Miracle-dz/UniFAR-FTUs huggingface dataset:
Moving Horizon Estimation of Air Data Parameters for UAVs

Local wind velocities, angle of attack, and lift coefficients of a fixed-wing unmanned aerial vehicle (UAV) are estimated by fusing kinematic, aerodynamic, and stochastic wind models with data from an inertial measurement unit, a global navigation satellite system receiver, and a pitot-static tube in a moving horizon estimator. Experimental validation with two different UAVs and two sensor sets of different quality shows promising results for both wind velocity and angle of attack estimation.
Streaming Adaptive Nonparametric Variational Autoencoder

We develop a data driven approach to perform clustering and end-to-end feature learning simultaneously for streaming data that can adaptively detect novel clusters in emerging data. Our approach, Adaptive Nonparametric Variational Autoencoder (AdapVAE), learns the cluster membership through a Bayesian Nonparametric (BNP) modeling framework with Deep Neural Networks (DNNs) for feature learning. We develop a joint online variational inference algorithm to learn feature representations and clustering assignments simultaneously via iteratively optimizing the Evidence Lower Bound (ELBO). We resolve the catastrophic forgetting \citep{kirkpatrick2017overcoming} challenges with streaming data by adopting generative samples from the trained AdapVAE using previous data, which avoids the need of storing and reusing past data. We demonstrate the advantages of our model including adaptive novel cluster detection without discarding useful information learned from past data, high quality sample generation and comparable clustering performance as end-to-end batch mode clustering methods on both image and text corpora benchmark datasets.
Self-similar compressible turbulent boundary layers with pressure gradients. Part 1. Direct numerical simulation and assessment of Morkovin’s hypothesis

A direct numerical simulation study of self-similar compressible flat-plate turbulent boundary layers (TBLs) with pressure gradients (PGs) has been performed for inflow Mach numbers of 0.5 and 2.0. All cases are computed with smooth PGs for both favourable and adverse PG distributions (FPG, APG) and thus are akin to experiments using a reflected-wave set-up. The equilibrium character allows for a systematic comparison between sub- and supersonic cases, enabling the isolation of pure PG effects from Mach-number effects and thus an investigation of the validity of common compressibility transformations for compressible PG TBLs. It turned out that the kinematic Rotta–Clauser parameter $\unicode[STIX]{x1D6FD}_{K}$ calculated using the incompressible form of the boundary-layer displacement thickness as length scale is the appropriate similarity parameter to compare both sub- and supersonic cases. Whereas the subsonic APG cases show trends known from incompressible flow, the interpretation of the supersonic PG cases is intricate. Both sub- and supersonic regions exist in the boundary layer, which counteract in their spatial evolution. The boundary-layer thickness $\unicode[STIX]{x1D6FF}_{99}$ and the skin-friction coefficient $c_{f}$ , for instance, are therefore in a comparable range for all compressible APG cases. The evaluation of local non-dimensionalized total and turbulent shear stresses shows an almost identical behaviour for both sub- and supersonic cases characterized by similar $\unicode[STIX]{x1D6FD}_{K}$ , which indicates the (approximate) validity of Morkovin’s scaling/hypothesis also for compressible PG TBLs. Likewise, the local non-dimensionalized distributions of the mean-flow pressure and the pressure fluctuations are virtually invariant to the local Mach number for same $\unicode[STIX]{x1D6FD}_{K}$ -cases. In the inner layer, the van Driest transformation collapses compressible mean-flow data of the streamwise velocity component well into their nearly incompressible counterparts with the same $\unicode[STIX]{x1D6FD}_{K}$ . However, noticeable differences can be observed in the wake region of the velocity profiles, depending on the strength of the PG. For both sub- and supersonic cases the recovery factor was found to be significantly decreased by APGs and increased by FPGs, but also to remain virtually constant in regions of approximated equilibrium.
Flow-induced swelling of poly(vinyl alcohol) gel

Flow and swelling behavior was investigated for poly(vinyl alcohol) (PVA) gel in the solvent flow field. It was found that the PVA gel swelled under solvent flow and the volume of the gel increased with increasing solvent pressure. The flow rate also increased with increasing solvent pressure. On the other hand, the characteristic times determined by swelling and flow kinetics were independent of the applied pressure, and were almost identical, suggesting that the swelling and flow properties of the gels were closely related to each other. A new type of diffusion equation, which can describe the time evolution of flow and volume change of polymer gels under flow, was proposed in this paper. Comparison between experiment and theory showed that the experimental results were explained fairly well by the theory.
Efficient and optimal designs for correlated observations.

