Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of third-order uniform acceleration and deceleration trajectory based on Simulink StateFlow

In order to improve the movement accuracy of the robot, let the movement of the robot trajectory smoother, we design a third-order uniform acceleration / deceleration trajectory based on the Jerk value. Through the establishment of the mathematical model of the trajectory, combined with the logic control algorithm, constituted the core of the trajectory control. Finally, the StateFlow toolbox in Simulink as the carrier. Using the state module in StateFlow combined with the logic control conversion module, the third-order uniform acceleration and deceleration trajectory design is realized.
Feynman-Enderlein Path Integral for Single-Molecule Nanofluidics

I present a photon statistics method for quasi-one dimensional sub-diffraction limited nanofluidic motions of single molecules using Feynman-Enderlein path integral approach. The theory is validated in Monte Carlo simulation platform to provide fundamental understandings of Knudsen type flow and diffusion of single molecule fluorescence in liquid. Distribution of single molecule burst size can be precise enough to detect molecular interaction. Realisation of this theoretical study considers several fundamental aspects of single-molecule nanofluidics, such as electrodynamics, photophysics, and multi-molecular events/molecular shot noise. I study two different sizes of molecules, one with 2 nm and another with 20 nm hydrodynamic radii driven by a wide range of flow velocities. The study reports distinctly different velocity dependent nanofluidic regimes, which have not been theoretically as well as experimentally reported earlier. Experimental single-molecule fluorescence bursts inside all-silica nanofluidic channels are used to validate the robustness of the method. It is not restricted to single molecule environment of uniform electrodynamic interactions and can be used to investigate complex refractive index mismatch related non-uniform single-molecule electrodynamic interactions as well. This fundamental investigation of single-molecule nanofluidics has a potential to accelerate the progress of dynamic and complex single-molecule experiments, such as dynamic heterogeneity, biomolecular interactions of misfolded proteins, and nanometric cavity electrodynamics.
Identify tooth cone beam computed tomography based on contourlet particle swarm optimization

In this paper certain type of biometric measurements has been used to identify the cone beam computed tomography (CBCT) radiograph of the subject in a fast and reliable way. Where the CBCT radiograph of a person is used as a data and stored in database for later use in a person’s recognition process. The aim of this research is to use various stages of the preprocessing operations of the CBCT radiograph to obtain the clearest possible image that will help us in the identification process more easily and precisely. The contourlet transformation was used for feature extraction of each particular CBCT image and the results were processed by a new hybrid particle swarm optimization (PSO) named "contourlet PSO" algorithm (CPSO), which is faster and produce more precise (due to apply contourlet algorithm) than traditional PSO. The proposed algorithm (CPSO) gave a detection ratio of 98% after its application on 100 CBCT radiographs.
Complete Structure of the Chloroplast Genome of a Legume, Lotus japonicus

The nucleotide sequence of the entire chloroplast genome (150,519 bp) of a legume, Lotus japonicus, has been determined. The circular double-stranded DNA contains a pair of inverted repeats of 25,156 bp which are separated by a small and a large single copy region of 18,271 bp and 81,936 bp, respectively. A total of 84 predicted protein-coding genes including 7 genes duplicated in the inverted repeat regions, 4 ribosomal RNA genes and 37 tRNA genes (30 gene species) representing 20 amino acids species were assigned on the genome based on similarity to genes previously identified in other chloroplasts. All the predicted genes were conserved among dicot plants except that rpl22, a gene encoding chloroplast ribosomal protein CL22, was missing in L. japonicus. Inversion of a 51-kb segment spanning rbcL to rpsl6 (positions 5161-56,176) in the large single copy region was observed in the chloroplast genome of L. japonicus. The sequence data and gene information are available on our World Wide Web database at http://www.kazusa. or.jp/en/plant/database.html.
Finite-time disturbance observer via continuous fractional sliding modes

