Miracle-dz/UniFAR-FTUs huggingface dataset:
Illumination-Insensitive Binary Descriptor for Visual Measurement Based on Local Inter-Patch Invariance

Binary feature descriptors have been widely used in various visual measurement tasks, particularly those with limited computing resources and storage capacities. Existing binary descriptors may not perform well for long-term visual measurement tasks due to their sensitivity to illumination variations. It can be observed that when image illumination changes dramatically, the relative relationship among local patches mostly remains intact. Based on the observation, consequently, this study presents an illumination-insensitive binary (IIB) descriptor by leveraging the local inter-patch invariance exhibited in multiple spatial granularities to deal with unfavorable illumination variations. By taking advantage of integral images for local patch feature computation, a highly efficient IIB descriptor is achieved. It can encode scalable features in multiple spatial granularities, thus facilitating a computationally efficient hierarchical matching from coarse to fine. Moreover, the IIB descriptor can also apply to other types of image data, such as depth maps and semantic segmentation results, when available in some applications. Numerical experiments on both natural and synthetic datasets reveal that the proposed IIB descriptor outperforms the state-of-the-art (SOTA) binary descriptors and some testing float descriptors. The proposed IIB descriptor has also been successfully employed in a demo system for long-term visual localization. The code of the IIB descriptor will be publicly available at https://github.com/roylin1229/IIB_descriptor.
Turbulent energy flux generated by shock/homogeneous-turbulence interaction

High-speed turbulent flows with shock waves are characterized by high localized surface heat transfer rates. Computational predictions are often inaccurate due to the limitations in modelling of the unclosed turbulent energy flux in the highly non-equilibrium regions of shock interaction. In this paper, we investigate the turbulent energy flux generated when homogeneous isotropic turbulence passes through a nominally normal shock wave. We use linear interaction analysis where the incoming turbulence is idealized as being composed of a collection of two-dimensional planar vorticity waves, and the shock wave is taken to be a discontinuity. The nature of the postshock turbulent energy flux is predicted to be strongly dependent on the angle of incidence of the incoming waves. The energy flux correlation is also decomposed into its vortical, entropy and acoustic contributions to understand its rapid non-monotonic variation behind the shock. Three-dimensional statistics, calculated by integrating two-dimensional results over a prescribed upstream energy spectrum, are compared with available data from direct numerical simulations. A detailed budget of the governing equation is also considered in order to gain insight into the underlying physics.
The T6SS-Dependent Effector Re78 of Rhizobium etli Mim1 Benefits Bacterial Competition

Simple Summary Rhizobium etli Mim1 (ReMim1) possesses a protein secretion system type VI (T6SS) that is active in free living and symbiosis. The T6SS is a nanosyringe that secretes proteins called effectors to both eukaryotic and prokaryotic target cells. The ReMim1 T6SS gene cluster encodes for a toxic effector (Re78) together with an immunity protein (Re79) as demonstrated when expressed in Escherichia coli. In addition, it was observed that the toxic role of the Re78 protein is outside the cytoplasm since its toxic effect on E. coli only occurred when a signal peptide was added to it. Re79 is found in ReMim1 periplasm and is T6SS-independent for its translocation. Additionally, the Re78/Re79 pair is involved in bacterial competition and nodule occupancy. A better understanding of the role of this secretion system can be very useful to select highly competitive rhizobia for inoculants. Abstract The genes of the type VI secretion system (T6SS) from Rhizobium etli Mim1 (ReMim1) that contain possible effectors can be divided into three modules. The mutants in them indicated that they are not required for effective nodulation with beans. To analyze T6SS expression, a putative promoter region between the tssA and tssH genes was fused in both orientations to a reporter gene. Both fusions are expressed more in free living than in symbiosis. When the module-specific genes were studied using RT-qPCR, a low expression was observed in free living and in symbiosis, which was clearly lower than the structural genes. The secretion of Re78 protein from the T6SS gene cluster was dependent on the presence of an active T6SS. Furthermore, the expression of Re78 and Re79 proteins in E. coli without the ReMim1 nanosyringe revealed that these proteins behave as a toxic effector/immunity protein pair (E/I). The harmful action of Re78, whose mechanism is still unknown, would take place in the periplasmic space of the target cell. The deletion of this ReMim1 E/I pair resulted in reduced competitiveness for bean nodule occupancy and in lower survival in the presence of the wild-type strain.
Heat transfer and fluid flow characteristics in helically coiled tube heat exchanger (HCTHE) using nanofluids: a review

