Miracle-dz/UniFAR-FTUs huggingface dataset:
Relay Selection Based Double-Differential Transmission for Cooperative Networks with Multiple Carrier Frequency Offsets: Model, Analysis, and Optimization

Abstract Due to the distributed nature, cooperative networks are generally subject to multiple carrier frequency offsets (MCFOs), which make the channels time-varying and drastically degrade the system performance. In this paper, to address the MCFOs problem in detect-andforward (DetF) multi-relay cooperative networks, a robust relay selection (RS) based double-differential (DD) transmission scheme, termed RSDDT, is proposed, where the best relay is selected to forward the source's double-differentially modulated signals to the destination with the DetF protocol. The proposed RSDDT scheme can achieve excellent performance over fading channels in the presence of unknown MCFOs. Considering double-differential multiple phase-shift keying (DDMPSK) is applied, we first derive exact expressions for the outage probability and average bit error rate (BER) of the RSDDT scheme. Then, we look into the high signal-to-noise ratio (SNR) regime and present simple and informative asymptotic outage probability and average BER expressions, which reveal that the proposed scheme can achieve full diversity. Moreover, to further improve the BER performance of the RSDDT scheme, we investigate the optimum power allocation strategy among the source and the relay nodes, and simple analytical solutions are obtained. Numerical results are provided to corroborate the derived analytical expressions and it is demonstrated that the proposed optimum power allocation strategy offers substantial BER performance improvement over the equal power allocation strategy.
High Schmidt-number turbulent advection and giant concentration fluctuations

We consider the effects of thermal noise on the Batchelor-Kraichnan theory of high Schmidt-number mixing in the viscous-dissipation range of turbulent flows. Using fluctuating hydrodynamics for a binary fluid mixture at low Mach numbers, we justify linearization around the deterministic Navier-Stokes solution in the dissipation range. For the latter solution we adopt the standard Kraichnan model and derive asymptotic high-Schmidt limiting equations for the concentration field, in which the thermal velocity fluctuations are exactly represented by a Gaussian random velocity which is white in time. We obtain the exact solution for the concentration spectrum in this high-Schmidt limiting model, showing that the Batchelor prediction in the viscous-convective range is unaltered. Thermal noise dramatically renormalizes the bare diffusivity in this range, but the effect is the same as in laminar flow and thus hidden phenomenologically. However, in the viscous-diffusive range at scales below the Batchelor length (typically micron scales) the predictions based on deterministic Navier-Stokes equations are drastically altered by thermal noise. Whereas the classical theories predict rapidly decaying spectra in the viscous-diffusive range, we obtain a $k^{-2}$ power-law spectrum starting just below the Batchelor length. This spectrum corresponds to non-equilibrium giant concentration fluctuations, due to the imposed concentration variations advected by thermal velocity fluctuations which are experimentally well-observed in quiescent fluids. At higher wavenumbers, the concentration spectrum instead goes to a $k^2$ equipartition spectrum due to equilibrium molecular fluctuations. We work out detailed predictions for water-glycerol and water-fluorescein mixtures. Finally, we discuss broad implications for turbulent flows and novel applications of our methods to experimentally accessible laminar flows.
Energy-Efficient Trajectory Design for UAV-Aided Maritime Data Collection in Wind

Unmanned aerial vehicles (UAVs), especially fixed-wing ones that withstand strong winds, have great potential for oceanic exploration and research. This paper studies a UAV-aided maritime data collection system with a fixed-wing UAV dispatched to collect data from marine buoys. We aim to minimize the UAV’s energy consumption in completing the task by jointly optimizing the communication time scheduling among the buoys and the UAV’s flight trajectory subject to wind effect. The conventional successive convex approximation (SCA) method can provide efficient sub-optimal solutions for collecting small/moderate data volume, whereas the solution heavily relies on trajectory initialization and has not explicitly considered wind effect, while the computational/trajectory complexity both become prohibitive for the task with large data volume. To this end, we propose a new cyclical trajectory design framework with tailored initialization algorithm that can handle arbitrary data volume efficiently, as well as a hybrid offline-online (HO2) design that leverages convex stochastic programming (CSP) offline based on wind statistics, and refines the solution by adapting online to real-time wind velocity. Numerical results show that our optimized trajectory can better adapt to various setups with different target data volume and buoys’ topology as well as various wind speed/direction/variance compared with benchmark schemes. In particular, our proposed HO2 design can effectively adapt to random wind variations with feasible and robust online operation, and proactively exploit the wind for further energy savings in both single-buoy and multi-buoy scenarios.
Narrow-beamwidth T-shaped monopole antenna fabricated from metamaterial wires