Recently, a great deal of effort has been devoted to the design of robust control techniques that compensate for lumped disturbances in mechanical robots and general electromechanical systems through disturbance observers. In this paper, assuming the Hamiltonian structure of Euler–Lagrange systems subject to a wider class of disturbances, and by exploiting some essential properties of fractional-order integro-differential operators, such as heritage and memory, a disturbance observer that is theoretically exact is proposed based on continuous fractional sliding modes, where exactness is understood in the sense of equality, in contrast to simple equivalence. The novelty of the proposal arises from the fact that the continuous fractional sliding-mode disturbance observer is exact, assuring finite-time disturbance estimation, in contrast with a classical integer-order sliding motion that is equivalent. Consequently, there arises a disturbance observer in finite time, including exact observation of continuous but not necessarily differentiable Hölder disturbances, as well as a clear compromise between regularity and robustness, which stands for a quite important issue overlooked in the conventional integer-order case. Representative simulations are discussed to highlight the reliability of the proposed method.
Interactive Physically-based Animation of Dense Surface Meshes Using Simplified Tetrahedral Meshes

In this paper we propose a novel interactive physically-based animation framework for dense meshes. We first compute an approximation of the input surface mesh. Then we build a tetrahedral mesh out of this approximation. It is used as a support for physically-based deformation and computation of reaction forces. For computing reaction forces we use pre-computed penetration depth values and gradients. These parameters, computed at each vertex, are handled efficiently as distance fields with a penalty approach. They are interpolated when collisions are detected between two meshes. Our method can animate dense meshes with collision management at interactive frame rates.
Monte Carlo Simulation of the X-Ray Halos of GX 5−1 and GX 13+1 to Test Models of Insterstellar Dust Grains

Simulated dust-scattered X-ray halos, generated by a three-dimensional ray-tracing Monte Carlo code, are fitted to the observed halos of GX 5−1 and GX 13+1 by automatic adjustment of parameters that specify the distributions of the dust grains in size and location. Three distributions in size a are tested for the quality of fit they yield: the WD01 distribution, the BARE-GR-B distribution, and power laws of the form . The best fits are obtained with multicloud models of the spatial distributions and power-law size distributions. Correlations between the fitted fractional distances of the dust clouds and the distance estimates of molecular clouds toward GX 5−1 and GX 13+1, derived from the velocity spectra of CO and the galactic rotation curve, favor distance estimates of 4.2 and 5.6 kpc for the two sources, respectively.
Fast computational aeroelastic analysis of helicopter rotor blades

The use of a new aeroelastic computer framework called Flexit is described and the framework is used to analyse the dynamic aeroelastic behaviour of a four-bladed helicopter main rotor. Flexit implements a loose coupling between unsteady vortex lattice method (UVLM) and numerical solution of the inhomogeneous Euler-Bernoulli partial differential equation (PDE). The framework is fast because most of the intensive computational functionality is performed on GPU using NVIDIA CUDA technology, and this makes it suitable for use in the early design stages. The UVLM algorithm uses a free wake model, and solution of the Euler-Bernoulli PDE is approximated using a finite difference algorithm that includes a term to take account of centrifugal forces. The results of simulations are compared with analysis performed with CFD and FSI tools.
Margin Adaptive Risk Bounds for Classification Trees

Margin adaptive risk bounds for Classification and Regression Trees (CART, Breiman et. al. 1984) classifiers are obtained in the binary supervised classification framework. These risk bounds are obtained conditionally on the construction of the maximal deep binary tree and permit to prove that the linear penalty used in the CART pruning algorithm is valid under margin condition. It is also shown that, conditionally on the construction of the maximal tree, the final selection by test sample does not alter dramatically the estimation accuracy of the Bayes classifier. In the two-class classification framework, the risk bounds that are proved, obtained by using penalized model selection, validate the CART algorithm which is used in many data mining applications such as Biology, Medicine or Image Coding.
Beyond the regret minimization barrier: optimal algorithms for stochastic strongly-convex optimization

