Comparing One with Many -- Solving Binary2source Function Matching Under Function Inlining

Binary2source function matching is a fundamental task for many security applications, including Software Component Analysis (SCA). The "1-to-1" mechanism has been applied in existing binary2source matching works, in which one binary function is matched against one source function. However, we discovered that such mapping could be "1-to-n" (one query binary function maps multiple source functions), due to the existence of function inlining. To help conduct binary2source function matching under function inlining, we propose a method named O2NMatcher to generate Source Function Sets (SFSs) as the matching target for binary functions with inlining. We first propose a model named ECOCCJ48 for inlined call site prediction. To train this model, we leverage the compilable OSS to generate a dataset with labeled call sites (inlined or not), extract several features from the call sites, and design a compiler-opt-based multi-label classifier by inspecting the inlining correlations between different compilations. Then, we use this model to predict the labels of call sites in the uncompilable OSS projects without compilation and obtain the labeled function call graphs of these projects. Next, we regard the construction of SFSs as a sub-tree generation problem and design root node selection and edge extension rules to construct SFSs automatically. Finally, these SFSs will be added to the corpus of source functions and compared with binary functions with inlining. We conduct several experiments to evaluate the effectiveness of O2NMatcher and results show our method increases the performance of existing works by 6% and exceeds all the state-of-the-art works

Single-shot, full characterization of the spatial wavefunction of light fields via Stokes tomography

Since the diffraction behavior of a light field is fully determined by its spatial wavefunction, i.e., its spatial complex amplitude (SCA), full characterization of spatial wavefunction, plays a vital role in modern optics from both the fundamental and applied aspects. In this work, we present a novel complex-amplitude profiler based on spatial Stokes tomography with the capability to fully determine the SCA of a light field in a single shot with high precision and resolution. The SCA slice observed at any propagation plane provides complete information about the light field, thus allowing us to further retrieve the complete beam structure in 3 dimensions space, as well as the exact modal constitution in terms of spatial degrees of freedom. The principle demonstrated here provides an important advancement for the full characterization of light beams with a broad spectrum of potential applications in various areas of optics, especially for the growing field of structured light

Approximate perturbed direct homotopy reduction method: infinite series reductions to two perturbed mKdV equations

An approximate perturbed direct homotopy reduction method is proposed and applied to two perturbed modified Korteweg-de Vries (mKdV) equations with fourth order dispersion and second order dissipation. The similarity reduction equations are derived to arbitrary orders. The method is valid not only for single soliton solution but also for the Painlev\'e II waves and periodic waves expressed by Jacobi elliptic functions for both fourth order dispersion and second order dissipation. The method is valid also for strong perturbations.Comment: 8 pages, 1 figur

Ansor : Generating High-Performance Tensor Programs for Deep Learning

High-performance tensor programs are crucial to guarantee efficient execution of deep neural networks. However, obtaining performant tensor programs for different operators on various hardware platforms is notoriously challenging. Currently, deep learning systems rely on vendor-provided kernel libraries or various search strategies to get performant tensor programs. These approaches either require significant engineering effort to develop platform-specific optimization code or fall short of finding high-performance programs due to restricted search space and ineffective exploration strategy. We present Ansor, a tensor program generation framework for deep learning applications. Compared with existing search strategies, Ansor explores many more optimization combinations by sampling programs from a hierarchical representation of the search space. Ansor then fine-tunes the sampled programs with evolutionary search and a learned cost model to identify the best programs. Ansor can find high-performance programs that are outside the search space of existing state-of-the-art approaches. In addition, Ansor utilizes a task scheduler to simultaneously optimize multiple subgraphs in deep neural networks. We show that Ansor improves the execution performance of deep neural networks relative to the state-of-the-art on the Intel CPU, ARM CPU, and NVIDIA GPU by up to $3.8\times$, $2.6\times$, and $1.7\times$, respectively.Comment: Published in OSDI 202

Effects of single session transcranial direct current stimulation on aerobic performance and one arm pull-down explosive force of professional rock climbers

Objective: To explore the effects of single-session transcranial direct current stimulation (tDCS) on aerobic performance and explosive force in the one-arm pull-down of long-term trained rock climbers.Method: Twenty athletes (twelve male and eight female) from the Rock Climbing Team of Hunan province (Hunan, China) were selected for a randomized double-blind crossover study. After baseline tests, All subjects visited laboratories twice to randomly receive either sham or a-tDCS at a current intensity of 2 mA for 20 min. The two visits were more than 72 h apart. Immediately after each stimulation, subjects completed a 9-min 3-level-load aerobic test and a one-arm pull-down test.Results: Differences in the heart rate immediately after 9-min incremental aerobic exercises revealed no statistical significance between each group (p > 0.05). However, the decrease in heart rate per unit time after exercise after real stimulation was significantly better than before stimulation (p < 0.05), and no statistical significance was observed between after sham stimulation and before stimulation (p > 0.05). One-arm pull-down explosive force on both sides after real stimulation was improved by a-tDCS compared with before stimulation, but with no significant difference (p > 0.05). Real stimulation was significantly improved, compared with sham stimulation on the right side (p < 0.05).Conclusion: Single-session tDCS could potentially benefit sports performance in professional athletes

