Miracle-dz/UniFAR-FTUs huggingface dataset:
Ontogenetic changes of geometrical and mechanical characteristics of the avian femur: a comparison between precocial and altricial birds

The mechanical performance of limb bones is closely associated with an animal's locomotor capability and is thus important to our understanding of animal behaviour. This study combined a geometrical analysis and three‐point bending tests to address the question of how the mechanical performance of the femurs of Japanese quail (Coturnix coturnix japonica) and pigeon (Columba livia domestica) respond to changing functional demands during ontogeny. Results showed that hatchling quails had stiff bone tissues, and the femoral ultimate loads scaled negatively with body mass, corresponding to high functional demands during early growth. The hatchling pigeon femora had weak material properties but they showed a dramatic increase in Young's modulus during growth. Consequently, although femoral cross‐sectional geometry showed negative allometry, the ultimate loads scaled positively with body mass. Older pigeons had more circular bone cross‐sections than younger pigeons, probably due to load stimulation changes occurred shortly after the onset of locomotion. Negative allometry and isometry of the cross‐sectional geometry of hind limb bones were observed in flying birds and ground‐dwelling birds, respectively. The correspondence between geometrical change and locomotor pattern suggests that ontogenetic changes in cross‐sectional geometry may be an effective indicator of avian locomotor behaviour.
Restoration of Cdk5, TrkB and Soluble N-ethylmaleimide-Sensitive Factor Attachment Protein Receptor Proteins after Chronic Methylphenidate Treatment in Spontaneous Hypertensive Rats, a Model for Attention-Deficit Hyperactivity Disorder

Objective Synaptic vesicle mobilization and neurite outgrowth regulation molecules were examined in modulation of effects of methylphenidate (MPH) in Spontaneous Hypertensive Rats (SHRs), a model for attention-deficit hyperactivity disorder (ADHD). Methods We compared the changes in the protein expression level of Cyclin dependent kinase 5 (Cdk5) and molecular substrates of Cdk5; tropomyosin receptor kinase B (TrkB), syntaxin 1A (STX1A) and synaptosomal-associated protein 25 (SNAP25). Comparisons were made in prefrontal cortex of vehicle (distilled water i.p. for 7 days)-treated SHRs, vehicle-treated Wistar Kyoto Rats (WKYs) and MPH (2 mg/kg i.p. for 7 days) treated SHRs. Results The Cdk5 level of vehicle-treated SHRs was significantly decreased compared to the Cdk5 level of vehicle-treated WKY rats, but was restored to the expression level of vehicle-treated WKYs in MPH-treated SHR. The ratio of p25/p35 was significantly decreased in MPH-treated SHR compared to vehicle-treated SHR. Moreover, TrkB, STX1A and SNAP25 of vehicle-treated SHRs were significantly decreased compared to vehicle-treated WKY rats, but were restored to the expression level of vehicle-treated WKYs in MPH-treated SHR. Conclusion The results show that Cdk5, TrkB, STX1A, and SNAP25 were involved in the modulation of MPH effects in prefrontal cortex of SHRs and play important role in treatment of ADHD.
Phase transitions of Best‐of‐two and Best‐of‐three on stochastic block models

This is concerned with voting processes on graphs where each vertex holds one of two different opinions. In particular, we study the Best‐of‐two and the Best‐of‐three. Here at each synchronous round, each vertex updates its opinion to match the majority among the opinions of two random neighbors and itself (the Best‐of‐two) or the opinions of three random neighbors (the Best‐of‐three). In this study, we consider the Best‐of‐two and the Best‐of‐three on the stochastic block model G(2n, p, q), which is a random graph consisting of two distinct Erdős–Rényi graphs G(n, p) joined by random edges with a density q ≤ p. We prove phase transition results for these processes: there is a threshold r∗ such that, if q/p > r∗ then the process reaches consensus within O(logn) rounds and the process requires exp(Ω(n)) rounds if q/p < r∗. For the Best‐of‐two and Best‐of‐three, the thresholds are r∗=5−2 and r∗ = 1/7, respectively.
Random Young Tower and Quenched Limit Law

