Sensitive-by-distance: quasi-health data in the algorithmic era

This article offers a new perspective on the boundaries between health and non-health data in the age of ‘Quantified-Self’ apps: the ‘data-sensitiveness-by-computational-distance’ approach-or, more simply, the ‘sensitive-by-distance’ approach. This approach takes into account two variables: the intrinsic sensitiveness (a static variable) of personal data and the computational distance (a dynamic variable) between some kinds of personal data and pure health (or sensitive) data, which depends upon computational capacity. From an objective perspective, computational capacity depends on the level of development of data retrieval technologies at a certain moment, the availability of ‘accessory data’, and the applicable legal restraints on processing data. From a subjective perspective, computational capacity depends on the specific data mining efforts (or the ability to invest in them) taken by a given data controller: economic resources, human resources, and the use of accessory data. A direct consequence of the expansion of augmented humanity in collecting and inferring personal data is the increasing loss of health data processing ‘legibility’ for data subjects. In order to address this issue, we propose exploiting the existing legal tools in the General Data Protection Regulation to empower data subjects (the right to data access, the right to know the logic involved in automated decision-making, data portability, etc.).     

A study of co-occurrence based local features for camera model identification

Camera model identification has great relevance for many forensic applications, and is receiving growing attention in the literature. Virtually all techniques rely on the traces left in the image by the long sequence of in-camera processes which are specific of each model. They differ in the prior assumptions, if any, and in how such evidence is gathered in expressive features. In this work we study a class of blind features, based on the analysis of the image residuals of all color bands. They are extracted locally, based on co-occurrence matrices of selected neighbors, and then used to train a classifier. A number of experiments are carried out on the well-known Dresden Image Database. Besides the full-knowledge case, where all models of interest are known in advance, other scenarios with more limited knowledge and partially corrupted images are also investigated. Experimental results show these features to provide a state-of-the-art performance.     

Algorithmic and architectural design for real-time and power-efficient Retinex image/video processing

This paper presents novel algorithmic and architectural solutions for real-time and power-efficient enhancement of images and video sequences. A programmable class of Retinex-like filters, based on the separation of the illumination and reflectance components, is proposed. The dynamic range of the input image is controlled by applying a suitable non-linear function to the illumination, while the details are enhanced by processing the reflectance. An innovative spatially recursive rational filter is used to estimate the illumination. Moreover, to improve the visual quality results of two-branch Retinex operators when applied to videos, a novel three-branch technique is proposed which exploits both spatial and temporal filtering. Real-time implementation is obtained by designing an Application Specific Instruction-set Processor (ASIP). Optimizations are addressed at algorithmic and architectural levels. The former involves arithmetic accuracy definition and linearization of non-linear operators; the latter includes customized instruction set, dedicated memory structure, adapted pipeline, bypasses, custom address generator, and special looping structures. The ASIP is synthesized in standard-cells CMOS technology and its performances are compared to known Digital signal processor (DSP) implementations of real-time Retinex filters. As a result of the comparison, the proposed algorithmic/architectural design outperforms state-of-art Retinex-like operators achieving the best trade-off between power consumption, flexibility, and visual quality.

Diagnostic and therapeutic update on primary duodenogastric reflux

The authors, after some remarks on the pathophysiology of primary alkaline gastritis, analyze the main methods, available at present, for diagnosis; they particularly consider the importance of endoscopy and histology, gastroesophageal 24-hours pH-metry and 99mTc HIDA scintigraphy. Successively, they present the criteria for the identification of patients who will predictably benefit from surgical treatment, and examine advantages and limits of the two main corrective surgical techniques for the duodenogastric reflux in patients not previously operated on the gastrointestinal tract: duodenal extramucosal myotomy according to Mattioli, relatively easy to perform, and the "duodenal switch" according to De Meester, technically more complex, but pathophysiologically more acceptable, completely eliminating the duodenogastric reflux.     

Male response to natural sex pheromone ofMigdolus fryanus westwood (Coleoptera: Cerambycidae) females as affected by daily climatic factors

Males of the sugarcane borer,Migdolus fryanus (Coleoptera: Cerambycidae), are attracted to females by means of a sex pheromone. Mating usually occurs during a few days from October to March under field conditions in São Paulo State, Brazil. This work reports on mating of this species as affected by daily climatic factors, during a single nuptial flight. Maximum male capture by the natural sex pheromone occurred from 1000 to 1100 AM at air and soil temperatures of 30.0°C and relative humidity of 57.0%. As these temperatures increased, females burrowed into the soil, as they are more sensitive to heat than males. Thus, it was concluded that sex pheromone-mediated mating in this cerambycid is directly affected by temperatures of air and soil.     

Improved LP-based algorithms for the closest string problem

In a recent paper by Liu et al. [Exact algorithm and heuristic for the closest string problem, Computers & Operations Research 2011;38:1513-20], a polynomial time heuristic procedure is proposed for the closest string problem (CSP). Such heuristic called LDDA_LSS is a combination of a previously published approximation algorithm and local search strategies. This paper points out that an instant algorithm deriving a feasible solution directly from the continuous relaxation solution of a standard ILP formulation of CSP already strongly outperforms LDDA_LSS both in terms of solution quality and computing time. Two core based procedures are then proposed that further improve the results of the instant algorithm. Based on these results, we conclude that such LP-based approaches for their efficiency and simplicity should be used as a benchmark for future heuristics on CSP.     

Multistage particle windows for fast and accurate object detection

The common paradigm employed for object detection is the sliding window (SW) search. This approach generates grid-distributed patches, at all possible positions and sizes, which are evaluated by a binary classifier: The tradeoff between computational burden and detection accuracy is the real critical point of sliding windows; several methods have been proposed to speed up the search such as adding complementary features. We propose a paradigm that differs from any previous approach since it casts object detection into a statistical-based search using a Monte Carlo sampling for estimating the likelihood density function with Gaussian kernels. The estimation relies on a multistage strategy where the proposal distribution is progressively refined by taking into account the feedback of the classifiers. The method can be easily plugged into a Bayesian-recursive framework to exploit the temporal coherency of the target objects in videos. Several tests on pedestrian and face detection, both on images and videos, with different types of classifiers (cascade of boosted classifiers, soft cascades, and SVM) and features (covariance matrices, Haar-like features, integral channel features, and histogram of oriented gradients) demonstrate that the proposed method provides higher detection rates and accuracy as well as a lower computational burden w.r.t. sliding window detection.     

Pairs of Complementary Unary Languages with “Balanced” Nondeterministic Automata

For each sufficiently large n, there exists a unary regular language L such that both L and its complement L ^c are accepted by unambiguous nondeterministic automata with at most n states, while the smallest deterministic automata for these two languages still require a superpolynomial number of states, at least \(e^{\Omega(\sqrt[3]{n\cdot\ln^{2}n})}\). Actually, L and L ^c are “balanced” not only in the number of states but, moreover, they are accepted by nondeterministic machines sharing the same transition graph, differing only in the distribution of their final states. As a consequence, the gap between the sizes of unary unambiguous self-verifying automata and deterministic automata is also superpolynomial.