Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of base flow modifications on noise amplifications: flow past a backward-facing step

Amplifications of flow past a backward-facing step with respect to optimal inflow and initial perturbations are investigated at Reynolds number 500. Two mechanisms of receptivity to inflow noise are identified: the bubble-induced inflectional point instability and the misalignment effect downstream of the secondary bubble. Further development of the misalignment results in decay of perturbations from $x=28$ onwards (the step is located at $x=0$ ), as has been observed in previous non-normality studies (Blackburn et al., J. Fluid Mech., vol. 603, 2008, pp. 271–304), and eventually limits the receptivity. The receptivity is found to be maximized at an inflow perturbation frequency of ${\it\omega}=0.50$ and a spanwise wavenumber of ${\it\beta}=0$ , where the inflow noise takes full advantage of both mechanisms and is amplified over two orders of magnitude in terms of the velocity magnitude. In direct numerical simulations (DNS) of the flow perturbed by optimal or random inflow noise, vortex shedding, flapping of bubbles, three-dimensionality and turbulence are observed in succession as the magnitude of the inflow noise increases. Similar features of linear and nonlinear receptivity are observed at higher Reynolds numbers. The Strouhal number of the bubble flapping is 0.08, at which the receptivity to inflow noise reaches a maximum. This Strouhal number is close to reported values extracted from DNS or large eddy simulations (LES) at larger Reynolds numbers (Le et al., J. Fluid Mech., vol. 330, 1997, pp. 349–374; Kaiktsis et al., J. Fluid Mech., vol. 321, 1996, pp. 157–187; Métais, New Trends in Turbulence, 2001, Springer; Wee et al., Phys. Fluids, vol. 16, 2004, pp. 3361–3373). Methods to further clarify the mechanisms of receptivity and to suppress the noise amplifications by modifying the base flow using a linearly optimal body force are proposed. It is observed that the mechanisms of optimal noise amplification are fully revealed by the distribution of the base flow modification, which weakens the bubble instabilities and misalignment effects and subsequently reduces the receptivity significantly. Comparing the base flow modifications with respect to amplifications of inflow and initial perturbations, it is found that the maximum receptivity to initial perturbations is highly correlated with the receptivity to inflow noise at the optimal frequency ${\it\omega}=0.50$ , and the correlation reduces as the inflow frequency deviates from this optimal value.
Contextual Knowledge-based Intent Processing for Management of Next Generation Cellular Networks

—Intent-based networking (IBN) facilitates the representation of consumer expectations in a declarative and domain-independent form. The deployment of intent in a network environment requires the extraction of the context from an intent expression, achieved via diﬀerent levels of network and consumer information to be made available to network administrators. The network and service management domain requires organizing the available information as service and resource models in order to aid the intent propagation to available deployment controllers. Knowledge graphs provide an eﬃcient conceptual framework for constructing contexts and organizing known information. This work proposes a knowledge organization methodology for intent processing and translation in IBN. The proposed knowledge model consists of an intent and associated extension models, and it builds on the TeleManagement forum’s intent common model. Additionally, we introduce an IBN framework that leverages the speciﬁed knowledge graphs of the service model for intent interpretation, improvement, and implementation. The proposed knowledge-based IBN and service management is validated through a demonstration for both mission-critical and non-mission-critical services.
On the modulating effect of three-dimensional instabilities in open cavity flows

Abstract Open cavity flows are known to select and enhance locked-on modes or tones. High-energy self-sustained oscillations arise within the shear layer, impinging onto the trailing edge of the cavity. These self-sustained oscillations are subject to amplitude modulations (AMs) at multiple low frequencies. However, only a few studies have addressed the identification of the lowest modulating frequencies. The present work brings to light salient AMs of the shear layer waves and identifies their source as three-dimensional dynamics existing inside the cavity. Indeed, the recirculating inner flow gives rise to centrifugal instabilities, which entail broad-band frequencies down two orders of magnitude lower than those of the self-sustained oscillations. Using time-resolved PIV (TRPIV) in two planes, the nonlinearly saturated dynamics is analysed in both space and time by means of proper orthogonal decomposition, global Fourier decomposition and Hilbert–Huang transforms. The inner flow can be decomposed as three-dimensional waves carried by the main recirculation. Bicoherence distributions are computed to highlight the nonlinear interactions between these spanwise-travelling waves inside the cavity and the locked-on modes. The modulated envelope of the shear layer oscillations is extracted and investigated with regards to the inner-flow dynamics. Strong cross-correlations, in time rather than in space, reveal a global coupling mechanism, possibly related to the beating of the spanwise-travelling waves.
Dynamic imprinting of nanoscale topological phases into an antiferromagnet