We obtain quenched almost sure invariance principle for Random Young Tower. We apply our result to i.i. d perturbations of non-uniformly expanding maps. In particular, we answer one open question in \cite{BBM}. In Appendix, we also give quenched (functional) central limit theorem for general random dynamical system under an average assumption.
Spatial process decomposition for quantitative imaging biomarkers using multiple images of varying shapes

Quantitative imaging biomarkers (QIB) are extracted from medical images in radiomics for a variety of purposes including noninvasive disease detection, cancer monitoring, and precision medicine. The existing methods for QIB extraction tend to be ad hoc and not reproducible. In this article, a general and flexible statistical approach is proposed for handling up to three‐dimensional medical images and reasonably capturing features with respect to specific spatial patterns. In particular, a model‐based spatial process decomposition is developed where the random weights are unique to individual patients for component functions common across patients. Model fitting and selection are based on maximum likelihood, while feature extractions are via optimal prediction of the underlying true image. Simulation studies are conducted to investigate the properties of the proposed methodology. For illustration, a cancer image data set is analyzed and QIBs are extracted in association with a clinical endpoint.
Radiative decays of f 1(1285) as the K*K̅ molecular state

Within a picture of the $f_1(1285)$ being a dynamically generated resonance from the $ K^*\bar K$ interactions, we estimate the rates for the radiative transitions of the $f_1(1285)$ meson to the vector mesons $\rho^0$,$\omega$ and $\phi$. These radiative decays proceed via the kaon loop diagrams. The calculated results are in fair agreement with the experimental measurements. Some predictions can be tested by experiments and their implementation and comparison with these predictions will be valuable to decode the nature of the $f_1(1285)$ state.
Discovering Association with Copula Entropy

Discovering associations is of central importance in scientific practices. Currently, most researches consider only linear association measured by correlation coefficient, which has its theoretical limitations. In this paper, we propose a new method for discovering association with copula entropy -- a universal applicable association measure for not only linear cases, but nonlinear cases. The advantage of the method based on copula entropy over traditional method is demonstrated on the NHANES data by discovering more biomedical meaningful associations.
Reprojection R-CNN: A Fast and Accurate Object Detector for 360{\deg} Images

360{\deg} images are usually represented in either equirectangular projection (ERP) or multiple perspective projections. Different from the flat 2D images, the detection task is challenging for 360{\deg} images due to the distortion of ERP and the inefficiency of perspective projections. However, existing methods mostly focus on one of the above representations instead of both, leading to limited detection performance. Moreover, the lack of appropriate bounding-box annotations as well as the annotated datasets further increases the difficulties of the detection task. In this paper, we present a standard object detection framework for 360{\deg} images. Specifically, we adapt the terminologies of the traditional object detection task to the omnidirectional scenarios, and propose a novel two-stage object detector, i.e., Reprojection R-CNN by combining both ERP and perspective projection. Owing to the omnidirectional field-of-view of ERP, Reprojection R-CNN first generates coarse region proposals efficiently by a distortion-aware spherical region proposal network. Then, it leverages the distortion-free perspective projection and refines the proposed regions by a novel reprojection network. We construct two novel synthetic datasets for training and evaluation. Experiments reveal that Reprojection R-CNN outperforms the previous state-of-the-art methods on the mAP metric. In addition, the proposed detector could run at 178ms per image in the panoramic datasets, which implies its practicability in real-world applications.
Direct numerical simulation of high-pressure mixing in turbulent jets.

