Trace and detect adversarial attacks on CNNs using feature response maps

The existence of adversarial attacks on convolutional neural networks (CNN) questions the fitness of such models for serious applications. The attacks manipulate an input image such that misclassification is evoked while still looking normal to a human observer – they are thus not easily detectable. In a different context, backpropagated activations of CNN hidden layers – “feature responses” to a given input – have been helpful to visualize for a human “debugger” what the CNN “looks at” while computing its output. In this work, we propose a novel detection method for adversarial examples to prevent attacks. We do so by tracking adversarial perturbations in feature responses, allowing for automatic detection using average local spatial entropy. The method does not alter the original network architecture and is fully human-interpretable. Experiments confirm the validity of our approach for state-of-the-art attacks on large-scale models trained on ImageNet

Project PROMETHEUS: Design and Construction of a Radio Frequency Quadrupole at TAEK

The PROMETHEUS Project is ongoing for the design and development of a 4-vane radio frequency quadrupole (RFQ) together with its H+ ion source, a low energy beam transport (LEBT) line and diagnostics section. The main goal of the project is to achieve the acceleration of the low energy ions up to 1.5 MeV by an RFQ (352 MHz) shorter than 2 meter. A plasma ion source is being developed to produce a 20 keV, 1 mA H+ beam. Simulation results for ion source, transmission and beam dynamics are presented together with analytical studies performed with newly developed RFQ design code DEMIRCI. Simulation results shows that a beam transmission 99% could be achieved at 1.7 m downstream reaching an energy of 1.5 MeV. As the first phase an Aluminum RFQ prototype, the so-called cold model, will be built for low power RF characterization. In this contribution the status of the project, design considerations, simulation results, the various diagnostics techniques and RFQ manufacturing issues are discussed.Comment: 4 pages, 8 figures, Proceedings of the 2nd International Beam Instrumentation Conference 2013 (IBIC'13), 16-19 Sep 2013, WEPC02, p. 65

Structural Material Property Tailoring Using Deep Neural Networks

Advances in robotics, artificial intelligence, and machine learning are ushering in a new age of automation, as machines match or outperform human performance. Machine intelligence can enable businesses to improve performance by reducing errors, improving sensitivity, quality and speed, and in some cases achieving outcomes that go beyond current resource capabilities. Relevant applications include new product architecture design, rapid material characterization, and life-cycle management tied with a digital strategy that will enable efficient development of products from cradle to grave. In addition, there are also challenges to overcome that must be addressed through a major, sustained research effort that is based solidly on both inferential and computational principles applied to design tailoring of functionally optimized structures. Current applications of structural materials in the aerospace industry demand the highest quality control of material microstructure, especially for advanced rotational turbomachinery in aircraft engines in order to have the best tailored material property. In this paper, deep convolutional neural networks were developed to accurately predict processing-structure-property relations from materials microstructures images, surpassing current best practices and modeling efforts. The models automatically learn critical features, without the need for manual specification and/or subjective and expensive image analysis. Further, in combination with generative deep learning models, a framework is proposed to enable rapid material design space exploration and property identification and optimization. The implementation must take account of real-time decision cycles and the trade-offs between speed and accuracy

Factorization and Scaling in Hadronic Diffraction

In standard Regge theory with a pomeron intercept a(0)=1+\epsilon, the contribution of the tripe-pomeron amplitude to the t=0 differential cross section for single diffraction dissociation has the form d\sigma/dM^2(t=0) \sim s^{2\epsilon}/(M^2)^{1+\epsilon}. For \epsilon>0, this form, which is based on factorization, does not scale with energy. From an analysis of p-p and p-pbar data from fixed target to collider energies, we find that such scaling actually holds, signaling a breakdown of factorization. Phenomenologically, this result can be obtained from a scaling law in diffraction, which is embedded in the hypothesis of pomeron flux renormalization introduced to unitarize the triple pomeron amplitude.Comment: 39 pages, Latex, 16 figure

