Strengthening Model Checking Techniques with Inductive Invariants

This paper describes optimized techniques to efficiently compute and reap benefits from inductive invariants within SAT-based model checking. We address sequential circuit verification, and we consider both equivalences and implications between pairs of nodes in the logic networks. First, we present a very efficient dynamic procedure, based on equivalence classes and incremental SAT, specifically oriented to reduce the set of checked invariants. Then, we show how to effectively integrate the computation of inductive invariants within state-of-the-art SAT-based model checking procedures. Experiments (on more than 600 designs) show the robustness of our approach on verification instances on which stand-alone techniques fai

Circuit Based Quantification: Back to State Set Manipulation within Unbounded Model Checking

In this paper a non-canonical circuit-based state set representation is used to efficiently perform quantifier elimination. The novelty of this approach lies in adapting equivalence checking and logic synthesis techniques, to the goal of compacting circuit based state set representations resulting from existential quantification. The method can be efficiently combined with other verification approaches such as inductive and SAT-based pre-image verifications

Does brief physician counseling promote weight loss?

While physician counseling alone isn't more effective for weight loss than usual care (strength of recommendation [SOR]: A, larger randomized controlled trials [RCTs]), counseling (adults) as part of a multidisciplinary intervention may promote modest (2-3 kg) weight loss over 1 year (SOR: B, a single RCT)

A paperfluidic platform to detect Neisseria gonorrhoeae in clinical samples

Globally, the microbe Neisseria gonorrhoeae (NG) causes 106 million newly documented sexually transmitted infections each year. Once appropriately diagnosed, NG infections can be readily treated with antibiotics, but high-risk patients often do not return to the clinic for treatment if results are not provided at the point of care. A rapid, sensitive molecular diagnostic would help increase NG treatment and reduce the prevalence of this sexually transmitted disease. Here, we report on the design and development of a rapid, highly sensitive, paperfluidic device for point-of-care diagnosis of NG. The device integrates patient swab sample lysis, nucleic acid extraction, thermophilic helicase-dependent amplification (tHDA), an internal amplification control (NGIC), and visual lateral flow detection within an 80 min run time. Limits of NG detection for the NG/NGIC multiplex tHDA assay were determined within the device, and clinical performance was validated retroactively against qPCR-quantified patient samples in a proof-of-concept study. This paperfluidic diagnostic has a clinically relevant limit of detection of 500 NG cells per device with analytical sensitivity down to 10 NG cells per device. In triplicate testing of 40 total urethral and vaginal swab samples, the device had 95% overall sensitivity and 100% specificity, approaching current laboratory-based molecular NG diagnostics. This diagnostic platform could increase access to accurate NG diagnoses to those most in need.This work was funded by the National Institute of Health National Institute of Allergy and Infectious Diseases award number R01 AI113927 to Boston University and the NIH National Institute of Biomedical and Bioengineering award number U54 EB007958 to Johns Hopkins University. (R01 AI113927 - National Institute of Health National Institute of Allergy and Infectious Diseases; U54 EB007958 - NIH National Institute of Biomedical and Bioengineering)Accepted manuscrip

Model-Checking Speculation-Dependent Security Properties: Abstracting and Reducing Processor Models for Sound and Complete Verification

Spectre and Meltdown attacks in modern microprocessors represent a new class of attacks that have been difficult to deal with. They underline vulnerabilities in hardware design that have been going unnoticed for years. This shows the weakness of the state-of-the-art verification process and design practices. These attacks are OS-independent, and they do not exploit any software vulnerabilities. Moreover, they violate all security assumptions ensured by standard security procedures, (e.g., address space isolation), and, as a result, every security mechanism built upon these guarantees. These vulnerabilities allow the attacker to retrieve leaked data without accessing the secret directly. Indeed, they make use of covert channels, which are mechanisms of hidden communication that convey sensitive information without any visible information flow between the malicious party and the victim. The root cause of this type of side-channel attacks lies within the speculative and out-of-order execution of modern high-performance microarchitectures. Since modern processors are hard to verify with standard formal verification techniques, we present a methodology that shows how to transform a realistic model of a speculative and out-of-order processor into an abstract one. Following related formal verification approaches, we simplify the model under consideration by abstraction and refinement steps. We also present an approach to formally verify the abstract model using a standard model checker. The theoretical flow, reliant on established formal verification results, is introduced and a sketch of proof is provided for soundness and correctness. Finally, we demonstrate the feasibility of our approach, by applying it on a pipelined DLX RISC-inspired processor architecture. We show preliminary experimental results to support our claim, performing Bounded Model-Checking with a state-of-the-art model checker

ErbB2 Receptor in Breast Cancer: Implications in Cancer Cell Migration, Invasion and Resistance to Targeted Therapy