This thesis considers some aspects of the problem of finding efficient and optimal designs when observations are correlated. The two main areas that are examined are nested row-column (NRC) designs and early generation variety trials (EGVTs). In NRC designs, the experimental area is divided into b blocks, and each block is divided into P1 rows and P2 columns (blocks of size P1 x P2). Here, optimal NRC designs, which can be constructed from semi-balanced arrays, are obtained under the assumption that within-block observations are correlated. For a stationary reflection symmetric dependence structure, optimal NRC designs with blocks of size 2 x 2 are obtained for models with fixed block effects, which may also include row and/or column effects. It is shown that the efficiency of binary designs can be very low for some correlation values. Also, optimal NRC designs for blocks of size 3 x 3 and P1 x 2 (P1 ≥ 3 ) are determined. The optimality region for blocks of size P1 x P2 (P1 P2 ≥ 2) under the AR( 1)* AR( 1) process is also specified. It is shown that optimal NRC designs are highly specific to the correlation values. The purpose of EGVTs is to select top performing new crop varieties for further testing. Recently there has been much interest in the spatial analysis of EGVTs, but there has been little work on the design of efficient EGVTs when a spatial analysis is intended. Several intuitively simple criteria to assess the efficiency of designs for EGVTs are examined, and simulation studies suggest that some of these criteria are well associated with probabilities of selecting the highest yielding new varieties. Also, the efficiency and robustness of some systematic designs for EGVTs is investigated over several models and dependence structures. For the examples considered, it is shown that designs in which the plots containing control varieties are at least a knight's move apart are robust.
Integrated Broadband Circularly Polarized Multibeam Antennas Using Berry-Phase Transmit-Arrays for $Ka$ -Band Applications

In this article, integrated millimeter-wave (mmW) broadband circularly polarized (CP) single-beam/multibeam antennas (MBAs) are reported. The antennas comprised a Berry-phase (BP) transmit-array (TA) containing three layers of slotted elliptical metallic resonator arrays and a cluster of substrate integrated waveguide (SIW) -fed CP source radiators with an anisotropic impedance surface (AIS) superstrate. By utilizing broadband mmW CP radiators, BP-based transmission phase compensation, and tailoring the dispersive transfer function of the TA cell, broadband CP high-gain beams can be generated with improved gain flatness and an antenna profile of only <inline-formula> <tex-math notation="LaTeX">$2.6\lambda _{0}$ </tex-math></inline-formula>. Moreover, by employing multifocal strategies in the design of TAs, the scanning loss for the multifeed MBAs can be alleviated, expanding their range of coverage. Two <italic>Ka</italic>-band prototypes were fabricated and characterized. The single-feed single-beam antenna experimentally achieves a peak gain of 22.6 dBic and a 2 dB gain bandwidth of 20.7%, within which the <inline-formula> <tex-math notation="LaTeX">$S_{11}$ </tex-math></inline-formula> is below −10 dB and axial ratio (AR) is smaller than 3 dB. The second prototype, which contains five source radiators and a bifocal TA, generates five pencil beams that cover a range between ±33°. It features a peak broadside gain of 22.2 dBic, a joint AR < 3 dB, and a 2 dB gain bandwidth of around 17.7%. The scanning loss and beam squinting are smaller than 1.2 dB and 1.7°, respectively. Within this band, the <inline-formula> <tex-math notation="LaTeX">$S_{11}$ </tex-math></inline-formula> is below −10 dB and mutual coupling is smaller than −20 dB. Finally, the numerical simulation of a broadband CP 21-beam antenna with a quad-focal TA is shown, which provides a 2-D coverage of 32° cone. The demonstrated single-beam antenna/MBA can be promising candidates for various mmW applications, such as satellite and fifth-generation (5G) communications.
An efficient analysis and optimization method for the powertrain mounting system with hybrid random and interval uncertainties

