Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Method for Feature Point Matching: Inner and Exterior Product

In this paper, a novel feature descriptor called gradient correlation descriptor (GCD) is proposed. The GCD descriptor uses the gradient correlation measure defined by the inner and exterior product to characterize the gradient distributions in neighborhoods of feature points, and it has the following advantages: Its construction is very simple because of only the inner and exterior product operations are used; Its distinctive performance is better than the region-based SIFT descriptors since the gradient correlation measure can effectively characterize the gradient distributions in neighborhoods of feature points; In the gradient correlation measure the use of gradient mean makes it is not sensitive to the estimate precision of main orientation of feature point, and thus can provide a better stabilization to image rotation; The gradient correlation measure makes it also has very good adaptability to image affine transform, image blur, JPEG compression as well as illumination change.
Attracting and Retaining Highly Effective Educators in Hard-to-Staff Schools

Efforts to attract and retain effective educators in high poverty public schools have had limited success. Dallas ISD addressed this challenge by using information produced by its evaluation and compensation reforms as the basis for effectiveness-adjusted payments that provided large compensating differentials to attract and retain effective teachers in its lowest achievement schools. The Accelerating Campus Excellence (ACE) program offers salary supplements to educators with records of high performance who are willing to work in the most educationally disadvantaged schools. We document that ACE resulted in immediate and sustained increases in student achievement, providing strong evidence that the multi-measure evaluation system identifies effective educators who foster the development of cognitive skills. The improvements at ACE schools were dramatic, bringing average achievement in the previously lowest performing schools close to the district average. When ACE stipends are largely eliminated, a substantial fraction of highly effective teachers leaves
Broadening of Nearly Neutral Iron Emission Line of GX 301–2 Observed with ASCA

We present results from detailed analyses of the ASCA data of the X-ray pulsar GX 301-2 during apastron, periastron, and intermediate orbital phases. We find that the iron Kα emission line possesses a nonzero energy width of 40-80 eV in Gaussian σ during all of the three orbital phases. The observed range in the width is far larger than that expected from velocity shifts in the stellar wind. Quantitative evaluation of the width indicates that the iron Kα line of GX 301-2 is emitted from a region within ~1010 cm from the neutron star via fluorescence. In addition, we have also discovered a broad wing with σ ≃ 135 eV in the profile of the iron Kα line during the periastron phase. Since the wing profile is modulated according to the spin phase of the pulsar, we conclude that the wing is composed of blue- and redshifted 6.4 keV iron lines emanating from the bipolar accretion columns. We find that, within the accretion column, the He II ionization front is formed at ~107 cm from the pulsar, outside of which iron can be kept in a nearly neutral ionization state. The observed equivalent width of the broad wing component (~100 eV) can be explained by emission in the accretion columns outside the He II ionization front.
Robustness and Dark-Matter Observation

Abstract Current cosmological observations place little constraints on the nature of dark matter, allowing the development of a large number of models and various methods for probing their properties, which seem to provide ideal grounds for the employment of robustness arguments. In this article, the extent to which such arguments can be used to overcome various methodological and theoretical challenges is examined. The conclusion is that although robustness arguments have a limited scope in the context of dark-matter research, they can still be used to increase the scientists’ confidence about the properties of specific models.
A Dependence Stability Bound based on the VC Dimension for Relational Classification

Relational classification (RC) is concerned with the application of statistical learning to relational data. RC models do not have improved stability to smooth the perturbations generated by variations in the correlation between the relational data. Therefore, few studies have attempted to derive a bound and develop a stability learning framework for RC models. To solve this problem, we derive a learning bound with a new measure dependence stability and a limited Vapnik–Chervonenkis (VC) dimension. Based on the learning bound, we then design a stable learning framework that serves as a guideline for the development of new learning algorithms for a broad class of RC models. Applying a Markov logic network on synthesized and real-world datasets, our experimental results demonstrate that our bound can be tight if the RC model has appropriate dependence stability and limited VC dimension and our learning framework increases the stability of RC models while reducing the deviation between empirical risk and true risk.
Single photon emission from a site-controlled quantum dot-micropillar cavity system

We demonstrate the deterministic integration of single site-controlled quantum dots (SCQDs) into micropillar cavities. Spatial resonance between single positioned QDs and GaAs/AlAs micropillar cavities was achieved using cross markers for precise SCQD-cavity alignment. Cavity effects are clearly reflected in an enhanced photoluminescence intensity when tuning SCQD emission lines through the fundamental cavity resonance. Single photon emission from a spatially and spectrally coupled SCQD-resonator system is confirmed by photon autocorrelation measurements yielding a g(2)(0) value of 0.12.
Placement of EV Fast Charging Station in Distribution System Based on Voltage Stability Index Strategy