Overexpression of ErbB2 is found in several types of human carcinomas. In breast tumors, ErbB2 overexpression is detected in up to 20% of patients. Breast cancers in with amplification of ErbB2 are characterized by rapid tumor growth, lower survival rate and increased disease progression. The molecular mechanisms underlying the oncogenic action of ErbB2 involve a complex signaling network that tightly regulates malignant cell migration and invasion and hence metastatic potential. Recent efforts have been made to identify gene expression signatures of ErbB2-positive invasive breast cancers that may represent important mediators of ErbB2-induced tumorigenesis and metastatic progression. In this chapter, we will discuss the canonical ErbB2 signaling pathways responsible for tumor growth and dissemination along with newly identified mediators such as adaptor protein p130Cas and miRNAs. From a therapeutic point of view, the treatment with anti-ErbB2 monoclonal antibody trastuzumab has greatly improved the outcomes of patients with ErbB2 aggressive cancer. Nevertheless, de novo and acquired resistance to trastuzumab therapy still represent a major clinical problem. In the second part of the chapter, we will provide an overview of the mechanisms so far implicated in the onset of resistance to targeted therapy and of the new strategies to overcome resistance

Comparative analysis of models and performance indicators for optimal service facility location

This study investigates the optimal process for locating generic service facilities by applying and comparing several well-known basic models from the literature. At a strategic level, we emphasize that selecting the right location model to use could result in a problematic and possibly misleading task if not supported by appropriate quantitative analysis. For this reason, we propose a general methodological framework to analyze and compare the solutions provided by several models to obtain a comprehensive evaluation of the location decisions from several different perspectives. Therefore, a battery of key performance indicators (KPIs) has been developed and calculated for the different models’ solutions. Additional insights into the decision process have been obtained through a comparative analysis. The indicators involve topological, coverage, equity, robustness, dispersion, and accessibility aspects. Moreover, a specific part of the analysis is devoted to progressive location interventions over time and identifying core location decisions. Results on randomly generated instances, which simulate areas characterized by realistic geographical or demographic features, are reported to analyze the models’ behavior in different settings and demonstrate the methodology’s general applicability. Our experimental campaign shows that the p-median model behaves very well against the proposed KPIs. In contrast, the maximal covering problem and some proposed back-up coverage models return very robust solutions when the location plan is implemented through several progressive interventions over time

A General-Purpose Graphics Processing Unit (GPGPU)-Accelerated Robotic Controller Using a Low Power Mobile Platform

Robotic controllers have to execute various complex independent tasks repeatedly. Massive processing power is required by the motion controllers to compute the solution of these computationally intensive algorithms. General-purpose graphics processing unit (GPGPU)-enabled mobile phones can be leveraged for acceleration of these motion controllers. Embedded GPUs can replace several dedicated computing boards by a single powerful and less power-consuming GPU. In this paper, the inverse kinematic algorithm based numeric controllers is proposed and realized using the GPGPU of a handheld mobile device. This work is the extension of a desktop GPU-accelerated robotic controller presented at DAS’16 where the comparative analysis of different sequential and concurrent controllers is discussed. First of all, the inverse kinematic algorithm is sequentially realized using Arduino-Due microcontroller and the field-programmable gate array (FPGA) is used for its parallel implementation. Execution speeds of these controllers are compared with two different GPGPU architectures (Nvidia Quadro K2200 and Nvidia Shield K1 Tablet), programmed with Compute Unified Device Architecture (CUDA) computing language. Experimental data shows that the proposed mobile platform-based scheme outperform s the FPGA by 5 and boasts a 100 speedup over the Arduino-based sequential implementation

Dbl oncogene expression in MCF-10 A epithelial cells disrupts mammary acinar architecture, induces EMT and angiogenic factor secretion.

The proteins of the Dbl family are guanine nucleotide exchange factors (GEFs) of Rho GTPases and are known to be involved in cell growth regulation. Alterations of the normal function of these proteins lead to pathological processes such as developmental disorders, neoplastic transformation, and tumor metastasis. We have previously demonstrated that expression of Dbl oncogene in lens epithelial cells modulates genes encoding proteins involved in epithelial-mesenchymal-transition (EMT) and induces angiogenesis in the lens. Our present study was undertaken to investigate the role of Dbl oncogene in epithelial cells transformation, providing new insights into carcinoma progression. To assess how Dbl oncogene can modulate EMT, cell migration, morphogenesis, and expression of pro-apoptotic and angiogenic factors we utilized bi- and three-dimensional cultures of MCF-10░A cells. We show that upon Dbl expression MCF-10░A cells undergo EMT. In addition, we found that Dbl overexpression sustain

GPGPU Accelerated Deep Object Classification on a Heterogeneous Mobile Platform

Deep convolutional neural networks achieve state-of-the-art performance in image classification. The computational and memory requirements of such networks are however huge, and that is an issue on embedded devices due to their constraints. Most of this complexity derives from the convolutional layers and in particular from the matrix multiplications they entail. This paper proposes a complete approach to image classification providing common layers used in neural networks. Namely, the proposed approach relies on a heterogeneous CPU-GPU scheme for performing convolutions in the transform domain. The Compute Unified Device Architecture(CUDA)-based implementation of the proposed approach is evaluated over three different image classification networks on a Tegra K1 CPU-GPU mobile processor. Experiments show that the presented heterogeneous scheme boasts a 50 speedup over the CPU-only reference and outperforms a GPU-based reference by 2, while slashing the power consumption by nearly 30%