Miracle-dz/UniFAR-FTUs huggingface dataset:
A Mutation-Enabled Proactive Defense Against Service-Oriented Man-in-The-Middle Attack in Kubernetes

Kubernetes (K8s) has become a core technology for cloud-native applications. However, a design flaw of the external IP in K8s leads to the service-oriented man-in-the-middle attack. Existing solutions (e.g., script monitor) attempt to address it passively, which allows attackers enough analysis time to bypass these static rule reviews. Differently, we propose a mutation-enabled proactive defense mechanism, aiming to change the asymmetry between attackers and defenders. It involves the address mutation (i.e., network identification) module and the connection ID (i.e., communication identification) mutation module. In the former module, we analyze mutation constraints and prove the corresponding mutation grouping problem to be NP-hard. Then, a maximally coloring-driven mutation grouping algorithm is developed. Since the address allocation time grows linearly with the service size, we design a prefetched address allocation algorithm. After designing the interaction flow between modules, we present a randomized algorithm in the latter module. Thus our mechanism does not affect methods oriented to other attacks. Eventually, it can continuously interrupt the attack and keep the service connection by incrementally updating K8s and the transport layer protocol. Experiments in the Alibaba cloud demonstrate that it can effectively defend against the attack with an acceptable performance loss.
Toward Identifying the Critical Ecological Habitat of Larval Fishes: An Environmental DNA Window into Fisheries Management

Environmental DNA (eDNA)-based ecological co-occurrence networks can provide a valuable tool for fisheries and conservation management. In the past, it was nearly impossible to explore the microscopic world of larval fishes in one sampling event. Now, eDNA data and ecological co-occurrence network modeling provide windows into ecosystems that support larval fishes and upon which subsequent fisheries rely. Thus, there is great potential for eDNA methods coupled with ecological network analyses to provide a holistic understanding of community composition and species interactions and to develop indicators for fisheries and ecosystem-based management.
Assessment of IEEE 1588-Based Timing System of the ITER Neutral Beam Test Facility

MITICA is one of the two ongoing experiments at the ITER neutral beam test facility (NBTF) located in Padua (Italy). MITICA aims to develop the full-size neutral beam injector of ITER and, as such, its Control and Data Acquisition System will adhere to ITER CODAC directives. In particular, its timing system will be based on the IEEE1588 PTPv2 protocol and it will use the ITER time communication network (TCN) on a dedicated Ethernet local-area network. Following the ITER device catalog, the National Instruments PXI-6683H precision time protocol (PTP) timing modules will be used to generate triggers and clocks synchronized with a PTP grandmaster clock (GMC). The “Lazy Trigger” technique, then, will be adopted to implement event-driven data acquisition without the need for any hardware link in addition to the Ethernet connections used to transfer data and timing synchronization. To evaluate the accuracy over time that can be achieved with different network topologies and configurations, a test system has been set up consisting of a GMC, two PXI-6683H devices, and two PTP-aware network switches. In addition, a detailed simulation of the network and the involved devices has been performed using the OMNET++ discrete event simulator. The simulation parameters include not only the network and switches configuration, but also the PID parameters used in the clock servo controllers. A comparison between simulated and measured statistics is reported, together with a discussion of the possible optimal configuration strategies.
Evolution of stream wise vortices and generation of small-scale motion in a plane mixing layer

