Miracle-dz/UniFAR-FTUs huggingface dataset:
Convolutional Cross-View Pose Estimation

We propose a novel end-to-end method for cross-view pose estimation. Given a ground-level query image and an aerial image that covers the query's local neighborhood, the 3 Degrees-of-Freedom camera pose of the query is estimated by matching its image descriptor to descriptors of local regions within the aerial image. The orientation-aware descriptors are obtained by using a translationally equivariant convolutional ground image encoder and contrastive learning. The Localization Decoder produces a dense probability distribution in a coarse-to-fine manner with a novel Localization Matching Upsampling module. A smaller Orientation Decoder produces a vector field to condition the orientation estimate on the localization. Our method is validated on the VIGOR and KITTI datasets, where it surpasses the state-of-the-art baseline by 72% and 36% in median localization error for comparable orientation estimation accuracy. The predicted probability distribution can represent localization ambiguity, and enables rejecting possible erroneous predictions. Without re-training, the model can infer on ground images with different field of views and utilize orientation priors if available. On the Oxford RobotCar dataset, our method can reliably estimate the ego-vehicle's pose over time, achieving a median localization error under 1 meter and a median orientation error of around 1 degree at 14 FPS.
Scatter-based common spatial patterns - a unified spatial filtering framework

The common spatial pattern (CSP) approach is known as one of the most popular spatial filtering techniques for EEG classification in motor imagery (MI) based brain-computer interfaces (BCIs). However, it still suffers some drawbacks such as sensitivity to noise, non-stationarity, and limitation to binary classification.Therefore, we propose a novel spatial filtering framework called scaCSP based on the scatter matrices of spatial covariances of EEG signals, which works generally in both binary and multi-class problems whereas CSP can be cast into our framework as a special case when only the range space of the between-class scatter matrix is used in binary cases.We further propose subspace enhanced scaCSP algorithms which easily permit incorporating more discriminative information contained in other range spaces and null spaces of the between-class and within-class scatter matrices in two scenarios: a nullspace components reduction scenario and an additional spatial filter learning scenario.The proposed algorithms are evaluated on two data sets including 4 MI tasks. The classification performance is compared against state-of-the-art competing algorithms: CSP, Tikhonov regularized CSP (TRCSP), stationary CSP (sCSP) and stationary TRCSP (sTRCSP) in the binary problems whilst multi-class extensions of CSP based on pair-wise and one-versus-rest techniques in the multi-class problems. The results show that the proposed framework outperforms all the competing algorithms in terms of average classification accuracy and computational efficiency in both binary and multi-class problems.The proposed scsCSP works as a unified framework for general multi-class problems and is promising for improving the performance of MI-BCIs.
Automatic Detection and Rectification of Paper Receipts on Smartphones

We describe the development of a real-time smartphone app that allows the user to digitize paper receipts in a novel way by"waving"their phone over the receipts and letting the app automatically detect and rectify the receipts for subsequent text recognition. We show that traditional computer vision algorithms for edge and corner detection do not robustly detect the non-linear and discontinuous edges and corners of a typical paper receipt in real-world settings. This is particularly the case when the colors of the receipt and background are similar, or where other interfering rectangular objects are present. Inaccurate detection of a receipt's corner positions then results in distorted images when using an affine projective transformation to rectify the perspective. We propose an innovative solution to receipt corner detection by treating each of the four corners as a unique"object", and training a Single Shot Detection MobileNet object detection model. We use a small amount of real data and a large amount of automatically generated synthetic data that is designed to be similar to real-world imaging scenarios. We show that our proposed method robustly detects the four corners of a receipt, giving a receipt detection accuracy of 85.3% on real-world data, compared to only 36.9% with a traditional edge detection-based approach. Our method works even when the color of the receipt is virtually indistinguishable from the background. Moreover, our method is trained to detect only the corners of the central target receipt and implicitly learns to ignore other receipts, and other rectangular objects. Including synthetic data allows us to train an even better model. These factors are a major advantage over traditional edge detection-based approaches, allowing us to deliver a much better experience to the user.
On Hybrid Radar Fusion for Integrated Sensing and Communication