We give novel algorithms for stochastic strongly-convex optimization in the gradient oracle model which return a O(1/T)-approximate solution after T iterations. The first algorithm is deterministic, and achieves this rate via gradient updates and historical averaging. The second algorithm is randomized, and is based on pure gradient steps with a random step size. his rate of convergence is optimal in the gradient oracle model. This improves upon the previously known best rate of O(log(T/T), which was obtained by applying an online strongly-convex optimization algorithm with regret O(log(T)) to the batch setting. We complement this result by proving that any algorithm has expected regret of Ω(log(T)) in the online stochastic strongly-convex optimization setting. This shows that any online-to-batch conversion is inherently suboptimal for stochastic strongly-convex optimization. This is the first formal evidence that online convex optimization is strictly more difficult than batch stochastic convex optimization.
GMOT-40: A Benchmark for Generic Multiple Object Tracking

The label format for proposed GMOT-40 is shown in the Table 1. We mainly follow the format of the widely-used MOT15 dataset [3]. The only difference is, MOT15 does not take some challenging targets, like small ones, into consideration for evaluation. It uses an extra flag to indicate that these labeled targets should be ignored. On the contrary, GMOT-40 includes all of them in evaluation, no matter how challenging the targets could be, and the flag is not used here. This is consistent with our motivation, i.e., trackers need to deal with these real world challenges.
The Milky Way in SDSS and in N-body Models

The Milky Way in SDSS and in N–body Models Sarah R. Loebman Chair of the Supervisory Committee: Professor Željko Ivezić Astronomy In this thesis, I present a comparison between recent Sloan Digital Sky Survey (SDSS) Galactic studies and N–body simulations to aid in the interpretation of observed structural features. I investigate the origin of the Galactic thin/thick disk system and the contents of the Galactic halo by drawing comparisons to two high resolution simulations produced using GASOLINE (Wadsley et al., 2004). My aim is twofold: (1) to assess the plausibility of radial migration as a main driver for shaping the thick disk, and (2) to evaluate via Jeans equations properties of the Galactic potential within the SDSS volume. I arrive as several key findings: • Radial migration can produce on a global scale a physically reasonable thin/thick disk system in an N–body simulation. • Secular evolution via radial migration could be the source of spatial, chemical and kinematic disk trends observed in the solar cylinder by SDSS. • Jeans equations can meaningfully recover mean accelerations from a non steady-state, non axisymmetric cosmologically derived N–body simulation. • The morphology of the acceleration maps generated using Jeans equations applied to SDSS data is consistent with the presence of dark matter and inconsistent with a baryon only model of the Milky Way. • An analytic technique utilizing Jeans equations can meaningfully recover an N–body simulation’s mean dark matter axis ratio. • The same analytic technique applied to SDSS Galactic halo data indicates the Milky Way dark matter axis ratio within the SDSS volume is qDM = 0.47 ± 0.14. Breakthroughs such as these were enabled by recent rapid progress in both observational surveys and simulation science. This thesis demonstrates the emergent and informative value of direct comparison between the two.
A fractal point of view towards Szemer\'edi's theorem

The main purpose of this paper is to explore some connections between Szemer\'edi's theorem and Hausdorff dimension of iterated sum sets. We show here that the Szemer\'edi's theorem is equivalent to a $F_{\sigma}$ sub-semi group having Hausdorff dimension less than $1$. An important ingredient here is a result by Lindenstrauss, Meiri and Peres on convolution of $S^1$ measures.
Terahertz wavelength encoding compressive imaging

Terahertz (THz) compressive imaging can obtain two dimensional image with a single or linear detector, which can overcome the bottleneck problem of lacking of THz two dimensional detectors. In this presentation, we propose a method to obtain two dimensional images using a linear detector. A plano-convex cylindrical lens is employed to perform Fourier transform and to encode one dimensional information of an object into wavelengths. After recording, both amplitude and phase information for different frequency at each pixel of the line detector are extracted, two dimensional image of the object can be reconstructed. Numerical simulation demonstrates the validity of the proposed method.
Overview of the Shared Task on Fake News Detection in Urdu at FIRE 2020