The evolution of streamwise vortices in a plane mixing layer and their role in the generation of small-scale three-dimensional motion are studied in a closed-return water facility. Spanwise-periodic streamwise vortices are excited by a time-harmonic wavetrain with span wise-periodic amplitude variations synthesized by a mosaic of 32 surface film heaters flush-mounted on the flow partition. For a given excitation frequency, virtually any span wise wavelength synthesizable by the heating mosaic can be excited and can lead to the formation of streamwise vortices before the rollup of the primary vortices is completed. The onset of streamwise vortices is accompanied by significant distortion in the transverse distribution of the streamwise velocity component. The presence of inflexion points, absent in corresponding velocity distributions of the unforced flow, suggests the formation of locally unstable regions of large shear in which broadband perturbations already present in the base flow undergo rapid amplification, followed by breakdown to small-scale motion. Furthermore, as a result of spanwise-non-uniform excitation the cores of the primary vortices are significantly altered. The three-dimensional features of the streamwise vortices and their interaction with the base flow are inferred from surfaces of r.m.s. velocity fluctuations and an approximation to cross-stream vorticity using three-dimensional single component velocity data. The striking enhancement of small-scale motion and the spatial modification of its distribution, both induced by the streamwise vortices, can be related to the onset of the mixing transition.
Tensor-Based Sparse Bayesian Learning With Intra-Dimension Correlation

Unlike the flat-view matrix, tensor provides an elegant representation to preserve the structure of data in the multidimensional nature. The wide use of tensor-based approaches highlights the limitation of matrix-based methods and has generated increasing needs in multidimensional data analytics. Driven by such demands, tensor decomposition with automatic rank determination has recently emerged as a promising tool for image processing, wireless communications and neural network compression. However, unlike the success in matrix-based schemes of exploring the correlation for performance improvement, existing tensor-based methods fail to consider the correlation structure in the tensor. In this paper, we propose to exploit the tensor intra-dimension correlation within the framework of sparse Bayesian learning. We provide the sparsity-inducing probabilistic models under both canonical polyadic decomposition and Tucker decomposition to capture the correlation structure effectively. We derive an efficient model inference method with a fast convergence rate based on the expectation-maximization algorithm. Furthermore, a complexity reduction method is proposed to speed up the computation by exploiting several properties of the Kronecker product. We further extend the proposed framework for the incomplete tensor corrupted by sparse outliers to achieve effective tensor completion and outlier mitigation. Simulation results demonstrate the superior performance of the proposed framework over those ignoring the intra-dimension correlation in terms of accuracy and sparsity.
Factors associated with depression, anxiety and severe mental illness among adults with atopic eczema or psoriasis: a systematic review and meta-analysis.

BACKGROUND Evidence suggests an association between atopic eczema (AE) or psoriasis and mental illness; however, the factors associated with mental illness are unclear. OBJECTIVES To synthesize and evaluate all available evidence on factors associated with depression, anxiety and severe mental illness (SMI) among adults with AE or psoriasis. METHODS We searched electronic databases, grey literature databases and clinical trial registries from inception to February 2022 for studies of adults with AE or psoriasis. Eligible studies included randomized controlled trials (RCTs), cohort, cross-sectional or case-control studies where effect estimates of factors associated with depression, anxiety or SMI were reported. We did not apply language or geographical restrictions. We assessed risk of bias using the Quality in Prognosis Studies tool. We synthesized results narratively, and if at least two studies were sufficiently homogeneous, we pooled effect estimates in a random effects meta-analysis. RESULTS We included 21 studies (11 observational, 10 RCTs). No observational studies in AE fulfilled our eligibility criteria. Observational studies in people with psoriasis mostly investigated factors associated with depression or anxiety - one cross-sectional study investigated factors associated with schizophrenia. Pooled effect estimates suggest that female sex and psoriatic arthritis were associated with depression [female sex: odds ratio (OR) 1.62, 95% confidence interval (CI) 1.09-2.40, 95% prediction intervals (PIs) 0.62-4.23, I2 = 24.90%, τ2 = 0.05; psoriatic arthritis: OR 2.26, 95% CI 1.56-3.25, 95% PI 0.21-24.23, I2 = 0.00%, τ2 = 0.00] and anxiety (female sex: OR 2.59, 95% CI 1.32-5.07, 95% PI 0.00-3956.27, I2 = 61.90%, τ2 = 0.22; psoriatic arthritis: OR 1.98, 95% CI 1.33-2.94, I2 = 0.00%, τ2 = 0.00). Moderate/severe psoriasis was associated with anxiety (OR 1.14, 95% CI 1.05-1.25, I2 0.00%, τ2 = 0.00), but not depression. Evidence from RCTs suggested that adults with AE or psoriasis given placebo had higher depression and anxiety scores compared with comparators given targeted treatment (e.g. biologic agents). CONCLUSIONS Our review highlights limited existing research on factors associated with depression, anxiety and SMI in adults with AE or psoriasis. Observational evidence on factors associated with depression or anxiety in people with psoriasis was conflicting or from single studies, but some identified factors were consistent with those in the general population. Evidence on factors associated with SMIs in people with AE or psoriasis was particularly limited. Evidence from RCTs suggested that AE and psoriasis treated with placebo was associated with higher depression and anxiety scores compared with skin disease treated with targeted therapy; however, follow-up was limited. Therefore, long-term effects on mental health are unclear.
Segmental Aperture Imaging Algorithm for Multirotor UAV-Borne MiniSAR

