\u3cem\u3eHP-DAEMON\u3c/em\u3e: \u3cem\u3eH\u3c/em\u3eigh \u3cem\u3eP\u3c/em\u3eerformance \u3cem\u3eD\u3c/em\u3eistributed \u3cem\u3eA\u3c/em\u3edaptive \u3cem\u3eE\u3c/em\u3energy-efficient \u3cem\u3eM\u3c/em\u3eatrix-multiplicati\u3cem\u3eON\u3c/em\u3e

The demands of improving energy efficiency for high performance scientific applications arise crucially nowadays. Software-controlled hardware solutions directed by Dynamic Voltage and Frequency Scaling (DVFS) have shown their effectiveness extensively. Although DVFS is beneficial to green computing, introducing DVFS itself can incur non-negligible overhead, if there exist a large number of frequency switches issued by DVFS. In this paper, we propose a strategy to achieve the optimal energy savings for distributed matrix multiplication via algorithmically trading more computation and communication at a time adaptively with user-specified memory costs for less DVFS switches, which saves 7.5% more energy on average than a classic strategy. Moreover, we leverage a high performance communication scheme for fully exploiting network bandwidth via pipeline broadcast. Overall, the integrated approach achieves substantial energy savings (up to 51.4%) and performance gain (28.6% on average) compared to ScaLAPACK pdgemm() on a cluster with an Ethernet switch, and outperforms ScaLAPACK and DPLASMA pdgemm() respectively by 33.3% and 32.7% on average on a cluster with an Infiniband switch

Bioaccumulation of eight heavy metals in cave animals from Dashui and Malang caves, Guizhou Province, China

Eight heavy metals content in the dominant animal groups, soil and water in Malang and Dashui caves were examined. The results showed that zinc contents in Porcellio scaber from Dashui and Malang caves were 448.80 and 598.00 mg/kg, respectively, which is the highest among all these 8 metals, while Pb was not detected in Diestrammena marmorata and Rhinolophidae pearsoni, suggesting that both animals were incapable of or poor in Pb accumulation. The highest average value of enrichment coefficient for soil-born Cd in animals from Dashui cave was 4.15, while that for water-born Zn was 91723.84. By contrast, the highest average value of enrichment coefficient for soil-born Cd in animals from Malang cave was 8.48, and that for water-born Zn was 708102.64.Key words: Bioaccumulation, heavy metal, cave animals, China

A Revisit of Fake News Dataset with Augmented Fact-checking by ChatGPT

The proliferation of fake news has emerged as a critical issue in recent years, requiring significant efforts to detect it. However, the existing fake news detection datasets are sourced from human journalists, which are likely to have inherent bias limitations due to the highly subjective nature of this task. In this paper, we revisit the existing fake news dataset verified by human journalists with augmented fact-checking by large language models (ChatGPT), and we name the augmented fake news dataset ChatGPT-FC. We quantitatively analyze the distinctions and resemblances between human journalists and LLM in assessing news subject credibility, news creator credibility, time-sensitive, and political framing. Our findings highlight LLM's potential to serve as a preliminary screening method, offering a promising avenue to mitigate the inherent biases of human journalists and enhance fake news detection

Correcting soft errors online in fast fourier transform

While many algorithm-based fault tolerance (ABFT) schemes have been proposed to detect soft errors offline in the fast Fourier transform (FFT) after computation finishes, none of the existing ABFT schemes detect soft errors online before the computation finishes. This paper presents an online ABFT scheme for FFT so that soft errors can be detected online and the corrupted computation can be terminated in a much more timely manner. We also extend our scheme to tolerate both arithmetic errors and memory errors, develop strategies to reduce its fault tolerance overhead and improve its numerical stability and fault coverage, and finally incorporate it into the widely used FFTW library - one of the today's fastest FFT software implementations. Experimental results demonstrate that: (1) the proposed online ABFT scheme introduces much lower overhead than the existing offline ABFT schemes; (2) it detects errors in a much more timely manner; and (3) it also has higher numerical stability and better fault coverage

Detection of different quorum-sensing signal molecules in a virulent Edwardsiella tarda strain LTB-4

Aims: The aim of this study was to elucidate the potential quorum-sensing (QS) signal molecules of an emerging pathogen (Edwardsiella tarda strain LTB-4) of cultured turbot (Scophthalmus maximus). Methods and Results: A sensitive and rapid double-layer plate method using biosensor strain Agrobacterium tumefaciens KYC55 was developed to detect the N-acylhomoserine lactone (AHL)-related compounds in bacteria. LTB-4 was found to have two QS systems, one was based on the AHLs and the other was based on the autoinducer-2 (AI-2). The AI-2 activity produced by LTB-4 was growth phase dependent and topped at OD600 of 1 center dot 0. The protocol to detect cholerae autoinducer 1 (CAI-1) activity in bacteria was modified, lowering the background luminescence of biosensor strain Vibrio harveyi JAF375. CAI-1 activity could not be detected in LTB-4. Conclusion: Edwardsiella tarda LTB-4 produced at least four kinds of AHLs during its whole growth phase. In comparison with the AHL-inducing QS, AI-2 may be the first predominant signal, functioning at early exponential phase. LTB-4 did not produce any CAI-1 activity. Significance and Impact of the Study: Different QS signal molecules of Edw. tarda LTB-4 were clarified by improved bioassays. In contrast to earlier studies detecting two types of AHLs, strain LTB-4 produced at least four kinds of AHLs, which seemed to be C-4-HSL, C-6-HSL, 3-oxo-C-6-HSL and an uncharacterized AHL molecule

A new species and new record of the leafhopper genus Seriana Dworakowska (Hemiptera, Cicadellidae, Typhlocybinae) from China

Seriana menglaensis sp. n. (Hemiptera: Cicadellidae: Typhlocybinae: Erythroneurini) is described and illustrated from Southwest China. Seriana equata (Singh, 1969) is recorded for the first time from China

Microstructure-Empowered Stock Factor Extraction and Utilization

High-frequency quantitative investment is a crucial aspect of stock investment. Notably, order flow data plays a critical role as it provides the most detailed level of information among high-frequency trading data, including comprehensive data from the order book and transaction records at the tick level. The order flow data is extremely valuable for market analysis as it equips traders with essential insights for making informed decisions. However, extracting and effectively utilizing order flow data present challenges due to the large volume of data involved and the limitations of traditional factor mining techniques, which are primarily designed for coarser-level stock data. To address these challenges, we propose a novel framework that aims to effectively extract essential factors from order flow data for diverse downstream tasks across different granularities and scenarios. Our method consists of a Context Encoder and an Factor Extractor. The Context Encoder learns an embedding for the current order flow data segment's context by considering both the expected and actual market state. In addition, the Factor Extractor uses unsupervised learning methods to select such important signals that are most distinct from the majority within the given context. The extracted factors are then utilized for downstream tasks. In empirical studies, our proposed framework efficiently handles an entire year of stock order flow data across diverse scenarios, offering a broader range of applications compared to existing tick-level approaches that are limited to only a few days of stock data. We demonstrate that our method extracts superior factors from order flow data, enabling significant improvement for stock trend prediction and order execution tasks at the second and minute level