This overview paper describes the first shared task on fake news detection in Urdu language. The task was posed as a binary classification task, in which the goal is to differentiate between real and fake news. We provided a dataset divided into 900 annotated news articles for training and 400 news articles for testing. The dataset contained news in five domains: (i) Health, (ii) Sports, (iii) Showbiz, (iv) Technology, and (v) Business. 42 teams from 6 different countries (India, China, Egypt, Germany, Pakistan, and the UK) registered for the task. 9 teams submitted their experimental results. The participants used various machine learning methods ranging from feature-based traditional machine learning to neural networks techniques. The best performing system achieved an F-score value of 0.90, showing that the BERT-based approach outperforms other machine learning techniques
The augmented Lagrangian method with full Jacobian decomposition and logarithmic-quadratic proximal regularization for multiple-block separable convex programming

We consider a separable convex minimization model whose variables are coupled by linear constraints and they are subject to the positive orthant constraints, and its objective function is in form of m functions without coupled variables. It is well recognized that when the augmented Lagrangian method (ALM) is applied to solve some concrete applications, the resulting subproblem at each iteration should be decomposed to generate solvable subproblems. When the Gauss-Seidel decomposition is implemented, this idea has inspired the alternating direction method of multiplier (form = 2) and its variants (form ≥ 3). When the Jacobian decomposition is considered, it has been shown that the ALM with Jacobian decomposition in its subproblem is not necessarily convergent even when m = 2 and it was suggested to regularize the decomposed subproblems with quadratic proximal terms to ensure the convergence. In this paper, we focus on the multiple-block case with m ≥ 3. We consider implementing the full Jacobian decomposition to ALM’s subproblems and using the logarithmic-quadratic proximal (LQP) terms to regularize the decomposed subproblems. The resulting subproblems are all unconstrained minimization problems because the positive orthant constraints are all inactive; and they are fully eligible for parallel computation. Accordingly, the ALM with full Jacobian decomposition and LQP regularization is proposed. We also consider its inexact version which allows the subproblems to be solved inexactly. For both the exact and inexact versions, we comprehensively discuss their convergence, including their global convergence, worst-case convergence rates measured by the iteration-complexity in both the ergodic and nonergodic senses, and linear convergence rates under additional assumptions. Some preliminary numerical results are reported to demonstrate the efficiency of the ALM with full Jacobian decomposition and LQP regularization. 2010 Mathematics Subject Classification. 90C25, 90C33, 65K05.
Time to relapse and remission of bipolar disorder: findings from a 1-year prospective study in Thailand

Background and methods: This study aimed to determine time to relapse and remission of mood episodes in Thai patients with bipolar disorder (BD). The Thai Bipolar Disorder Registry was a multicenter, prospective, naturalistic, observational study conducted in Thailand. Participants were adult inpatients or outpatients with Diagnostic and Statistical Manual of Mental Disorders bipolar disorder. The diagnosis of bipolar disorder, current psychiatric comorbidity, mood relapse, and mood remission were determined by using the Mini International Neuropsychiatric Interview. Relapse and remission were assessed every 2 months. Results: Of 424 BD participants, 404 (95.3%) were BD I, and 258 (60.8%) were female. At entry, 260 (61.3%) had recovered, and 49 (11.6%) were recovering. During 1-year follow-up (381.7 person-years), 92 participants (21.7%) had 119 relapses or 0.31 (95% confidence interval 0.25–0.35) episodes per person-year. Among 119 relapses, 58 (48.7%), 39 (32.7%), and 21 (17.6%) of them were depressive, hypomanic, and manic episodes, respectively. Using the Kaplan–Meier method, we found that 25% of the participants relapsed in 361 days. Of the 400 participants who reached remission, 113 (28.2%) had mood relapses. Of 173 mood events accountable for remission analysis, the median time to remission was 67.5 days (72.5 days for depressive episodes versus 58.0 days for manic episodes, log rank P = 0.014). Conclusions: The 1-year relapse rate in Thai patients with BD was 21.7% or 0.31 episodes per person-year. About one-fifth of recovered patients had mood relapses within 371 days. On average, a mood episode would remit in 67.5 days.
Electromagnetic backscattering from one-dimensional drifting fractal sea surface I: Wave–current coupled model