Controlling the magnetic order of antiferromagnets is challenging due to the vanishing net magnetisation. For this reason, the study of topologically protected real-space states of antiferromagnets is restricted by the difficulty in nucleating such states. Here, using atomistic simulations we demonstrate how to nucleate predefined, localised spin textures in a thin film antiferromagnet, $\gamma$-IrMn$_3$. Utilising the exchange bias coupling between a ferromagnet and an antiferromagnet, we imprint the topological states on the former onto the latter by means of a thermal cycling procedure. The imprinted textures are shown to be stable against field perturbations. We also discuss how various material parameters affect the efficiency of the imprinting and the characteristics of the imprinted textures. This work paves way for further studies on topologically protected real-space phases in antiferromagnets and promotes the development of denser and faster spintronic devices.
Measuring distribution risk in discrete models

Model risk measures consequences of choosing a model in a class of possible alternatives. We find analytical and simulated bounds for payoff functions on classes of plausible alternatives of a given discrete model. We measure the impact of choosing a risk-neutral measure on convex derivative pricing in incomplete markets. We find analytical bounds for prices of European and American options in the class of all risk-neutral measures, and we also find simulated bounds for given classes of perturbations of the minimal martingale equivalent measure.
Quantum Computing Provides Exponential Regret Improvement in Episodic Reinforcement Learning

In this paper, we investigate the problem of \textit{episodic reinforcement learning} with quantum oracles for state evolution. To this end, we propose an \textit{Upper Confidence Bound} (UCB) based quantum algorithmic framework to facilitate learning of a finite-horizon MDP. Our quantum algorithm achieves an exponential improvement in regret as compared to the classical counterparts, achieving a regret of $\Tilde{\mathcal{O}}(1)$ as compared to $\Tilde{\mathcal{O}}(\sqrt{K})$ \footnote{$\Tilde{\mathcal{O}}(\cdot)$ hides logarithmic terms. }, $K$ being the number of training episodes. In order to achieve this advantage, we exploit efficient quantum mean estimation technique that provides quadratic improvement in the number of i.i. d. samples needed to estimate the mean of sub-Gaussian random variables as compared to classical mean estimation. This improvement is a key to the significant regret improvement in quantum reinforcement learning. We provide proof-of-concept experiments on various RL environments that in turn demonstrate performance gains of the proposed algorithmic framework.
Caffeine ingestion stimulates plasma carnitine clearance in humans