Combustion in automotive and aerospace applications employing diesel, gas turbine and liquid rocket engines is preceded by injection and mixing of fuel and oxidizer at high pressures, often exceeding mixture critical values. Experimental observations indicate that the jets injected at supercritical pressures exhibit significantly different dynamics than the jets at subcritical conditions, owing to the lack of distinct liquid and gas phases in supercritical state. As a result, the averaged flow quantities such as the potential core length, jet spatial growth rate and velocity decay profiles differ in the two conditions, resulting in different mixed-fluid distributions. In this study, turbulent jet direct numerical simulations (DNS) are performed to examine the variations in statistics between injection of Nitrogen ($\mathrm{N_{2}}$) in Nitrogen ($\mathrm{N_{2}}$) at subcritical (perfect-gas) and supercritical conditions. Isothermal round jets at Reynolds number ($Re_{D}$), based on jet diameter ($D$) and jet orifice velocity ($U_{0}$), of $5000$ and Mach number of $0.6$ are considered. For mixing analyses, a passive scalar transported with the flow is examined.
Some results on the Ryser design conjecture

A Ryser design has equally many points as blocks with the provision that every two blocks intersect in a fixed number of points λ . An improper Ryser design has only one replication number and is thus symmetric design. A proper Ryser design has two replication numbers. The only known construction of a Ryser design is the complementation of a symmetric design. Such a Ryser design is called a Ryser design of type 1. Let D denote a Ryser design of order v , index λ and replication numbers r 1 , r 2 . Let e i denote the number of points of D with replication number r i (with i = 1 , 2 ). Call a block A small (respectively large) if | A | < 2 λ (respectively | A | > 2 λ ) and average if | A | = 2 λ . Let D denote the integer e 1 − r 2 and let ρ > 1 denote the rational number r 1 − 1 r 2 − 1 . Main results of the present article are the following. For every block A , r 1 ≥ | A | ≥ r 2 (this improves an earlier known inequality | A | ≥ r 2 ). If there is no small block (respectively no large block) in D , then D ≤ − 1 (respectively D ≥ 0 ). With an extra assumption e 2 > e 1 an earlier known upper bound on v is improved from a cubic to a quadratic in λ . It is also proved that if v ≤ λ 2 + λ + 1 and if ρ equals λ or λ − 1 , then D is of type 1. Finally, a Ryser design with 2 n + 1 points is shown to be of type 1.
A Survey on Hardware-based Security Mechanisms for Internet of Things

The vast areas of applications for IoTs in future smart cities, industrial automation, smart transportation systems, and smart health facilities represent a thriving surface for several security attacks with enormous economic, environmental and societal impacts. This survey paper presents a review of several security challenges of emerging IoT networks and discusses some of the attacks and their countermeasures based on different domains in IoT networks. Most conventional security solutions for IoT networks are adopted from communication networks while noting the particular characteristics of IoT networks such as the huge number of nodes, heterogeneity of the network, and the limited energy, communication, and computation of the nodes, these conventional security methods are not really adequate. One important challenge toward utilizing common secret key-based cryptographic methods in very large scale IoT networks is the problem of secret key generation, distribution, and storage in IoT devices and more importantly protecting these secret keys from physical attacks. Physically unclonable functions (PUFs) are recently utilized as a promising hardware security solution for identification and authentication in IoT networks. Since PUFs extract the unique hardware characteristics, they potentially offer an affordable and practical solution for secret key generation. However, several barriers limit the applications of different types of PUFs for key generation purposes. We discuss the advantages of PUF-based key generation methods, review the state-of-the-art techniques in this field and propose a reliable PUF-based solution for secret key generation using resistive random-access memories (RERAMs) embedded in IoTs.
Hand-Gesture-Recognition Based Text Input Method for AR/VR Wearable Devices

