Miracle-dz/UniFAR-FTUs huggingface dataset:
The secrets of profiling for side-channel analysis: feature selection matters

Profiled side-channel attacks feature a number of steps one needs to take. One significant step, importance of which is sometimes ignored, is selection of the points of interest (features) within side-channel measurement traces. A large majority of the related works on profiling in side-channel analysis starts with an assumption that the features are somehow selected and distinct attack methods are compared in order to find the best approach for the key recovery. Contrary to this, in this work we concentrate on the feature selection step and show that if an optimal selection is done, most of the attack techniques perform well i.e., result in the key recovery. Consequently, in this paper, we investigate in details how more advanced feature selection techniques stemming from the machine learning domain can be used to improve the attack efficiency. To this end, we look into relevant aspects and we provide a systematic evaluation of machine learning methods of interest. Our results show that the so-called Hybrid feature selection methods perform with the best classification accuracy over a wide range of test scenarios and number of features selected. The experiments are performed on several real-world data sets containing software and hardware implementations of AES, and even including the random delay countermeasure. We emphasize the L1 regularization technique, which consistently performed well and in many cases resulted in significantly higher accuracy than the second best technique. Further on, we consider even Principal Component Analysis (PCA) as a typical dimensionality reduction method and show that feature selection combined with the ML classification remains the method of choice (when confronted with PCA).
2015 Ieee International Conference on Big Data (big Data) Core Decomposition in Large Temporal Graphs

—Core decomposition has been applied widely in the visualization and analysis of massive networks. However, existing studies of core decomposition were only limited to non-temporal graphs, while many real-world graphs can be naturally modeled as temporal graphs (e.g., the interaction between users at different time in online social networks, the phone call or messaging records between friends over time, etc.). In this paper, we define the problem of core decomposition in a temporal graph, propose efficient distributed algorithms to compute the cores in massive temporal graphs, and discuss how the technique can be used in temporal graph analysis.
Pipe Phantoms With Applications in Molecular Imaging and System Characterization

Pipe (vessel) phantoms mimicking human tissue and blood flow are widely used for cardiovascular related research in medical ultrasound. Pipe phantom studies require the development of materials and liquids that match the acoustic properties of soft tissue, blood vessel wall, and blood. Over recent years, pipe phantoms have been developed to mimic the molecular properties of the simulated blood vessels. In this paper, the design, construction, and functionalization of pipe phantoms are introduced and validated for applications in molecular imaging and ultrasound imaging system characterization. There are three major types of pipe phantoms introduced: 1) a gelatin-based pipe phantom; 2) a polydimethylsiloxane-based pipe phantom; and 3) the “Edinburgh pipe phantom. ” These phantoms may be used in the validation and assessment of the dynamics of microbubble-based contrast agents and, in the case of a small diameter tube phantom, for assessing imaging system spatial resolution/contrast performance. The materials and procedures required to address each of the phantoms are described.
Degeneration in VAE: in the Light of Fisher Information Loss

Variational Autoencoder (VAE) is one of the most popular generative models, and enormous advances have been explored in recent years. Due to the increasing complexity of the raw data and the model architecture, deep networks are needed in VAE models while few works discuss their impacts. According to our observation, VAE does not always benefit from deeper architecture: 1) Deeper encoder makes VAE learn more comprehensible latent representations, while results in blurry reconstruction samples; 2) Deeper decoder ensures more high-quality generations, while the latent representations become abstruse; 3) When encoder and decoder both go deeper, abstruse latent representation occurs with blurry reconstruction samples at same time. In this paper, we deduce a Fisher information measure for the corresponding analysis. With such measure, we demonstrate that information loss is ineluctable in feed-forward networks and causes the previous three types of degeneration, especially when the network goes deeper. We also demonstrate that skip connections benefit the preservation of information amount, thus propose a VAE enhanced by skip connections, named SCVAE. In the experiments, SCVAE is shown to mitigate the information loss and to achieve a promising performance in both encoding and decoding tasks. Moreover, SCVAE can be adaptive to other state-of-the-art variants of VAE for further amelioration.
A MultiSite Gateway Toolkit for Rapid Cloning of Vertebrate Expression Constructs with Diverse Research Applications

Recombination-based cloning is a quick and efficient way to generate expression vectors. Recent advancements have provided powerful recombinant DNA methods for molecular manipulations. Here, we describe a novel collection of three-fragment MultiSite Gateway cloning system-compatible vectors providing expanded molecular tools for vertebrate research. The components of this toolkit encompass a broad range of uses such as fluorescent imaging, dual gene expression, RNA interference, tandem affinity purification, chemically-inducible dimerization and lentiviral production. We demonstrate examples highlighting the utility of this toolkit for producing multi-component vertebrate expression vectors with diverse primary research applications. The vectors presented here are compatible with other Gateway toolkits and collections, facilitating the rapid generation of a broad range of innovative DNA constructs for biological research.
SeekAView: An intelligent dimensionality reduction strategy for navigating high-dimensional data spaces

