Training Set Optimization in an Artificial Neural Network Constructed for High Bandwidth Interconnects Design

In this article, a novel training set optimization method in an artificial neural network (ANN) constructed for high bandwidth interconnects design is proposed based on rigorous probability analysis. In general, the accuracy of an ANN is enhanced by increasing training set size. However, generating large training sets is inevitably time-consuming and resource-demanding, and sometimes even impossible due to limited prototypes or measurement scenarios. Especially, when the number of channels in required design are huge such as graphics double data rate (GDDR) memory and high bandwidth memory (HBM). Therefore, optimizing the training set selection process is crucial to minimizing the training datasets for developing an efficient ANN. According to rigorous mathematical analysis of the uniformity of the training data by probability distribution function, optimization flow of the range selection is proposed to improve accuracy and efficiency. The optimal number of training data samples is further determined by studying the prediction error rates. The performance of the proposed method in terms of accuracy is validated by comparing the scattering parameters of arbitrarily chosen strip and microstrip type GDDR interconnects obtained from EM simulations with those predicted by ANNs using default and the proposed training-set selection methods

Towards Generalizable and Faithful Logic Reasoning over Natural Language via Resolution Refutation

Large language models (LLMs) have achieved significant performance in various natural language reasoning tasks. However, they still struggle with performing first-order logic reasoning over formal logical theories expressed in natural language. This is because the previous LLMs-based reasoning systems have the theoretical incompleteness issue. As a result, it can only address a limited set of simple reasoning problems, which significantly decreases their generalization ability. To address this issue, we propose a novel framework, named Generalizable and Faithful Reasoner (GFaiR), which introduces the paradigm of resolution refutation. Resolution refutation has the capability to solve all first-order logic reasoning problems by extending reasoning rules and employing the principle of proof by contradiction, so our system's completeness can be improved by introducing resolution refutation. Experimental results demonstrate that our system outperforms previous works by achieving state-of-the-art performances in complex scenarios while maintaining performances in simple scenarios. Besides, we observe that GFaiR is faithful to its reasoning process.Comment: LREC-Coling 202

HCV genotyping using statistical classification approach

The genotype of Hepatitis C Virus (HCV) strains is an important determinant of the severity and aggressiveness of liver infection as well as patient response to antiviral therapy. Fast and accurate determination of viral genotype could provide direction in the clinical management of patients with chronic HCV infections. Using publicly available HCV nucleotide sequences, we built a global Position Weight Matrix (PWM) for the HCV genome. Based on the PWM, a set of genotype specific nucleotide sequence "signatures" were selected from the 5' NCR, CORE, E1, and NS5B regions of the HCV genome. We evaluated the predictive power of these signatures for predicting the most common HCV genotypes and subtypes. We observed that nucleotide sequence signatures selected from NS5B and E1 regions generally demonstrated stronger discriminant power in differentiating major HCV genotypes and subtypes than that from 5' NCR and CORE regions. Two discriminant methods were used to build predictive models. Through 10 fold cross validation, over 99% prediction accuracy was achieved using both support vector machine (SVM) and random forest based classification methods in a dataset of 1134 sequences for NS5B and 947 sequences for E1. Prediction accuracy for each genotype is also reported

The Emergence of Chromosomally Located blaCTX-M-55 in Salmonella From Foodborne Animals in China

The emergence and increase in prevalence of resistance to cephalosporins amongst isolates of Salmonella from food animals imposes a public health threat. The aim of the present study was to investigate the prevalence and characteristics of CTX-M-producing Salmonella isolates from raw meat and food animals. 27 of 152 (17.76%) Salmonella isolates were ESBL-positive including 21/70 (30%) from food animals and 6/82 (7.32%) from raw meat. CTX-M-55 was the most prevalent ESBL type observed (12/27, 44.44%). 7 of 12 CTX-M-55-positive Salmonella isolates were Salmonella Indiana, 2 were Salmonella Typhimurium, 2 were Salmonella Chester, and the remaining isolate was not typeable. Eight CTX-M-55-positive Salmonella isolates were highly resistant to fluoroquinolones (MICCIP = 64 ug/mL) and co-harbored aac(6')-Ib-cr and oqxAB. Most of the CTX-M-55 positive isolates (11/12) carried blaCTX-M-55 genes on the chromosome, with the remaining isolate carrying this gene on a transferable 280 kb IncHI2 plasmid. A chromosomal blaCTX-M-55 gene from one isolate transferred onto a 250 kb IncHI2 plasmid which was subsequently conjugated into recipient strain J53. PFGE and MLST profiles showed a wide range of strain types were carrying blaCTX-M-55. Our study demonstrates the emergence and prevalence of foodborne Salmonella harboring a chromosomally located blaCTX-M-55 in China. The co-existence of PMQR genes with blaCTX-M-55 in Salmonella isolates suggests co-selection and dissemination of resistance to both fluoroquinolones and cephalosporins in Salmonella via the food chain in China represents a public health concern

Prepreg and Core Dielectric Permittivity (ϵr) Extraction for Fabricated Striplines\u27 Far-End Crosstalk Modeling

