On the solution of maximization problems of optimal design by geometric programming

Summary A geometric programming method, recently developed for the constrained maximization of posynomials, is presented and illustrated by an application to the optimal design of a torsion bar spring.Research supported by the National Science Foundation, Project GP 13546.     

Key Recovery Attacks on Iterated Even–Mansour Encryption Schemes

Iterated Even–Mansour (EM) encryption schemes (also named “key-alternating ciphers”) were extensively studied in recent years as an abstraction of commonly used block ciphers. A large amount of previous works on iterated EM concentrated on security in an information-theoretic model. A central question studied in these papers is: What is the minimal number of rounds for which the resulting cipher is indistinguishable from an ideal cipher? In this paper, we study a similar question in the computational model: What is the minimal number of rounds, assuring that no attack can recover the secret key faster than trivial attacks (such as exhaustive search)? We study this question for the two natural key scheduling variants that were considered in most previous papers: the identical subkeys variant and the independent subkeys variant. In the identical subkeys variant, we improve the best known attack by an additional round and show that \(r=3\) rounds are insufficient for assuring security, by devising a key recovery attack whose running time is about \(n/\log (n)\) times faster than exhaustive search for an \(n\)-bit key. In the independent subkeys variant, we also extend the known results by one round and show that for \(r=2\), there exists a key recovery attack whose running time is faster than the benchmark meet-in-the-middle attack. Despite their generic nature, we show that the attacks can be applied to improve the best known attacks on several concrete ciphers, including the full \({\hbox {AES}^{2}}\) (proposed at Eurocrypt 2012) and reduced-round LED-128 (proposed at CHES 2012).     

Medical image categorization and retrieval for PACS using the GMM-KL framework.

This paper presents an image representation and matching framework for image categorization in medical image archives. Categorization enables one to determine automatically, based on the image content, the examined body region and imaging modality. It is a basic step in content-based image retrieval (CBIR) systems, the goal of which is to augment text-based search with visual information analysis. CBIR systems are currently being integrated with picture archiving and communication systems for increasing the overall search capabilities and tools available to radiologists. The proposed methodology is comprised of a continuous and probabilistic image representation scheme using Gaussian mixture modeling (GMM) along with information-theoretic image matching via the Kullback-Leibler (KL) measure. The GMM-KL framework is used for matching and categorizing X-ray images by body regions. A multidimensional feature space is used to represent the image input, including intensity, texture, and spatial information. Unsupervised clustering via the GMM is used to extract coherent regions in feature space that are then used in the matching process. A dominant characteristic of the radiological images is their poor contrast and large intensity variations. This presents a challenge to matching among the images, and is handled via an illumination-invariant representation. The GMM-KL framework is evaluated for image categorization and image retrieval on a dataset of 1500 radiological images. A classification rate of 97.5% was achieved. The classification results compare favorably with reported global and local representation schemes. Precision versus recall curves indicate a strong retrieval result as compared with other state-of-the-art retrieval techniques. Finally, category models are learned and results are presented for comparing images to learned category models.     

Zero-knowledge proofs of identity

In this paper we extend the notion of interactive proofs of assertions to interactive proofs of knowledge. This leads to the definition of unrestricted input zero-knowledge proofs of knowledge in which the prover demonstrates possession of knowledge without revealing any computational information whatsoever (not even the one bit revealed in zero-knowledge proofs of assertions). We show the relevance of these notions to identification schemes, in which parties prove their identity by demonstrating their knowledge rather than by proving the validity of assertions. We describe a novel scheme which is provably secure if factoring is difficult and whose practical implementations are about two orders of magnitude faster than RSA-based identification schemes. The advantages of thinking in terms of proofs of knowledge rather than proofs of assertions are demonstrated in two efficient variants of the scheme: unrestricted input zero-knowledge proofs of knowledge are used in the construction of a scheme which needs no directory; a version of the scheme based on parallel interactive proofs (which are not known to be zero knowledge) is proved secure by observing that the identification protocols are proofs of knowledge.     

