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

Emerging Optics from Structured Nanoscale Optical Cavities

Miniaturized and rationally assembled nanostructures exhibit extraordinarily distinct physical properties beyond their individual units. This review will focus on structured small-scale optical cavities that show unique electromagnetic near fields and collective optical coupling. By harnessing different material systems and structural designs, various light-matter interactions can be engineered, such as nanoscale lasing, nonlinear optics, exciton-polariton coupling, and energy harvesting. Key device performance of nanoscale lasers, including low power threshold, optical multiplexing, and electrical pump, will be discussed. This review will also cover emerging applications of nanoscale optical cavities in quantum engineering and topological photonics. Structured nanocavities can serve as a scalable platform for integrated photonic circuits and hybrid quantum photonic systems

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 parents’ perceptions of Chinese language maintenance, factors which influence their children’s Chinese literacy development, and the strategies they used to maintain their children’s 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) ($\rho_c(\omega)=\rho_0/|\omega|^{r}$, $0<r<1$) in materials, we investigate a multichannel Kondo model involving conduction electrons near the higher-order van Hove filling. This model considers $M$ channel and $N$ spin degrees of freedom. Employing a renormalization group analysis and dynamical large-$N$ 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 $r$ or the ratio $\kappa=M/N$ 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

ALGO: Synthesizing Algorithmic Programs with LLM-Generated Oracle Verifiers

Large language models (LLMs) excel at implementing code from functionality descriptions but struggle with algorithmic problems that require not only implementation but also identification of the suitable algorithm. Moreover, LLM-generated programs lack guaranteed correctness and require human verification. To address these challenges, we propose ALGO, a framework that synthesizes Algorithmic programs with LLM-Generated Oracles to guide the generation and verify their correctness. ALGO first generates a reference oracle by prompting an LLM to exhaustively enumerate all the combinations of relevant variables. This oracle is then utilized to guide an arbitrary search strategy in exploring the algorithm space and to verify the synthesized algorithms. Our study shows that the LLM-generated oracles are correct for 88% of the cases. With the oracles as verifiers, ALGO can be integrated with any existing code generation model in a model-agnostic manner to enhance its performance. Experiments show that when equipped with ALGO, we achieve an 8x better one-submission pass rate over the Codex model and a 2.6x better one-submission pass rate over CodeT, the current state-of-the-art model on CodeContests. We can also get 1.3x better pass rate over the ChatGPT Code Interpreter on unseen problems. The problem set we used for testing, the prompts we used, the verifier and solution programs, and the test cases generated by ALGO are available at https://github.com/zkx06111/ALGO.Comment: NeurIPS 202

Ultra-low-threshold InGaN/GaN quantum dot micro-ring lasers.

In this work, we demonstrate ultra-low-threshold, optically pumped, room-temperature lasing in GaN microdisk and micro-ring cavities containing InGaN quantum dots and fragmented quantum wells, with the lowest measured threshold at a record low of 6.2  μJ/cm2. When pump volume decreases, we observe a systematic decrease in the lasing threshold of micro-rings. The photon loss rate, γ, increases with increasing inner ring diameter, leading to a systematic decrease in the post-threshold slope efficiency, while the quality factor of the lasing mode remains largely unchanged. A careful analysis using finite-difference time-domain simulations attributes the increased γ to the loss of photons from lower-quality higher-order modes during amplified spontaneous emission