A simple T-shaped monopole is converted into a metamaterial antenna by loading lumped capacitors and inductors on the metal wire. The new metamaterial antenna could sustain a half-wave resonance even when its length exceeds several free-space wavelengths. An 80 mm-long monopole initially resonating at 1.3 GHz is modified to operate at 3.6 GHz with a reduced 3 dB H-plane beamwidth of 48deg.
Multi-Domain Balanced Sampling Improves Out-of-Generalization of Chest X-ray Pathology Prediction Models

Learning models that generalize under different distribution shifts in medical imaging has been a long-standing research challenge. There have been several proposals for efﬁcient and robust visual representation learning among vision research practitioners, especially in the sensitive and critical biomedical domain. In this paper, we propose an idea for out-of-distribution generalization of chest X-ray pathologies that uses a simple balanced batch sampling technique. We observed that balanced sampling between the multiple training datasets improves the performance over baseline models trained without balancing. Code for this work is available on Github. 1
A new model to compute the desired steering torque for steer-by-wire vehicles and driving simulators

This paper deals with the control of the hand wheel actuator in steer-by-wire (SbW) vehicles and driving simulators (DSs). A novel model for the computation of the desired steering torque is presented. The introduced steering torque computation does not only aim to generate a realistic steering feel, which means that the driver should not miss the basic steering functionality of a modern conventional steering system such as an electric power steering (EPS) or hydraulic power steering (HPS), and this in every driving situation. In addition, the modular structure of the steering torque computation combined with suitably selected tuning parameters has the objective to offer a high degree of customisability of the steering feel and thus to provide each driver with his preferred steering feel in a very intuitive manner. The task and the tuning of each module are firstly described. Then, the steering torque computation is parameterised such that the steering feel of a series EPS system is reproduced. For this purpose, experiments are conducted in a hardware-in-the-loop environment where a test EPS is mounted on a steering test bench coupled with a vehicle simulator and parameter identification techniques are applied. Subsequently, how appropriate the steering torque computation mimics the test EPS system is objectively evaluated with respect to criteria concerning the steering torque level and gradient, the feedback behaviour and the steering return ability. Finally, the intuitive tuning of the modular steering torque computation is demonstrated for deriving a sportier steering feel configuration.
Real-time ab initio description of the photon-echo mechanisms in extended systems: the case study of bulk GaAs

In this paper we present an ab-initio real-time analysis of free polarization decay and photon echo in extended systems. As a prototype material, we study bulk GaAs driven by ultra-short laser pulses with a FWHM of 10 fs. We compute the electronic polarization P(t) and define a computational procedure to extract the echo signal. Results are obtained in both the low and high field regime, and compared with a two-levels system (TLS) model, with parameters extracted from the ab-initio simulations. An optimal agreement with the TLS is found in the low-field case, whereas some differences are observed in the high-field regime where the multi-band nature of GaAs becomes relevant.
Three‐dimensional FE analysis of reinforced concrete structures using the lattice equivalent continuum method

The lattice equivalent continuum model (LECM) has proved to be very effective in analysing reinforced concrete structures in two‐dimensional cases. That model is extended here to three dimensions and is fitted to a finite element formulation for analysing three‐dimensional reinforced concrete structures. In any of the three principal directions, the stress–strain behaviour of concrete will be affected by the stress state in the other two directions. Consequently, stress‐strain curves for concrete will shift from a uniaxial pattern. This phenomenon is considered in this work by selecting concrete peak stresses in three alternative approaches. The effectiveness of those approaches is also evaluated. The results of the calculations show good reproduction of the test data, which indicates the validity of this proposed 3D modelling of reinforced concrete.
Asymptotic derivation of multicomponent compressible flows with heat conduction and mass diffusion

A Type-I model of a multicomponent system of fluids with non-constant temperature is derived as the high-friction limit of a Type-II model via a Chapman-Enskog expansion. The asymptotic model is shown to fit into the general theory of hyperbolic-parabolic systems, by exploiting the entropy structure inherited through the asymptotic procedure. Finally, by deriving the relative entropy identity for the Type-I model, two convergence results for smooth solutions are presented, from the system with mass-diffusion and heat conduction to the corresponding system without mass-diffusion but including heat conduction and to its hyperbolic counterpart.
A Comprehensive Review of Myoelectric Prosthesis Control