This article takes on the challenges of synthetic aperture radar (SAR) imaging for miniaturized SAR (MiniSAR) onboard a multirotor unmanned aerial vehicle (UAV). Several unique challenges are systematically analyzed, and a corresponding analytical phase error model is established, which accurately models the effects of both translational and rotational motions of UAVs. A segmental aperture imaging (SAI) algorithm, an autofocus algorithm based on strong scatterers, is proposed. It simply divides the platform trajectory into uneven segments, which are first independently focused with motion compensation and then stitched together to form a complete SAR image. Both the theoretical derivation of the signal model and the implementation of the imaging algorithm are presented. A simulation analysis with actual UAV trajectory and attitude data is conducted, which demonstrates the efficacy and performance of the proposed imaging algorithm. It shows that the ideal focusing effect can be achieved as evaluated by various metrics, and the proposed algorithm has superior performance compared to the subaperture phase gradient autofocus (PGA) and minimum entropy autofocus (MEA) methods. Finally, the multirotor-borne MiniSAR system FUSAR-Ku is used for experiments to verify the proposed algorithm. Experimental results show that the proposed algorithm can achieve the theoretical decimeter-resolution imaging performance as measured by various metrics.
Simulated X-ray cycles in rapidly rotating solar-like stars

It is generally accepted that the presence of a hot magnetic corona provides the source of X-ray emission in cool stars. With this connection one could expect to see the variation of magnetic flux in the activity cycle of a star mirrored by a similar variation in the stars X-ray emission. Using magnetic maps produced from flux emergence and transport simulations and assuming a potential field for the corona, we can extrapolate the coronal magnetic field and hence calculate the variation of the X-ray emission. We consider three types of activity cycle that successfully reproduce the pattern of intermingled magnetic flux at high latitudes, a feature observed with Zeeman-Doppler imaging. The three different cycles take the form of (1) an enhanced butterfly pattern where flux emergence is extended to a latitude of 70°, (2) an extended emergence profile as before but with an overlap of 4 yr in the butterfly diagram and (3) where no butterfly diagram is used. The cyclic variation in the X-ray emission is around two orders of magnitude for cases (1) and (3), but less than one order of magnitude for case (2). For all three cases, the rotational modulation of the X-ray emission is greatest at cycle minimum, but the emission measure weighted density varies little over the cycle. For cases (1) and (2) the fraction of the total flux that is open (along which a wind can escape) varies little over the cycle, but for case (3) this is three times larger at cycle minimum than at maximum. Our results clearly show that although magnetic cycles may exist for stars they are not necessarily observable in the X-ray emission.
Polarization aberration analysis using Pauli-Zernike representation

Polarized illumination is a viable technique for improving the image quality and process latitude of hyper-NA lithography. On investigation of polarization effects, it is often assumed that the lens system can maintain the polarization state through optical path, which may not be the case with actual lenses. These polarization changes may cause CD variations and pattern placement errors. In this paper, we investigated a method of polarization analysis across the pupil and showed some examples of polarization aberrations. Also, we analyzed CD sensitivity and pattern placement errors from polarization aberrations. Specific terms of the Pauli Zernike representation have effects on CD and pattern placement errors, like the Zernike representation of conventional aberrations. The Pauli-Zernike representation is intuitive and useful for understanding and specifying polarization aberrations.
Distributed and Scalable Cooperative Formation of Unmanned Ground Vehicles Using Deep Reinforcement Learning

