491 research outputs found
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
Revisiting Event Argument Extraction: Can EAE Models Learn Better When Being Aware of Event Co-occurrences?
Event co-occurrences have been proved effective for event extraction (EE) in
previous studies, but have not been considered for event argument extraction
(EAE) recently. In this paper, we try to fill this gap between EE research and
EAE research, by highlighting the question that ``Can EAE models learn better
when being aware of event co-occurrences?''. To answer this question, we
reformulate EAE as a problem of table generation and extend a SOTA prompt-based
EAE model into a non-autoregressive generation framework, called TabEAE, which
is able to extract the arguments of multiple events in parallel. Under this
framework, we experiment with 3 different training-inference schemes on 4
datasets (ACE05, RAMS, WikiEvents and MLEE) and discover that via training the
model to extract all events in parallel, it can better distinguish the semantic
boundary of each event and its ability to extract single event gets
substantially improved. Experimental results show that our method achieves new
state-of-the-art performance on the 4 datasets. Our code is avilable at
https://github.com/Stardust-hyx/TabEAE.Comment: Accepted to ACL 2023 main conferenc
Characterizing Alzheimer's Disease Biomarker Cascade Through Non-linear Mixed Effect Models
Alzheimer's Disease (AD) research has shifted to focus on biomarker
trajectories and their potential use in understanding the underlying AD-related
pathological process. A conceptual framework was proposed in such modern AD
research that hypothesized biomarker cascades as a result of underlying AD
pathology. In this paper, we leverage the idea of biomarker cascades and
develop methods that use a non-linear mixed effect model to depict AD biomarker
trajectories as a function of the latent AD disease progression. We tailored
our methods to address a number of real-data challenges present in BIOCARD and
ADNI studies. We illustrate the proposed methods with simulation studies as
well as analysis results on the BIOCARD and ADNI data showing the ordering of
various biomarkers from the CSF, MRI, and cognitive domains. We investigated
cascading patterns of AD biomarkers in these datasets and presented prediction
results for individual-level profiles over time. These findings highlight the
potential of the conceptual biomarker cascade framework to be leveraged for
diagnoses and monitoring.Comment: 28 pages, 2 figures, 3 table
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
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
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
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