B→τμ(X)B\to\tau\mu (X) decays in SUSY models without R-parity

Being strictly forbidden in the standard model, experimental detection of the lepton flavor violating decays $B(\bar B)\to\tau^+\mu^-$ and $b(\bar b)\to X\tau^+\mu^-$ would constitute an unmistakable indication of new physics. We study these decays in supersymmetric models without R-parity and without lepton number. In order to derive order of magnitude predictions for the branching ratios, we assume a horizontal U(1) symmetry with horizontal charges chosen to explain the magnitude of fermion masses and quark mixing angles. We find that the branching ratios for decays with a $\tau\mu$ pair in the final state are not particularly suppressed with respect to the lepton flavor conserving channels. In general in these models {\rm B}[b\to\mu^+\mu^-(X)]\lsim {\rm B}[b(\bar b)\to\tau^+\mu^-(X)] \lsim {\rm B}[b\to\tau^+\tau^-(X)]. While in some cases the rates for final states $\tau^+\tau^-$ can be up to one order of magnitude larger than the lepton flavor violating channel, due to better efficiencies for muon detection and to the absence of standard model contributions, decays into $\tau\mu$ final states appear to be better suited to reveal this kind of new physics.Comment: 15 pages, LaTeX, 3 ps-figures (uses epsfig.sty) Minor typos corrected, one normalization factor added to Eq. (3.11). To be published on Phys. Rev.

Accuracy of coronary computed tomography angiography for bioresorbable scaffold luminal investigation: a comparison with optical coherence tomography

To establish the accuracy of coronary computed tomography angiography (CTA) for in-scaffold quantitative evaluation with opt

The interplay between learner beliefs and foreign language anxiety: insights from the Turkish EFL context

Foreign language learning in classroom settings has long been found to be associated with anxiety (Horwitz, 1986). Though it is known that sources of foreign language anxiety are varied (Young, 1991), whether anxiety is related to learners’ inherent beliefs about language learning is understudied. To this end, the present study examines the possible relationship between learner beliefs and language anxiety in the under-researched Turkish EFL context. A population of university-level Turkish EFL learners (n=153) completed the Beliefs about Language Learning Inventory (BALLI) and the Foreign Language Classroom Anxiety scale (FLCAS). The underlying constructs in both instruments were explored using an Exploratory Factor Analysis (EFA). A series of Pearson Product-Moment Correlation Coefficient (PPMCC) tests were performed on the factor scores from both instruments. The findings revealed that fear of ambiguity was positively linked to classroom performance anxiety and negative feelings towards English. In addition, the more confident learners feel themselves using English, the more desire they seem to have to interact with native speakers. Overall, positive beliefs about language learning may be helpful in reducing anxiety and boosting confidence in language learning. These findings not only enhance our understanding of the complex psychology of language learning but also have important implications for instructed EFL/ESL settings

Clinical utility of chromogranin A and octreotide in large cell neuro endocrine carcinoma of the uterine corpus

Primary neuroendocrine tumors of the female genital tract have been described in the cervix, ovaries and uterus. Large cell neuroendocrine carcinoma (LCNC) of the uterine corpus is the least common and appears to behave the most aggressively. We report a rare case of a large cell neuroendocrine tumor of the endometrium. These tumors are not well characterized, unlike neuroendocrine tumors of the uterine cervix. Consequently, the optimal management remains still unclear. The treatment of our case consisted of surgery, radiotherapy, chemotherapy, and octreotide. Despite the aggressive treatment, the patient died of disease progression 12 months after the initial diagnosis. We discuss the diagnosis, prognosis, and treatment options for LCNC of the genital tract, and potential future therapeutics

Identifying the drivers of circular food packaging: a comprehensive review for the current state of the food supply chain to be sustainable and circular

The resilience of food systems is jeopardized by using food packaging materials that have adverse impacts on the environment, food quality, food safety, shelf-life, food loss, and waste. Therefore, a transition into a more sustainable system can only be possible by adopting circular economy principles and practices that can facilitate the elimination of unsustainable packaging, irresponsible disposal behaviors, and waste management. This paper mainly focuses on circular packaging practices in the existing literature to reveal the drivers of circular food packaging applications. The study also displays the triple combinations of material-sector, material-CE, and sector-CE principles. As a methodology, a systematic literature review (SLR) has been used for this study. Furthermore, this study investigates the literature findings, such as the most frequently mentioned food sector and sub-sector, CE principles, materials adopted for food packaging, and so on. The primary contribution of this study to the body of literature is the synthesis and mapping of the literature as a whole from the perspectives of CE principles, both sector-based and national, and the materials used through circular food packaging, and the attempt to facilitate this transition into a more circular system by outlining the drivers of circular food packaging