Chain-Of-Thought Prompting Under Streaming Batch: A Case Study

Recently, Large Language Models (LLMs) have demonstrated remarkable capabilities. Chain-of-Thought (CoT) has been proposed as a way of assisting LLMs in performing complex reasoning. However, developing effective prompts can be a challenging and labor-intensive task. Many studies come out of some way to automatically construct CoT from test data. Most of them assume that all test data is visible before testing and only select a small subset to generate rationales, which is an unrealistic assumption. In this paper, we present a case study on how to construct and optimize chain-of-thought prompting using batch data in streaming settings

Symmetric failures in symmetric control systems

AbstractThis paper discusses the fault-tolerance of symmetric systems with respect to controllability, which is a fundamental characteristic of control systems. In particular, we reveal the underlying mathematical mechanism of the loss of controllability for symmetric systems induced by failures. Based on the decomposition of the symmetric systems into subsystems under the symmetry, the controllability of the entire system can be discussed by checking that of each subsystem. The analysis of the fault-tolerance in this paper is an extension of this idea with the aid of the chain-adapted transformation matrix for the decomposition. The result is shown as a necessary condition for symmetric systems to retain the controllability despite some symmetric failures. We also discuss sufficient conditions

Luxury Fashion Consumption of Chinese Overseas Students: Motivation for Purchase

This dissertation aimed to answer the main research question: What are the motivations for Chinese overseas students to purchase luxury fashion products? After reviewing the literature, the motivation of luxury consumption mainly include social-oriented motivation and personal-oriented motivation (Leibenstein, 1950, Vigneron and Johnson, 1999). Through the qualitative research method, data was collected by in-depth interview from 12 Chinese overseas students in UK. The main motivations identified by this research reflect and support the academic theories proposed by other literatures. Chinese overseas students are motivated by social-oriented motivation (bandwagon effect, prestige value, conspicuous consumption, gift giving) and personal-oriented motivation (hedonic value, quality, self-expression). In addition, some specific motivations due to they living abroad were found. For example, the price advantage, the authenticity of the goods and more channels to purchase in UK are other specific motivations that drive them to purchasing luxury products. Finally, practical and managerial implications are further discussed

Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning

The relational data model was designed to facilitate large-scale data management and analytics. We consider the problem of how to differentiate computations expressed relationally. We show experimentally that a relational engine running an auto-differentiated relational algorithm can easily scale to very large datasets, and is competitive with state-of-the-art, special-purpose systems for large-scale distributed machine learning.Comment: ICML 202

An automated and intelligent microfluidic platform for microalgae detection and monitoring

Microalgae not only play a vital role in the ecosystem but also hold promising commercial applications. Conventional methods of detecting and monitoring microalgae rely on field sampling followed by transportation to the laboratory for manual analysis, which is both time-consuming and laborious. Although machine learning (ML) algorithms have been introduced for microalgae detection in the laboratory, no integrated platform approach has yet emerged to enable real-time, on-site sampling and analysing. To solve this problem, here, we develop an automated and intelligent microfluidic platform (AIMP) that can offer automated system control, intelligent data analysis, and user interaction, providing an economical and portable solution to alleviate the drawbacks of conventional methods for microalgae detection and monitoring. We demonstrate the feasibility of the AIMP by detecting and classifying four microalgal species (Cosmarium, Closterium, Micrasterias, and Haematococcus Pluvialis) that exhibit varying sizes (from a few to hundreds of microns) and morphologies. The trained microalgae species detection network (MSDN, based on YOLOv5 architecture) achieves a high overall mean average precision at 0.5 intersection-over-union ([email protected]) of 92.8%. Furthermore, the versatility of the AIMP is demonstrated by long-term monitoring of astaxanthin production from Haematococcus Pluvialis over a period of 30 days. The AIMP achieved 97.5% accuracy in the detection of Haematococcus Pluvialis and 96.3% in further classification based on astaxanthin accumulation. This study opens up a new path towards microalgae detection and monitoring using portable intelligent devices, providing new ideas to accelerate progress in the ecological studies and commercial exploitation of microalgae

Recent advances in non-optical microfluidic platforms for bioparticle detection

The effective analysis of the basic structure and functional information of bioparticles are of great significance for the early diagnosis of diseases. The synergism between microfluidics and particle manipulation/detection technologies offers enhanced system integration capability and test accuracy for the detection of various bioparticles. Most microfluidic detection platforms are based on optical strategies such as fluorescence, absorbance, and image recognition. Although optical microfluidic platforms have proven their capabilities in the practical clinical detection of bioparticles, shortcomings such as expensive components and whole bulky devices have limited their practicality in the development of point-of-care testing (POCT) systems to be used in remote and underdeveloped areas. Therefore, there is an urgent need to develop cost-effective non-optical microfluidic platforms for bioparticle detection that can act as alternatives to optical counterparts. In this review, we first briefly summarise passive and active methods for bioparticle manipulation in microfluidics. Then, we survey the latest progress in non-optical microfluidic strategies based on electrical, magnetic, and acoustic techniques for bioparticle detection. Finally, a perspective is offered, clarifying challenges faced by current non-optical platforms in developing practical POCT devices and clinical applications.</p