion and personality annotations. Subsequently, we analyze the challenges in this task, i.e., (1) heterogeneously integrating personality and emotional factors and (2) extracting multi-granularity emotional information in the dialog context. Finally, we propose to model the personality as the transition weight by simulating the mood transition process in the dialog system and solve the challenges above. We conduct extensive experiments on PELD for evaluation. Results suggest that by adopting our method, the emotion generation performance is improved by 13% in macro-F1 and 5% in weighted-F1 from the BERT-base model.Comment: Accepted by ACM Transactions on Information System