As the data rate and density of digital high-speed systems are getting higher, far-end crosstalk (FEXT) noise becomes one of the major issues that limit signal integrity performance. It was commonly believed that FEXT would be eliminated for strip lines routed in a homogeneous dielectric, but in reality, FEXT can always be measured in strip lines on the fabricated printed circuit boards. A slightly different dielectric permittivity (ϵr) of prepreg and core may be one of the major contributors to the FEXT. This article is focusing on providing a practical FEXT modeling methodology for strip lines by introducing an approach to extract ϵr of prepreg and core. Using the known cross-sectional geometry and measured S-parameters of the coupled strip line, the capacitance components in prepreg and core are separated using a two-dimensional solver, and the ϵr of prepreg and core is determined. A more comprehensive FEXT modeling approach is proposed by applying extracted inhomogeneous dielectric material information

High pressure evolution of Fe2_{2}O3_{3} electronic structure revealed by X-ray absorption

We report the first high pressure measurement of the Fe K-edge in hematite (Fe$_2$O$_3$) by X-ray absorption spectroscopy in partial fluorescence yield geometry. The pressure-induced evolution of the electronic structure as Fe$_2$O$_3$ transforms from a high-spin insulator to a low-spin metal is reflected in the x-ray absorption pre-edge. The crystal field splitting energy was found to increase monotonically with pressure up to 48 GPa, above which a series of phase transitions occur. Atomic multiplet, cluster diagonalization, and density-functional calculations were performed to simulate the pre-edge absorption spectra, showing good qualitative agreement with the measurements. The mechanism for the pressure-induced phase transitions of Fe$_2$O$_3$ is discussed and it is shown that ligand hybridization significantly reduces the critical high-spin/low-spin pressure.Comment: 5 pages, 4 figures and 1 tabl

GGPPS1 predicts the biological character of hepatocellular carcinoma in patients with cirrhosis

Background: Hepatocellular carcinoma (HCC) has been associated with diabetes and obesity, but a possible connection with the metabolic syndrome (MetS) and its potential interaction with hepatitis and cirrhosis are open to discussion. Our previous investigations have shown that GGPPS1 plays a critical role during hyperinsulinism. In this report, the expression and distribution of GGPPS1 in liver cancer, and its clinical significance were investigated. Methods: 70 patients with hepatocellular carcinoma (HCC) were included in this study. Three different types of tissues from each HCC patient were assembled immediately after surgical resection: tumor-free tissue >5 cm far from tumor edge (TF), adjacent nonmalignant tissue within 2 cm (AT), and tissue from the tumor (TT). Normal liver tissues from 10 liver transplant donors served as healthy control (HC) while 10 patients with liver cirrhosis as cirrhosis control (CC). The expression and distribution of GGPPS1 were detected by immunohistochemistry, western blots, or real-time PCR. The relationship between the expression of GGPPS1 and clinic pathologic index were analyzed. Results: We found that GGPPS1 was intensified mainly in the cytoplasm of liver tumor cells. Both the expression of GGPPS1 mRNA and protein were upregulated in TT comparing to AT or TF. Meanwhile, HCC patients with cirrhosis had relative higher expression of GGPPS1. In addition, many pathologic characters show close correlation with GGPPS1, such as tumor stage, vessel invasion, and early recurrence. Conclusion: GGPPS1 may play a critical role during the development of HCC from cirrhosis and is of clinical significance for predicting biological character of HCC

A Dnn-Ensemble Method for Error Reduction and Training Data Selection in Dnn based Modeling

Deep neural networks (DNNs) have been widely adopted in modeling electromagnetic compatibility (EMC) problems, but the training data acquisition is usually time-consuming through various simulators. This paper presents a powerful approach using an ensemble of DNN s to effectively reduce the training data size in DNN-based modeling problems. A batch of training data with the largest uncertainties is selected using active learning through the variance among the ensemble of DNNs. Subsequently, a greedy sampling algorithm is applied to select a data subset using diversity. Thus, the proposed method can achieve both uncertainty and diversity in data selection. By averaging the outputs of the DNN ensemble, the prediction error can be further reduced. Simple mathematical functions are used to validate the proposed method, and a high-dimensional strip line modeling problem also demonstrates the effectiveness of this DNN-ensemble approach. The proposed method is task agnostic and can be used in other surrogate modeling problems with DNN s

Tin Assisted Fully Exposed Platinum Clusters Stabilized on Defect-Rich Graphene for Dehydrogenation Reaction

Tin assisted fully exposed Pt clusters are fabricated on the core-shell nanodiamond@graphene (ND@G) hybrid support (a-PtSn/ND@G). The obtained atomically dispersed Pt clusters, with an average Pt atom number of 3, were anchored over the ND@Gsupport by the assistance of Sn atoms as a partition agent and through the Pt-C bond between Pt clusters and defect-rich graphene nanoshell. The atomically dispersed Pt clusters guaranteed a full metal availability to the reactants, a high thermal stability, and an optimized adsorption/desorption behavior. It inhibits the side reactions and enhances catalytic performance in direct dehydrogenation of n-butane at a low temperature of 450 °C, leading to \u3e98% selectivity toward olefin products, and the turnover frequency (TOF) of a-PtSn/ND@G is approximately 3.9 times higher than that of the traditional Pt3Sn alloy catalyst supported on Al2O3 (Pt3Sn/Al2O3)