Treatment of hepatic venous system hemorrhage and carbon dioxide gas embolization during laparoscopic hepatectomy via hepatic vein approach

With the improvement of laparoscopic surgery, the feasibility and safety of laparoscopic hepatectomy have been affirmed, but intraoperative hepatic venous system hemorrhage and carbon dioxide gas embolism are the difficulties in laparoscopic hepatectomy. The incidence of preoperative hemorrhage and carbon dioxide gas embolism could be reduced through preoperative imaging evaluation, reasonable liver blood flow blocking method, appropriate liver-breaking device, controlled low-center venous pressure technology, and fine-precision precision operation. In the case of blood vessel rupture bleeding in the liver vein system, after controlling and reducing bleeding, confirm the type and severity of vascular damage in the liver and venous system, take appropriate measures to stop the bleeding quickly and effectively, and, if necessary, transfer the abdominal treatment in time. In addition, to strengthen the understanding, prevention and emergency treatment of severe CO2 gas embolism in laparoscopic hepatectomy is also the key to the success of surgery. This study aims to investigate the methods to deal with hepatic venous system hemorrhage and carbon dioxide gas embolization based on author’s institutional experience and relevant literature. We retrospectively analyzed the data of 60 patients who received laparoscopic anatomical hepatectomy of hepatic vein approach for HCC. For patients with intraoperative complications, corresponding treatments were given to cope with different complications. After the operation, combined with clinical experience and literature, we summarized and discussed the good treatment methods in the face of such situations so that minimize the harm to patients as much as possible

AI of Brain and Cognitive Sciences: From the Perspective of First Principles

Nowadays, we have witnessed the great success of AI in various applications, including image classification, game playing, protein structure analysis, language translation, and content generation. Despite these powerful applications, there are still many tasks in our daily life that are rather simple to humans but pose great challenges to AI. These include image and language understanding, few-shot learning, abstract concepts, and low-energy cost computing. Thus, learning from the brain is still a promising way that can shed light on the development of next-generation AI. The brain is arguably the only known intelligent machine in the universe, which is the product of evolution for animals surviving in the natural environment. At the behavior level, psychology and cognitive sciences have demonstrated that human and animal brains can execute very intelligent high-level cognitive functions. At the structure level, cognitive and computational neurosciences have unveiled that the brain has extremely complicated but elegant network forms to support its functions. Over years, people are gathering knowledge about the structure and functions of the brain, and this process is accelerating recently along with the initiation of giant brain projects worldwide. Here, we argue that the general principles of brain functions are the most valuable things to inspire the development of AI. These general principles are the standard rules of the brain extracting, representing, manipulating, and retrieving information, and here we call them the first principles of the brain. This paper collects six such first principles. They are attractor network, criticality, random network, sparse coding, relational memory, and perceptual learning. On each topic, we review its biological background, fundamental property, potential application to AI, and future development.Comment: 59 pages, 5 figures, review articl

MSH2 and MSH6 in mismatch repair system account for soybean (Glycine max (L.) Merr.) tolerance to cadmium Toxicity by determining DNA damage response

Our aim was to investigate DNA mismatch repair (MMR) genes regulating cadmium tolerance in two soybean cultivars. Cultivars Liaodou 10 (LD10, Cd-sensitive) and Shennong 20 (SN20, Cd-tolerant) seedlings were grown hydroponically on Murashige and Skoog (MS) media containing 0–2.5 mg·L–1 Cd for 4 days. Cd stress induced less random amplified polymorphism DNA (RAPD) polymorphism in LD10 than in SN20 roots, causing G1/S arrest in LD10 and G2/M arrest in SN20 roots. Virus-induced gene silencing (VIGS) of MLH1 in LD10-TRV-MLH1 plantlets showed markedly diminished G1/S arrest but enhanced root length/area under Cd stress. However, an increase in G1/S arrest and reduction of G2/M arrest occurred in SN20-TRV-MSH2 and SN20-TRV-MSH6 plantlets with decreased root length/area under Cd stress. Taken together, we conclude that the low expression of MSH2 and MSH6, involved in the G2/M arrest, results in Cd-induced DNA damage recognition bypassing the MMR system to activate G1/S arrest with the assistance of MLH1. This then leads to repressed root growth in LD10, explaining the intervarietal difference in Cd tolerance in soybean