Audit Committees Oversight of Information Technology Risk

This exploratory study examines the role of the audit committee in overseeing information technology (IT) risk. We address the degree of audit committee oversight of specific IT risks, as well as factors associated with variations in audit committee IT oversight. Based on responses from 39 audit committee members, we found (1) little audit committee emphasis on oversight of IT risks, (2) audit committees involved with IT oversight focus on more traditional risks (e.g., monitoring), while very little attention is devoted to IT acquisition and implementation, and (3) the amount of IT oversight is positively associated with the responding members auditing experience and prior familiarity with the COBIT model for assessing IT risks. Audit committee independence, diligence, and expertise, company size, and industry were not significantly associated with IT oversight

Audit Committees Oversight Of Information Technology Risk

This exploratory study examines the role of the audit committee in overseeing information technology (IT) risk. We address the degree of audit committee oversight of specific IT risks, as well as factors associated with variations in audit committee IT oversight. Based on responses from 39 audit committee members, we found (1) little audit committee emphasis on oversight of IT risks, (2) audit committees involved with IT oversight focus on more traditional risks (e.g., monitoring), while very little attention is devoted to IT acquisition and implementation, and (3) the amount of IT oversight is positively associated with the responding members auditing experience and prior familiarity with the COBIT model for assessing IT risks. Audit committee independence, diligence, and expertise, company size, and industry were not significantly associated with IT oversight

Audit Committee Effectiveness: A Synthesis of the Empirical Audit Committee Literature

The article describes the factors that contribute to audit committee effectiveness. An effective audit committee has qualified members with the authority and resources to protect stakeholder interests by ensuring reliable financial reporting, internal controls, and risk management through its diligent oversight efforts. The determinants of audit committee effectiveness includes the audit committee composition, authority, resources and diligence. The major U.S. stock exchanges require that audit committees be composed of at least three independent, financially literate directors. Team issues also are relevant when considering audit committee composition. The audit committee derives its authority from the full board of directors, federal law and exchange listing requirements. Authority is viewed as a function of the audit committees responsibilities and influence. Audit committee authority also depends on the audit committees relationships with management, external and internal auditors and the board as a whole. The resource component of audit committee effectiveness highlights that effective oversight is contingent upon the audit committee having adequate resources to do its job. Diligence is the process factor that is needed to achieve audit committee effectiveness

Charged particle tracking via edge-classifying interaction networks

Recent work has demonstrated that geometric deep learning methods such as graph neural networks (GNNs) are well suited to address a variety of reconstruction problems in high energy particle physics. In particular, particle tracking data is naturally represented as a graph by identifying silicon tracker hits as nodes and particle trajectories as edges; given a set of hypothesized edges, edge-classifying GNNs identify those corresponding to real particle trajectories. In this work, we adapt the physics-motivated interaction network (IN) GNN toward the problem of particle tracking in pileup conditions similar to those expected at the high-luminosity Large Hadron Collider. Assuming idealized hit filtering at various particle momenta thresholds, we demonstrate the IN's excellent edge-classification accuracy and tracking efficiency through a suite of measurements at each stage of GNN-based tracking: graph construction, edge classification, and track building. The proposed IN architecture is substantially smaller than previously studied GNN tracking architectures; this is particularly promising as a reduction in size is critical for enabling GNN-based tracking in constrained computing environments. Furthermore, the IN may be represented as either a set of explicit matrix operations or a message passing GNN. Efforts are underway to accelerate each representation via heterogeneous computing resources towards both high-level and low-latency triggering applications.Comment: This is a post-peer-review, pre-copyedit version of this article. The final authenticated version is available online at: https://doi.org/10.1007/s41781-021-00073-

Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs

We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks. The two complementary FPGA designs are based on OpenCL, a framework for writing programs that execute across heterogeneous platforms, and hls4ml, a high-level-synthesis-based compiler for neural network to firmware conversion. We evaluate and compare the resource usage, latency, and tracking performance of our implementations based on a benchmark dataset. We find a considerable speedup over CPU-based execution is possible, potentially enabling such algorithms to be used effectively in future computing workflows and the FPGA-based Level-1 trigger at the CERN Large Hadron Collider.Comment: 8 pages, 4 figures, To appear in Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020

Physics and Computing Performance of the Exa.TrkX TrackML Pipeline

The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. The Exa.TrkX tracking pipeline clusters detector measurements to form track candidates and filters them. The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-like tracking detector), has been demonstrated on various detectors, including the DUNE LArTPC and the CMS High-Granularity Calorimeter. This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS and CMS data. The pipeline achieves tracking efficiency and purity similar to production tracking algorithms. Crucially for future HEP applications, the pipeline benefits significantly from GPU acceleration, and its computational requirements scale close to linearly with the number of particles in the event