Controllability and controller-observer design for a class of linear time-varying systems

“The final publication is available at Springer via http://dx.doi.org/10.1007/s10852-012-9212-6"In this paper a class of linear time-varying control systems is considered. The time variation consists of a scalar time-varying coefficient multiplying the state matrix of an otherwise time-invariant system. Under very weak assumptions of this coefficient, we show that the controllability can be assessed by an algebraic rank condition, Kalman canonical decomposition is possible, and we give a method for designing a linear state-feedback controller and Luenberger observer

Deep Nested Clustering Auto-Encoder for Anomaly-Based Network Intrusion Detection

Anomaly-based intrusion detection system(AIDS) plays an increasingly important role in detecting complex,multi-stage network attacks, especially zero-day attacks. Although there have been improvements both in practical applications and the research environment, there are still many unresolved accuracy-related concerns. The two fundamental limitations that contribute to these concerns are: i) the succinct, concise, latent representation learning of the normal network data, and ii) the optimization volume of normal regions in latent space. Recent studies have suggested many ways to learn the latent representation of normal network data in a semi-supervised manner to construct AIDS. However, these approaches are still affected by the above limitations,mainly due to the inability to process high data dimensionality or ineffectively explore the underlying architecture of the data. In this paper, we propose a novel Deep Nested Clustering Auto Encoder (DNCAE ) model to thoroughly overcome the aforementioned difficulties and improve the performance o fnetwork attack detection. The proposed model consists of two nested Deep Auto-Encoders(DAE) to learn the informative and tighter data representation space. In addition, the DNCAE model integrates the clustering technique into the latent layer of the outer DAE to learn the optimal arrangement of datapoints in the latent space. This harmonious combination allows us to effectively deal with the limitations outlined. The performance of the proposed model is evaluated using standard datasets including NSL-KDD,UNSW-NB15, and six scenarios of CIC-IDS2017(Tuesday, Wednesday, Thursday-Morning, Friday-Morning, Friday-Afternoon Port Scan,Friday-Afternoon DDoS).The experimental results strongly confirm that the proposed model clearly out performs th baselines and the existing methods for network anomaly detection. IndexTerms—Latent Representation, DeepAuto-Encoder, Clustering, AnomalyDetection, Intrusion Detection Syste

Rubber Impact on 3D Textile Composites

A low velocity impact study of aircraft tire rubber on 3D textile-reinforced composite plates was performed experimentally and numerically. In contrast to regular unidirectional composite laminates, no delaminations occur in such a 3D textile composite. Yarn decohesions, matrix cracks and yarn ruptures have been identified as the major damage mechanisms under impact load. An increase in the number of 3D warp yarns is proposed to improve the impact damage resistance. The characteristic of a rubber impact is the high amount of elastic energy stored in the impactor during impact, which was more than 90% of the initial kinetic energy. This large geometrical deformation of the rubber during impact leads to a less localised loading of the target structure and poses great challenges for the numerical modelling. A hyperelastic Mooney-Rivlin constitutive law was used in Abaqus/Explicit based on a step-by-step validation with static rubber compression tests and low velocity impact tests on aluminium plates. Simulation models of the textile weave were developed on the meso- and macro-scale. The final correlation between impact simulation results on 3D textile-reinforced composite plates and impact test data was promising, highlighting the potential of such numerical simulation tools

On the Equivalence between Neural Network and Support Vector Machine

Recent research shows that the dynamics of an infinitely wide neural network (NN) trained by gradient descent can be characterized by Neural Tangent Kernel (NTK) [27]. Under the squared loss, the infinite-width NN trained by gradient descent with an infinitely small learning rate is equivalent to kernel regression with NTK [4]. However, the equivalence is only known for ridge regression currently [6], while the equivalence between NN and other kernel machines (KMs), e.g. support vector machine (SVM), remains unknown. Therefore, in this work, we propose to establish the equivalence between NN and SVM, and specifically, the infinitely wide NN trained by soft margin loss and the standard soft margin SVM with NTK trained by subgradient descent. Our main theoretical results include establishing the equivalence between NN and a broad family of `2 regularized KMs with finite-width bounds, which cannot be handled by prior work, and showing that every finite-width NN trained by such regularized loss functions is approximately a KM. Furthermore, we demonstrate our theory can enable three practical applications, including (i) non-vacuous generalization bound of NN via the corresponding KM; (ii) nontrivial robustness certificate for the infinite-width NN (while existing robustness verification methods would provide vacuous bounds); (iii) intrinsically more robust infinite-width NNs than those from previous kernel regression

Birth data accessibility via primary care health records to classify health status in a multi-ethnic population of children: an observational study

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Mid-infrared frequency comb spanning an octave based on an Er fiber laser and difference-frequency generation