Increasing skeletal muscle carnitine content can manipulate fuel metabolism and improve exercise performance. Intravenous insulin infusion during hypercarnitinemia increases plasma carnitine clearance and Na+‐dependent muscle carnitine accretion, likely via stimulating Na+/K+ ATPase pump activity. We hypothesized that the ingestion of high‐dose caffeine, also known to stimulate Na+/K+ ATPase activity, would stimulate plasma carnitine clearance during hypercarnitinemia in humans. In a randomized placebo‐controlled study, six healthy young adults (aged 24 ± 5 years, height 175 ± 8 cm, and weight 70 ± 13 kg) underwent three 5‐h laboratory visits involving the primed continuous intravenous infusion of l‐carnitine (CARN and CARN + CAFF) or saline (CAFF) in parallel with ingestion of caffeine (CARN + CAFF and CAFF) or placebo (CARN) at 0, 2, 3, and 4 h. Regular blood samples were collected to determine concentrations of blood Na+ and K+, and plasma carnitine and caffeine, concentrations. Caffeine ingestion (i.e., CAFF and CARN + CAFF conditions) and l‐carnitine infusion (i.e., CARN and CARN + CAFF) elevated steady‐state plasma caffeine (to ~7 μg·mL−1) and carnitine (to ~400 μmol·L−1) concentrations, respectively, throughout the 5 h infusions. Plasma carnitine concentration was ~15% lower in CARN + CAFF compared with CARN during the final 90 min of the infusion (at 210 min, 356 ± 96 vs. 412 ± 94 μmol·L−1; p = 0.0080: at 240 min, 350 ± 91 vs. 406 ± 102 μmol·L−1; p = 0.0079: and at 300 min, 357 ± 91 vs. 413 ± 110 μmol·L−1; p = 0.0073, respectively). Blood Na+ concentrations were greater in CAFF and CARN + CAFF compared with CARN. Ingestion of high‐dose caffeine stimulates plasma carnitine clearance during hypercarnitinemia, likely via increased Na+/K+ ATPase activity. Carnitine co‐ingestion with caffeine may represent a novel muscle carnitine loading strategy in humans, and therefore manipulate skeletal muscle fuel metabolism and improve exercise performance.
Optimization of the CRISPR/Cas9 system using adh1 promoter derivatives in fission yeast

The CRSIPR/Cas9 system has been applied to fission yeast, but there remain some rooms for improvement. Here we report that the weaker versions of the adh1 + promoter, adh11 and adh41 promoters, for the potentially cytotoxic Cas9 achieved highly efficient mutagenesis and gene deletion at the ade6 + locus. Employing a drug-selectable marker instead of conventional auxotrophic markers, our new vector system is compatible with a variety of experimental settings including prototrophic/auxotrophic strains and complete/minimal media.
Robust pole assignment in matrix descriptor second-order linear systems

The present paper considers eigenvalue assignment with minimum sensitivity in matrix descriptor second-order linear (MDSOL) systems via proportional-derivative plus partial second-derivative state feedback. Based on a result in the perturbation theory of the eigenvalue problem of MDSOL systems, a closed-loop eigenvalue sensitivity measure in terms of the closed-loop normalized right and left eigenvectors is established directly in the matrix second-order framework. Also a general parametric eigenstructure assignment for MDSOL systems via proportional-derivative plus partial second-derivative state feedback is proposed. Based on this, the robust pole assignment problem is converted into an independent minimization problem. The closed-loop eigenvalues may be easily taken as part of the design parameters and optimized within certain desired fields on the complex plane to improve robustness. Using a simple sequential-order algorithm, the optimality of the obtained solution to the robust pole assignment problem is totally dependent on the solution to the independent minimization problem. An example of mass-spring system demonstrates the effect of the proposed approach.
New criteria for exponential stability of nonlinear time‐varying differential systems

General nonlinear time‐varying differential systems are considered. An explicit criterion for exponential stability is presented. Furthermore, an explicit robust stability bound for systems subjected to nonlinear time‐varying perturbations is given. In particular, it is shown that the generalized Aizerman conjecture holds for positive linear systems. Some examples are given to illustrate obtained results.Copyright © 2012 John Wiley & Sons, Ltd.
Hybrid approach of selecting hyperparameters of support vector machine for regression

To select the hyperparameters of the support vector machine for regression (SVR), a hybrid approach is proposed to determine the kernel parameter of the Gaussian kernel function and the epsilon value of Vapnik's epsilon-insensitive loss function. The proposed hybrid approach includes a competitive agglomeration (CA) clustering algorithm and a repeated SVR (RSVR) approach. Since the CA clustering algorithm is used to find the nearly "optimal" number of clusters and the centers of clusters in the clustering process, the CA clustering algorithm is applied to select the Gaussian kernel parameter. Additionally, an RSVR approach that relies on the standard deviation of a training error is proposed to obtain an epsilon in the loss function. Finally, two functions, one real data set (i.e., a time series of quarterly unemployment rate for West Germany) and an identification of nonlinear plant are used to verify the usefulness of the hybrid approach.
Determination of nuclear magnetic resonance T2 cutoff value based on multifractal theory — An application in sandstone with complex pore structure