Static and dynamic hand movements are basic way for human-machine interactions. To recognize and classify these movements, first these movements are captured by the cameras mounted on the augmented reality (AR) or virtual reality (VR) wearable devices. The hand is segmented using segmentation method and its gestures are passed to hand gesture recognition algorithm, which depends on depth-wise separable convolutional neural network for training, testing and finally running smoothly on mobile AR/VR devices, while maintaining the accuracy and balancing the load. A number of gestures are processed for identification of right gesture and to classify the gesture and ignore the all intermittent gestures. With proposed method, a user can write letters and numbers in air by just moving his/her hand in air. Gesture based operations are performed, and trajectory of hand is recorded as handwritten text. Finally, that handwritten text is processed for the text recognition.
Integer Programming, Constraint Programming, and Hybrid Decomposition Approaches to Discretizable Distance Geometry Problems

Given an integer dimension K and a simple, undirected graph G with positive edge weights, the Distance Geometry Problem (DGP) aims to find a realization function mapping each vertex to a coordinate in [Formula: see text] such that the distance between pairs of vertex coordinates is equal to the corresponding edge weights in G. The so-called discretization assumptions reduce the search space of the realization to a finite discrete one, which can be explored via the branch-and-prune (BP) algorithm. Given a discretization vertex order in G, the BP algorithm constructs a binary tree where the nodes at a layer provide all possible coordinates of the vertex corresponding to that layer. The focus of this paper is on finding optimal BP trees for a class of discretizable DGPs. More specifically, we aim to find a discretization vertex order in G that yields a BP tree with the least number of branches. We propose an integer programming formulation and three constraint programming formulations that all significantly outperform the state-of-the-art cutting-plane algorithm for this problem. Moreover, motivated by the difficulty in solving instances with a large and low-density input graph, we develop two hybrid decomposition algorithms, strengthened by a set of valid inequalities, which further improve the solvability of the problem. Summary of Contribution: We present a new model to solve a combinatorial optimization problem on graphs, MIN DOUBLE, which comes from the highly active area of distance geometry and has applications in a wide variety of fields. We use integer programming (IP) and present the first constraint programming (CP) models and hybrid decomposition methods, implemented as a branch-and-cut procedure, for MIN DOUBLE. Through an extensive computational study, we show that our approaches advance the state of the art for MIN DOUBLE. We accomplish this by not only combining generic techniques from IP and CP but also exploring the structure of the problem in developing valid inequalities and variable fixing rules. Our methods significantly improve the solvability of MIN DOUBLE, which we believe can also provide insights for tackling other problem classes and applications.
Specular- and Diffuse-reflection-based Face Liveness Detection for Mobile Devices

In light of the rising demand for biometric-authentication systems, preventing face spoofing attacks is a critical issue for the safe deployment of face recognition systems. Here, we propose an efficient face presentation attack detection (PAD) algorithm that requires minimal hardware and only a small database, making it suitable for resource-constrained devices such as mobile phones. Utilizing one monocular visible light camera, the proposed algorithm takes two facial photos, one taken with a flash, the other without a flash. The proposed $SpecDiff$ descriptor is constructed by leveraging two types of reflection: (i) specular reflections from the iris region that have a specific intensity distribution depending on liveness, and (ii) diffuse reflections from the entire face region that represents the 3D structure of a subject's face. Classifiers trained with $SpecDiff$ descriptor outperforms other flash-based PAD algorithms on both an in-house database and on publicly available NUAA, Replay-Attack, and SiW databases. Moreover, the proposed algorithm achieves statistically significantly better accuracy to that of an end-to-end, deep neural network classifier, while being approximately six-times faster execution speed.
An empirical evaluation of recent texture features for the classification of natural images

ABSTRACT Feature extraction and classification are considered to be major tasks in image processing applications. This paper presents the performance of three feature extraction algorithms: the Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), and Dominant Rotation Local Binary Pattern (DRLBP). The SIFT is robust to occlusion and clutter since it considers local features. The SURF is used to generate features for small objects and the extraction of interest points is also faster. The DRLBP is computationally efficient, given that it considers dominant and frequently occurring patterns. The texture performance of all these descriptors is measured in terms of accuracy on rotation invariant, scale invariant, and illumination invariant images taken from the Brodatz and Outex texture image databases. The texture images are classified using the K-Nearest Neighbor (KNN) and Naïve Bayes classifiers. The experimental result shows that the DRLBP with the KNN classifier provides better classification accuracy against various challenges.
Large thermoelectric power factor of high-mobility transition-metal dichalcogenides with 
1T″
 phase