Cooperative formation control of unmanned ground vehicles (UGVs) has become one of the important research hotspots in the application of UGV and attracted more and more attention in the military and civil fields. Compared with traditional formation control algorithms, reinforcement-learning-based algorithms can provide a new solution with a lower complexity for real-time formation control by equipping UGVs with artificial intelligence. Therefore, in this paper, a distributed deep-reinforcement-learning-based cooperative formation control algorithm is proposed to solve the navigation, maintenance, and obstacle avoidance tasks of UGV formations. More importantly, the hierarchical triangular formation structure and the newly designed Markov decision process for UGV formations of leader and follower attributes make the control strategy learned by the algorithm reusable, so that the formation can arbitrarily increase the number of UGVs and realize a more flexible expansion. The effectiveness and scalability of the algorithm is verified by formation simulation experiments of different scales.
A Comparison of Spectral Continuity Measures as a Join Cost in Concatenative Speech Synthesis

The quality of concatenative speech synthesis depends on the cost function employed for unit selection. Cost functions for spectral continuity are difficult to define and standard measures used for this task often do not accurately reflect human perception of discontinuity across a concatenated join. We compare a set of standard distance measures for the task of detecting audible discontinuities and introduce a new measure. A perceptual experiment is described that was used to relate each measure to human perception of discontinuities. The impact of window length on feature extraction and subsequent detection of discontinuities is investigated. The distance measure approach to detecting audible discontinuities is extended to a feature space based representation and feature transformations are investigated as a means of improving discontinuity detection. Receiver operating characteristic (ROC) curves is used to compare the results, which indicate that the feature space approach improves on the performance of standard measures.
Evaluation of Bacterial Composition and Viability of Equine Feces after Processing for Transplantation

Fecal microbiota transplantation (FMT) has been used empirically for decades in equine medicine to treat intestinal dysbiosis but evidence-based information is scarce. This in vitro study aimed at assessing the effect of a commonly used pre-FMT processing method on the bacterial composition and viability of the fecal filtrate. Three samples of fresh equine manure (T0) were processed identically: the initial manure was mixed with 1 L of lukewarm water and chopped using an immersion blender to obtain a mixture (T1), which was left uncovered during 30 min (T2) and percolated through a sieve to obtain a fecal filtrate (T3). Samples were taken throughout the procedure (Tn) and immediately stored at 4 °C until processing. The 16S rDNA amplicon profiling associated with propidium monoazide treatment was performed on each sample to select live bacteria. Analyses of α and β diversity and main bacterial populations and quantitative (qPCR) analysis were performed and statistically compared (significance p < 0.05) between time points (T0–T3). No significant differences in ecological indices or mean estimated total living bacteria were found in the final fecal filtrate (T3) in regard to the original manure (T0); however, relative abundances of some minor genera (Fibrobacter, WCHB1-41_ge and Akkermansia) were significantly different in the final filtrate. In conclusion, the results support the viability of the major bacterial populations in equine feces when using the described pre-FMT protocol.
Generalized Chandrasekhar recursions from the generalized Schur algorithm

Presents a new approach to the Chandrasekhar recursions and some generalizations thereof. The derivation uses the generalized Schur recursions, which are O(N/sup 2/) recursions for the triangular factorization of N/spl times/N matrices having a certain Toeplitz-like displacement structure. It is shown that when the extra structure provided by an underlying state-space model is properly incorporated into the generalized Schur algorithm, it reduces to the Chandrasekhar recursions, which are O(Nn/sup 2/) recursions for estimating the n-dimensional state of a time-invariant (or constant-parameter) system from N measured outputs. It is further noted that the generalized Schur algorithm factors more general structured matrices, and this fact is readily used to extend the Chandrasekhar recursions to a class of time-variant state-space models, special cases of which often arise in adaptive filtering. >
“Faith Is Not Enough?” Ego-Resiliency and Religiosity as Coping Resources with Pandemic Stress—Mediation Study

