Digital services of regional centers for scientific and technical information in China

The article aims to study the current level of digital services of the regional subsystem of the National System of Scientific and Technical Information of the People's Republic of China and to determine its optimization directions.A content analysis of 28 provincial institutes of scientific and technical information’s official sites was carried out; the most powerful of them were identified in terms of resource and service potential, the level of organization of corporate cooperation based on consolidated digital platforms of multifunctional user service. It is proved that the level of efficiency of digital services of regional scientific and technical information systems directly depends on the level of the province’s economic development and the ability of its government to finance and technologically equip information industry centers activities, and to establish sustainable interaction of all subjects of the information market. Summarizing the results of the content analysis made it possible to identify reserves for improving the service capabilities of the Chinese information industry’s regional clusters, to design vectors for diversifying consulting, expert-analytical, cognitive services of provincial institutes of scientific and technical information, the development of integrated innovation-oriented intelligent service platforms operating based on artificial intelligence technologies

Vibration measurement in a metro depot with trains running in the top story

Metro depots are places for subway train to get parked and maintained. To avoid the waste of large city areas occupied by depots, there is a need of developing depots for commercial and/or residential use as well, and in that case the train-induced vibrations become the major concern. This paper presents a unique case study on the vibration measurement in a 3-story metro depot, where the first two stories are developed for offices and shops and the third story is used as the maintenance garage with trains moving in/out through the connecting viaducts. Acceleration time histories of rails and floors in the three stories were measured. Amplitudes and frequency contents of the vibrations at different locations are compared through the corresponding frequency spectra and 1/3 octave band root-mean-square (RMS) spectra. The influence of track positions on floor vibration is investigated, and the vibration level of the building is evaluated using two indicators. Finally, numerical simulation is carried out so as to provide some references to the vibration control

A Case-Based Reasoning Framework for Adaptive Prompting in Cross-Domain Text-to-SQL

Recent advancements in Large Language Models (LLMs), such as Codex, ChatGPT and GPT-4 have significantly impacted the AI community, including Text-to-SQL tasks. Some evaluations and analyses on LLMs show their potential to generate SQL queries but they point out poorly designed prompts (e.g. simplistic construction or random sampling) limit LLMs' performance and may cause unnecessary or irrelevant outputs. To address these issues, we propose CBR-ApSQL, a Case-Based Reasoning (CBR)-based framework combined with GPT-3.5 for precise control over case-relevant and case-irrelevant knowledge in Text-to-SQL tasks. We design adaptive prompts for flexibly adjusting inputs for GPT-3.5, which involves (1) adaptively retrieving cases according to the question intention by de-semantizing the input question, and (2) an adaptive fallback mechanism to ensure the informativeness of the prompt, as well as the relevance between cases and the prompt. In the de-semanticization phase, we designed Semantic Domain Relevance Evaluator(SDRE), combined with Poincar\'e detector(mining implicit semantics in hyperbolic space), TextAlign(discovering explicit matches), and Positector (part-of-speech detector). SDRE semantically and syntactically generates in-context exemplar annotations for the new case. On the three cross-domain datasets, our framework outperforms the state-of-the-art(SOTA) model in execution accuracy by 3.7\%, 2.5\%, and 8.2\%, respectively

Retrieval-augmented GPT-3.5-based Text-to-SQL Framework with Sample-aware Prompting and Dynamic Revision Chain

Text-to-SQL aims at generating SQL queries for the given natural language questions and thus helping users to query databases. Prompt learning with large language models (LLMs) has emerged as a recent approach, which designs prompts to lead LLMs to understand the input question and generate the corresponding SQL. However, it faces challenges with strict SQL syntax requirements. Existing work prompts the LLMs with a list of demonstration examples (i.e. question-SQL pairs) to generate SQL, but the fixed prompts can hardly handle the scenario where the semantic gap between the retrieved demonstration and the input question is large. In this paper, we propose a retrieval-augmented prompting method for a LLM-based Text-to-SQL framework, involving sample-aware prompting and a dynamic revision chain. Our approach incorporates sample-aware demonstrations, which include the composition of SQL operators and fine-grained information related to the given question. To retrieve questions sharing similar intents with input questions, we propose two strategies for assisting retrieval. Firstly, we leverage LLMs to simplify the original questions, unifying the syntax and thereby clarifying the users' intentions. To generate executable and accurate SQLs without human intervention, we design a dynamic revision chain which iteratively adapts fine-grained feedback from the previously generated SQL. Experimental results on three Text-to-SQL benchmarks demonstrate the superiority of our method over strong baseline models

Recursively Summarizing Enables Long-Term Dialogue Memory in Large Language Models

Most open-domain dialogue systems suffer from forgetting important information, especially in a long-term conversation. Existing works usually train the specific retriever or summarizer to obtain key information from the past, which is time-consuming and highly depends on the quality of labeled data. To alleviate this problem, we propose to recursively generate summaries/ memory using large language models (LLMs) to enhance long-term memory ability. Specifically, our method first stimulates LLMs to memorize small dialogue contexts and then recursively produce new memory using previous memory and following contexts. Finally, the LLM can easily generate a highly consistent response with the help of the latest memory. We evaluate our method using ChatGPT and text-davinci-003, and the experiments on the widely-used public dataset show that our method can generate more consistent responses in a long-context conversation. Notably, our method is a potential solution to enable the LLM to model the extremely long context. Code and scripts will be released later

Unconscious structural knowledge of tonal symmetry: Tang poetry redefines limits of implicit learning

a b s t r a c t The study aims to help characterize the sort of structures about which people can acquire unconscious knowledge. It is already well established that people can implicitly learn n-grams (chunks) and also repetition patterns. We explore the acquisition of unconscious structural knowledge of symmetry. Chinese Tang poetry uses a specific sort of mirror symmetry, an inversion rule with respect to the tones of characters in successive lines of verse. We show, using artificial poetry to control both n-gram structure and repetition patterns, that people can implicitly learn to discriminate inversions from non-inversions, presenting a challenge to existing models of implicit learning

Controlled order rearrangement encryption for quantum key distribution

A novel technique is devised to perform orthogonal state quantum key distribution. In this scheme, entangled parts of a quantum information carrier are sent from Alice to Bob through two quantum channels. However before the transmission, the orders of the quantum information carrier in one channel is reordered so that Eve can not steal useful information. At the receiver's end, the order of the quantum information carrier is restored. The order rearrangement operation in both parties is controlled by a prior shared control key which is used repeatedly in a quantum key distribution session.Comment: 5 pages and 2 figure