The following paper introduces a novel integrated sensing and communication (ISAC) scenario termed hybrid radar fusion. In this setting, the dual-functional radar and communications (DFRC) base station (BS) acts as a mono-static radar in the downlink (DL), for sensing purposes, while performing its DL communication tasks. Meanwhile, the communication users act as distributed bi-static radar nodes in the uplink (UL) following a frequency-division duplex protocol. The DFRC BS fuses the information available at different DL and UL resource bands to estimate the angles-of-arrival (AoAs) of the multiple targets existing in the scene. In this work, we derive the maximum likelihood (ML) criterion for the hybrid radar fusion problem at hand. Additionally, we design efficient estimators; the first algorithm is based on an alternating optimization approach to solve the ML criterion, while the second one designs an optimization framework that leads to an alternating subspace approach to estimate AoAs for both the target and users. Finally, we demonstrate the superior performance of both algorithms in different scenarios, and the gains offered by these proposed methods through numerical simulations.
A new mixed finite element for the linear elasticity problem in 3D

This paper constructs the first mixed finite element for the linear elasticity problem in 3D using $P_3$ polynomials for the stress and discontinuous $P_2$ polynomials for the displacement on tetrahedral meshes under some mild mesh conditions. The degrees of freedom of the stress space as well as the corresponding nodal basis are established by characterizing a space of some piecewise constant symmetric matrices on a patch around each edge. Macro-element techniques are used to define a stable interpolation to prove the discrete inf-sup condition. Optimal convergence is obtained theoretically.
Propagation of strain waves in cylindrical helical springs

This paper discusses the vibrations of a coil, which is excited axially, in helical compression springs. The mathematical formula is composed of a system of four hyperbolic partial differential equations of first order with unknown variables, which are angular and axial deformations and velocities. The numerical resolution is based on the conservative finite difference scheme of Lax-Wendroff. The impedance method is applied to calculate the frequency spectrum. The spring is excited by a sinusoidal axial velocity at its end. The results obtained by using this method are used to analyze the evolution in time of deformations and velocities in different sections. These results clearly show the effect of the interaction between the slow axial waves and the fast angular waves. Indeed, the amplification was more important for the axial strain than for the rotational one that was caused by the effect of coupling Poisson. In addition to this, the resonance phenomenon and other phenomena related to wave propagations such as wave reflections and beat are analyzed.
Bootstrapping Dynamic Distance Oracles

Designing approximate all-pairs distance oracles in the fully dynamic setting is one of the central problems in dynamic graph algorithms. Despite extensive research on this topic, the first result breaking the $O(\sqrt{n})$ barrier on the update time for any non-trivial approximation was introduced only recently by Forster, Goranci and Henzinger [SODA'21] who achieved $m^{1/\rho+o(1)}$ amortized update time with a $O(\log n)^{3\rho-2}$ factor in the approximation ratio, for any parameter $\rho \geq 1$. In this paper, we give the first constant-stretch fully dynamic distance oracle with a small polynomial update and query time. Prior work required either at least a poly-logarithmic approximation or much larger update time. Our result gives a more fine-grained trade-off between stretch and update time, for instance we can achieve constant stretch of $O(\frac{1}{\rho^2})^{4/\rho}$ in amortized update time $\tilde{O}(n^{\rho})$, and query time $\tilde{O}(n^{\rho/8})$ for a constant parameter $\rho<1$. Our algorithm is randomized and assumes an oblivious adversary. A core technical idea underlying our construction is to design a black-box reduction from decremental approximate hub-labeling schemes to fully dynamic distance oracles, which may be of independent interest. We then apply this reduction repeatedly to an existing decremental algorithm to bootstrap our fully dynamic solution.
Quantum imaging of biological organisms through spatial and polarization entanglement