A validation study of the Keyboard Personal Computer Style instrument (K-PeCS) for use with children

This study examines a potential instrument for measurement of typing postures of children. This paper describes inter-rater, test-retest reliability and concurrent validity of the Keyboard Personal Computer Style instrument (K-PeCS), an observational measurement of postures and movements during keyboarding, for use with children. Two trained raters independently rated videos of 24 children (aged 7-10 years). Six children returned one week later for identifying test-retest reliability. Concurrent validity was assessed by comparing ratings obtained using the K-PECS to scores from a 3D motion analysis system. Inter-rater reliability was moderate to high for 12 out of 16 items (Kappa: 0.46 to 1.00; correlation coefficients: 0.77-0.95) and test-retest reliability varied across items (Kappa: 0.25 to 0.67; correlation coefficients: r = 0.20 to r = 0.95). Concurrent validity compared favourably across arm pathlength, wrist extension and ulnar deviation. In light of the limitations of other tools the K-PeCS offers a fairly affordable, reliable and valid instrument to address the gap for measurement of typing styles of children, despite the shortcomings of some items. However further research is required to refine the instrument for use in evaluating typing among children.     

On the impact of serializing contention management on STM performance

Transactional memory (TM) is an emerging concurrent programming abstraction. Numerous software-based transactional memory (STM) implementations have been developed in recent years. STM implementations must guarantee transaction atomicity and isolation. In order to ensure progress, an STM implementation must resolve transaction collisions by consulting a contention manager (CM).     

Effects of THC on driving performance, physiological state and subjective feelings relative to alcohol

BACKGROUND: The effects of marijuana or THC on driving has been tested in several studies, but usually not in conjunction with physiological and subjective responses and not in comparison to alcohol effects on all three types of measures. OBJECTIVE: To assess the effects of two dosages of THC relative to alcohol on driving performance, physiological strain, and subjective feelings. METHOD: We tested the subjective feelings and driving abilities after placebo, smoking two dosages of THC (13 mg and 17 mg), drinking (0.05% BAC) and 24 h after smoking the high dose THC cigarette, while monitoring physiological activity of the drugs by heart rate. Fourteen healthy students, all recreational marijuana users, participated in the study. RESULTS: Both levels of THC cigarettes significantly affected the subjects in a dose-dependent manner. The moderate dose of alcohol and the low THC dose were equally detrimental to some of the driving abilities, with some differences between the two drugs. THC primarily caused elevation in physical effort and physical discomfort during the drive while alcohol tended to affect sleepiness level. After THC administration, subjects drove significantly slower than in the control condition, while after alcohol ingestion, subjects drove significantly faster than in the control condition. No THC effects were observed after 24 h on any of the measures.     

A NEW VERSION OF STRUCTURAL PERSISTENCE IN PREDICTION

Abstract. A data analysis estimation method called structural persistence is presented in this paper. Prediction applications of the method to time series with trend and seasonal components are discussed. The basic underlying assumption is that the structure of a given series does not change in the forecasting range. Therefore, when forecasts are made, the values which measure the structure (e.g. the trend shape) of the original and of the forecast-extended time series should be the same. We propose relaxed 'model-free' use of the principle which nevertheless provides an explicit prediction formula for the one-stepahead prediction P _{n +1}. Missing data can also be estimated using this method. The procedure is applied to some previously published time series data and the prediction results are compared with those obtained using the Box-Jenkins approach.     

Carbon and Nitrogen Based Nanosheets as Fluorescent Probes with Tunable Emission

2D carbon and nitrogen based semiconductors (CN) have attracted widespread attention for their possible use as low‐cost and environmentally friendly materials for various applications. However, their limited solution‐dispersibility and the difficulty in preparing exfoliated sheets with tunable photophysical properties restrain their exploitation in imaging‐related applications. Here, the synthesis of carbon and nitrogen organic scaffolds with highly tunable optical properties, excellent dispersion in water and DMSO, and good bioimaging properties is reported. Tailored photophysical and chemical properties are acquired by the synthesis of new starting monomers containing different substituent chemical groups with varying electronic properties. Upon monomer condensation at moderate temperature, 350 °C, the starting chemical groups are fully preserved in the final CN. The low condensation temperature and the effective molecular‐level modification of the CN scaffold lead to well‐dispersed photoluminescent CN thin sheets with a wide range of emission wavelengths. The good bioimaging properties and the tunable fluorescence properties are exemplified by in situ visualization of giant unilamellar vesicles in a buffered aqueous solution as a model system. This approach opens the possibility for the design of tailor‐made CN materials with tunable photophysical and chemical properties toward their exploitation in various fields, such as photocatalysis, bioimaging, and sensing.