64 research outputs found
From Ad-Hoc to Systematic: A Strategy for Imposing General Boundary Conditions in Discretized PDEs in variational quantum algorithm
We proposed a general quantum-computing-based algorithm that harnesses the
exponential power of noisy intermediate-scale quantum (NISQ) devices in solving
partial differential equations (PDE). This variational quantum eigensolver
(VQE)-inspired approach transcends previous idealized model demonstrations
constrained by strict and simplistic boundary conditions. It enables the
imposition of arbitrary boundary conditions, significantly expanding its
potential and adaptability for real-world applications, achieving this "from
ad-hoc to systematic" concept. We have implemented this method using the
fourth-order PDE (the Euler-Bernoulli beam) as example and showcased its
effectiveness with four different boundary conditions. This framework enables
expectation evaluations independent of problem size, harnessing the
exponentially growing state space inherent in quantum computing, resulting in
exceptional scalability. This method paves the way for applying quantum
computing to practical engineering applications.Comment: 16 pages, 8 figure
Acoustic Holographic Rendering with Two-dimensional Metamaterial-based Passive Phased Array.
Acoustic holographic rendering in complete analogy with optical holography are useful for various applications, ranging from multi-focal lensing, multiplexed sensing and synthesizing three-dimensional complex sound fields. Conventional approaches rely on a large number of active transducers and phase shifting circuits. In this paper we show that by using passive metamaterials as subwavelength pixels, holographic rendering can be achieved without cumbersome circuitry and with only a single transducer, thus significantly reducing system complexity. Such metamaterial-based holograms can serve as versatile platforms for various advanced acoustic wave manipulation and signal modulation, leading to new possibilities in acoustic sensing, energy deposition and medical diagnostic imaging
CMB: A Comprehensive Medical Benchmark in Chinese
Large Language Models (LLMs) provide a possibility to make a great
breakthrough in medicine. The establishment of a standardized medical benchmark
becomes a fundamental cornerstone to measure progression. However, medical
environments in different regions have their local characteristics, e.g., the
ubiquity and significance of traditional Chinese medicine within China.
Therefore, merely translating English-based medical evaluation may result in
\textit{contextual incongruities} to a local region. To solve the issue, we
propose a localized medical benchmark called CMB, a Comprehensive Medical
Benchmark in Chinese, designed and rooted entirely within the native Chinese
linguistic and cultural framework. While traditional Chinese medicine is
integral to this evaluation, it does not constitute its entirety. Using this
benchmark, we have evaluated several prominent large-scale LLMs, including
ChatGPT, GPT-4, dedicated Chinese LLMs, and LLMs specialized in the medical
domain. It is worth noting that our benchmark is not devised as a leaderboard
competition but as an instrument for self-assessment of model advancements. We
hope this benchmark could facilitate the widespread adoption and enhancement of
medical LLMs within China. Check details in
\url{https://cmedbenchmark.llmzoo.com/}
AceGPT, Localizing Large Language Models in Arabic
This paper explores the imperative need and methodology for developing a
localized Large Language Model (LLM) tailored for Arabic, a language with
unique cultural characteristics that are not adequately addressed by current
mainstream models like ChatGPT. Key concerns additionally arise when
considering cultural sensitivity and local values. To this end, the paper
outlines a packaged solution, including further pre-training with Arabic texts,
supervised fine-tuning (SFT) using native Arabic instructions and GPT-4
responses in Arabic, and reinforcement learning with AI feedback (RLAIF) using
a reward model that is sensitive to local culture and values. The objective is
to train culturally aware and value-aligned Arabic LLMs that can serve the
diverse application-specific needs of Arabic-speaking communities.
Extensive evaluations demonstrated that the resulting LLM called `AceGPT' is
the SOTA open Arabic LLM in various benchmarks, including instruction-following
benchmark (i.e., Arabic Vicuna-80 and Arabic AlpacaEval), knowledge benchmark
(i.e., Arabic MMLU and EXAMs), as well as the newly-proposed Arabic cultural \&
value alignment benchmark. Notably, AceGPT outperforms ChatGPT in the popular
Vicuna-80 benchmark when evaluated with GPT-4, despite the benchmark's limited
scale. % Natural Language Understanding (NLU) benchmark (i.e., ALUE)
Codes, data, and models are in https://github.com/FreedomIntelligence/AceGPT.Comment: https://github.com/FreedomIntelligence/AceGP
Crystal structure of rhodopsin bound to arrestin by femtosecond X-ray laser.
G-protein-coupled receptors (GPCRs) signal primarily through G proteins or arrestins. Arrestin binding to GPCRs blocks G protein interaction and redirects signalling to numerous G-protein-independent pathways. Here we report the crystal structure of a constitutively active form of human rhodopsin bound to a pre-activated form of the mouse visual arrestin, determined by serial femtosecond X-ray laser crystallography. Together with extensive biochemical and mutagenesis data, the structure reveals an overall architecture of the rhodopsin-arrestin assembly in which rhodopsin uses distinct structural elements, including transmembrane helix 7 and helix 8, to recruit arrestin. Correspondingly, arrestin adopts the pre-activated conformation, with a ∼20° rotation between the amino and carboxy domains, which opens up a cleft in arrestin to accommodate a short helix formed by the second intracellular loop of rhodopsin. This structure provides a basis for understanding GPCR-mediated arrestin-biased signalling and demonstrates the power of X-ray lasers for advancing the frontiers of structural biology
Systematic assessment of long-read RNA-seq methods for transcript identification and quantification
The Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. The consortium generated over 427 million long-read sequences from cDNA and direct RNA datasets, encompassing human, mouse, and manatee species, using different protocols and sequencing platforms. These data were utilized by developers to address challenges in transcript isoform detection and quantification, as well as de novo transcript isoform identification. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. When aiming to detect rare and novel transcripts or when using reference-free approaches, incorporating additional orthogonal data and replicate samples are advised. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis
Применение программного продукта «Яндекс.Сервер» для организации поиска в электронном каталоге библиотеки
The huge amounts of information accumulated by libraries in recent years put before developers a problem of the organization of fast and qualitative search which decision is possible with the use of modern search tools of Web-technology. The author examines one of these tools the software product “Yandex. Server”, allowing to organize optimum search in the electronic library catalog. The software product “Yandex. Server” gives a chance to carry out optimum search taking into account morphology of Russian and English languages, as well as the various logical conditions that provides effective and flexible search in the electronic library catalog.Накопленные библиотеками за последние годы огромные массивы информации ставят перед разработчиками задачу организации быстрого и качественного поиска, решение которой возможно с использованием современных поисковых инструментов веб-технологии. Автор рассматривает один из таких инструментов - программный продукт «Яндекс. Сервер», позволяющий организовать оптимальный поиск в электронном каталоге библиотеки с учетом морфологии русского и английского языков, а также различных логических условий
Joint DOA and DOD Estimation Based on Tensor Subspace with Partially Calibrated Bistatic MIMO Radar
A joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation algorithm based on tensor subspace approach for partially calibrated bistatic multiple-input multiple-output (MIMO) radar is proposed. By exploiting the multidimensional structure of the received data, a third-order measurement tensor is constructed. Consequently, the tensor-based signal subspace is achieved using the higher-order singular value decomposition (HOSVD). To achieve accurate DOA estimation with partially calibrated array, a closed-form solution is provided to estimate the gain-phase uncertainties of the transmit and receive arrays by modeling the imperfections of the arrays. Simulation results demonstrate the effectiveness of the proposed calibration algorithm
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