Dealing with the curse of dimensionality is a key challenge in high-dimensional data visualization. We present SeekAView to address three main gaps in the existing research literature. First, automated methods like dimensionality reduction or clustering suffer from a lack of transparency in letting analysts interact with their outputs in real-time to suit their exploration strategies. The results often suffer from a lack of interpretability, especially for domain experts not trained in statistics and machine learning. Second, exploratory visualization techniques like scatter plots or parallel coordinates suffer from a lack of visual scalability: it is difficult to present a coherent overview of interesting combinations of dimensions. Third, the existing techniques do not provide a flexible workflow that allows for multiple perspectives into the analysis process by automatically detecting and suggesting potentially interesting subspaces. In SeekAView we address these issues using suggestion based visual exploration of interesting patterns for building and refining multidimensional subspaces. Compared to the state-of-the-art in subspace search and visualization methods, we achieve higher transparency in showing not only the results of the algorithms, but also interesting dimensions calibrated against different metrics. We integrate a visually scalable design space with an iterative workflow guiding the analysts by choosing the starting points and letting them slice and dice through the data to find interesting subspaces and detect correlations, clusters, and outliers. We present two usage scenarios for demonstrating how SeekAView can be applied in real-world data analysis scenarios.
Proteogenomic studies on cancer drug resistance: towards biomarker discovery and target identification

ABSTRACT Introduction: Chemoresistance is a major obstacle for current cancer treatment. Proteogenomics is a powerful multi-omics research field that uses customized protein sequence databases generated by genomic and transcriptomic information to identify novel genes (e.g. noncoding, mutation and fusion genes) from mass spectrometry-based proteomic data. By identifying aberrations that are differentially expressed between tumor and normal pairs, this approach can also be applied to validate protein variants in cancer, which may reveal the response to drug treatment. Areas covered: In this review, we will present recent advances in proteogenomic investigations of cancer drug resistance with an emphasis on integrative proteogenomic pipelines and the biomarker discovery which contributes to achieving the goal of using precision/personalized medicine for cancer treatment. Expert commentary: The discovery and comprehensive understanding of potential biomarkers help identify the cohort of patients who may benefit from particular treatments, and will assist real-time clinical decision-making to maximize therapeutic efficacy and minimize adverse effects. With the development of MS-based proteomics and NGS-based sequencing, a growing number of proteogenomic tools are being developed specifically to investigate cancer drug resistance.
High concordance of daidzein-metabolizing phenotypes in individuals measured 1 to 3 years apart

Particular intestinal bacteria are capable of metabolizing the soya isoflavone daidzein to equol and/or O-desmethylangolensin (O-DMA), and the presence of these metabolites in urine after soya consumption are markers of particular intestinal bacteria profiles. Prevalences of equol producers and O-DMA producers are approximately 30–50 % and 80–90 %, respectively, and limited observations have suggested that these daidzein-metabolizing phenotypes are stable within individuals over time. Characterizing stability of these phenotypes is important to understand their potential as markers of long-term exposure to particular intestinal bacteria and their associations with disease risk. We evaluated concordance within an individual for the equol-producer and O-DMA-producer phenotypes measured at two time points (T1, T2), 1–3 years apart. Phenotypes were ascertained by analysing equol and O-DMA using GC-MS in a spot urine sample collected after 3 d soya (source of daidzein) supplementation. In ninety-two individuals without recent (within 3 months before phenotyping) or current antibiotics use, 41 % were equol producers at T1 and 45 % were equol producers at T2, and 90 % were O-DMA producers at T1 and 95 % were O-DMA producers at T2. The percentage agreement for the equol-producer phenotype was 82 and for the O-DMA-producer phenotype was 89. These results indicate that these phenotypes are stable in most individuals over time, suggesting that they provide a useful biomarker for evaluating disease risk associated with harbouring particular intestinal bacteria responsible for, or associated with, the metabolism of the soya isoflavone daidzein.
Numerical Simulation of Secondary Air Penetration Depth in a 300 MW Single-furnace Circulating Fluidized Bed Boiler

Aiming at the characteristics of the secondary air jet in large-scale circulating fluidized beds,numerical simulation was carried out for the furnace of a 300 MW large-scale circulating fluidized bed boiler with single furnace by use of software Fluent.The influence of following factors on secondary air penetration and gas-solid two phase mixture were studied,such as secondary air port elevation,secondary air jet angle,secondary air velocity,primary air velocity and particle concentration in the furnace.Results show that the secondary air jet penetration decreases with the increase of primary air velocity and particle concentration.Jet penetration increases with increasing secondary air velocity.Besides,secondary air jet angle and secondary air port position also have effects on secondary air jet penetration.
Digitizing Tablet and Fahn–Tolosa–Marín Ratings of Archimedes Spirals have Comparable Minimum Detectable Change in Essential Tremor

