Towards hybrid cloud service certification models

In this paper, we introduce a hybrid approach for certifying security properties of cloud services that combines monitoring and testing data. The paper argues about the need for hybrid certification and examines some basic characteristics of hybrid certification models

LogicWiSARD: Memoryless synthesis of weightless neural networks

Weightless neural networks (WNNs) are an alternative pattern recognition technique where RAM nodes function as neurons. As both training and inference require mostly table lookups, few additions, and no multiplications, WNNs are suitable for high-performance and low-power embedded applications. This work introduces a novel approach to implement WiSARD, the leading WNN state-of-the-art architecture, completely eliminating memories and arithmetic circuits and utilizing only logic functions. The approach creates compressed minimized implementations by converting trained WNN nodes from lookup tables to logic functions. The proposed LogicWiSARD is implemented in FPGA and ASIC technologies to illustrate its suitability for edge inference. Experimental results show more than 80% reduction in energy consumption when the proposed LogicWiSARD model is compared with a multilayer perceptron network (MLP) of equivalent accuracy. Compared to previous work on FPGA implementations for WNNs, convolutional neural networks, and binary neural networks, the energy savings of LogicWiSARD range between 32.2% and 99.6%.info:eu-repo/semantics/acceptedVersio

ULEEN: A novel architecture for ultra low-energy edge neural networks

"Extreme edge"1 devices, such as smart sensors, are a uniquely challenging environment for the deployment of machine learning. The tiny energy budgets of these devices lie beyond what is feasible for conventional deep neural networks, particularly in high-throughput scenarios, requiring us to rethink how we approach edge inference. In this work, we propose ULEEN, a model and FPGA-based accelerator architecture based on weightless neural networks (WNNs). WNNs eliminate energy-intensive arithmetic operations, instead using table lookups to perform computation, which makes them theoretically well-suited for edge inference. However, WNNs have historically suffered from poor accuracy and excessive memory usage. ULEEN incorporates algorithmic improvements and a novel training strategy inspired by binary neural networks (BNNs) to make significant strides in addressing these issues. We compare ULEEN against BNNs in software and hardware using the four MLPerf Tiny datasets and MNIST. Our FPGA implementations of ULEEN accomplish classification at 4.0-14.3 million inferences per second, improving area-normalized throughput by an average of 3.6× and steady-state energy efficiency by an average of 7.1× compared to the FPGA-based Xilinx FINN BNN inference platform. While ULEEN is not a universally applicable machine learning model, we demonstrate that it can be an excellent choice for certain applications in energy- and latency-critical edge environments.info:eu-repo/semantics/publishedVersio

ULEEN: A Novel Architecture for Ultra Low-Energy Edge Neural Networks

The deployment of AI models on low-power, real-time edge devices requires accelerators for which energy, latency, and area are all first-order concerns. There are many approaches to enabling deep neural networks (DNNs) in this domain, including pruning, quantization, compression, and binary neural networks (BNNs), but with the emergence of the "extreme edge", there is now a demand for even more efficient models. In order to meet the constraints of ultra-low-energy devices, we propose ULEEN, a model architecture based on weightless neural networks. Weightless neural networks (WNNs) are a class of neural model which use table lookups, not arithmetic, to perform computation. The elimination of energy-intensive arithmetic operations makes WNNs theoretically well suited for edge inference; however, they have historically suffered from poor accuracy and excessive memory usage. ULEEN incorporates algorithmic improvements and a novel training strategy inspired by BNNs to make significant strides in improving accuracy and reducing model size. We compare FPGA and ASIC implementations of an inference accelerator for ULEEN against edge-optimized DNN and BNN devices. On a Xilinx Zynq Z-7045 FPGA, we demonstrate classification on the MNIST dataset at 14.3 million inferences per second (13 million inferences/Joule) with 0.21 $\mu$s latency and 96.2% accuracy, while Xilinx FINN achieves 12.3 million inferences per second (1.69 million inferences/Joule) with 0.31 $\mu$s latency and 95.83% accuracy. In a 45nm ASIC, we achieve 5.1 million inferences/Joule and 38.5 million inferences/second at 98.46% accuracy, while a quantized Bit Fusion model achieves 9230 inferences/Joule and 19,100 inferences/second at 99.35% accuracy. In our search for ever more efficient edge devices, ULEEN shows that WNNs are deserving of consideration.Comment: 14 pages, 14 figures Portions of this article draw heavily from arXiv:2203.01479, most notably sections 5E and 5F.

COVID‑19 and SARS‑CoV‑2 host cell entry mediators: Expression profiling of TMRSS4 in health and disease

Copyright: © Katopodis et al. Severe acute respiratory syndrome (SARS) coronavirus‑2 (SARS‑CoV‑2), the causative viral agent for the ongoing COVID‑19 pandemic, enters its host cells primarily via the binding of the SARS‑CoV‑2 spike (S) proteins to the angiotensin‑converting enzyme 2 (ACE2). A number of other cell entry mediators have also been identified, including neuropilin‑1 (NRP1) and transmembrane protease serine 2 (TMPRSS2). More recently, it has been demonstrated that transmembrane protease serine 4 (TMPRSS4) along with TMPRSS2 activate the SARS‑CoV‑2 S proteins, and enhance the viral infection of human small intestinal enterocytes. To date, a systematic analysis of TMPRSS4 in health and disease is lacking. In the present study, using in silico tools, the gene expression and genetic alteration of TMPRSS4 were analysed across numerous tumours and compared to controls. The observations were also expanded to the level of the central nervous system (CNS). The findings revealed that TMPRSS4 was overexpressed in 11 types of cancer, including lung adenocarcinoma, lung squamous cell carcinoma, cervical squamous cell carcinoma, thyroid carcinoma, ovarian cancer, cancer of the rectum, pancreatic cancer, colon and stomach adenocarcinoma, uterine carcinosarcoma and uterine corpus endometrial carcinoma, whilst it was significantly downregulated in kidney carcinomas, acute myeloid leukaemia, skin cutaneous melanoma and testicular germ cell tumours. Finally, a high TMPRSS4 expression was documented in the olfactory tubercle, paraolfactory gyrus and frontal operculum, all brain regions which are associated with the sense of smell and taste. Collectively, these data suggest that TMPRSS4 may play a role in COVID‑19 symptomatology as another SARS‑CoV‑2 host cell entry mediator responsible for the tropism of this coronavirus both in the periphery and the CNS

