Rational Sensibility: LLM Enhanced Empathetic Response Generation Guided by Self-presentation Theory

Having the ability to empathize is crucial for accurately representing human behavior during conversations. Despite numerous research aim to improve the cognitive capability of models by incorporating external knowledge, there has been limited attention on the sensible and rational expression of the conversation itself, which are crucial components of the cognitive empathy. Guided by self-presentation theory in sociology, we have designed an innovative categorical approach that segregates historical dialogues into sensible and rational sentences and subsequently elucidate the context through the designed attention mechanism. However, the rational information within the conversation is restricted and the external knowledge used in previous methods have limitations of semantic contradiction and narrow vision field. Considering the impressive performance of LLM in the domain of intelligent agent. We employ LLaMA2-70b as a rational brain to analyze the profound logical information maintained in conversations, which assists the model assessing the balance of sensibility and rationality to produce quality empathetic responses. Experimental evaluations demonstrate that our method outperforms other comparable methods on both automatic and human evaluations

Integrated Sensing and Communication for Network-Assisted Full-Duplex Cell-Free Distributed Massive MIMO Systems

In this paper, we combine the network-assisted full-duplex (NAFD) technology and distributed radar sensing to implement integrated sensing and communication (ISAC). The ISAC system features both uplink and downlink remote radio units (RRUs) equipped with communication and sensing capabilities. We evaluate the communication and sensing performance of the system using the sum communication rates and the Cramer-Rao lower bound (CRLB), respectively. We compare the performance of the proposed scheme with other ISAC schemes, the result shows that the proposed scheme can provide more stable sensing and better communication performance. Furthermore, we propose two power allocation algorithms to optimize the communication and sensing performance jointly. One algorithm is based on the deep Q-network (DQN) and the other one is based on the non-dominated sorting genetic algorithm II (NSGA-II). The proposed algorithms provide more feasible solutions and achieve better system performance than the equal power allocation algorithm.Comment: 14 pages, 7 figures,submit to China Communication February 28, 2023, date of major revision July 09, 202

Key ingredients in Verbena officinalis and determination of their anti-atherosclerotic effect using a computer-aided drug design approach

Lipid metabolism disorders may considerably contribute to the formation and development of atherosclerosis (AS). Traditional Chinese medicine has received considerable attention in recent years owing to its ability to treat lipid metabolism disorders using multiple components and targets. Verbena officinalis (VO), a Chinese herbal medicine, exhibits anti-inflammatory, analgesic, immunomodulatory, and neuroprotective effects. Evidence suggests that VO regulates lipid metabolism; however, its role in AS remains unclear. In the present study, an integrated network pharmacology approach, molecular docking, and molecular dynamics simulation (MDS) were applied to examine the mechanism of VO against AS. Analysis revealed 209 potential targets for the 11 main ingredients in VO. Further, 2698 mechanistic targets for AS were identified, including 147 intersection targets between VO and AS. Quercetin, luteolin, and kaempferol were considered key ingredients for the treatment of AS based on a potential ingredient target–AS target network. GO analysis revealed that biological processes were primarily associated with responses to xenobiotic stimuli, cellular responses to lipids, and responses to hormones. Cell components were predominantly focused on the membrane microdomain, membrane raft, and caveola nucleus. Molecular functions were mainly focused on DNA-binding transcription factor binding, RNA polymerase II-specific DNA-binding transcription factor binding, and transcription factor binding. KEGG pathway enrichment analysis identified pathways in cancer, fluid shear stress, and atherosclerosis, with lipid and atherosclerosis being the most significantly enriched pathways. Molecular docking revealed that three key ingredients in VO (i.e., quercetin, luteolin, and kaempferol) strongly interacted with three potential targets (i.e., AKT1, IL-6, and TNF-α). Further, MDS revealed that quercetin had a stronger binding affinity for AKT1. These findings suggest that VO has beneficial effects on AS via these potential targets that are closely related to the lipid and atherosclerosis pathways. Our study utilized a new computer-aided drug design to identify key ingredients, potential targets, various biological processes, and multiple pathways associated with the clinical roles of VO in AS, which provides a comprehensive and systemic pharmacological explanation for the anti-atherosclerotic activity of VO

