Binary code similarity analysis based on naming function and common vector space.

Binary code similarity analysis is widely used in the field of vulnerability search where source code may not be available to detect whether two binary functions are similar or not. Based on deep learning and natural processing techniques, several approaches have been proposed to perform cross-platform binary code similarity analysis using control flow graphs. However, existing schemes suffer from the shortcomings of large differences in instruction syntaxes across different target platforms, inability to align control flow graph nodes, and less introduction of high-level semantics of stability, which pose challenges for identifying similar computations between binary functions of different platforms generated from the same source code.

High-frequency rTMS over the left DLPFC improves the response inhibition control of young healthy participants: an ERP combined H-MRS study.

Introduction: Unlike the effect of repetitive transcranial magnetic stimulation (rTMS) in treating neuropsychiatric diseases, little is known about how personal factors might account for the disparity of results from studies of cognition and rTMS. In this study, we investigated the effects of high-frequency rTMS on response inhibition control and explored the time course changes in cognitive processing and brain metabolic mechanisms after rTMS using event-related potentials (ERPs) and magnetic resonance spectroscopy (H-MRS).

Methods: Participants were all right-handed and were naive to rTMS and the Go/NoGo task.

Knockdown of PDCD4 ameliorates neural cell apoptosis and mitochondrial injury through activating the PI3K/AKT/mTOR signal in Parkinson's disease.

Background: Parkinson's disease (PD) is a complex neurodegenerative disorder and hampers normal living. It has been reported that programmed cell death 4 (PDCD4) is associated with tumor suppression, inflammatory response, and apoptosis.

Objective: The aim of this study was to investigate the role of PDCD4 in PD.

Influence of Management of Intensive Weight, Blood Pressure, and Lipids on Disease Severity in Patients with Carotid Atherosclerosis.

Context: Cardiovascular diseases (CVDs caused by atherosclerosis, such as coronary heart disease and stroke, have become major causes of death and disability worldwide. Atherosclerosis is the primary pathological factor causing CVDs. Managing weight, blood pressure, and lipids is one of the tenets of chronic-disease management, including atherosclerosis.

A new method of software vulnerability detection based on a quantum neural network.

In the field of network security, although there has been related work on software vulnerability detection based on classic machine learning, detection ability is directly proportional to the scale of training data. A quantum neural network has been proven to solve the memory bottleneck problem of classical machine learning, so it has far-reaching prospects in the field of vulnerability detection. To fill the gap in this field, we propose a quantum neural network structure named QDENN for software vulnerability detection.

A Method of Short Text Representation Fusion with Weighted Word Embeddings and Extended Topic Information.

Short text representation is one of the basic and key tasks of NLP. The traditional method is to simply merge the bag-of-words model and the topic model, which may lead to the problem of ambiguity in semantic information, and leave topic information sparse. We propose an unsupervised text representation method that involves fusing word embeddings and extended topic information.

Record Atomistic Simulation of Crystalline Silicon: Bridging Microscale Structures and Macroscale Properties.

Based on the molecular dynamics software package CovalentMD 2.0, the fastest molecular dynamics simulation for covalent crystalline silicon with bond-order potentials has been implemented on the third highest performance supercomputer "Sunway TaihuLight" in the world (before June 2019), and already obtained 16.0 Pflops (10 floating point operation per second) in double precision for the simulation of crystalline silicon, which is recordly high for rigorous atomistic simulation of covalent materials.

Serum long noncoding RNA LRB1 as a potential biomarker for predicting the diagnosis and prognosis of human hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a type of malignant tumor with a high mortality rate. Long non-coding RNAs (lncRNAs) serve important roles in cellular processes and gene regulation. Identifying novel prognostic biomarkers is important for the monitoring and treatment of HCC.