We describe a coherent mid-infrared continuum source with 700 cm-1 usable bandwidth, readily tuned within 600 - 2500 cm-1 (4 - 17 \mum) and thus covering much of the infrared "fingerprint" molecular vibration region. It is based on nonlinear frequency conversion in GaSe using a compact commercial 100-fs-pulsed Er fiber laser system providing two amplified near-infrared beams, one of them broadened by a nonlinear optical fiber. The resulting collimated mid-infrared continuum beam of 1 mW quasi-cw power represents a coherent infrared frequency comb with zero carrier-envelope phase, containing about 500,000 modes that are exact multiples of the pulse repetition rate of 40 MHz. The beam's diffraction-limited performance enables long-distance spectroscopic probing as well as maximal focusability for classical and ultraresolving near-field microscopies. Applications are foreseen also in studies of transient chemical phenomena even at ultrafast pump-probe scale, and in high-resolution gas spectroscopy for e.g. breath analysis.Comment: 8 pages, 2 figures revised version, added reference

Characteristics and genomic epidemiology of colistin-resistant Enterobacterales from farmers, swine, and hospitalized patients in Thailand, 2014-2017

BACKGROUND: Colistin is one of the last resort therapeutic options for treating carbapenemase-producing Enterobacterales, which are resistant to a broad range of beta-lactam antibiotics. However, the increased use of colistin in clinical and livestock farming settings in Thailand and China, has led to the inevitable emergence of colistin resistance. To better understand the rise of colistin-resistant strains in each of these settings, we characterized colistin-resistant Enterobacterales isolated from farmers, swine, and hospitalized patients in Thailand. METHODS: Enterobacterales were isolated from 149 stool samples or rectal swabs collected from farmers, pigs, and hospitalized patients in Thailand between November 2014-December 2017. Confirmed colistin-resistant isolates were sequenced. Genomic analyses included species identification, multilocus sequence typing, and detection of antimicrobial resistance determinants and plasmids. RESULTS: The overall colistin-resistant Enterobacterales colonization rate was 26.2% (n = 39/149). The plasmid-mediated colistin-resistance gene (mcr) was detected in all 25 Escherichia coli isolates and 9 of 14 (64.3%) Klebsiella spp. isolates. Five novel mcr allelic variants were also identified: mcr-2.3, mcr-3.21, mcr-3.22, mcr-3.23, and mcr-3.24, that were only detected in E. coli and Klebsiella spp. isolates from farmed pigs. CONCLUSION: Our data confirmed the presence of colistin-resistance genes in combination with extended spectrum beta-lactamase genes in bacterial isolates from farmers, swine, and patients in Thailand. Differences between the colistin-resistance mechanisms of Escherichia coli and Klebsiella pneumoniae in hospitalized patients were observed, as expected. Additionally, we identified mobile colistin-resistance mcr-1.1 genes from swine and patient isolates belonging to plasmids of the same incompatibility group. This supported the possibility that horizontal transmission of bacterial strains or plasmid-mediated colistin-resistance genes occurs between humans and swine

Contributions of lean mass and fat mass to bone mineral density: a study in postmenopausal women

<p>Abstract</p> <p>Background</p> <p>The relative contribution of lean and fat to the determination of bone mineral density (BMD) in postmenopausal women is a contentious issue. The present study was undertaken to test the hypothesis that lean mass is a better determinant of BMD than fat mass.</p> <p>Methods</p> <p>This cross-sectional study involved 210 postmenopausal women of Vietnamese background, aged between 50 and 85 years, who were randomly sampled from various districts in Ho Chi Minh City (Vietnam). Whole body scans, femoral neck, and lumbar spine BMD were measured by DXA (QDR 4500, Hologic Inc., Waltham, MA). Lean mass (LM) and fat mass (FM) were derived from the whole body scan. Furthermore, lean mass index (LMi) and fat mass index (FMi) were calculated as ratio of LM or FM to body height in metre squared (m²).</p> <p>Results</p> <p>In multiple linear regression analysis, both LM and FM were independent and significant predictors of BMD at the spine and femoral neck. Age, lean mass and fat mass collectively explained 33% variance of lumbar spine and 38% variance of femoral neck BMD. Replacing LM and FM by LMi and LMi did not alter the result. In both analyses, the influence of LM or LMi was greater than FM and FMi. Simulation analysis suggested that a study with 1000 individuals has a 78% chance of finding the significant effects of both LM and FM, and a 22% chance of finding LM alone significant, and zero chance of finding the effect of fat mass alone.</p> <p>Conclusions</p> <p>These data suggest that both lean mass and fat mass are important determinants of BMD. For a given body size -- measured either by lean mass or height --women with greater fat mass have greater BMD.</p

Generic Mechanism of Emergence of Amyloid Protofilaments from Disordered Oligomeric aggregates

The presence of oligomeric aggregates, which is often observed during the process of amyloid formation, has recently attracted much attention since it has been associated with neurodegenerative conditions such as Alzheimer's and Parkinson's diseases. We provide a description of a sequence-indepedent mechanism by which polypeptide chains aggregate by forming metastable oligomeric intermediate states prior to converting into fibrillar structures. Our results illustrate how the formation of ordered arrays of hydrogen bonds drives the formation of beta-sheets within the disordered oligomeric aggregates that form early under the effect of hydrophobic forces. Initially individual beta-sheets form with random orientations, which subsequently tend to align into protofilaments as their lengths increases. Our results suggest that amyloid aggregation represents an example of the Ostwald step rule of first order phase transitions by showing that ordered cross-beta structures emerge preferentially from disordered compact dynamical intermediate assemblies.Comment: 14 pages, 4 figure