One of the most common methods used to transfer heat is by using various types of heat exchangers. The efficiency of heat exchangers can be improved through active or passive techniques. Active technique involves the use of external forces such as vibration, rotation, electric field, etc. Where else the passive techniques involve fluid additives or modified surface geometries. Helical coiled tubes have been known to exhibit excellent heat transfer characteristics compared to regular straight tubes and for this reason it is widely used in industrial applications. Another known passive technique to enhance heat transfer is by altering the fluid thermal conductivity. Recent studies have shown that nanofluids (nano sized particles suspended in conventional heat transfer fluids) posses superior heat transfer capabilities due to its high thermal conductivity. The amount of research done in this area is fairly new and limited. Most studies done on helical coiled tubes and nanofluids show enhanced heat transfer capabilities and results that challenge conventional theories and limitations on heat transfer devices and fluids. Several important aspects of HCTHE that affect the performance such as geometry, fluid inlet and outlet arrangement, and types of HCTHE are discussed based on the reported findings from experimental and numerical studies. This review also focuses on the vital aspects of nanofluids such as types, properties and heat transfer characteristics and limitations towards the application of nanofluids.
Informative combination of multiple partial order relations

Michael Rademaker∗, Bernard De Baets∗ and Hans De Meyer† ∗Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Coupure links 653, 9000 Gent, Belgium (michael.rademaker@ugent.be, bernard.debaets@ugent.be) †Department of Applied Mathematics and Computer Science, Ghent University, Krijgslaan 281 (S9), 9000 Gent, Belgium (hans.demeyer@ugent.be) Multicriteria Ordering and Ranking: Partial Orders, Ambiguities and Applied Issues Jan W. Owsinski and Rainer Bruggemann, Editors
Matching Even-template with Argument Structure of Verbs:Towards a Verb-driven Approach of Information Extraction

This paper demonstrates how to establish the ma tc hing relation between event-template of an information extraction (briefly, IE) system and the argument structure of the related verbs basing on the analysis o f the succession texts according to [2] testing data for his IE sys te m InfoX. It firstly divides the succession verbs into six classes (appoint, hold , remove, resign, dispatch, transfer) according to their syntactic and semantic features. Then it describes the argument structure of these six classes of verbs , especially the thematic role of the arguments and their syntactic arrangement. Finally, it establishes the matching relation between succession event-templat e elements and the argument roles of these six classes of succession verbs, revea ls the orientation function of the related verbs in screen and merger of texts, and illustrates the possibility of developing a verb-driven approach of IE.
Soluble Urokinase-Type Plasminogen Activator Receptor (suPAR) Is a Biomarker Associated with Left Ventricular Hypertrophy in the Elderly, Specifically in Women

Left ventricular hypertrophy (LVH) may result in the development of heart failure, which is widespread among people of advanced age. The pathophysiology of LVH is complex and its biochemical pathways are not fully understood in this group. Elevated soluble urokinase-type plasminogen activator receptor (suPAR), a biomarker of immune activation, including fibrosis, reflects subclinical organ damage in systematic diseases. The present study assesses the clinical role of suPAR measurement in determination of LVH-associated cardiac disorders in the elderly. The studied population consisted of 238 individuals aged 76–91 years; of these, 139 (58%) were diagnosed with LVH. Serum biomarkers measurement (suPAR, troponin T, NT-proBNP and CRP) and echocardiography were performed in all subjects. The suPAR level was significantly higher in the LVH group (4.01 vs. 3.82 ng/mL, p = 0.033) and correlated with the parameters of cardiac diastolic function. Stepwise logistic regression found suPAR level (OR = 1.55, p = 0.016), BMI (OR = 1.17, p = 0.0003) and hypertension (OR = 2.42, p = 0.046) to be independently associated with LVH in women. In men, the strongest predictors of LVH were hypertension (OR = 7.52, p = 0.014) and BMI (OR = 1.42, p = 0.032). The observations indicate suPAR as a promising marker reflecting LVH, especially in women at advanced age, independent of age-associated cardiac remodeling.
MSI-A: An Energy Efficient Approximated Cache Coherence Protocol

Energy consumption has become an essential factor in designing modern computer system architecture. Because of physical limits, the termination of Moore’s law and Dennard’s scaling has forced the computer design community to investigate new approaches to meet the requirements for computing resources. Approximate computing has emerged as a promising method for reducing energy consumption while trading a controllable quality loss. This paper asserts that an approximated cache coherence protocol preserves overall energy for computation. We can approximate the cache coherence protocol by adding approximated cache lines to a certain level without hindering the output. This paper introduces an enhanced approximated version of the MSI (Modified Shared Invalid) cache coherence protocol MSI-A (Modified Shared Invalid-Approx). We have verified MSI-A and MSI by employing LTL specifications in the NuSMV model checker. To illustrate the benefits of MSI-A, we have added DTMC (Discrete-Time Markov Chain) with PCTL (Probabilistic Computational Tree Logic). Although the PCTL proves the theory of approximation, we have also simulated the MSI-A in the TEJAS hardware simulator on PARSEC 3.0 to investigate the energy gains and cycle gains of MSI-A in varied applications. The cache lines considered to be approx are between 10 and 30 percent. Each application benefited from approximation according to its nature, and VIPS has indicated a total energy gain of 30.18 percent.
Optimum Dose Levels when Males and Females Differ in Response

