Self-Evolved Diverse Data Sampling for Efficient Instruction Tuning

Enhancing the instruction-following ability of Large Language Models (LLMs) primarily demands substantial instruction-tuning datasets. However, the sheer volume of these imposes a considerable computational burden and annotation cost. To investigate a label-efficient instruction tuning method that allows the model itself to actively sample subsets that are equally or even more effective, we introduce a self-evolving mechanism DiverseEvol. In this process, a model iteratively augments its training subset to refine its own performance, without requiring any intervention from humans or more advanced LLMs. The key to our data sampling technique lies in the enhancement of diversity in the chosen subsets, as the model selects new data points most distinct from any existing ones according to its current embedding space. Extensive experiments across three datasets and benchmarks demonstrate the effectiveness of DiverseEvol. Our models, trained on less than 8% of the original dataset, maintain or improve performance compared with finetuning on full data. We also provide empirical evidence to analyze the importance of diversity in instruction data and the iterative scheme as opposed to one-time sampling. Our code is publicly available at https://github.com/OFA-Sys/DiverseEvol.git

Synthesis and anticancer activities of diquinazoline diselenides compounds

A series of novel diquinazoline diselenide compounds was designed and synthesized with substituted 4-chloroquinazoline and sodium diselenide. Their structures were confirmed by IR, 1H NMR, 13C NMR, and elemental analyses.The antitumor activity of the new compounds was evaluated by MTT method. Compound 1a, 1c, 1h and 1i were found to have activities against MDA-MB-435, A549,MDA-MB-231, SiHa, and HeLa cells. Moreover, compared with the commercial anticancer drugs Gefitinib, Oxaliplatin,Taxol, 10-Hydroxycamptothec in, and Epirubicin Hydrochloride,1a exerted better antitumor effects on corresponding cell lines at 10 μM

Qwen Technical Report

Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans. In this work, we introduce Qwen, the first installment of our large language model series. Qwen is a comprehensive language model series that encompasses distinct models with varying parameter counts. It includes Qwen, the base pretrained language models, and Qwen-Chat, the chat models finetuned with human alignment techniques. The base language models consistently demonstrate superior performance across a multitude of downstream tasks, and the chat models, particularly those trained using Reinforcement Learning from Human Feedback (RLHF), are highly competitive. The chat models possess advanced tool-use and planning capabilities for creating agent applications, showcasing impressive performance even when compared to bigger models on complex tasks like utilizing a code interpreter. Furthermore, we have developed coding-specialized models, Code-Qwen and Code-Qwen-Chat, as well as mathematics-focused models, Math-Qwen-Chat, which are built upon base language models. These models demonstrate significantly improved performance in comparison with open-source models, and slightly fall behind the proprietary models.Comment: 59 pages, 5 figure

Peregrine and saker falcon genome sequences provide insights into evolution of a predatory lifestyle

As top predators, falcons possess unique morphological, physiological and behavioral adaptations that allow them to be successful hunters: for example, the peregrine is renowned as the world's fastest animal. To examine the evolutionary basis of predatory adaptations, we sequenced the genomes of both the peregrine (Falco peregrinus) and saker falcon (Falco cherrug), and we present parallel, genome-wide evidence for evolutionary innovation and selection for a predatory lifestyle. The genomes, assembled using Illumina deep sequencing with greater than 100-fold coverage, are both approximately 1.2 Gb in length, with transcriptome-assisted prediction of approximately 16,200 genes for both species. Analysis of 8,424 orthologs in both falcons, chicken, zebra finch and turkey identified consistent evidence for genome-wide rapid evolution in these raptors. SNP-based inference showed contrasting recent demographic trajectories for the two falcons, and gene-based analysis highlighted falcon-specific evolutionary novelties for beak development and olfaction and specifically for homeostasis-related genes in the arid environment–adapted saker

Power and Voltage Control for Single-Phase Cascaded H-Bridge Multilevel Converters under Unbalanced Loads

The conventional control method for a single-phase cascaded H-bridge (CHB) multilevel converter is vector (dq) control; however, dq control requires complicated calculations and additional time delays. This paper presents a novel power control strategy for the CHB multilevel converter. A power-based dc-link voltage balance control is also proposed for unbalanced load conditions. The new control method is designed in a virtual αβ stationary reference frame without coordinate transformation or phase-locked loop (PLL) to avoid the potential issues related to computational complexity. Because only imaginary voltage construction is needed in the proposed control method, the time delay from conventional imaginary current construction can be eliminated. The proposed method can obtain a sinusoidal grid current waveform with unity power factor. Compared with the conventional dq control method, the power control strategy possesses the advantage of a fast dynamic response. The stability of the closed-loop system with the dc-link voltage balance controller is evaluated. Simulation and experimental results are presented to verify the accuracy of the proposed power and voltage control method

MiR-222 in Cardiovascular Diseases: Physiology and Pathology

MicroRNAs (miRNAs and miRs) are endogenous 19–22 nucleotide, small noncoding RNAs with highly conservative and tissue specific expression. They can negatively modulate target gene expressions through decreasing transcription or posttranscriptional inducing mRNA decay. Increasing evidence suggests that deregulated miRNAs play an important role in the genesis of cardiovascular diseases. Additionally, circulating miRNAs can be biomarkers for cardiovascular diseases. MiR-222 has been reported to play important roles in a variety of physiological and pathological processes in the heart. Here we reviewed the recent studies about the roles of miR-222 in cardiovascular diseases. MiR-222 may be a potential cardiovascular biomarker and a new therapeutic target in cardiovascular diseases

Neuroepithelial Cell Transforming Gene 1 Acts as an Oncogene and Is Mediated by miR-22 in Human Non-Small-Cell Lung Cancer

Abnormal expression of neuroepithelial cell transforming gene 1 (NET1) has been authenticated in many human cancers, including lung cancer. We have previously reported that NET1 functioned as an oncogene and promoted human non-small-cell lung cancer (NSCLC) growth and migration. However, the correlation between NET1 and its upstream miRNAs needed further illustration. Our present work demonstrated that miR-22 had a relatively low expression, and NET1 had a relatively high expression in both NSCLC samples and lung adenocarcinoma cell lines compared with corresponding normal controls. Moreover, miR-22 directly regulated NET1 and was verified to weaken cancer cell proliferation and migration, as well as enhance cell apoptosis by suppressing NET1. Furthermore, the inhibitory effect of miR-22 can be reversed via overexpressing NET1 using an ectopic expression vector in NSCLC cells. Our findings showed that miR-22/NET-1 axis may contribute to the inhibition of NSCLC growth and migration and represents a promising therapeutic target for NSCLC

Sequential Human Activity Recognition Based on Deep Convolutional Network and Extreme Learning Machine Using Wearable Sensors

Human activity recognition (HAR) problems have traditionally been solved by using engineered features obtained by heuristic methods. These methods ignore the time information of the streaming sensor data and cannot achieve sequential human activity recognition. With the use of traditional statistical learning methods, results could easily plunge into the local minimum other than the global optimal and also face the problem of low efficiency. Therefore, we propose a hybrid deep framework based on convolution operations, LSTM recurrent units, and ELM classifier; the advantages are as follows: (1) does not require expert knowledge in extracting features; (2) models temporal dynamics of features; and (3) is more suitable to classify the extracted features and shortens the runtime. All of these unique advantages make it superior to other HAR algorithms. We evaluate our framework on OPPORTUNITY dataset which has been used in OPPORTUNITY challenge. Results show that our proposed method outperforms deep nonrecurrent networks by 6%, outperforming the previous reported best result by 8%. When compared with neural network using BP algorithm, testing time reduced by 38%