The oligotrophic southeastern Mediterranean Sea (SEMS) is frequently exposed to desert-dust deposition which supplies nutrients, trace metals and a wide array of viable airbornemicroorganisms. In this study, we experimentally examined the impact of aerosol addition, collected during an intense dust storm event in early September 2015, on the biomass and activity of pico-phytoplankton and hetero...

Preparing for robot soccer competitions by empirically evaluating different possible game strategies has been rather limited in leagues using real robots. Such limitation comes from factors related to the difficulty of extensively experimenting with games with real robots, such as their inevitable wear and tear and their usual limited number. RoboCup real robot teams have therefore developed si...

Semi-supervised learning (SSL) is the process of training decision functions using small amounts of labeled and relatively large amounts of unlabeled data. In many applications, annotating training data is time-consuming and error prone. Speech recognition is the typical example, which requires large amounts of meticulously annotated speech data (Evermann et al., 2005) to produce an accurate sy...

Feeding nymphs of the silverleaf whitefly, Bemisia argentifolii (formerly known as Bemisia tabaci Gennadius, B strain), cause a physiological disorder known as squash silverleaf (SSL) in summer squash (Cucurbita pepo L.). This disorder is characterized by a progressive silvering on the upper leaf surface caused by the development of large air spaces between the palisade mesophyll cells and the ...

A high-level fault modeling and testing philosophy is proposed which is aimed at ensuring,full detection of lowlevel, physical faults, as well as the industry-standard single stuck-line (SSL) faults. A set of independent functional faults and the corresponding ,functional tests are derived (induced) from the circuit under test; ofparticulur interest are SSL-induced functional faults or SIFs. We...

Semi-supervised learning (SSL) addresses the problem of training a classifier using a small number of labeled examples and many unlabeled examples. Most previous work on SSL focused on how availability of unlabeled data can improve the accuracy of the learned classifiers. In this work we study how unlabeled data can be beneficial for constructing faster classifiers. We propose an SSL algorithmi...

Most SSL/TLS-based e-commerce applications employ conventional mechanisms for user authentication. These mechanisms—if decoupled from SSL/TLS session establishment—are vulnerable to manin-the-middle (MITM) attacks. In this paper, we elaborate on the feasibility of MITM attacks, survey countermeasures, introduce the notion of SSL/TLS session-aware user authentication (TLS-SA), and present a proo...

Priority resolver is ubiquitous in digital logic circuits as it is frequently used to optimize large number of inputs into a minimum number of outputs. This particular circuit exclusively demonstrate manifestation of outputs, controlled by the priority of logic interventions of the inputs. CMOS Logic made circuits with higher number of inputs experiences an excessive power dissipation. On the o...

As a countermeasure against the famous Bleichenbacher attack on RSA based ciphersuites, all TLS RFCs starting from RFC 2246 (TLS 1.0) propose “to treat incorrectly formatted messages in a manner indistinguishable from correctly formatted RSA blocks”. In this paper we show that this objective has not been achieved yet (cf. Table 1): We present four new Bleichenbacher side channels, and three suc...

We propose a novel method for semi-supervised learning (SSL) based on data-driven distributionally robust optimization (DRO) using optimal transport metrics. Our proposed method enhances generalization error by using the unlabeled data to restrict the support of the worst case distribution in our DRO formulation. We enable the implementation of our DRO formulation by proposing a stochastic grad...