In this modern era, to keep the environment pollution-free, electric vehicles have taken a lead in the transportation system. With rising EV demand, increasing the number of charging stations in the distribution network is extremely important. In this context, fast charging is very efficient for public services as it reduces the waiting time of EV users. However, the power requirement of the fast-charging station is very high which further degrades the voltage profile, and voltage stability and increases the total losses of the distribution system. Therefore, proper allocation of the charging station in the distribution network is an important aspect. The improper allocation may further deteriorate the system’s condition. In this paper, the Voltage Stability Index is used to identify the healthier and weaker zone of the distribution network. Based on this, charging stations are placed in the healthier zone of the distribution network as weaker zones are on the verge of voltage collapse. In the present work, the placement is done without violating the operational constraints such as power equality, voltage limit, and power flow limit. The research work is being carried out using 33 bus Radial Distribution Network and 28 bus Indian Rural Distribution Network and data on vehicle range and battery capacity is being collected for recently used vehicles. The backward forward sweep method is used to solve the power flow problem. MATLAB – 9.8 (R2020a) tool is used as a programming platform for the present work.
Activation of TRPV1-Expressing Renal Sensory Nerves of Rats with N-Oleoyldopamine Attenuates High-Fat-Diet-Induced Impairment of Renal Function

Enhanced renal sympathetic nerve activity (RSNA) contributes to obesity-induced renal disease, while the role of afferent renal nerve activity (ARNA) is not fully understood. The present study tested the hypothesis that activating the transient receptor potential vanilloid 1 (TRPV1) channel in afferent renal nerves suppresses RSNA and prevents renal dysfunction and hypertension in obese rats. N-oleoyldopamine (OLDA, 1 ng/kg, daily) was administrated intrathecally (T8-L3) via an indwelled catheter to chronically activate, TRPV1-positive afferent renal nerves in rats fed a chow diet or high-fat diet (HFD) for 8 weeks. HFD intake significantly increased the body weight, impaired glucose and insulin tolerance, decreased creatinine clearance, and elevated systolic blood pressure in rats compared with the levels of the chow-fed rats (all p < 0.05). An intrathecal OLDA treatment for 8 weeks did not affect the fasting glucose level, glucose tolerance, and insulin tolerance in rats fed either chow or HFD. As expected, the chronic OLDA treatment significantly increased the levels of plasma calcitonin gene-related peptide and substance P and ARNA in the HFD-fed rats (all p < 0.05). Interestingly, the OLDA treatment decreased the urinary norepinephrine level and RSNA in rats fed HFD (both p < 0.05). Importantly, the OLDA treatment attenuated HFD-induced decreases in creatinine clearance and urinary Na+ excretion and increases in the plasma urea level, urinary albumin level, and systolic blood pressure at the end of an 8-week treatment (all p < 0.05). Taken together, the intrathecal administration of OLDA ameliorates the enhancement of RSNA, renal dysfunction, and hypertension in obese rats. These findings shed light on the roles of TRPV1-positive renal afferent nerves in obesity-related renal dysfunction and hypertension.
Databases on the Indonesian Prefixes PE- and PEN-

This paper provides the theoretical grounding in constituting databases related to PE- and PEN-, two Indonesian nominalizing prefixes, which have various meanings (e.g., patient, agent, or instrument). The first database contains the words with PE- and PEN- whereas the second database provides the cosine similarity between two words of interest. Using a written Indonesian corpus as the primary source (Leipzig Corpora Collection), the databases contain the following information: PE- or PEN- prefixes, allomorph of PEN-, base word, semantics role, morphological variation, cosine similarity, as well as the word frequency. Furthermore, this paper elaborates the theoretical consideration on how each information was cultivated. In building the databases, Indonesian morphological parser and Word to Vector were used to analyze the Indonesian morphological status and to put the words in the corpus into a vector. In addition, manual verification for the data against the Indonesian comprehensive dictionary was also conducted. In the end, the databases are available for free so that the data could be used as materials for a corpus-based analysis on Indonesian morphology. This research shed light to a careful and thorough classification of the open-access databases of PE- and PEN- from their allomorphs, base word, semantics role, and morphological variation. The information provided in this article is hoped to be contributive in Indonesian morphology specifically, and other linguistics fields (e.g., corpus linguistics and quantitative linguistics) in general.
XSS Detection Using Hybrid Machine Learning Methods