Quantum imaging holds potential benefits over classical imaging but has faced challenges such as poor signal-to-noise ratios, low resolvable pixel counts, difficulty in imaging biological organisms, and inability to quantify full birefringence properties. Here, we introduce quantum imaging by coincidence from entanglement (ICE), using spatially and polarization-entangled photon pairs to overcome these challenges. With spatial entanglement, ICE offers higher signal-to-noise ratios, greater resolvable pixel counts, and the ability to image biological organisms. With polarization entanglement, ICE provides quantitative quantum birefringence imaging capability, where both the phase retardation and the principal refractive index axis angle of an object can be remotely and instantly quantified without changing the polarization states of the photons incident on the object. Furthermore, ICE enables 25 times greater suppression of stray light than classical imaging. ICE has the potential to pave the way for quantum imaging in diverse fields, such as life sciences and remote sensing.
Parameter identification of Jiles‐Atherton model using SFLA

Purpose – The purpose of this paper is to implement a simple, fast and accurate heuristic method for parameter determination of Jiles‐Atherton (JA) hysteresis model for representing magnetization in electrical steel sheets. The performance of the method is validated using measured data and comparison with previous methods.Design/methodology/approach – JA model requires five parameters to represent the hysteretic behavior of ferromagnetic materials. In order to determine these parameters, measured hysteresis loop is used here to calculate a fitness function which is defined by comparing the measured and simulated magnetization loops. This fitness function is minimized by optimization algorithms.Findings – In total, four different measured hysteresis loops are studied in this paper. Each optimization algorithm is executed 50 times to investigate the convergence, speed, and accuracy of six methods. All methods begin with the same randomly generated initial parameters. Physical boundaries are used for paramet.
Projection evolution and quantum spacetime

We discuss the problem of time in quantum mechanics. In the traditional formulation time enters the model as a~parameter, not an observable. In our model time is a quantum observable as any other quantum quantity and it is also a component of the spacetime position operator. In this case, instead of the unitary time evolution, other operators, usually projection or POVM operators which map the space of initial states into the space of final states at each step of the evolution can be used. The quantum evolution itself is a stochastic process. This allows to treat time as a quantum observable in a consistent, observer independent way, which is a very important feature to resolve some quantum paradoxes and the time problem in cosmology. An idea of construction of a quantum spacetime as a special set of the allowed states is presented. An example of a structureless quantum Minkowski-like spacetime is also considered. We present the projection evolution model and show how the traditional Schroedinger evolution and relativistic equations can be obtained from it, in the flat structureless spacetime. We propose the form of the time operator which satisfies the energy-time uncertainty relation based on the same inequality as the space position and spatial momenta observables. The sign of the temporal component of the four-momentum operator defines the basic arrow of time in spacetime.
Selection rules for ultrafast laser excitation and detection of spin correlation dynamics in a cubic antiferromagnet

Exchange interactions determine the correlations between microscopic spins in magnetic materials. Probing the dynamics of these spin correlations on ultrashort length and time scales is, however rather challenging, since it requires simultaneously high spatial and high temporal resolution. Recent experimental demonstrations of laser-driven two-magnon modes - zone-edge excitations in antiferromagnets governed by exchange coupling - posed questions about the microscopic nature of the observed spin dynamics, the mechanism underlying its excitation, and their macroscopic manifestation enabling detection. Here, on the basis of a simple microscopic model, we derive the selection rules for cubic systems that describe the polarization of pump and probe pulses required to excite and detect dynamics of nearest-neighbor spin correlations, and can be employed to isolate such dynamics from other magnetic excitations and magneto-optical effects. We show that laser-driven spin correlations contribute to optical anisotropy of the antiferromagnet even in the absence of spin-orbit coupling. In addition, we highlight the role of subleading anisotropy in the spin system and demonstrate that the dynamics of the antiferromagnetic order parameter occurs only in next-to-leading order, determined by the smallness of the magnetic anisotropy as compared to the isotropic exchange interactions in the system. We expect that our results will stimulate and support further studies of magnetic correlations on the shortest length and time scale.
Jahn-Teller transition in La 1 − x Sr x MnO 3 in the low-doping region ( 0 x 0 . 1 )