Based on the concepts of Pargament’s adaptational functions of religiosity, Huber’s centrality of religiosity, and Block’s conceptualisation of ego-resiliency as psychosocial resources, a nonexperimental, moderated mediation project was designed for a group of 175 women and 57 men who voluntarily participated in an online study to determine whether and to what extent religiosity mediated or moderated the relationship between ego-resiliency and the severity of PTSD and depression during the COVID-19 epidemic. The analyses carried out showed that the studied variables, ego-resiliency and centrality of religiosity, were predictors of the intensity of some psychopathological reactions caused by the COVID-19 pandemic but were not connected via a mediation relationship. Therefore, one question remains open: what is the role of ego-resiliency and the nature of the stated immunogenic effect of the centrality of religiosity in dealing with the critical threat to mental health that is the COVID-19 pandemic?
Development of a Parametric Structural Analysis Environment to Support the Design, Manufacturing, and Production of a Composite UAV Wing

As digital and physical systems become more complex, collect voluminous quantities of data, and move towards greater integration over time, the concept of digital engineering has become of great interest in the engineering world. The integration of digital methods with traditional engineering approaches in product lifecycle management has posed challenges on how techniques such as digital twins can be best used during the design and manufacturing phases of the product lifecycle. To address this need, this research supports the integration of design, manufacturing, and production by assessing the structural integrity of various designs of a parametric UAV wing built with a composite material. A systematic and efficient environment is developed to modify the wing design parameters, develop and analyze the finite element model, obtain structural data, and identify feasible design regions for decision making. The sharing of models, data, and analyses with the manufacturing and production segments of the lifecycle permits integration of the various disciplines in early design phases to allow greater design freedom and avoid great costs during the design of the product. The re-sults indicate that (1) the need for a trade-off analysis between key disciplinary considerations in UAV wing design decision making can be addressed and that (2) the developed capability enables decision makers to choose the configurations to be studied in later design stages after the structural integrity and weight considerations are assessed for multiple wing designs.
Advanced Reinforcement Learning and Its Connections with Brain Neuroscience

In recent years, brain science and neuroscience have greatly propelled the innovation of computer science. In particular, knowledge from the neurobiology and neuropsychology of the brain revolutionized the development of reinforcement learning (RL) by providing novel interpretable mechanisms of how the brain achieves intelligent and efficient decision making. Triggered by this, there has been a boom in research about advanced RL algorithms that are built upon the inspirations of brain neuroscience. In this work, to further strengthen the bidirectional link between the 2 communities and especially promote the research on modern RL technology, we provide a comprehensive survey of recent advances in the area of brain-inspired/related RL algorithms. We start with basis theories of RL, and present a concise introduction to brain neuroscience related to RL. Then, we classify these advanced RL methodologies into 3 categories according to different connections of the brain, i.e., micro-neural activity, macro-brain structure, and cognitive function. Each category is further surveyed by presenting several modern RL algorithms along with their mathematical models, correlations with the brain, and open issues. Finally, we introduce several important applications of RL algorithms, followed by the discussions of challenges and opportunities for future research.
Tethered Multicopter Guidance in GPS-Denied Environments Through Reinforcement Learning

: This paper presents a novel reinforcement learning (RL) approach for a tethered drone to follow a predeﬁned three-dimensional trajectory in a GPS-denied environment. The adopted Q-learning strategy determines high-level actions using raw observations from the onoard accelerometers, gyros, and altimeter, which facilitates a low-level proportional-integral-derivative (PID) controller to drive the drone through the desired waypoints on a reference trajectory. The eﬀectiveness of the proposed approach is demonstrated in a simulated environment.