Towards Robust Text Retrieval with Progressive Learning

Retrieval augmentation has become an effective solution to empower large language models (LLMs) with external and verified knowledge sources from the database, which overcomes the limitations and hallucinations of LLMs in handling up-to-date and domain-specific information. However, existing embedding models for text retrieval usually have three non-negligible limitations. First, the number and diversity of samples in a batch are too restricted to supervise the modeling of textual nuances at scale. Second, the high proportional noise are detrimental to the semantic correctness and consistency of embeddings. Third, the equal treatment to easy and difficult samples would cause sub-optimum convergence of embeddings with poorer generalization. In this paper, we propose the PEG, a progressively learned embeddings for robust text retrieval. Specifically, we increase the training in-batch negative samples to 80,000, and for each query, we extracted five hard negatives. Concurrently, we incorporated a progressive learning mechanism, enabling the model to dynamically modulate its attention to the samples throughout the entire training process. Additionally, PEG is trained on more than 100 million data, encompassing a wide range of domains (e.g., finance, medicine, and tourism) and covering various tasks (e.g., question-answering, machine reading comprehension, and similarity matching). Extensive experiments conducted on C-MTEB and DuReader demonstrate that PEG surpasses state-of-the-art embeddings in retrieving true positives, highlighting its significant potential for applications in LLMs. Our model is publicly available at https://huggingface.co/TownsWu/PEG

A Two-Level Game Theory Approach for Joint Relay Selection and Resource Allocation in Network Coding Assisted D2D Communications

Device-to-device (D2D) communication, which enables direct transmissions between mobile devices to improve spectrum efficiency, is one of the preferable candidate technologies for the next generation cellular network. Network coding, on the other hand, is widely used to improve throughput in ad hoc networks. Thus, the performance of D2D communications in cellular networks can potentially benefit from network coding. Aiming to improve the achievable capacity of D2D communications, we propose a system with inter-session network coding enabled to assist D2D transmissions. We formulate the joint problem of relay selection and resource allocation in network coding assisted D2D communications, and obtain the overall capacity of the network under complex interference conditions as a function of the relay selection and resource allocation. To solve the formulated problem, we propose a two-level decentralized approach termed NC-D2D, which solves the relay selection and resource allocation problems alternatively to obtain stable solutions for these two problems. Specifically, a coalition formation game associates relays with D2D pairs to enable network coding aided transmissions, and a greedy algorithm based game allocates limited cellular resources to D2D pairs and relays in NC-D2D, respectively. The performances of the proposed scheme is evaluated through extensive simulations to prove its superiority

Геморрагические диатезы у детей: диагностика и тактика ведения

геморрагические диатезыдети, службы охраны здоровьядиате

Deploying Edge Computing Nodes for Large-scale IoT:A Diversity Aware Approach

The recent advances in microelectronics and communications have led to the development of large-scale IoT networks, where tremendous sensory data is generated and needs to be processed. To support real-time processing for large-scale IoT, deploying edge servers with storage and computational capability is a promising approach. In this paper, we carefully analyze the impacting factors and key challenges for edge node deployment. We then propose a novel three-phase deployment approach which considers both traffic diversity and the wireless diversity of IoT. The proposed work aims at providing real-time processing service for the IoT network and reducing the required number of edge nodes. We conducted extensive simulation experiments, the results show that compared to the existing works that overlooked the two kinds of diversities, the proposed work greatly reduces the number of edge nodes and improves the throughput between IoT and edge nodes

Experimental verification and identifying biomarkers related to insomnia

IntroductionInsomnia is the most common form of sleep deprivation (SD) observed in clinics. Although there are differences between insomnia and SD, they have similar symptoms and the same animal model. Currently, there is a lack of microarray data on insomnia. Therefore, for now, we are going to apply the SD data to insomnia. Although many studies have explained the possible mechanisms associated with insomnia, no previous studies have considered the key genes associated with insomnia or the relationship between insomnia and immune cells. In this study, we analyzed the relationship between key genes and immune cells by identifying biomarkers for the diagnosis of insomnia. Next, we verified the efficacy of these biomarkers experimentally.MethodsFirst, we downloaded four microarrays (GSE11755, GSE12624, GSE28750, and GSE48080) from the Gene Expression Omnibus (GEO) database, which included data from 239 normal human blood samples and 365 blood specimens from patients with SD. Then, we analyzed two groups of differentially expressed genes (DEGs) and used Support Vector Machine Recursive Feature Elimination (SVM-RFE) analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model to investigate these key genes. Next, we used CIBERSORT to investigate the composition of 22 immune cell components of key genes in SD patients. Finally, the expression levels of key biomarkers in sleep-deprived patients were examined by quantitative real-time polymerase chain reaction (qRT-PCR).ResultsA total of 50 DEGs were identified: six genes were significantly upregulated, and 44 genes were significantly downregulated. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that Salmonella infection, NOD-like receptor (NLR) signaling pathway, Kaposi sarcoma-associated herpesvirus infection, and Th17 cell differentiation were significant. Based on machine learning, we identified C2CD2L, SPINT2, APOL3, PKNOX1, and A2M as key genes for SD; these were confirmed by receiver operating characteristic (ROC) analysis. Immune cell infiltration analysis showed that C2CD2L, SPINT2, APOL3, PKNOX1, and A2M were related in different degrees to regulatory T cells (Tregs), follicular T helper cells, CD8 cells, and other immune cells. The qRT-PCR experiments confirmed that the expression levels of C2CD2L concurred with the results derived from machine learning, but PKNOX1 and APOL3 did not.DiscussionIn summary, we identified a key gene (C2CD2L) that may facilitate the development of biomarkers for insomnia

Catalyst-free synthesis of cycloalkenyl phosphonates

The reactions described provide a facile and efficient access to cycloalkenyl phosphonates with good to excellent yields via Diels-Alder cycloadditions between alkynyl phosphonates and 1,3-dienes under catalyst-free conditions. This journal is ? the Partner Organisations 2014

Prompt-to-afterglow transition of optical emission in a long gamma-ray burst consistent with a fireball

Long gamma-ray bursts (GRBs), which signify the end-life collapsing of very massive stars, are produced by extremely relativistic jets colliding into circumstellar medium. Huge energy is released both in the first few seconds, namely the internal dissipation phase that powers prompt emissions, and in the subsequent self-similar jet-deceleration phase that produces afterglows observed in broad-band electromagnetic spectrum. However, prompt optical emissions of GRBs have been rarely detected, seriously limiting our understanding of the transition between the two phases. Here we report detection of prompt optical emissions from a gamma-ray burst (i.e. GRB 201223A) using a dedicated telescope array with a high temporal resolution and a wide time coverage. The early phase coincident with prompt {\gamma}-ray emissions show a luminosity in great excess with respect to the extrapolation of {\gamma}-rays, while the later luminosity bump is consistent with onset of the afterglow. The clearly detected transition allows us to differentiate physical processes contributing to early optical emissions and to diagnose the composition of the jetComment: Authors' version of article published in Nature Astronomy, see their website for official versio