We have investigated the Jahn-Teller transition in ${\mathrm{La}}_{1\ensuremath{-}x}{\mathrm{Sr}}_{x}{\mathrm{MnO}}_{3}$ by high-temperature powder neutron diffraction in the low-doping region $(0lxl~0.1). $ The Jahn-Teller transition temperature which is about ${T}_{\mathrm{JT}}\ensuremath{\approx}750\mathrm{K}$ for stoichiometric ${\mathrm{LaMnO}}_{3}$ is drastically reduced on doping with Sr, becoming ${T}_{\mathrm{JT}}\ensuremath{\approx}475\mathrm{K}$ for $x=0.10. $ The previously identified metrically cubic orthorhombic O phase (Pmnb) for $x=0$ becomes clearly (also metrically) orthorhombic for small doping $x=0.05$ and remains so in the doping range investigated. The Jahn-Teller transition is accompanied only by the partial reduction of the distortion of the ${\mathrm{MnO}}_{6}$ octahedra. From the distortion of the ${\mathrm{MnO}}_{6}$ octahedra we have determined the orbital mixing coefficients.
Recursive cyclotomic factorization--A new algorithm for calculating the discrete Fourier transform

In this paper, a new recursive algorithm for calculating the discrete Fourier transformation is presented. This new, so-called recursive cyclotomic factorization algorithm (RCFA) is more efficient than the fast Fourier transformation (FFT) algorithm. Moreover, due to its recursive nature, the RCFA can also be easily implemented, using only a limited number of different computation cells.
The Efficacy of Chlorella Supplementation on Multiple Indices of Cycling Performance

Abstract This study investigated the effects of chlorella supplementation on submaximal endurance, time trial performance, lactate threshold, and power indices during a repeated sprint performance test by fourteen male trained cyclists. Participants ingested 6 g/day of chlorella or placebo for 21-days in a double-blinded randomized counter-balanced cross-over design, with a fourteen-day washout period between trials. Each completed a 2-day testing period comprising a 1-hour submaximal endurance test at 55% external power output max and a 16.1 km time trial (Day-1), followed by a lactate threshold (Dmax) and repeated sprint performance tests (3 X 20 s sprints interspersed by 4-mins) (Day-2). Heart rate (b.min−1), RER, V̇O2 (ml·kg−1·min−1), lactate and glucose (mmol/L), time (secs), power output (W/kg), and hemoglobin (g/L) were compared across conditions. Following chlorella supplementation (chlorella vs. placebo for each measurement) average lactate and heart rate were significantly lower (p < 0.05) during submaximal endurance tests (1.68 ± 0.50 mmol/L vs. 1.91 ± 0.65 mmol/L & 138 ± 11b.min−1 vs. 144 ± 10b.min−1), average power and peak power (W/kg) were significantly higher during repeated sprint bouts (9.5 ± 0.7 W/kg vs. 9.0 ± 0.7 W/kg & 12.0 ± 1.2 W/kg vs. 11.4 ± 1.4 W/kg), hemoglobin significantly increased (149.1 ± 10.3 g/L) in comparison to placebo (143.4 ± 8.7 g/L) (p = 0.05). No differences existed between conditions for all oxygen consumption values, 16.1 km time trial measures and lactate threshold tests (p > 0.05). In conclusion, chlorella may pose as an additional supplement for cyclists to consider, particularly for those cyclists who want to improve their sprinting.
Electron acceleration by megahertz waves during OEDIPUS C