Background Drawing Archimedes spirals is a popular and valid method of assessing action tremor in the upper limbs. We performed the first blinded comparison of Fahn–Tolosa–Marín (FTM) ratings and tablet measures of essential tremor to determine if a digitizing tablet is better than 0–4 ratings in detecting changes in essential tremor that exceed random variability in tremor amplitude. Methods The large and small spirals of FTM were drawn with each hand on two consecutive days by 14 men and four women (age 60±8.7 years [mean±SD]) with mild to severe essential tremor. The drawings were simultaneously digitized with a digitizing tablet. Tremor in each digitized drawing was computed with spectral analysis in an independent laboratory, blinded to the clinical ratings. The mean peak-to-peak tremor displacement (cm) in the four spirals and mean FTM ratings were compared statistically. Results Test–retest intraclass correlations (ICCs) (two-way random single measures, absolute agreement) were excellent for the FTM ratings (ICC 0.90, 95% CI 0.76–0.96) and tablet (ICC 0.97, 95% CI 0.91–0.99). Log10 tremor amplitude (T) and FTM were strongly correlated (logT=αFTM + β, α≈0.6, β≈–1.27, r=0.94). The minimum detectable change for the tablet and FTM were 51% and 67% of the initial assessment. Discussion Digitizing tablets are much more precise than clinical ratings, but this advantage is mitigated by the natural variability in tremor. Nevertheless, the digitizing tablet is a robust method of quantifying tremor that can be used in lieu of or in combination with clinical ratings.
Chaotic cellular neural network‐based true random number generator

This paper presents implementation of a chaotic cellular neural network (CNN)‐based true random number generator on a field programmable gate array (FPGA) board. In this implementation, discrete time model of the chaotic CNN is used as the entropy source. Random number series are generated for three scenarios. Obtained number series are tested by using NIST 800.22 statistical test suite. Also, the scale index technique is carried out for these three scenarios to determine the degree of non‐periodicity for key stream. Copyright © 2017 John Wiley & Sons, Ltd.
Skeleton motion history based human action recognition using deep learning

Nowadays, deep learning is very popular in a variety of research field due to its outperformance over the existing machine learning methods and its high generality over raw inputs. According to recent surveys, deep learning can give high performance in visual object recognition system. Human Action Recognition (HAR) is a promising research area over the computer vision research field due to its enormous applicability. Most of the conventional HAR need to extract the handcrafted features in advance before classifying the actions and the environments are fixed. Those limitations make HAR to depend too much on the problem. In real-world, it is difficult to choose the suitable feature depending on the problem and difficult to fix the environment. In this paper, we applied the deep learning technology over the Skeleton Motion History Image (Skl MHI) of human actions to implement HAR that can work independently on the problem domain. According to the experimental results, the proposed system achieves the high recognition accuracy with low computational cost under the various environments.
Differential roles of glucosinolates and camalexin at different stages of Agrobacterium‐mediated transformation

Summary Agrobacterium tumefaciens is the causal agent of crown gall disease in a wide range of plants via a unique interkingdom DNA transfer from bacterial cells into the plant genome. Agrobacterium tumefaciens is capable of transferring its T‐DNA into different plant parts at different developmental stages for transient and stable transformation. However, the plant genes and mechanisms involved in these transformation processes are not well understood. We used Arabidopsis thaliana Col‐0 seedlings to reveal the gene expression profiles at early time points during Agrobacterium infection. Common and differentially expressed genes were found in shoots and roots. A gene ontology analysis showed that the glucosinolate (GS) biosynthesis pathway was an enriched common response. Strikingly, several genes involved in indole glucosinolate (iGS) modification and the camalexin biosynthesis pathway were up‐regulated, whereas genes in aliphatic glucosinolate (aGS) biosynthesis were generally down‐regulated, on Agrobacterium infection. Thus, we evaluated the impacts of GSs and camalexin during different stages of Agrobacterium‐mediated transformation combining Arabidopsis mutant studies, metabolite profiling and exogenous applications of various GS hydrolysis products or camalexin. The results suggest that the iGS hydrolysis pathway plays an inhibitory role on transformation efficiency in Arabidopsis seedlings at the early infection stage. Later in the Agrobacterium infection process, the accumulation of camalexin is a key factor inhibiting tumour development on Arabidopsis inflorescence stalks. In conclusion, this study reveals the differential roles of GSs and camalexin at different stages of Agrobacterium‐mediated transformation and provides new insights into crown gall disease control and improvement of plant transformation.
Feature-Distributed SVRG for High-Dimensional Linear Classification