In the traditional uncertainty-based analyses and optimizations of the automotive powertrain mounting system (PMS), uncertain parameters are usually treated as either random variables or interval variables. In this article, an efficient analysis and optimization method is proposed for PMS designs with hybrid random and interval uncertainties. An efficient analysis method, named the hybrid perturbation–finite difference method (HPFDM), is first derived to calculate the uncertain responses of the natural frequencies (NFs) and the decoupling ratios (DRs) of the PMS. Then, an optimization model of the PMS with hybrid uncertainties is established based on the HPFDM, where the uncertain responses of the relevant NFs and DRs are taken to create optimization constraints and optimization objectives. With the aid of the HPFDM, the nested optimization model can be solved efficiently. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed method.
Appropriate Location of an Accelerometer for Gait Analysis: A Comparative Study Based on Cross-correlation Coefficients

〔目的〕本研究は，歩行分析における最適な加速度計装着部位を同定することを目的とした．〔対象〕健常成人男性15名とした．〔方法〕快適歩行にて鉛直・前後・左右成分の体幹加速度と床反力を同時に測定した．加速度計は第10胸椎（Th10），第3腰椎（L3），第2仙椎（S2）に装着した．左右下肢合成床反力と加速度より算出した床反力推定値との一致度の指標として，3つの装着部位での相互相関係数（CC）により比較した．〔結果〕鉛直・前後成分のCCはL3にて最も高く，左右成分のCCはいずれも低かった．〔結語〕歩行分析では加速度計をL3に装着することにより，鉛直・前後成分では実際の床反力に近似した波形パターンを得られることが示された．
Streamflow modeling of five major rivers that flow into the Gulf of Mexico using SWAT

It has been shown that detailed and continuous riverine discharge and nutrient input data are fundamental to the successful simulation of biogeochemical processes in the ocean using numerical models. Currently, no discharge data for the river mouths on the Mexican side of the Gulf of Mexico (GoM) is available. In the present study, the Soil and Water Assessment Tool (SWAT) was used to simulate the daily discharge of five major rivers that flow directly into the GoM. Two periods were used in the analysis: 1997 to 2013 for the Grijalva basin and 1989 to 2013 for the Usumacinta, Papaloapan, Coatzacoalcos, and Panuco basins. The model was successfully calibrated for monthly time steps using an automatized technique for the five watersheds. The sensitivity analysis of the calibration process showed that for most of the watersheds, the model was sensitive to groundwater and soil moisture parametrizations.
Amino Acid Compositions of Common Edible Mushrooms

野生および栽培キノコ13種20試料についてアミノ酸組成分析を行った. 全体的な傾向として, 含硫アミノ酸, トリプトファン, ヒスチジンは低含量, グルタミソ酸は高含量であった. また, ヒラタケ, コウタケ, サクラシメジはアミノ酸量が多く, ナラタケ, ハナイグチ, ムキタケは少なかった. コウタケを除く12種のキノコの第一制限アミノ酸は, 含硫アミノ酸であった. コウタケの第一制限アミノ酸はロイシソであった. 第二制限アミノ酸は, ホンシメジを除くすべてのキノコでロイシソあるいはリジソであった. アミノ酸スコアはムキタケの33. 7からコウタケの86. 7の範囲であったが, 全体的に低い傾向にあった. また, 1985年 (学齢期前2-5歳) の評点パタソと比較した場合の第一制限アミノ酸は, エノキタケではロイシソ, シイタケ, ニオウシメジ, ハナイグチ, ムキタケでは含硫アミノ酸, その他のキノコではリジンであった. この場合のアミノ酸スコアはサクラシメジの39. 9からコウタケの84. 6となった.
Filtered lattice Boltzmann collision formulation enforcing isotropy and Galilean invariance

