Quenched QCD with domain wall fermions

We report on simulations of quenched QCD using domain wall fermions, where we focus on basic questions about the formalism and its ability to produce expected low energy hadronic physics for light quarks. The work reported here is on quenched $8^3 \times 32$ lattices at $\beta = 5.7$ and 5.85, using values for the length of the fifth dimension between 10 and 48. We report results for parameter choices which lead to the desired number of flavors, a study of undamped modes in the extra dimension and hadron masses.Comment: Contribution to Lattice '98. Presented by R. Mawhinney. 3 pages, 3 figure

Dynamical QCD thermodynamics with domain wall fermions

We present results from numerical simulations of full, two flavor QCD thermodynamics at N_t=4 with domain wall fermions. For the first time a numerical simulation of the full QCD phase transition displays a low temperature phase with spontaneous chiral symmetry breaking but intact flavor symmetry and a high temperature phase with the full SU(2) x SU(2) chiral flavor symmetry.Comment: LATTICE98(hightemp

The domain wall fermion chiral condensate in quenched QCD

We examine the chiral limit of domain wall fermions in quenched QCD. One expects that in a quenched simulation, exact fermion zero modes will give a divergent, 1/m behavior in the chiral condensate for sufficiently small valence quark masses. Unlike other fermion formulations, domain wall fermions clearly demonstrate this behavior.Comment: LATTICE98(spectrum), G. R. Fleming presented talk, 5 pages, 3 figures, corrected typos in printed versio

Static detection of control-flow-related vulnerabilities using graph embedding

© 2019 IEEE. Static vulnerability detection has shown its effectiveness in detecting well-defined low-level memory errors. However, high-level control-flow related (CFR) vulnerabilities, such as insufficient control flow management (CWE-691), business logic errors (CWE-840), and program behavioral problems (CWE-438), which are often caused by a wide variety of bad programming practices, posing a great challenge for existing general static analysis solutions. This paper presents a new deep-learning-based graph embedding approach to accurate detection of CFR vulnerabilities. Our approach makes a new attempt by applying a recent graph convolutional network to embed code fragments in a compact and low-dimensional representation that preserves high-level control-flow information of a vulnerable program. We have conducted our experiments using 8,368 real-world vulnerable programs by comparing our approach with several traditional static vulnerability detectors and state-of-the-art machine-learning-based approaches. The experimental results show the effectiveness of our approach in terms of both accuracy and recall. Our research has shed light on the promising direction of combining program analysis with deep learning techniques to address the general static analysis challenges

Light-Front Quantization and AdS/QCD: An Overview

We give an overview of the light-front holographic approach to strongly coupled QCD, whereby a confining gauge theory, quantized on the light front, is mapped to a higher-dimensional anti de Sitter (AdS) space. The framework is guided by the AdS/CFT correspondence incorporating a gravitational background asymptotic to AdS space which encodes the salient properties of QCD, such as the ultraviolet conformal limit at the AdS boundary at $z \to 0$, as well as modifications of the geometry in the large $z$ infrared region to describe confinement and linear Regge behavior. There are two equivalent procedures for deriving the AdS/QCD equations of motion: one can start from the Hamiltonian equation of motion in physical space time by studying the off-shell dynamics of the bound state wavefunctions as a function of the invariant mass of the constituents. To a first semiclassical approximation, where quantum loops and quark masses are not included, this leads to a light-front Hamiltonian equation which describes the bound state dynamics of light hadrons in terms of an invariant impact variable $\zeta$ which measures the separation of the partons within the hadron at equal light-front time. Alternatively, one can start from the gravity side by studying the propagation of hadronic modes in a fixed effective gravitational background. Both approaches are equivalent in the semiclassical approximation. This allows us to identify the holographic variable $z$ in AdS space with the impact variable $\zeta$. Light-front holography thus allows a precise mapping of transition amplitudes from AdS to physical space-time. The internal structure of hadrons is explicitly introduced and the angular momentum of the constituents plays a key role.Comment: Invited talk presented by GdT at the XIV School of Particles and Fields, Morelia, Mexico, November 8-12, 201

Flow2Vec: Value-flow-based precise code embedding

© 2020 Owner/Author. Code embedding, as an emerging paradigm for source code analysis, has attracted much attention over the past few years. It aims to represent code semantics through distributed vector representations, which can be used to support a variety of program analysis tasks (e.g., code summarization and semantic labeling). However, existing code embedding approaches are intraprocedural, alias-unaware and ignoring the asymmetric transitivity of directed graphs abstracted from source code, thus they are still ineffective in preserving the structural information of code. This paper presents Flow2Vec, a new code embedding approach that precisely preserves interprocedural program dependence (a.k.a value-flows). By approximating the high-order proximity, i.e., the asymmetric transitivity of value-flows, Flow2Vec embeds control-flows and alias-aware data-flows of a program in a low-dimensional vector space. Our value-flow embedding is formulated as matrix multiplication to preserve context-sensitive transitivity through CFL reachability by filtering out infeasible value-flow paths. We have evaluated Flow2Vec using 32 popular open-source projects. Results from our experiments show that Flow2Vec successfully boosts the performance of two recent code embedding approaches codevec and codeseq for two client applications, i.e., code classification and code summarization. For code classification, Flow2Vec improves codevec with an average increase of 21.2%, 20.1% and 20.7% in precision, recall and F1, respectively. For code summarization, Flow2Vec outperforms codeseq by an average of 13.2%, 18.8% and 16.0% in precision, recall and F1, respectively

An exploratory study of autopilot software bugs in unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) are becoming increasingly important and widely used in modern society. Software bugs in these systems can cause severe issues, such as system crashes, hangs, and undefined behaviors. Some bugs can also be exploited by hackers to launch security attacks, resulting in catastrophic consequences. Therefore, techniques that can help detect and fix software bugs in UAVs are highly desirable. However, although there are many existing studies on bugs in various types of software, the characteristics of UAV software bugs have never been systematically studied. This impedes the development of tools for assuring the dependability of UAVs. To bridge this gap, we conducted the first large-scale empirical study on two well-known open-source autopilot software platforms for UAVs, namely PX4 and Ardupilot, to characterize bugs in UAVs. Through analyzing 569 bugs from these two projects, we observed eight types of UAV-specific bugs (i.e., limit, math, inconsistency, priority, parameter, hardware support, correction, and initialization) and learned their root causes. Based on the bug taxonomy, we summarized common bug patterns and repairing strategies. We further identified five challenges associated with detecting and fixing such UAV-specific bugs. Our study can help researchers and practitioners to better understand the threats to the dependability of UAV systems and facilitate the future development of UAV bug diagnosis tools