Prosthetic hands can be used to support upper-body amputees. Myoelectric prosthesis, one of the externally-powered active prosthesis categories, requires proper processing units in addition to recording electrodes and instrumentation amplifiers. In this paper, the following myoelectric prosthesis control methods were discussed in detail: On-off and finite-state, proportional, direct, and posture, simultaneous, classification and regression-based control, and deep learning methods. Myoelectric control performance indices, such as completion time and rate, throughput, lag, and path length, were reviewed. The advantages and disadvantages of the control methods were also discussed. Some of myoelectric prosthesis control's significant challenges are comfort, durability, cost, the application of under-sampled signals, and electrode shift. Moreover, the proposed algorithms must be usually tuned after each don and doff, which is not comfortable for the users. Realtime simultaneous and proportional myoelectric control, resampling human’s arm, has brought much attention. However, increasing the degree of freedom reduces the overall performance. Applying a 3D printed prosthesis arm and under-sampled electromyographic signals could reduce the fabrication cost and improve the application of such methods in practice. There are many technological and clinical challenges in this area to reduce the prosthesis rejection rate.
Low-voltage, low-power current monitor for deep-submicron testing

An on-chip low-power circuit for both quiescent current I ddq and transient current I ddt monitoring is presented. The current monitor performs faster and is significantly smaller than those reported previously. The monitor is designed for low-voltage digital CMOS circuits (1.5V). The same design can be used in the testing of analogue and mixed signal circuits. The effect on the circuit under test performance is negligible. Testing speeds of up to 25MHz can be achieved (including the 4-bit A/D converter, 100MHz without the converter). The monitor has been implemented in 0. 5μm and 0. 35μm CMOS technology and tested successfully on parallel chains of inverters as circuit under test. Two types of fault (an open fault and a short fault) have been observed. Simulation and experimental results are included and analysed.
3-D Metamaterials: Trends on Applied Designs, Computational Methods and Fabrication Techniques

Metamaterials are artificially engineered devices that go beyond the properties of conventional materials in nature. Metamaterials allow for the creation of negative refractive indexes; light trapping with epsilon-near-zero compounds; bandgap selection; superconductivity phenomena; non-Hermitian responses; and more generally, manipulation of the propagation of electromagnetic and acoustic waves. In the past, low computational resources and the lack of proper manufacturing techniques have limited attention towards 1-D and 2-D metamaterials. However, the true potential of metamaterials is ultimately reached in 3-D configurations, when the degrees of freedom associated with the propagating direction are fully exploited in design. This is expected to lead to a new era in the field of metamaterials, from which future high-speed and low-latency communication networks can benefit. Here, a comprehensive overview of the past, present, and future trends related to 3-D metamaterial devices is presented, focusing on efficient computational methods, innovative designs, and functional manufacturing techniques.
A machine learning analysis of the relationship between some underlying medical conditions and COVID-19 susceptibility

For the past couple years, the Coronavirus, commonly known as COVID-19, has significantly affected the daily lives of all citizens residing in the United States by imposing several, fatal health risks that cannot go unnoticed. In response to the growing fear and danger COVID-19 inflicts upon societies in the USA, several vaccines and boosters have been created as a permanent remedy for individuals to take advantage of. In this paper, we investigate the relationship between the COVID-19 vaccines and boosters and the total case count for the Coronavirus across multiple states in the USA. Additionally, this paper discusses the relationship between several, selected underlying health conditions with COVID-19. To discuss these relationships effectively, this paper will utilize statistical tests and machine learning methods for analysis and discussion purposes. Furthermore, this paper reflects upon conclusions made about the relationship between educational attainment, race, and COVID-19 and the possible connections that can be established with underlying health conditions, vaccination rates, and COVID-19 total case and death counts.
Disentanglement by Cyclic Reconstruction