The central framework of a filtered lattice Boltzmann collision operator formulation is to remove hydrodynamic moments that are not supported by the order of isotropy of a given lattice velocity set. Due to the natural moment orthogonality of the Hermite polynomials, the form of a filtered collision operator is obtained directly via truncation of the Hermite expansion. In this paper, we present an extension of the filtered collision operator formulation to enforce Galilean invariance. This is accomplished by representing hydrodynamic moments in the relative reference frame with respect to local fluid velocity. The resulting collision operator has a compact and fully Galilean invariant form, and it can then be exactly expressed in terms of an infinite Hermite expansion. Giving a lattice velocity set of specific order of isotropy, a proper truncation of this expansion can be directly determined. Higher order terms are retained in the truncation if a higher order lattice velocity set is used, so that Galilean invariance is attained asymptotically. The previously known filtered collision operator forms can be seen as a limit of zero fluid velocity.
Single Feed Square Aperture Circular Polarized Antenna

In recent years, circular polarized antennas become more important in wireless communication systems because of the ability of radiating circular polarized wave. In this paper, the design of single-feed square-aperture antenna with and without reflector towards the circular polarized antenna performance is presented. The antenna made of square ground plane, square-aperture and a probing strip feed by using microstrip transmission line. The design has achieved a target axial ratio, which is less than 3 dB at frequency range of 4 GHz to 6.5 GHz. When a square ground plane reflector is integrated on the CP antenna for the unidirectional pattern, the presented antenna enhanced gain about 8 dB.
Tree-structured multiclass probability estimators

Faculty of Computing and Mathematical Sciences Department of Computer Science Doctor of Philosophy Tree-Structured Multiclass Probability Estimators by Tim LEATHART Nested dichotomies are used as a method of transforming a multiclass classification problem into a series of binary problems. A binary tree structure is constructed over the label space that recursively splits the set of classes into subsets, and a binary classification model learns to discriminate between the two subsets of classes at each node. Several distinct nested dichotomy structures can be built in an ensemble for superior performance. In this thesis, we introduce two new methods for constructing more accurate nested dichotomies. Random-pair selection is a subset selection method that aims to group similar classes together in a non-deterministic fashion to easily enable the construction of accurate ensembles. Multiple subset evaluation takes this, and other subset selection methods, further by evaluating several different splits and choosing the best performing one. Finally, we also discuss the calibration of the probability estimates produced by nested dichotomies. We observe that nested dichotomies systematically produce under-confident predictions, even if the binary classifiers are well calibrated, and especially when the number of classes is high. Furthermore, substantial performance gains can be made when probability calibration methods are also applied to the internal models.
Support stability of maximizing measures for shifts of finite type

This paper establishes a fundamental difference between $\mathbb{Z}$ subshifts of finite type and $\mathbb{Z}^{2}$ subshifts of finite type in the context of ergodic optimization. Specifically, we consider a subshift of finite type $X$ as a subset of a full shift $F$ . We then introduce a natural penalty function $f$ , defined on $F$ , which is 0 if the local configuration near the origin is legal and $-1$ otherwise. We show that in the case of $\mathbb{Z}$ subshifts, for all sufficiently small perturbations, $g$ , of $f$ , the $g$ -maximizing invariant probability measures are supported on $X$ (that is, the set $X$ is stably maximized by $f$ ). However, in the two-dimensional case, we show that the well-known Robinson tiling fails to have this property: there exist arbitrarily small perturbations, $g$ , of $f$ for which the $g$ -maximizing invariant probability measures are supported on $F\setminus X$ .
Secure State Estimation for Multiagent Systems With Faulty and Malicious Agents

This paper investigates the secure state estimation problem of multiagent systems with misbehaving agents. Compared with the existing works which only consider malicious behaviors generated elaborately, a class of faulty behaviors generated randomly is also taken into account. By assuming that faulty behaviors cannot be altered by the adversaries, the notion of orthogonal complement matrix is introduced to characterize the state observability of the multiagent systems with both faulty and malicious agents. It is shown that more misbehaving agents can be tolerated by distinguishing faulty agents from malicious ones. Then, for single agent, two algorithms are provided to estimate the state based on its own value and the measurements obtained from its neighbors. Finally, two simulations are provided to illustrate the correctness and effectiveness of the proposed conditions and methods.
Dwarfs or giants? Stellar metallicities and distances in the Canada-France-Imaging-Survey from $ugrizG$ multi-band photometry.

