293 research outputs found
Learn from Mistakes through Cooperative Interaction with Study Assistant
Large language models have demonstrated their ability to self-reflect and
refine their generation, which can further improve their performance. However,
this feedback mechanism faces challenges such as no guarantee of correctness
and the lack of global insight into the model's weaknesses. In this paper, we
propose a novel framework, Study Assistant for Large Language Model (SALAM), to
aid LLMs in the reflection and refinement process. Motivated by the human study
assistant, this framework grades previous responses with the ground truth and
collects mistakes in the training phase. During inference, it identifies common
misunderstandings based on the mistake collections and provides guidelines for
the model to help the model avoid similar mistakes during inference. SALAM is a
model-agnostic framework, focusing on providing general feedback and can adapt
to any base model. Our evaluation of SALAM on two challenging benchmarks
demonstrated a significant improvement over various baselines
Sino-Canadian parents' perceptions of their children's Chinese literacy development
This qualitative study was conducted in a Northwestern Ontario urban community where
the population of Sino-Canadian people is approximately 300 members. The purpose of
the study was to describe Sino-Canadian parentsā perceptions of Chinese language
maintenance, factors which influence their childrenās Chinese literacy development, and
the strategies they used to maintain their childrenās family literacy. Data were collected
from interviews with six Chinese parents who had school aged children. Three themes
emerged from the analysis of the data: general perceptions of language maintenance,
family literacy practices, and concerns and issues. The children, parents, and the literacy
and language environment of children all play an important role in achieving Chinese
language maintenance. Family literacy is a vehicle for promoting Chinese language and
culture
The impact of exchange rate volatility on foreign direct investment (FDI) in BRIC countries
1 online resource ( iv, 29 p.) : col. ill.Includes abstract.Includes bibliographical references (p. 25-29).The paper is aimed at exploring the relationship between exchange rate volatility and foreign direct investment in selected emerging economies, specifically, Brazil, Russia, India, and China (BRIC). The sample of data was selected over the period of 1994-2012 for both exchange rate volatility and foreign direct investment for all countries. The standard deviation of monthly exchange rate changes is applied to examine the exchange rate volatility and its influence upon foreign direct investment using an Autoregressive Distributed Lag (ARDL) approach and the Cointegration and Error Correction Model,
developed by Pesaran, Shin and Smith (2001).
The results indicate a negative long-run relationship between exchange rate volatility and foreign direct investment for India and Russia. The existence of a short-run association was found in China, India, and Russia. However, for Brazil no connection between the two variables was observed
Crossover from Non-Fermi-Liquid to Pseudogap Behavior in the Spectral of Local Impurity in Power-Law Diverging Multichannel Kondo Model
Motivated by the emergence of higher-order van Hove singularities (VHS) with
power-law divergent density of states (DOS)
(, ) in materials, we investigate a
multichannel Kondo model involving conduction electrons near the higher-order
van Hove filling. This model considers channel and spin degrees of
freedom. Employing a renormalization group analysis and dynamical large-
approach, our results reveal a crossover from a non-Fermi liquid to pseudogap
behavior in the spectral properties of the local impurity at the overscreened
fixed point. At this critical fixed point, we precisely determine the
conditions under which the crossover occurs, either by tuning the exponent
or the ratio to a critical value. The results of this study
provide novel insights into the non-Fermi liquid and pseudogap behaviors
observed in strongly correlated systems, shedding light on the intriguing
interplay between higher-order van Hove singularities and multichannel Kondo
physics.Comment: 5 pages, 5 fugure
A frequency-domain full waveform inversion method of elastic waves in quantitative defection investigation
857-866Full waveform inversion is a challenging data-fitting procedure based on full wave field modeling to extract quantitative information on elastic properties of subsurface structures. We developed a frequency-domain full-waveform inversion method of elastic waves for stratified media, adopting a quasi-linearization method coupled with a random search algorithm. The inversion process of this method is irrelevant to hypocenter function and can be considered as a kind of combination between the heuristic and non-heuristic inversion methods. To verify our method, we apply it to three numerical two-dimensional models with different intermediate structures (dipping, arched and hollow), and their structures are well revealed. With some pretreatments on response waveforms, such as filtering, normalization and correlation analysis, the full-waveform inversion method is extended to models with damaged area and its feasibility and accuracy verified. Alignment of full waveform inversion method and its cost of computing, several strategies exist to treat this quantitative detecting problem. In Chengdu-Chongqing guest emergency project, the application of full waveform inversion method saves a lot of time. In this method, each section only needs 2 detectors and only need to be hammered twice, while the traditional CT (Computed Tomography) test requires 11 detection filters and at least 11 hammering, and each section has 121 waveform data. In some cases, we can obtain some important priori information through field investigation. The priori information can be used to accelerate the inversion process
Accelerating Antimicrobial Peptide Discovery with Latent Structure
Antimicrobial peptides (AMPs) are promising therapeutic approaches against
drug-resistant pathogens. Recently, deep generative models are used to discover
new AMPs. However, previous studies mainly focus on peptide sequence attributes
and do not consider crucial structure information. In this paper, we propose a
latent sequence-structure model for designing AMPs (LSSAMP). LSSAMP exploits
multi-scale vector quantization in the latent space to represent secondary
structures (e.g. alpha helix and beta sheet). By sampling in the latent space,
LSSAMP can simultaneously generate peptides with ideal sequence attributes and
secondary structures. Experimental results show that the peptides generated by
LSSAMP have a high probability of antimicrobial activity. Our wet laboratory
experiments verified that two of the 21 candidates exhibit strong antimicrobial
activity. The code is released at https://github.com/dqwang122/LSSAMP.Comment: KDD 202
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