Nowadays, the increased use of Web services has resulted in a rise in the danger of web application attacks. These types of attacks are become more severe by the day. Intrusion detection systems (IDS) are essential in proactively detecting Web threats. The network traffic contains a significant number of characteristics. Identification of irrelevant and essential features is a primary task in machine learning. Pre-processing, feature selection, and naive Bayes (NB) classifier proposed in this study as part of IDS for Web application attack. The system is evaluated on the CICIDS-2017 dataset and employs a random forest to select significant features for cross-site scripting (XSS) attack classification. Experimental results show that the feature selection using random forest enhanced the classifier's performance in terms of accuracy, detection rate and execution time, are also reducing false alarms.in comparison with using all features in the classification method.
The Interfacial Thermodynamics of Micro Heat Pipes

Successful analysis and modeling of micro heat pipes requires a complete understanding of the vapor-liquid interface. A thermodynamic model of the vapor-liquid interface in micro heat pipes has been formulated that includes axial pressure and temperature differences, changes in local interfacial curvature, Marangoni effects, and the disjoining pressure. Relationships were developed for the interfacial mass flux in an extended meniscus, the heat transfer rate in the intrinsic meniscus, the 'thermocapillary' heat-pipe limitation, as well as the nonevaporating superheated liquid film thickness that exists between adjacent menisci and occurs during liquid dry out in the evaporator. These relationships can be used to define quantitative restrictions and/or requirements necessary for proper operation of micro heat pipes. They also provide fundamental insight into the critical mechanisms required for proper heat pipe operation. 29 refs., 6 figs.
Chemical sensing in slotted photonic crystal heterostructure cavities

We fabricated slotted photonic crystal waveguides and cavities supporting resonant modes in air. Their peculiar geometry enables the detection of refractive index changes in a given analyte with high sensitivity because of the large overlap between the optical mode and the analyte. This yields a high figure of merit for the sensitivity of the device and we are able to report values of S=Δλ/Δn over 1500. By applying a photonic crystal heterostructure to the slotted geometry, we are able to create high quality-factor cavities essential for realizing low detection limits up to Q=50 000.
Physiological Rhythms and Biological Variation of Biomolecules: The Road to Personalized Laboratory Medicine

The concentration of biomolecules in living systems shows numerous systematic and random variations. Systematic variations can be classified based on the frequency of variations as ultradian (<24 h), circadian (approximately 24 h), and infradian (>24 h), which are partly predictable. Random biological variations are known as between-subject biological variations that are the variations among the set points of an analyte from different individuals and within-subject biological variation, which is the variation of the analyte around individuals’ set points. The random biological variation cannot be predicted but can be estimated using appropriate measurement and statistical procedures. Physiological rhythms and random biological variation of the analytes could be considered the essential elements of predictive, preventive, and particularly personalized laboratory medicine. This systematic review aims to summarize research that have been done about the types of physiological rhythms, biological variations, and their effects on laboratory tests. We have searched the PubMed and Web of Science databases for biological variation and physiological rhythm articles in English without time restrictions with the terms “Biological variation, Within-subject biological variation, Between-subject biological variation, Physiological rhythms, Ultradian rhythms, Circadian rhythm, Infradian rhythms”. It was concluded that, for effective management of predicting, preventing, and personalizing medicine, which is based on the safe and valid interpretation of patients’ laboratory test results, both physiological rhythms and biological variation of the measurands should be considered simultaneously.
Reduced rank stochastic regression with a sparse singular value decomposition

Summary. For a reduced rank multivariate stochastic regression model of rank r*, the regression coefficient matrix can be expressed as a sum of r* unit rank matrices each of which is proportional to the outer product of the left and right singular vectors. For improving predictive accuracy and facilitating interpretation, it is often desirable that these left and right singular vectors be sparse or enjoy some smoothness property. We propose a regularized reduced rank regression approach for solving this problem. Computation algorithms and regularization parameter selection methods are developed, and the properties of the new method are explored both theoretically and by simulation. In particular, the regularization method proposed is shown to be selection consistent and asymptotically normal and to enjoy the oracle property. We apply the proposed model to perform biclustering analysis with microarray gene expression data.
Pyramide résiduelle pour la compression d'images

We review here the Laplacian pyramid originally defined for image compression. We study the expansion principle of Gaussian pyramid layers which is essential to build a Laplacian pyramid. The standard expansion is a simple interpolation scheme. We show that such scheme is unsuited, and that a layer must be expanded by induction. This new scheme based on inductive expansion yields a new pyramidal model called “residual pyramid”. We use this new model for lossless image compression.
Effects of surface elasticity and residual surface tension on the natural frequency of microbeams

Surface effects often play a significant role in the physical properties of micro- and nanosized materials and structures. In this letter, the authors presented a theoretical model directed towards investigation of the effects of both surface elasticity and residual surface tension on the natural frequency of microbeams. A thin surface layer was introduced on the upper and lower surfaces to rationalize the near-surface material properties that are different from the bulk material. An explicit solution is derived for the natural frequency of microbeams with surface effects. This study might be helpful for the design of microbeam-based sensors and some related measurement techniques.
Security Classification of Mobile Intelligent Terminal Based on Multi-source Data Fusion