We present a new fully data-driven algorithm that uses photometric data from the Canada-France-Imaging-Survey (CFIS; $u$), Pan-STARRS 1 (PS1; $griz$), and Gaia ($G$) to discriminate between dwarf and giant stars and to estimate their distances and metallicities. The algorithm is trained and tested using the SDSS/SEGUE spectroscopic dataset and Gaia photometric/astrometric dataset. At [Fe/H]$<-1.2$, the algorithm succeeds in identifying more than 70% of the giants in the training/test set, with a dwarf contamination fraction below 30% (with respect to the SDSS/SEGUE dataset). The photometric metallicity estimates have uncertainties better than 0.2 dex when compared with the spectroscopic measurements. The distances estimated by the algorithm are valid out to a distance of at least $\sim 80$ kpc without requiring any prior on the stellar distribution, and have fully independent uncertainities that take into account both random and systematic errors. These advances allow us to estimate these stellar parameters for approximately 12 million stars in the photometric dataset. This will enable studies involving the chemical mapping of the distant outer disc and the stellar halo, including their kinematics using the Gaia proper motions. This type of algorithm can be applied in the Southern hemisphere to the first release of LSST data, thus providing an almost complete view of the external components of our Galaxy out to at least $\sim 80$ kpc. Critical to the success of these efforts will be ensuring well-defined spectroscopic training sets that sample a broad range of stellar parameters with minimal biases. A catalogue containing the training/test set and all relevant parameters within the public footprint of CFIS is available online.
Variation and learnability in constraints on A-bar movement

Title of dissertation: VARIATION AND LEARNABILITY IN CONSTRAINTS ON A-BAR MOVEMENT Nick Huang Doctor of Philosophy, 2019 Dissertation directed by: Professor Colin Phillips and Professor Howard Lasnik Department of Linguistics A classic problem in linguistics is explaining how learners come to know so much about their native languages, despite receiving limited and noisy input. This learning problem becomes especially acute when the linguistic properties in question are obscure and show subtle variation across languages. Cross-linguistic variation means that learners must identify the appropriate points of variation for their language, even though the direct evidence that they need is often hard to detect or even non-existent. This dissertation presents two case studies on constraints in A-bar movement. Because constraints are by nature abstract and di cult to observe directly, a classic solution to the learning problem posed by constraints claims that knowledge of these abstract or negative linguistic properties is innate. However, a number of these constraints show cross-linguistic variation, raising questions about how they are represented and how linguistic experience might (or might not) shape linguistic
What ails me

Shortly after a backpacking trip in Michigan in 2009, 20-year-old Sarah Sheridan came down with what seemed to be a nasty case of the flu. Unlike the flu, however, her symptoms only got worse with time. Blood tests, MRI scans, spinal taps and other investigations came back normal or inconclusive. Sheridan spent the next three years in and out of hospital, all to no avail. Her insurance claims swelled to over $100,000. It wasn't until a chance encounter with someone who'd had Lyme disease that she finally found relief. New web-based tools seek to spare others from a similar ordeal. CrowdMed, launched on Apr 16 at the TedMed conference in Washington DC, uses crowds to solve tough medical cases. Anyone can join CrowdMed and analyze cases, regardless of their background or training. Participants are given points that they can then use to bet on the correct diagnosis from lists of suggestions. This creates a prediction market, with diagnoses falling and rising in value based on their popularity.
Krylov methods for adjoint‐free singular vector based perturbations in dynamical systems

The estimation of weather forecast uncertainty with ensemble systems requires a careful selection of perturbations to establish a reliable sampling of the error growth potential in the phase space of the model. Usually, the singular vectors of the tangent linear model propagator are used to identify the fastest growing modes (classical singular vector perturbation (SV) method). In this article we present an efficient matrix‐free block Krylov method for generating fast‐growing perturbations in high‐dimensional dynamical systems. A specific matrix containing the nonlinear evolution of perturbations is introduced, which we call Evolved Increment Matrix (EIM). Instead of solving an equivalent eigenvalue problem, we use the Arnoldi method for a direct approximation of the leading singular vectors of this matrix, which however is never computed explicitly. This avoids linear and adjoint models but requires forecasts with the full nonlinear system. The performance of the approximated perturbations is compared with singular vectors of a full EIM (not with the classical SV method). We show promising results for the Lorenz 96 differential equations and a shallow‐water model, where we obtain good approximations of the fastest‐growing perturbations by using only a small number of Arnoldi iterations.