62 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
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/}
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
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 βΌ20Β° 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
ΠΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ΄ΡΠΊΡΠ° Β«Π―Π½Π΄Π΅ΠΊΡ.Π‘Π΅ΡΠ²Π΅ΡΒ» Π΄Π»Ρ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΈ ΠΏΠΎΠΈΡΠΊΠ° Π² ΡΠ»Π΅ΠΊΡΡΠΎΠ½Π½ΠΎΠΌ ΠΊΠ°ΡΠ°Π»ΠΎΠ³Π΅ Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠΈ
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 βYandex. Serverβ, allowing to organize optimum search in the electronic library catalog. The software product βYandex. Serverβ 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.ΠΠ°ΠΊΠΎΠΏΠ»Π΅Π½Π½ΡΠ΅ Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠ°ΠΌΠΈ Π·Π° ΠΏΠΎΡΠ»Π΅Π΄Π½ΠΈΠ΅ Π³ΠΎΠ΄Ρ ΠΎΠ³ΡΠΎΠΌΠ½ΡΠ΅ ΠΌΠ°ΡΡΠΈΠ²Ρ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΡΡΠ°Π²ΡΡ ΠΏΠ΅ΡΠ΅Π΄ ΡΠ°Π·ΡΠ°Π±ΠΎΡΡΠΈΠΊΠ°ΠΌΠΈ Π·Π°Π΄Π°ΡΡ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΈ Π±ΡΡΡΡΠΎΠ³ΠΎ ΠΈ ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΏΠΎΠΈΡΠΊΠ°, ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ ΠΊΠΎΡΠΎΡΠΎΠΉ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΠΏΠΎΠΈΡΠΊΠΎΠ²ΡΡ
ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠΎΠ² Π²Π΅Π±-ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ. ΠΠ²ΡΠΎΡ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅Ρ ΠΎΠ΄ΠΈΠ½ ΠΈΠ· ΡΠ°ΠΊΠΈΡ
ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠΎΠ² - ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΡΠΉ ΠΏΡΠΎΠ΄ΡΠΊΡ Β«Π―Π½Π΄Π΅ΠΊΡ. Π‘Π΅ΡΠ²Π΅ΡΒ», ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠΈΠΉ ΠΎΡΠ³Π°Π½ΠΈΠ·ΠΎΠ²Π°ΡΡ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΡΠΉ ΠΏΠΎΠΈΡΠΊ Π² ΡΠ»Π΅ΠΊΡΡΠΎΠ½Π½ΠΎΠΌ ΠΊΠ°ΡΠ°Π»ΠΎΠ³Π΅ Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠΈ Ρ ΡΡΠ΅ΡΠΎΠΌ ΠΌΠΎΡΡΠΎΠ»ΠΎΠ³ΠΈΠΈ ΡΡΡΡΠΊΠΎΠ³ΠΎ ΠΈ Π°Π½Π³Π»ΠΈΠΉΡΠΊΠΎΠ³ΠΎ ΡΠ·ΡΠΊΠΎΠ², Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
Π»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΡΠ»ΠΎΠ²ΠΈΠΉ
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
- β¦