Observations of Electric Field Distributions in the Ionospheric Plasma - A Unique Strategy (OEDIPUS C) was a tethered mother-son experiment that was launched northward from the Poker Flat rocket range at 0638 UT on November 7, 1995, across a sequence of auroral structures. During the flight's upleg the magnetically aligned tether was deployed to a separation of ∼1.2 km and then cut at both ends. The forward payload contained a 50-kHz to 8-MHz stepped-frequency transmitter. Receivers were carried on both forward and aft payloads. The transmitter swept through the frequency range every 0.5 s. During each of the 3-ms steps the transmitter emitted only for the first 0.3 ms. The scientific complement also included multiangular electrostatic analyzers on both payloads that were sensitive to fluxes of electrons with energies from 20 eV to 20 keV. The durations of sampling and frequency steps were matched. During the flight the electron gyrofrequency was approximately twice the plasma frequency. When the transmitter swept through the local gyrofrequency, the particle detectors on both payloads detected sounder-accelerated electrons (SAEs) independent of the energy steps being sampled. In addition, SAEs were detected at the aft payload out to separations of several hundred meters for wave emissions at harmonics of the electron gyrofrequency as well as in the upper hybrid and whistler bands. As the vehicle separation increased, significant time differences developed between the wave-emission pulses and the onsets/durations of SAE detections. The data indicate that electrons were heated through strong wave-particle interactions. However, a simple resonant-interaction explanation appears inadequate. We outline requirements for any models purporting to explain OEDIPUS C measurements.
Two-way Analysis of Variance with Correlated Errors

Summary The usual two-way analysis of variance may be inappropriate if one of the criteria of partition is the time, because of serial correlations. In the present paper we discuss the model obtained by assuming that the errors within rows constitute a stationary Gaussian process. This model was studied by Box (1954b). In the present paper approximations to the distributions of the usual test statistics of no column effect and of no row effect are derived. The behaviour of the approximations is studied by simulation in the cases of the first-order autoregressive and the first-order moving average models. These simulations prove that the approximations are excellent and that if we disregard the correlations it may lead to seriously misleading conclusions. Some aspects of the problem of identifying the stationary Gaussian process are discussed and the suggested methods are illustrated by an example.
Increased risk of depression after Guillain‐Barré syndrome

Guillain‐Barré syndrome (GBS) is a potentially life‐threatening disorder, and some patients may develop subsequent depression related to traumatic stress or permanent loss of motor function. We determined the short‐term (0 to 2 years) and long‐term (>2 years) risk of depression after GBS.
An Alternative Estimator for the Censored Quantile Regression Model

This paper introduces an alternative estimator for the linear censored quantile regression model. The estimator also applies to cases where the censoring point is unknown. Since the objective function is globally convex and the estimator is a solution to a linear programming problem, a global minimizer is obtained in a finite number of simplex iterations. The estimator has a square root of n-convergence rate and is asymptotically normal. A Monte Carlo study performed shows that the suggested estimator has very desirable small sample properties.
Kolmogorov superposition theorem and its applications

Hilbert’s 13th problem asked whether every continuous multivariate function can be written as superposition of continuous functions of 2 variables. Kolmogorov and Arnold show that every continuous multivariate function can be represented as superposition of continuous univariate functions and addition in a universal form and thus solved the problem positively. In Kolmogorov’s representation, only one univariate function (the outer function) depends on and all the other univariate functions (inner functions) are independent of the multivariate function to be represented. This greatly inspired research on representation and superposition of functions using Kolmogorov’s superposition theorem (KST). However, the numeric applications and theoretic development of KST is considerably limited due to the lack of smoothness of the univariate functions in the representation. Therefore, we investigate the properties of the outer and inner functions in detail. We show that the outer function for a given multivariate function is not unique, does not preserve the positivity of the multivariate function and has a largely degraded modulus of continuity. The structure of the set of inner functions only depends on the number of variables of the multivariate function. We show that inner functions constructed in Kolmogorov’s representation for continuous functions of a fixed number of variables can be reused by extension or projection to represent continuous functions of a different number of variables. After an investigation of the functions in KST, we combine KST with Fourier transform and write a formula regarding the change of the outer functions under different inner functions for a given multivariate function. KST is also applied to estimate the optimal cost between measures in high dimension by the optimal cost between measures in low dimension. Furthermore, we apply KST to image encryption and show that the maximal error can be obtained in the encryption schemes we suggested.