Linear classification has been widely used in many high-dimensional applications like text classification. To perform linear classification for large-scale tasks, we often need to design distributed learning methods on a cluster of multiple machines. In this paper, we propose a new distributed learning method, called feature-distributed stochastic variance reduced gradient (FD-SVRG) for high-dimensional linear classification. Unlike most existing distributed learning methods which are instance-distributed, FD-SVRG is feature-distributed. FD-SVRG has lower communication cost than other instance-distributed methods when the data dimensionality is larger than the number of data instances. Experimental results on real data demonstrate that FD-SVRG can outperform other state-of-the-art distributed methods for high-dimensional linear classification in terms of both communication cost and wall-clock time, when the dimensionality is larger than the number of instances in training data.
Practical Considerations before Installing Ground-Based Geodetic Infrastructure for Integrated InSAR and cGNSS Monitoring of Vertical Land Motion

Continuously operating Global Navigation Satellite Systems (cGNSS) can be used to convert relative values of vertical land motion (VLM) derived from Interferometric Synthetic Aperture Radar (InSAR) to absolute values in a global or regional reference frame. Artificial trihedral corner reflectors (CRs) provide high-intensity and temporally stable reflections in SAR time series imagery, more so than naturally occurring permanent scatterers. Therefore, it is logical to co-locate CRs with cGNSS as ground-based geodetic infrastructure for the integrated monitoring of VLM. We describe the practical considerations for such co-locations using four case-study examples from Perth, Australia. After basic initial considerations such as land access, sky visibility and security, temporary test deployments of co-located CRs with cGNSS should be analysed together to determine site suitability. Signal to clutter ratios from SAR imagery are used to determine potential sites for placement of the CR. A significant concern is whether the co-location of a deliberately designed reflecting object generates unwanted multipath (reflected signals) in the cGNSS data. To mitigate against this, we located CRs >30 m from the cGNSS with no inter-visibility. Daily RMS values of the zero-difference ionosphere-free carrier-phase residuals, and ellipsoidal heights from static precise point positioning GNSS processing at each co-located site were then used to ascertain that the CR did not generate unwanted cGNSS multipath. These steps form a set of recommendations for the installation of such geodetic ground-infrastructure, which may be of use to others wishing to establish integrated InSAR-cGNSS monitoring of VLM elsewhere.
Resource depletion through primate stone technology

Tool use has allowed humans to become one of the most successful species. However, tool-assisted foraging has also pushed many of our prey species to extinction or endangerment, a technology-driven process thought to be uniquely human. Here, we demonstrate that tool-assisted foraging on shellfish by long-tailed macaques (Macaca fascicularis) in Khao Sam Roi Yot National Park, Thailand, reduces prey size and prey abundance, with more pronounced effects where the macaque population size is larger. We compared availability, sizes and maturation stages of shellfish between two adjacent islands inhabited by different-sized macaque populations and demonstrate potential effects on the prey reproductive biology. We provide evidence that once technological macaques reach a large enough group size, they enter a feedback loop – driving shellfish prey size down with attendant changes in the tool sizes used by the monkeys. If this pattern continues, prey populations could be reduced to a point where tool-assisted foraging is no longer beneficial to the macaques, which in return may lessen or extinguish the remarkable foraging technology employed by these primates.
Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization

We present the first accelerated randomized algorithm for solving linear systems in Euclidean spaces. One essential problem of this type is the matrix inversion problem. In particular, our algorithm can be specialized to invert positive definite matrices in such a way that all iterates (approximate solutions) generated by the algorithm are positive definite matrices themselves. This opens the way for many applications in the field of optimization and machine learning. As an application of our general theory, we develop the {\em first accelerated (deterministic and stochastic) quasi-Newton updates}. Our updates lead to provably more aggressive approximations of the inverse Hessian, and lead to speed-ups over classical non-accelerated rules in numerical experiments. Experiments with empirical risk minimization show that our rules can accelerate training of machine learning models.
Double Constrained NMF for Partial Multi-View Clustering

Many computer vision problems involve data with multiple views, where each view corresponds to a certain type of feature. To integrate information from multiple views in the unsupervised setting, multi-view clustering methods have been developed to cluster multiple views simultaneously. Most existing methods only consider the case that each example appears in all the views. However, data with partial views is often occurred in real applications. For example, several certain sensors sometimes have faults, then the data may not be captured completely, which will lead to the case that only partial views are available. In this paper, we propose a nonnegative matrix factorization (NMF) method for partial multi-view clustering, which incorporates the cluster similarity and manifold preserving constraints into an unified framework. The basic idea of the proposed method, named Double Constrained NMF (DCNMF), is to push clustering solutions of different views from the same examples towards a common membership matrice, and to maintain the latent geometric structure of the views simultaneously. Moreover, we develop an efficient optimization scheme for the proposed method. Experiments on several two-view datasets demonstrate the advantages of the proposed method on partial multi-view clustering tasks.