Passive Integrated Sensing and Communication Scheme based on RF Fingerprint Information Extraction for Cell-Free RAN

This paper investigates how to achieve integrated sensing and communication (ISAC) based on a cell-free radio access network (CF-RAN) architecture with a minimum footprint of communication resources. We propose a new passive sensing scheme. The scheme is based on the radio frequency (RF) fingerprint learning of the RF radio unit (RRU) to build an RF fingerprint library of RRUs. The source RRU is identified by comparing the RF fingerprints carried by the signal at the receiver side. The receiver extracts the channel parameters from the signal and estimates the channel environment, thus locating the reflectors in the environment. The proposed scheme can effectively solve the problem of interference between signals in the same time-frequency domain but in different spatial domains when multiple RRUs jointly serve users in CF-RAN architecture. Simulation results show that the proposed passive ISAC scheme can effectively detect reflector location information in the environment without degrading the communication performance.Comment: 11 pages, 6 figures, submitted on 28-Feb-2023, China Communication, Accepted on 14-Sep-202

Fair Causal Feature Selection

Causal feature selection has recently received increasing attention in machine learning. Existing causal feature selection algorithms select unique causal features of a class variable as the optimal feature subset. However, a class variable usually has multiple states, and it is unfair to select the same causal features for different states of a class variable. To address this problem, we employ the class-specific mutual information to evaluate the causal information carried by each state of the class attribute, and theoretically analyze the unique relationship between each state and the causal features. Based on this, a Fair Causal Feature Selection algorithm (FairCFS) is proposed to fairly identifies the causal features for each state of the class variable. Specifically, FairCFS uses the pairwise comparisons of class-specific mutual information and the size of class-specific mutual information values from the perspective of each state, and follows a divide-and-conquer framework to find causal features. The correctness and application condition of FairCFS are theoretically proved, and extensive experiments are conducted to demonstrate the efficiency and superiority of FairCFS compared to the state-of-the-art approaches

Enhancing Human-like Multi-Modal Reasoning: A New Challenging Dataset and Comprehensive Framework

Multimodal reasoning is a critical component in the pursuit of artificial intelligence systems that exhibit human-like intelligence, especially when tackling complex tasks. While the chain-of-thought (CoT) technique has gained considerable attention, the existing ScienceQA dataset, which focuses on multimodal scientific questions and explanations from elementary and high school textbooks, lacks a comprehensive evaluation of diverse approaches. To address this gap, we present COCO Multi-Modal Reasoning Dataset(COCO-MMRD), a novel dataset that encompasses an extensive collection of open-ended questions, rationales, and answers derived from the large object dataset COCO. Unlike previous datasets that rely on multiple-choice questions, our dataset pioneers the use of open-ended questions in the context of multimodal CoT, introducing a more challenging problem that effectively assesses the reasoning capability of CoT models. Through comprehensive evaluations and detailed analyses, we provide valuable insights and propose innovative techniques, including multi-hop cross-modal attention and sentence-level contrastive learning, to enhance the image and text encoders. Extensive experiments demonstrate the efficacy of the proposed dataset and techniques, offering novel perspectives for advancing multimodal reasoning

MAP3K19 regulatory variation in populations with African ancestry may increase COVID-19 severity

To identify ancestry-linked genetic risk variants associated with COVID-19 hospitalization, we performed an integrative analysis of two genome-wide association studies and resolved four single nucleotide polymorphisms more frequent in COVID-19-hospitalized patients with non-European ancestry. Among them, the COVID-19 risk SNP rs16831827 shows the largest difference in minor allele frequency (MAF) between populations with African and European ancestry and also shows higher MAF in hospitalized COVID-19 patients among cohorts of mixed ancestry (odds ratio [OR] = 1.20, 95% CI: 1.10-1.30) and entirely African ancestry (OR = 1.30, 95% CI: 1.02-1.67). rs16831827 is an expression quantitative trait locus of MAP3K19. MAP3K19 expression is induced during ciliogenesis and most abundant in ciliated tissues including lungs. Single-cell RNA sequencing analyses revealed that MAP3K19 is highly expressed in multiple ciliated cell types. As rs16831827∗T is associated with reduced MAP3K19 expression, it may increase the risk of severe COVID-19 by reducing MAP3K19 expression