In dose level experiments on insects males and females in general respond differently but may not be readily separable at the time of the experiment, although dead insects may be sexed. Each application of a specified dose therefore yields data on the response of males and of females. The theory of optimum experimental design is extended to this novel situation and applied to give designs for parameter estimation and for the estimation of the dose for a required probability of death, typically the LD 95 -value. Monte Carlo sampling is used to incorporate prior information into the design criteria
Formation of the nanodomain structures after pulse laser heating in lithium tantalate: experiment and computer simulation

ABSTRACT Formation of the micro- and nanodomain structures in congruent lithium tantalate single crystal under the action of pyroelectric field induced by pulse infrared laser irradiation at elevated temperatures was investigated. Three types of the self-organized domain structures were obtained: (1) the self-similar domain structure formed in crystal regions heated below Curie temperature, (2) the maze-type domain structure, and (3) irregular-shaped isolated domains formed in the regions heated above Curie temperature. The results of calculation of the time dependent spatial distribution of the temperature and pyroelectric field have been used for explanation of the peculiarities of domain structure formation.
One-time assignment algorithm to solve assignment problem

Hungarian algorithm is an optimal global algorithm to solve assignment problem.However,there are some deficien- cies in the classic Hungarian algorithm,such as the hardly achieving and slowly processing.This paper presents an improved algorithm on the base of the Hungarian algorithm,and improves the order of finding separate zero in Hungarian algorithm, as a result,it avoids the disadvantage of repeating assignment in Hungarian algorithm.By comparing the performance of the two algorithms for improved and classic Hungarian algorithm in complexity,running time and association accuracy,the results show that the improved Hungary arithmetic is a high-precision approximative algorithm,can be easily achieved and fastly processed,and can be applyed to the application of real-time engineering.
Unsupervised Domain Adaptation for Preference Learning Based Speech Emotion Recognition

Retrieving speech samples that have specific expressive content has many applications. It is desirable to build a preference learning framework that ranks speech samples according to emotional attribute values that generalize well to new domains. A popular architecture for preference learning is the RankNet framework, which uses a function to obtain the preference between pairs of speech sentences. This study explores implementing this function with alternative feature representations that are explicitly selected to reduce the mismatch between source and target domains. In particular, we implement our preference-learning based speech emotion recognition (SER) system using ladder networks and adversarial domain adaptation. The study also proposes a novel combination of these two unsupervised domain adaptation strategies. The experimental results in cross-corpus evaluations using the MSP-Podcast and MSP-IMPROV datasets reveal that the proposed adversarial domain adaptation on a ladder network-based feature representation performs the best across different conditions. The results also show that preference learning leads to better precision for retrieval tasks than comparable SER systems built to directly predict absolute emotional attribute scores.
A Clustering-Based Niching Particle Swarm Optimization

A simple clustering algorithm is used in niching particle swarm optimization(PSO) to replace the circular topological field based method for creating niche,and a clustering-based niching PSO is constructed. In the new algorithm,l-best PSO and a clustering algorithm are processed synchronously.When the number of particles in a cluster reaches the defined least particle number of a subswarm,a niche is formed based on this cluster.This algorithm can overcome the disadvantages of NichePSO by forming niches with different sizes and shapes.
Direction of Arrival Algorithm for Acoustic Sensors Operating Near Resonance

Recently, it has been possible to detect sound direction using phase-sensitive sensors placed much closer to each other than the sound wavelength. A typical setup combines two phase-sensitive sensors placed perpendicular to each other with a third omnidirectional sensor, the latter to determine the bearing quadrant. So far, algorithms that extract the sound direction from the sensor voltages have only been applicable to sensors with identical frequency responses. However, we have developed microelectromechanical systems (MEMS) sensors with simple harmonic oscillator-like resonance behavior for which these algorithms no longer apply. The operation near resonance greatly increases their performance, but the frequency dependencies of any two sensors are different from each other and from the omnidirectional sensor. In this letter, we show how to accommodate these differences, first, by forcing the two sensor peaks to conform to each other in both magnitude and phase, and second, by adjusting the phase of the omnidirectional sensor to approximately match the phases of the resonant sensors near their resonant frequencies. We then present two algorithms to extract the sound direction. We lay out the theoretical basis for our algorithms and present measurements of acoustic noise to show the algorithms’ effectiveness.
Noise mechanisms in a transonic high-pressure turbine stage

