Model-enhanced Vector Index

Embedding-based retrieval methods construct vector indices to search for document representations that are most similar to the query representations. They are widely used in document retrieval due to low latency and decent recall performance. Recent research indicates that deep retrieval solutions offer better model quality, but are hindered by unacceptable serving latency and the inability to support document updates. In this paper, we aim to enhance the vector index with end-to-end deep generative models, leveraging the differentiable advantages of deep retrieval models while maintaining desirable serving efficiency. We propose Model-enhanced Vector Index (MEVI), a differentiable model-enhanced index empowered by a twin-tower representation model. MEVI leverages a Residual Quantization (RQ) codebook to bridge the sequence-to-sequence deep retrieval and embedding-based models. To substantially reduce the inference time, instead of decoding the unique document ids in long sequential steps, we first generate some semantic virtual cluster ids of candidate documents in a small number of steps, and then leverage the well-adapted embedding vectors to further perform a fine-grained search for the relevant documents in the candidate virtual clusters. We empirically show that our model achieves better performance on the commonly used academic benchmarks MSMARCO Passage and Natural Questions, with comparable serving latency to dense retrieval solutions

Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models

We introduce Jais and Jais-chat, new state-of-the-art Arabic-centric foundation and instruction-tuned open generative large language models (LLMs). The models are based on the GPT-3 decoder-only architecture and are pretrained on a mixture of Arabic and English texts, including source code in various programming languages. With 13 billion parameters, they demonstrate better knowledge and reasoning capabilities in Arabic than any existing open Arabic and multilingual models by a sizable margin, based on extensive evaluation. Moreover, the models are competitive in English compared to English-centric open models of similar size, despite being trained on much less English data. We provide a detailed description of the training, the tuning, the safety alignment, and the evaluation of the models. We release two open versions of the model -- the foundation Jais model, and an instruction-tuned Jais-chat variant -- with the aim of promoting research on Arabic LLMs. Available at https://huggingface.co/inception-mbzuai/jais-13b-chatComment: Arabic-centric, foundation model, large-language model, LLM, generative model, instruction-tuned, Jais, Jais-cha

Neutrino Physics with JUNO

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purposeunderground liquid scintillator detector, was proposed with the determinationof the neutrino mass hierarchy as a primary physics goal. It is also capable ofobserving neutrinos from terrestrial and extra-terrestrial sources, includingsupernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos,atmospheric neutrinos, solar neutrinos, as well as exotic searches such asnucleon decays, dark matter, sterile neutrinos, etc. We present the physicsmotivations and the anticipated performance of the JUNO detector for variousproposed measurements. By detecting reactor antineutrinos from two power plantsat 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4sigma significance with six years of running. The measurement of antineutrinospectrum will also lead to the precise determination of three out of the sixoscillation parameters to an accuracy of better than 1\%. Neutrino burst from atypical core-collapse supernova at 10 kpc would lead to ~5000inverse-beta-decay events and ~2000 all-flavor neutrino-proton elasticscattering events in JUNO. Detection of DSNB would provide valuable informationon the cosmic star-formation rate and the average core-collapsed neutrinoenergy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400events per year, significantly improving the statistics of existing geoneutrinosamples. The JUNO detector is sensitive to several exotic searches, e.g. protondecay via the $p\to K^++\bar\nu$ decay channel. The JUNO detector will providea unique facility to address many outstanding crucial questions in particle andastrophysics. It holds the great potential for further advancing our quest tounderstanding the fundamental properties of neutrinos, one of the buildingblocks of our Universe

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Extracellular vesicle‐based nucleic acid delivery

Abstract Extracellular vesicles (EVs) are a heterogeneous class of natural vesicles that facilitate intercellular communication by functional transfer of lipids and biomolecular cargoes, such as miRNAs, mRNAs and proteins. As a naturally occurring delivery vehicle for nucleic acids, EVs are characterized by multiple advantageous characteristics, such as unique size and structure, excellent biocompatibility, immunologically inert, increased stability in circulation, intrinsic targeting capacity and the capability of membrane fusion and crossing biological barriers. Of note, the delivery properties of EVs can be further improved by genetic engineering of donor cells or direct modification of EVs. Over the last decade, EVs have sparkled intensive interest for delivery of small RNAs, including small interfering RNAs (siRNAs) and microRNAs (miRNAs). In recent years, increasing attention has been focused on exploring a variety of strategies to harness EVs for delivery of more nucleic acid types. In the present perspective, we provide a capsule overview of the latest accomplishments and trends in the field of EV‐based delivery systems for siRNAs, miRNAs, messenger RNAs (mRNAs), clustered regularly interspaced short palindromic repeats‐associated endonuclease (CRISPR/Cas) systems, antisense oligonucleotides (ASOs), circular RNA (circRNAs), long noncoding RNAs (lncRNAs) and DNAs. This perspective may offer insights into the rational design of more cutting‐edge extracellular vesicle‐based nucleic acid delivery nanoplatforms

Enhancing Dehumidification in the Cable Room of a Ring Main Unit through CFD-EMAG Coupling Simulation and Experimental Verification

The cable room, located at the base of the ring main unit, is prone to water vapor due to its proximity to damp cable holes and its relatively enclosed structure. This may penetrate internally and ultimately affect operational safety. Therefore, a dehumidifier was introduced to utilize dry air for internal circulation. To enhance the dehumidification in the cable room, the cable room device was designed for experimental research. Meanwhile, computational fluid dynamics (CFD)-electromagnetic (EMAG) coupling simulation is used to calculate the power loss of heat sources and their influence on multiple physical fields in numerical simulations. The feasibility of this study was confirmed by comparing the relative humidity, temperature, and velocity values between the experimental and numerical approaches. Furthermore, the layout of the ventilation pipes was modified to a vertical distribution, with upward supply and downward suction, to improve the airflow. The results indicate that the maximum relative errors in temperature, relative humidity, and velocity are only 3.61%, 7.14%, and 7.14%, respectively, which fall within an acceptable range. On this basis, additional simulation analysis was conducted on the humidity, dew point temperature, and airflow within the cable room, using an optimized model with a more comprehensive internal structure and cables. After implementing an optimized ventilation pipe layout, the relative humidity at the corresponding measuring points can decrease by up to 10.6%. The dew point temperature has decreased by 2.61 °C and the airflow has become more stable

Gut microbial diversity in two insectivorous bats: Insights into the effect of different sampling sources

Abstract The gut microbiota is now known as a key factor in mammalian physiology and health. Our understanding of the gut microbial communities and their effects on ecology and evolution of their hosts is extremely limited in bats which represent the second largest mammalian order. In the current study, gut microbiota of three sampling sources (small intestine, large intestine, and feces) were characterized in two sympatric and insectivorous bats (Rhinolophus sinicus and Myotis altarium) by high‐throughput sequencing of the V3‐V4 region of the 16S rRNA gene. Combining with published studies, this work reveals that Gammaproteobacteria may be a dominant class in the whole Chiroptera and Fusobacteria is less observed in bats although it has been proven to be dominant in other mammals. Our results reveal that the sampling source influences alpha diversity of the microbial community in both studied species although no significant variations of beta diversity were observed, which support that fecal samples cannot be used as a proxy of the microbiota in other gut regions in wild animals