Runners with better cardiorespiratory fitness had higher prefrontal cortex activity during both single and exercise-executive function dual tasks: an fNIRS study

Objective: This study investigated the relationship between executive function and prefrontal cortex oxygenation during exercise in young adults with different Cardiorespiratory fitness (CRF) levels.Methods: A total of 28 amateur runners (n = 14) and sedentary college students (n = 14) were recruited. The maximum oxygen uptake estimated for the sub-maximal intensity run (4.97 miles/h) was used to indicate the different CRF levels. After 1 week, participants must complete the Stroop and 2-Back tasks in silence while performing moderate-intensity exercise. Using 19-channel functional near-infrared spectroscopic (fNIRS) to examine changes in prefrontal cortex oxyhemoglobin.Results: There was no significant difference in the correctness of the Stroop and 2-Back tasks between the two groups during exercise, but the amateur runner group showed an acceleration in reaction time. fNIRS results showed that during the exercise 2-Back task, the left dorsolateral prefrontal cortex oxyhemoglobin was higher in the amateur runner group than in the sedentary group.Conclusion: Executive function during exercise was similarly improved in participants with better fitness, suggesting that CRF provides an excellent metabolic reserve and directed allocation for cognitive tasks during exercise

Mixed-Signal Parallel Compressive Spectrum Sensing for Cognitive Radios

Wideband spectrum sensing for cognitive radios requires very demanding analog-to-digital conversion (ADC) speed and dynamic range. In this paper, a mixed-signal parallel compressive sensing architecture is developed to realize wideband spectrum sensing for cognitive radios at sub-Nqyuist rates by exploiting the sparsity in current frequency usage. Overlapping windowed integrators are used for analog basis expansion, that provides flexible filter nulls for clock leakage spur rejection. A low-speed experimental system, built with off-the-shelf components, is presented. The impact of circuit nonidealities is considered in detail, providing insight for a future integrated circuit implementation

Adaptive Steered Molecular Dynamics Combined With Protein Structure Networks Revealing the Mechanism of Y68I/G109P Mutations That Enhance the Catalytic Activity of D-psicose 3-Epimerase From Clostridium Bolteae

The scarcity, richness, and other important physiological functions of D-psicose make it crucial to increase the yield of D-psicose. The production of D-psicose can be accomplished by D-psicose 3-epimerase (DPEase) from Clostridium bolteae (CbDPEase) catalyzing the substrate D-fructose. Although the catalytic efficiency of the CbDPEase has been raised via using the site-directed mutagenesis (Y68I/G109P) technique, structure-activity relationship in the wild-type CbDPEase and Y68I/G109P mutant is currently poorly understood. In our study, a battery of molecular modeling methods [homology modeling, adaptive steered molecular dynamics (ASMD) simulations, and Molecular Mechanics/Generalized Born Surface Area (MM-GB/SA)], combined with protein structure networks, were employed to theoretically characterize the reasons for the differences in the abilities of the D-fructose catalyzed by the wild-type CbDPEase and Y68I/G109P mutant. Protein structure networks demonstrated that site-directed mutagenesis enhanced the connectivity between D-fructose and CbDPEase, leading to the increased catalytic efficiency mediated by the functional residues with high betweenness. During the dissociation of the D-fructose from the Y68I/G109P mutant, planes of benzene rings of F248 and W114 could be continuously parallel to the stretching direction of D-fructose. It made the tunnel have an open state and resulted in the stable donor-π interactions between D-fructose and the benzene rings around 18Å. The stronger substrate-protein interactions were detected in the Y68I/G109P mutant, instead of in the wild-type CbDPEase, which were consistent with the binding free energy and Potential Mean of Force (PMF) results. The theoretical results illustrated the reasons that Y68I/G109P mutations increased the catalytic efficiency of CbDPEase and could be provided the new clue for further DPEase engineering