To study the electromagnetic backscattering from a one-dimensional drifting fractal sea surface, a fractal sea surface wave–current model is derived, based on the mechanism of wave–current interactions. The numerical results show the effect of the ocean current on the wave. Wave amplitude decreases, wavelength and kurtosis of wave height increase, spectrum intensity decreases and shifts towards lower frequencies when the current occurs parallel to the direction of the ocean wave. By comparison, wave amplitude increases, wavelength and kurtosis of wave height decrease, spectrum intensity increases and shifts towards higher frequencies if the current is in the opposite direction to the direction of ocean wave. The wave–current interaction effect of the ocean current is much stronger than that of the nonlinear wave–wave interaction. The kurtosis of the nonlinear fractal ocean surface is larger than that of linear fractal ocean surface. The effect of the current on skewness of the probability distribution function is negligible. Therefore, the ocean wave spectrum is notably changed by the surface current and the change should be detectable in the electromagnetic backscattering signal.
Training the brain or tending a garden? Students’ metaphors of learning predict their self-reported learning patterns

Conceptions of learning are seen as an important factor in shaping students’ patterns of learning. However, conceptions are often implicit and difficult to assess. Metaphors have been proposed as a method to assess conceptions, because metaphors are closely linked to the conceptual system. Therefore, in our study we assessed which conceptions of learning are visible in students’ metaphors of learning and examined whether these metaphors predict differences in students’ learning patterns. Altogether, N = 91 students of educational science from a German university filled in a questionnaire on their personal metaphors of learning, their learning strategy use, epistemological beliefs, and their motivation. Four kinds of metaphors could be differentiated: regulation-related metaphors, learning as knowledge acquisition, learning as problem solving, or as personality development. A discriminant analysis revealed that students with personality development metaphors and with problem solving metaphors were more intrinsically motivated and more aware of the relativism of knowledge than students with regulation-related or knowledge acquisition metaphors. Students with personality development metaphors differed from students with problem solving metaphors in their stronger use of deep processing strategies, their lower extrinsic motivation and their stronger rejection of a dualism of knowledge. The study demonstrates that metaphors of learning are a suitable tool for assessing students’ conceptions of learning and gives new insights on using this innovative method as an assessment tool.
Control of open quantum systems

Known as decoherence, the unavoidable interaction of a quantum system with its surrounding environment is usually considered to be detrimental for quantum information processing. In this thesis the coherent, open loop control of such open systems is studied. Concepts from quantum control theory and the theory of open quantum system are adopted in order to fight decoherence and implement quantum gates in a noiseless manner. In particular, Lie algebraic methods and numerical optimization tools are used to investigate the control properties of a single spin interacting with a spin environment. We show that, independent of the size of the environment, every unitary transformation can be implemented on the system spin through a single control field. We proceed by investigating dynamical decoupling, a method to suppress the interactions with the environment, for finiteand for infinite dimensional systems. We prove that every finite dimensional system can be protected from decoherence, even if the environment is infinite dimensional, whereas for noise described by a Lindblad master equation dynamical decoupling will never succeed. This will lead to a new method to distinguish decoherence from intrinsic noise terms. We further prove that not every infinite dimensional system can be protected from decoherence through dynamical decoupling. Afterwards we investigate dynamical decoupling of systems that are described by quadratic Hamiltonians, showing that such interactions can always be suppressed with two simple operations. In the last part we investigate the coherent control of a Lindblad master equation. We show that a strong noise process exhibiting a decoherence free subspace can substantially increase the number of unitary operations that can be implemented, allowing us to fully control parts of the system. Afterwards we develop a scheme to make Hamiltonians and Lindbladians commutative by adding an auxiliary system. The old, possibly non-commutative dynamics, is recovered through a non-selective measurement.