In modern ultra-high by-pass ratio turboengines, the noise contribution of both turbine stages and combustion chamber is expected to increase drastically. In the present work, both noise sources are evaluated in the realistic, fully three-dimensional transonic high-pressure turbine stage MT1 using large-eddy simulations (LES). An analysis of the basic flow field and the different turbine noise generation mechanisms is performed for two configurations: one with a steady inflow and one with an unsteady inflow, where a plane entropy wave train at a given frequency is injected at the inlet and propagates across the stage generating indirect noise. The noise is evaluated through Fourier analysis, dynamic mode decomposition of the flow field, and estimates of propagation coefficients. The steady case show three different dominant noise mechanisms: the usual stator wake brushing of the rotor blades, the several pulsating shocks in the stage and the vortex shedding. The forced results have additional tonal noise caused by the indirect noise mechanism generated by the acceleration and distortion of entropy spots. They are compared with previous two-dimensional (2D) simulations of a similar stator/rotor configuration, as well as with the compact theory. Results show that the upstream propagating entropy noise is reduced due to the choked turbine nozzle guide vane. Downstream acoustic waves are found to be of similar strength as the 2D case and larger than the blade passing frequency of the wake-interaction mechanism, highlighting the potential impact of indirect combustion noise on the overall noise signature of the engine.
What Has China Learned from Pandemics? The Evolution and Innovation of China’s Pandemic Response and Emergency Management Systems

Abstract Public sectors typically learn from crises, providing them the opportunity to improve the performance of crisis management. Through thematic analysis, this study maps out the evolution process of China’s pandemic response and emergency management systems and summarizes the characteristics of China’s crisis learning process, crisis learning subject, and crisis learning content. The findings indicate that China’s pandemic response and emergency management systems have the characteristics of crisis learning with gradual adjustments and continuous innovation. Specifically, under the impetus of China’s political factors, its pandemic response and emergency management systems have been able to learn from crises and have a complete crisis learning process. This crisis learning process includes adaptive learning, as well as single, double, and triple-loop learning. There is also a clear selection preference at various government levels, corresponding crisis learning processes and stages, and the path dependence of crisis learning content. Moreover, political accountability, attention, and pressure are the key factors opening the window of crisis learning, but the decision-making authority is the decisive factor of crisis learning in China’s centralized context. This study provides a theoretical framework for understanding the evolution and changes in the government’s crisis learning model and puts forward policy implications.
Hole spins in GE-SI nanowires

In a universal quantum computer, coherent control over the state of a quantum mechanical two-level system is needed. This requires interactions of the quantum state with its environment. Inherently, such interactions also lead to decoherence and thus limit the performance of the quantum computer. A profound knowledge of the relevant interaction mechanisms is therefore key to the realization of a quantum computer. In this thesis we use Ge-Si core-shell nanowires to investigate holes confined to one dimension. Mixing of heavy and light hole states leads to a strong, anisotropic spin-orbit interaction in this system. We define highly stable quantum dots of different lengths in the nanowire and controllably split up longer quantum dots into double quantum dots. The effective g-factor in these one-dimensional hole quantum dots is found to be highly anisotropic with respect to the nanowire axis as well as the electric-field axis. In double quantum dots, we observe shell filling of new orbitals and Pauli spin blockade of the second hole entering the orbital. The leakage current in the spin-blocked state is highly anisotropic with spin-flip cotunnelling as the dominant leakage mechanism. At finite magnetic fields, we also find signatures of leakage current induced by spin-orbit coupling and anisotropic Coulomb effects.
Digital Phenotype Representation by Statistical, Information Theory, Data-Driven Approach with Digital Health Data

Digital phenotyping (DP) is a multidisciplinary field of science that quantifies the individual level phenotype through active and passive data. Although DP is a multidisciplinary field, there lacks a technical and a systematic approach to representing DP. This work proposes the development of digital phenotype profile (DPP) to represent a user’s physical and behavioural health baseline through systematic investigations with an emphasis on robustness and explainability. To achieve this, a Statistical, Information Theory, and Data-driven (SID) pipeline will develop the foundation of the DPP. SID evaluates the non-linearity of the signal to offer inference for domain-specific feature extraction, evaluates the information theory to rank the DPP parameters, and imputes missing data for robust analysis, respectively. SID was applied to a 24-hr Multi-Level dataset and was able to represent individual DPPs. The respective DPPs were visualized and clusters of awake and asleep were used for individual specific modelling.