Failure to complete adjuvant chemotherapy is associated with adverse survival in stage III colon cancer patients

Two recent North American studies have shown that completion of 5-fluorouracil (5FU)-based adjuvant chemotherapy is a major prognostic factor for the survival of elderly stage III colon cancer patients. The aim of the present study was to confirm this finding in a population-based series from Australia. The study cohort comprised 851 stage III colon cancer patients treated by surgery alone and 461 who initiated the Mayo chemotherapy regime. One-third of patients who initiated chemotherapy failed to complete more than three cycles of treatment. Independent predictors for failure to complete were treatment in district or rural hospitals, low socioeconomic index and treatment by a low-volume surgeon. Patients who failed to complete chemotherapy showed worse cancer-specific survival compared not only to those who completed treatment (HR=2.24; 95% confidence interval (CI) (1.66–3.03), P<0.001) but also to those treated by surgery alone (HR=1.37; 95% CI (1.09–1.72), P=0.008). The current and previous studies demonstrate the importance of completing adjuvant 5-FU-based chemotherapy for colon cancer. Further prospective studies are required to identify better the physiological and socioeconomic factors responsible for failure to complete chemotherapy so that appropriate improvements in health service delivery can be made

Fusidic acid and clindamycin resistance in community-associated, methicillin-resistant Staphylococcus aureus infections in children of Central Greece

<p>Abstract</p> <p>Introduction</p> <p>In Greece, fusidic acid and clindamycin are commonly used for the empiric therapy of suspected staphylococcal infections.</p> <p>Methods</p> <p>The medical records of children examined at the outpatient clinics or admitted to the pediatric wards of the University General Hospital of Larissa, Central Greece, with community-associated staphylococcal infections from January 2003 to December 2009 were reviewed.</p> <p>Results</p> <p>Of 309 children (0-14 years old), 21 (6.8%) had invasive infections and 288 (93.2%) skin and soft tissue infections (SSTIs). Thirty-five patients were ≤30 days of age. The proportion of staphylococcal infections caused by a community-associated methicillin-resistant <it>Staphylococcus aureus </it>(CA-MRSA) isolate increased from 51.5% (69 of 134) in 2003-2006 to 63.4% (111 of 175) in 2007-2009 (<it>P </it>= 0.037). Among the CA-MRSA isolates, 88.9% were resistant to fusidic acid, 77.6% to tetracycline, and 21.1% to clindamycin. Clindamycin resistance increased from 0% (2003) to 31.2% (2009) among the CA-MRSA isolates (<it>P </it>= 0.011). Over the 7-year period, an increase in multidrug-resistant CA-MRSA isolates was observed (<it>P </it>= 0.004). One hundred and thirty-one (93.6%) of the 140 tested MRSA isolates were Panton-Valentine leukocidin-positive. Multilocus sequence typing of 72 CA-MRSA isolates revealed that they belonged to ST80 (n = 61), ST30 (n = 6), ST377 (n = 3), ST22 (n = 1), and ST152 (n = 1). Resistance to fusidic acid was observed in ST80 (58/61), ST30 (1/6), and ST22 (1/1) isolates.</p> <p>Conclusion</p> <p>In areas with high rate of infections caused by multidrug-resistant CA-MRSA isolates, predominantly belonging to the European ST80 clone, fusidic acid and clindamycin should be used cautiously as empiric therapy in patients with suspected severe staphylococcal infections.</p

Description and performance of track and primary-vertex reconstruction with the CMS tracker

A description is provided of the software algorithms developed for the CMS tracker both for reconstructing charged-particle trajectories in proton-proton interactions and for using the resulting tracks to estimate the positions of the LHC luminous region and individual primary-interaction vertices. Despite the very hostile environment at the LHC, the performance obtained with these algorithms is found to be excellent. For tbar t events under typical 2011 pileup conditions, the average track-reconstruction efficiency for promptly-produced charged particles with transverse momenta of pT > 0.9GeV is 94% for pseudorapidities of |η| < 0.9 and 85% for 0.9 < |η| < 2.5. The inefficiency is caused mainly by hadrons that undergo nuclear interactions in the tracker material. For isolated muons, the corresponding efficiencies are essentially 100%. For isolated muons of pT = 100GeV emitted at |η| < 1.4, the resolutions are approximately 2.8% in pT, and respectively, 10μm and 30μm in the transverse and longitudinal impact parameters. The position resolution achieved for reconstructed primary vertices that correspond to interesting pp collisions is 10–12μm in each of the three spatial dimensions. The tracking and vertexing software is fast and flexible, and easily adaptable to other functions, such as fast tracking for the trigger, or dedicated tracking for electrons that takes into account bremsstrahlung