ABSTRACTThe cutoff value of nuclear magnetic resonance (NMR) transversal relaxation time T2 is vital for pore structure characterization, permeability prediction, and irreducible water saturation calculation. Conventional default values often lead to inaccurate results for rocks with complex pore structure. Based on NMR experiments and multifractal theory, we have developed an effective statistical method to predict T2 cutoff values without other petrophysical information. The method is based on multifractal theory to analyze the NMR T2 spectrum with the assumption that the T2 spectrum is an indicator of pore size distribution. Multifractal parameters, such as multifractal dimension, singularity strength, and mass exponent, are calculated to investigate the multifractal behavior of T2 spectrum via NMR experiments and a dyadic scaling-down algorithm. To obtain the optimal T2 cutoff value, the rotation speed and time of centrifugation are enlarged increasingly to optimal centrifugal state. A predicating mod.
Confinors and Bounded-Time Patterns in Chua's Circuit and the Double Scroll Family

We study the patterns in the time waveforms of the spiral attractors of the double scroll equations for the theoretical Chua's parameters. We introduce several mathematical tools linked to these equations (half-Poincare maps, isochronic lines, selecting curves and antiselecting curves, spirals and antispirals, theorems of the turnstile, patterns and confinors). We prove that for given values of the parameters, only patterns in the time waveforms which are bounded in time can exist.
Dynamic behavior and locking of a semiconductor laser subjected to external injection

In this paper, we analyze the phenomena arising when a monomode semiconductor laser is subjected to external injection from another laser. The system stability is investigated as a function of detuning and of the relative injected power. Different regimes, spanning from phase locking to chaos and coherence collapse, are described by analytical and numerical methods for weak and moderate injection. Previous theoretical studies are extended by describing the inverse transition from chaos to stability and by deriving the final locking condition. Also, further investigation on the coherence collapse regime has been performed. Besides contributing to the exploration of an interesting fundamental phenomenon, the results of this analysis are useful for different applications, including coherent detection and chaotic cryptography.
A Novel Epitope Dataset: Performance of the MCL-Based Algorithms to Generate Dataset for Graph Learning Model

Naturally, the epitope dataset can be presented as a graph. Dataset preparation in the previous methods is part of model development. There are many graph-based classification and regression methods. Still, it is difficult to identify their performance on the conformational epitope prediction model because datasets in a suitable format are unavailable. This research aims to build a dataset in a suitable format to evaluate kernel graph and graph convolution network. This dataset, which results from graph clustering on graph antigens, can be used to identify the performance of many graph neural network-based algorithms for conformational epitope prediction. The Ag-Ab complexes that meet the criteria for forming a conformational epitope prediction dataset from previous studies were downloaded from the Protein Data Bank. Raw datasets in the form of specific exposed antigen chain residues are labeled as epitope or non-epitope based on their proximity to the paratope. The engineering features in the raw dataset are derived from the structure of the antigen-antibody complex and the propensity score. Aggregating atomic-level interactions into residual levels create an initial graph of the antigen chain. The MCL, MLR-MCL, and PS-MCL are graph clustering algorithms to obtain labeled sub-clusters from the initial graph. A balance factor parameter is set to several values to identify the optimal dataset formation based on minimal fragmentation. The output of the MCL algorithm is used as a baseline. As a result of the fragmentation analysis that occurs, the MLR-MCL algorithm gives the best model performance at a balance factor equal to 2. PS-MCL gives the best performance at a value of 0.9. Based on the minimum fragmentation, the MLR-MCL algorithm provides the best model performance compared to MCL and PS-MCL. The dataset in a format according to benchmarking dataset can be used to identify the characteristics of antigen subgraphs formed from the graph clustering process and to explore the performance of graph-based learning conformational epitope prediction models such as graph convolution networks.
Variational Study of the Impact of Call Graphs on Precision of Android Taint Analysis

