Reversing Stealthy Dopant-Level Circuits

A successful detection of the stealthy dopant-level circuit (trojan), proposed by Becker et al. at CHES 2013, is reported. Contrary to an assumption made by Becker et al., dopant types in active region are visible with either scanning electron microscopy (SEM) or focused ion beam (FIB) imaging. The successful measurement is explained by an LSI failure analysis technique called the passive voltage contrast. The experiments are conducted by measuring a dedicated chip. The chip uses the diffusion programmable device: an anti-reverse-engineering technique by the same principle as the stealthy dopant-level trojan. The chip is delayered down to the contact layer, and images are taken with (1) an optical microscope, (2) SEM, and (3) FIB. As a result, the four possible dopant-well combinations, namely (i) p+/n-well, (ii) p+/p-well, (iii) n+/n-well and (iv) n+/p-well are distinguishable in the SEM images. Partial but sufficient detection is also achieved with FIB. Although the stealthy dopant-level circuits are visible, however, they potentially make a detection harder. That is because the contact layer should be measured. We show that imaging the contact layer is at most 16-times expensive than that of a metal layer in terms of the number of image

Antibiotic-dependent instability of homeostatic plasticity for growth and environmental load

Reducing antibiotic usage in livestock animals has become an urgent issue worldwide to prevent antimicrobial resistance. Here, abuse of chlortetracycline (CTC), a versatile antibacterial agent, on the performance, blood components, fecal microbiota, and organic acid concentration in calves was investigated. Japanese Black calves were fed milk replacer containing CTC at 10 g/kg (CON) or 0 g/kg (EXP). Growth performance was not affected by CTC administration. However, CTC administration altered the correlation between fecal organic acids and bacterial genera. Machine learning methods such as association analysis, linear discriminant analysis, and energy landscape analysis revealed that CTC administration affected according to certain rules the population of various types of fecal bacteria. It is particularly interesting that the population of several methane-producing bacteria was high in the CON, and that of Lachnospiraceae, a butyrate-producing bacteria, was high in the EXP at 60 d of age. Furthermore, statistical causal inference based on machine learning data estimated that CTC treatment affects the entire intestinal environment, inhibiting butyrate production for growth and biological defense, which may be attributed to methanogens in feces. Thus, these observations highlight the multiple harmful impacts of antibiotics on intestinal health and the potential production of greenhouse gas in the calves

A prospective study of the association between drainage volume within 24 hours after thoracoscopic lobectomy and postoperative morbidity

ObjectivesWe prospectively analyzed the association between drainage volume and development of complications to clarify the safety of early removal of chest tube after thoracoscopic lobectomy.MethodsBetween November 2001 and October 2007, 136 patients with suspected or histologically documented lung cancer were enrolled. Patients with no air leak and increased drainage underwent removal of the chest tube on the day after thoracoscopic lobectomy independent of the drainage volume. Patients were classified into three groups as tertiles according to the drainage volume. Demographic and perioperative variables were compared among the three groups. Age–sex adjusted odds ratios of the clinical variables associated with development of complications were estimated. In addition, the odds ratios of the drainage volume for development of complications were estimated after adjusting for potentially important factors.ResultsOne hundred patients underwent early removal of the chest tube. Almost all demographic and perioperative variables showed no differences among the three groups (0–289 mL, n = 33; 290–399 mL, n = 33; and ≥400 mL, n = 34). Tumors in a lower lobe, preoperative stage II or higher, 5 or more anatomic segments resected, and advanced disease were all factors that were associated with higher odds ratios for complications. The drainage volume was not associated with an increased morbidity, even after adjusting for these factors.ConclusionsEarly removal of chest tube on the day after thoracoscopic lobectomy, independently of the drainage volume, appears to be safe in well-selected patients

A Fast Algorithm for Discovering Optimal String Patterns in Large Text Databases

. We consider a data mining problem in a large collection of unstructured texts based on association rules over subwords of texts. A two-words association pattern is an expression such as (TATA, 30, AGGAGGT) ) C that expresses a rule that if a text contains a subword TATA followed by another subword AGGAGGT with distance no more than 30 letters then a property C will hold with high probability. The optimized confidence pattern problem is to compute frequent patterns (ff; k; fi) that optimize the confidence with respect to a given collection of texts. Although this problem is solved in polynomial time by a straightforward algorithm that enumerates all the possible patterns in time O(n 5 ), we focus on the development of more efficient algorithms that can be applied to large text databases. We present an algorithm that solves the optimized confidence pattern problem in time O(maxfk; mgn 2 ) and space O(kn), where m and n are the number and the total length of classification example..

An Efficient Algorithm for Text Data Mining with Optimal String Patterns

We study a data mining problem in a large collection of unstructured texts based on association rules over subwords of texts. A two-word association rule is an expression such as (TATA, 30, AGGAGGT) ⇒ C that expresses a rule that if a text contains a subword α followed by another subword β with distance no more than k then a condition C will holds with a probability. We present an efficient algorithm for computing frequent patterns that optimizes the confidence with respect to a given collection of texts. The algorithm runs in time O(mn² log² n) and in space O(kmn log n), where m and n are the number and the total length of classification examples, respectively, and k is a small constant around 30 ∼ 50. The algorithm employs the suffix tree data structure from string pattern matching and the orthogonal range query techniques from computational geometry. We also give a faster version that runs in time O(mn²) and in space O(kmn)

Protein Motif Discovery from Positive Examples by Minimal Multiple Generalization over Regular Patterns

Recently, several attempts have been made at applying machine learning method to protein motif discovery, but most of these methods require negative examples in addition to positive examples. This paper proposes an efficient method for learning protein motif from positive examples. A regular pattern is a string consisting of constant symbols and mutually distinct variables, and represents the set of the constant strings obtained by substituting nonempty constant strings for variables. Regular patterns and their languages are called extended if empty substitutions are allowed. Our learning algorithm, called k-minimal multiple generalization (k-mmg), finds a minimally general collection of at most k regular patterns that explains all the positive examples. We have implemented this algorithm for subclasses for regular patterns and extended regular patterns where the number of variables are bounded by a small constant, and run experiments on protein data taken from GenBank and PIR database..