Fault Sneaking Attack: a Stealthy Framework for Misleading Deep Neural Networks

Despite the great achievements of deep neural networks (DNNs), the vulnerability of state-of-the-art DNNs raises security concerns of DNNs in many application domains requiring high reliability.We propose the fault sneaking attack on DNNs, where the adversary aims to misclassify certain input images into any target labels by modifying the DNN parameters. We apply ADMM (alternating direction method of multipliers) for solving the optimization problem of the fault sneaking attack with two constraints: 1) the classification of the other images should be unchanged and 2) the parameter modifications should be minimized. Specifically, the first constraint requires us not only to inject designated faults (misclassifications), but also to hide the faults for stealthy or sneaking considerations by maintaining model accuracy. The second constraint requires us to minimize the parameter modifications (using L0 norm to measure the number of modifications and L2 norm to measure the magnitude of modifications). Comprehensive experimental evaluation demonstrates that the proposed framework can inject multiple sneaking faults without losing the overall test accuracy performance.Comment: Accepted by the 56th Design Automation Conference (DAC 2019

Effect of pulsed methylprednisolone on pain, in patients with HTLV-1-associated myelopathy

HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is an immune mediated myelopathy caused by the human T-lymphotropic virus type 1 (HTLV-1). The efficacy of treatments used for patients with HAM/TSP is uncertain. The aim of this study is to document the efficacy of pulsed methylprednisolone in patients with HAM/TSP. Data from an open cohort of 26 patients with HAM/TSP was retrospectively analysed. 1g IV methylprednisolone was infused on three consecutive days. The outcomes were pain, gait, urinary frequency and nocturia, a range of inflammatory markers and HTLV-1 proviral load. Treatment was well tolerated in all but one patient. Significant improvements in pain were: observed immediately, unrelated to duration of disease and maintained for three months. Improvement in gait was only seen on Day 3 of treatment. Baseline cytokine concentrations did not correlate to baseline pain or gait impairment but a decrease in tumour necrosis factor-alpha (TNF-α) concentration after pulsed methylprednisolone was associated with improvements in both. Until compared with placebo, treatment with pulsed methylprednisolone should be offered to patients with HAM/TSP for the treatment of pain present despite regular analgesia

Strategies in Outsourcing R&D Processes to Maintain Market Competitiveness

In the 21st century, managing outsourced research and development (R&D) processes is critical to an organization\u27s success. Guided by the logistic outsourcing theory developed by de Boer, Gaytan, and Arroyo, the purpose of this single case study was to explore strategies and processes organizational leaders used to manage outsourced R&D to maintain market competitiveness. Semistructured interviews were conducted with 5 purposefully selected business leaders who were responsible for outsourcing R&D in a single Fortune 500 corporation in the Mid-Atlantic region of the United States. Company records were also gathered as data. Yin\u27s 5-step process for a case study and key words in context analysis were used to analyze the data. Findings included 3 main themes: (a) the outsourcing decision-making process with internal and external constraints, (b) the effectiveness of managing outsourcing services and processes, and (c) the influence of outsourcing on business effectiveness and new products. Findings also indicated no practical system to measure effectiveness of outsourced R&D services on market competitiveness. The lack of measurement effectiveness was due to a lack of processes in place to measure R&D performance and no practical approach to measure impact of R&D on market competitiveness. Findings offered insight into strategies used by business leaders to manage outsourced R&D processes. Findings may also have implications for positive social change such as impacting communities through employment, generating government revenues through taxes, and creating a positive impact on job creation in the industries that promote R&D outsourcing

A Fatal Case of Metastatic Pulmonary Calcification during the Puerperium

We present an unusual case of a fatal respiratory failure in a young woman developed two weeks after she gave birth at home. Circumstantial and clinical features of the case were strongly suggestive for a 'classical' septic origin of the respiratory symptoms. Autopsy, together with histopathological and immunohistochemical analyses allowed demonstrating a massive calcium redistribution consisting of an important osteolysis, especially from cranial bones and abnormal accumulation in lungs and other organs. Such physiopathology was driven by a primary hyperparathyroidism secondary to a parathyroid carcinoma as demonstrated by immunohistochemistry. This very rare case is furthermore characterised by a regular pregnancy course, ended with the birth of a healthy new-born. A complex interaction between pregnancy physiology and hyperparathyroidism might be hypothesised, determining the discrepancy between the relative long period of wellness and the tumultuous cascade occurred in the puerperium

Exact solutions to the focusing nonlinear Schrodinger equation

A method is given to construct globally analytic (in space and time) exact solutions to the focusing cubic nonlinear Schrodinger equation on the line. An explicit formula and its equivalents are presented to express such exact solutions in a compact form in terms of matrix exponentials. Such exact solutions can alternatively be written explicitly as algebraic combinations of exponential, trigonometric, and polynomial functions of the spatial and temporal coordinates.Comment: 60 pages, 18 figure

ImageNet-Patch: A dataset for benchmarking machine learning robustness against adversarial patches

Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-learning model to misclassify it. However, their optimization is computationally demanding, and requires careful hyperparameter tuning, potentially leading to suboptimal robustness evaluations. To overcome these issues, we propose ImageNet-Patch, a dataset to benchmark machine-learning models against adversarial patches. The dataset is built by first optimizing a set of adversarial patches against an ensemble of models, using a state-of-the-art attack that creates transferable patches. The corresponding patches are then randomly rotated and translated, and finally applied to the ImageNet data. We use ImageNet-Patch to benchmark the robustness of 127 models against patch attacks, and also validate the effectiveness of the given patches in the physical domain (i.e., by printing and applying them to real-world objects). We conclude by discussing how our dataset could be used as a benchmark for robustness, and how our methodology can be generalized to other domains. We open source our dataset and evaluation code at https://github.com/pralab/ImageNet-Patch