The experimental studies about monolayer transition metal dichalcogenides in the recent year reveal this kind of compounds have many metastable phases with unique physical properties, not just 1H phases. Here, we focus on the 1T'' phase and systematically investigate the electronic structures and transport properties of MX2 (M=Mo, W; X=S, Se, Te) using the first-principles calculations with Boltzmann transport theory. And among them, only three molybdenum compounds has small direct bandgap at K point, which derive from the distortion of octahedral-coordination [MoS6]. For these three cases, hole carrier mobility of MoSe2 is estimated as 690 cm^2/Vs at room temperature, far more high than that of other two MoX2. For the reason, the combination of the modest carrier effective mass and weak electron-phonon coupling lead to the outstanding transport performance of MoSe2. The Seebeck coefficient of MoSe2 is also evaluated as high as 300 10^-6 V/K at room temperature. Due to the temperature dependent mobility of T^-1.9 and higher Seebeck coefficient at low temperature, it is found that MoSe2 has a large thermoelectric power factor around 6 10^-3 W/mK^2 in the low to intermediate temperature range. The present results suggests 1T'' MoSe2 maybe a excellent candidate for thermoelectric material.
Enumeration of regular graphs by using the cluster in high efficiency

In this note, we proposed a method to enumerate regular graphs on the cluster in high efficiency after partitioning. Here, we chose the GENREG, a classical regular graph generation, to adapt with supercomputers and clusters by using MPI, which can implement loading balance and support the parallel scale of thousands of CPU cores after task partitioning. Using this method and modified version of the generation, we have got some optimal regular graphs with the minimum diameter. Moreover, we got the exact counting number of 4-regular graphs for order 23, which expand the sequence of A006820 in OEIS. This study improves the efficiency of enumerating regular graphs with current available HPC resources and makes the work of regular graph counting more convenient on the supercomputer.
Nonlinear spectroscopy of a three level atom strongly interacting with a quantized cavity mode

We present numerical simulation of the previously unexplored multiphoton spectra of a realistic three level atom one of whose transitions is strongly coupled to a cavity mode while the other is driven by a classical light employing only standard dipole and rotating wave approximations. We show that, if the atom-cavity coupling constant is much larger than atomic and cavity dissipation rates, a relatively weak signal can excite multiphoton transitions. We also show that under resonant atom-cavity conditions, the dark state gets dressed by the cavity photons which leads to a Raman spectra with richly complex structure involving dark-state mediated discrete multiphoton transitions as well as direct Raman transitions.
A stabilized logical quantum bit encoded in grid states of a superconducting cavity

The majority of quantum information tasks require error-corrected logical qubits whose coherence times are vastly longer than that of currently available physical qubits. Among the many quantum error correction codes, bosonic codes are particularly attractive as they make use of a single quantum harmonic oscillator to encode a correctable qubit in a hardware-efficient manner. One such encoding, based on grid states of an oscillator, has the potential to protect a logical qubit against all major physical noise processes. By stroboscopically modulating the interaction of a superconducting microwave cavity with an ancillary transmon, we have successfully prepared and permanently stabilized these grid states. The lifetimes of the three Bloch vector components of the encoded qubit are enhanced by the application of this protocol, and agree with a theoretical estimate based on the measured imperfections of the experiment.
G-flocking: Flocking Model Optimization based on Genetic Framework

Flocking model has been widely used to control robotic swarm. However, with the increasing scalability, there exist complex conflicts for robotic swarm in autonomous navigation, brought by internal pattern maintenance, external environment changes, and target area orientation, which results in poor stability and adaptability. Hence, optimizing the flocking model for robotic swarm in autonomous navigation is an important and meaningful research domain.