The application of mobile intelligent terminal in the environment is very complex, and its own computing capacity is also very limited, so it is vulnerable to malicious attacks. The security classification of mobile intelligent terminals can effectively ensure the security of their use. Therefore, a security classification method for mobile intelligent terminals based on multi-source data fusion is proposed. The Boolean value is used to count the multi-source data of the mobile intelligent terminal, and the word frequency method is used to calculate the weight of the multi-source data of the mobile intelligent terminal. The D-S evidence theory is used to complete the multi-source data fusion of the mobile intelligent terminal and implement the multi-source data fusion processing of the mobile intelligent terminal. On this basis, the security level permission value of mobile intelligent terminal is calculated to achieve the security level division of mobile intelligent terminal based on multi-source data fusion. The experimental results show that the accuracy of mobile intelligent terminal security classification is higher than 96% and the classification time is less than 3.8 ms after the application of the proposed method. Therefore, the security level of mobile intelligent terminals after the application of this method is high, and the security performance of mobile intelligent terminals is strong, which can effectively improve the accuracy of security classification and shorten the time of security classification.
THE COUPLED EVOLUTION OF ELECTRONS AND IONS IN CORONAL MASS EJECTION-DRIVEN SHOCKS

We present simulations of coronal mass ejections (CMEs) performed with a new two-temperature coronal model developed at the University of Michigan, which is able to address the coupled thermodynamics of the electron and proton populations in the context of a single fluid. This model employs heat conduction for electrons, constant adiabatic index (γ = 5/3), and includes Alfvén wave pressure to accelerate the solar wind. The Wang–Sheeley–Arge empirical model is used to determine the Alfvén wave pressure necessary to produce the observed bimodal solar wind speed. The Alfvén waves are dissipated as they propagate from the Sun and heat protons on open magnetic field lines to temperatures above 2 MK. The model is driven by empirical boundary conditions that includes GONG magnetogram data to calculate the coronal field, and STEREO/EUVI observations to specify the density and temperature at the coronal boundary by the Differential Emission Measure Tomography method. With this model, we simulate the propagation of fast CMEs and study the thermodynamics of CME-driven shocks. Since the thermal speed of the electrons greatly exceeds the speed of the CME, only protons are directly heated by the shock. Coulomb collisions low in the corona couple the protons and electrons allowing heat exchange between the two species. However, the coupling is so brief that the electrons never achieve more than 10% of the maximum temperature of the protons. We find that heat is able to conduct on open magnetic field lines and rapidly propagates ahead of the CME to form a shock precursor of hot electrons.
The Observation Record Length Necessary to Generate Robust Soil Moisture Percentiles

AbstractThe ability to use in situ soil moisture for large-scale soil moisture monitoring, model and satellite validation, and climate investigations is contingent on properly standardizing soil moisture observations. Percentiles are a useful method for homogenizing in situ soil moisture. However, very few stations have been continuously monitoring in situ soil moisture for 20 years or more. Therefore, one challenge in evaluating soil moisture is determining whether the period of record is sufficient to produce a stable distribution from which to generate percentiles. In this study daily in situ soil moisture observations, measured at three separate depths in the soil column at 15 stations in the United States and Canada, are used to determine the record length that is necessary to generate a stable soil moisture distribution. The Anderson–Darling test is implemented, both with and without a Bonferroni adjustment, to quantify the necessary record length. The authors evaluate how the necessary record lengt.
A Universal Test Sequence for CMOS Scan

A systematic method for analyzing all possible faults within the scan path of a scan-based CMOS circuit is given. The analysis shows that both logic and current monitoring are necessary in order to detect all irredundant faults. A universal test sequence is derived based on the analysis of single bridging faults. This sequence also detects all irredundant stuck-at and stuck-open faults. In this paper all possible faults in the scan path of a scan- based system are considered. We start by examining the effects of BFs in scan registers. We show that a systematic analysis for all possible BFs is necessary to guarantee high quality tests. A classification method for all possible BFs is then described and BFs in each category analyzed. We show that all but one BF is detectable if both current supply and scan output data are monitored. The undetectable fault is actually a redundant one that cannot affect the normal operation of the circuit. For each irredundant fault we derive a test (sequence). A test sequence that contains all test sequences for individual faults is then found. This sequence also detects all irredundant stuck-at and stuck- open faults. 2 Scan registers can be implemented in several ways(ll). The dis- cussion in this paper is based on the implementation shown in Figure 1. Our analysis can easily be applied to other types of scan registers. Figure 1(a) and (b) are the switch- and gate-level descrip