With the growing advent and usage of Android applications, security of sensitive user information remains to be of paramount concern. A popular way to identify security leaks in Android applications is by performing taint analysis that tries to enlist possible paths in the program through which sources of critical information may get connected to potential sinks that may propagate leaks. Notably, the precision of such “taint information” is heavily dependent on the elements that are responsible for constructing an interprocedural path in a program – primarily, the call graph. This paper is a step towards a larger study to identify the common patterns through which information gets tainted in Android applications, aiming to suggest points in the program analysis space that could lead to their detection in a precise yet efficient manner. To begin with, we invoke FlowDroid (a popular taint-analysis tool) to analyze Android apps from a variety of domains, and measure the impact of varying the underlying call graph on the computed taint information. We observe that taint information depends significantly on the used call graph, and that certain spurious leaks can be mapped to particular causes of removable imprecision. We further classify the identified leaks into various kinds, and hope to extend this study to identify exact parts of the program that popularly leak out particular kinds of information. Our final goal is to help security analysts select the right interprocedural analysis toolset for identifying bugs in Android apps, as well as to frame app-design guidelines for helping developers first-hand avoid common sources of information leaks from their future artifacts.
Advances in Automated Pedagogical Compile-time Error Repair

Automated pedagogical error repair (APER) is the task of suggesting fixes to buggy programs written by beginner or novice programmers. APER tools have been shown to greatly improve the learning experience for students for whom error messages offered by compilers or runtime environments are either unhelpful and often misleading. Consequently, several APER tools have been proposed in literature using a variety of powerful machine learning techniques including sequence-to-sequence modelling (TRACER), reinforcement learning (RLAssist), graph attention (DrRepair) and decision trees (MACER). Despite offering high repair rates, these tools are often bulky, requiring several days of training and extensive GPU resources. This paper describes CAPER, a novel APER tool for the C programming language that offers 4-5% higher repair accuracy than existing APER tools on multiple benchmark error repair datasets. CAPER has the added advantage of being significantly lighter and faster than most existing tools, being able to train on a CPU and yet offering training speedups of 2 × over TRACER, 600 × over RLAssist and 700 × over DrRepair. The paper also describes PyPER, an extension of CAPER to the Python programming language. Code for CAPER is available at https://github.com/purushottamkar/caper/
TH-iSSD: Design and Implementation of a Generic and Reconfigurable Near-Data Processing Framework

We present the design and implementation of TH-iSSD, a near-data processing framework to address the data movement problem. TH-iSSD does not pose any restriction to the hardware selection and is highly reconfigurable—its core components, such as the on-device compute unit (e.g., FPGA, embedded CPUs) and data collectors (e.g., camera, sensors), can be easily replaced to adapt to different use cases. TH-iSSD achieves this goal by incorporating highly flexible computation and data paths. In the data path, TH-iSSD adopts an efficient device-level data switch that exchanges data with both host CPUs and peripheral sensors; it also enables direct accesses between the sensing, computation, and storage hardware components, which completely eliminates the redundant data movement overhead, and thus delivers both high performance and energy efficiency. In the computation path, TH-iSSD provides an abstraction of filestream for developers, which abstracts a collection of data along with the related computation task as a file. Since existing applications are familiar with POSIX-like interfaces, they can be ported on top of our platform with minimal code modification. Moreover, TH-iSSD also introduces mechanisms including pipelined near-data processing and priority-aware I/O scheduling to make TH-iSSD perform more effectively. We deploy TH-iSSD to accelerate two types of applications: the content-based information retrieval system and the edge zero-streaming system. Our experimental results show that TH-iSSD achieves up to 1.6× higher throughput and 36% lower latency than compute-centric designs.
Support Vector Machine Based on Type-2 Fuzzy Training Samples

In order to deal with the classification problems of type-2 fuzzy training samples on generalized credibility space. Firstly the type-2 fuzzy training samples are reduced to ordinary fuzzy samples by the mean reduction method. Secondly the definition of strong fuzzy linear separable data for type-2 fuzzy samples on generalized credibility space is introduced. Further, by utilizing fuzzy chance-constrained programming and classic support vector machine, a support vector machine based on type-2 fuzzy training samples and established on generalized credibility space is given. An example shows the efficiency of the support vector machine.