Deep neural networks have demonstrated their ability to automatically extract meaningful features from data. However, in supervised learning, information specific to the dataset used for training, but irrelevant to the task at hand, may remain encoded in the extracted representations. This remaining information introduces a domain-specific bias, weakening the generalization performance. In this work, we propose splitting the information into a task-related representation and its complementary context representation. We propose an original method, combining adversarial feature predictors and cyclic reconstruction, to disentangle these two representations in the single-domain supervised case. We then adapt this method to the unsupervised domain adaptation (UDA) problem, consisting of training a model capable of performing on both a source and a target domain. In particular, our method promotes disentanglement in the target domain, despite the absence of training labels. This enables the isolation of task-specific information from both domains and a projection into a common representation. The task-specific representation allows the efficient transfer of knowledge acquired from the source domain to the target domain. In the single-domain case, we demonstrate the quality of our representations on information retrieval tasks and the generalization benefits induced by sharpened task-specific representations. We then validate the proposed method on several classical domain adaptation (DA) benchmarks and illustrate the benefits of disentanglement for DA.
Nonnegative canonical tensor decomposition with linear constraints: nnCANDELINC

There is an emerging interest for tensor factorization applications in big‐data analytics and machine learning. To speed up the factorization of extra‐large datasets, organized in multidimensional arrays (also known as tensors), easy to compute compression‐based tensor representations, such as, Tucker and tensor train formats, are used to approximate the initial large‐tensor. Further, tensor factorization is used to extract latent features that can facilitate discoveries of new mechanisms and signatures hidden in the data, where the explainability of the latent features is of principal importance. Nonnegative tensor factorization extracts latent features that are naturally sparse and parts of the data, which makes them easily interpretable. However, to take into account available domain knowledge and subject matter expertise, often additional constraints need to be imposed, which lead us to canonical decomposition with linear constraints (CANDELINC), a canonical polyadic decomposition with rank deficient factors. In CANDELINC, Tucker compression is used as a preprocessing step, which lead to a larger residual error but to more explainable latent features. Here, we propose a nonnegative CANDELINC (nnCANDELINC) accomplished via a specific nonnegative Tucker decomposition; we refer to as minimal or canonical nonnegative Tucker. We derive several results required to understand the specificity of nnCANDELINC, focusing on the difficulties of preserving the nonnegative rank of a tensor to its Tucker core and comparing the real valued to nonnegative case. Finally, we demonstrate nnCANDELINC performance on synthetic and real‐world examples.
Lipschitz continuity in the Hurst parameter of functionals of stochastic differential equations driven by a fractional Brownian motion

Sensitivity analysis w.r. t. the long-range/memory noise parameter for probability distributions of functionals of solutions to stochastic differential equations is an important stochastic modeling issue in many applications. In this paper we consider solutions {X t }t∈R+ to stochastic differential equations driven by fractional Brownian motions. We develop two innovative sensitivity analyses when the Hurst parameter H of the noise tends to the critical Brownian parameter H = 12 from above or from below. First, we examine expected smooth functions of X at a fixed time horizon T . Second, we examine Laplace transforms of functionals which are irregular with regard to Malliavin calculus, namely, first passage times of X at a given threshold. In both cases we exhibit the Lipschitz continuity w.r. t. H around the value 1 2 . Therefore, our results show that the Markov Brownian model is a good proxy model as long as the Hurst parameter remains close to 12 .
Topological superconductivity induced by magnetic texture crystals

We present a detailed investigation of the topological phases and Majorana fermion (MF) excitations that arise from the bulk interplay between (un)conventional one/two-band spin-singlet superconductivity and a number of magnetic texture crystals. The latter define inhomogeneous magnetization profiles which consist of a periodically-repeating primitive cell. Here we focus on magnetic texture crystals with a primitive cell of the helix, whirl, and skyrmion types, which feature distinct symmetry properties. We identify a multitude of accessible topological phases which harbor flat, uni- or bi-directional, (quasi-)helical, or chiral MF edge modes. This rich variety originates from the interplay between topological phases with gapped and nodal bulk energy spectra. The types of the emerging topological superconducting phases and the features of the arising MFs are solely determined by the properties and compatibility of the so-called magnetic and pairing point/space groups. Our analysis is general and does not rely on specific parameters of the models employed here to exemplify the topological scenarios which become accessible. Therefore, our results can be extended to systems with multiple bands, are relevant for a wide range of layered materials and hybrid devices, and provide predictions for strong, weak and crystalline topological phases.
Kolmogorov complexity of unitary transformations and quantum operators

We introduce a notion of Kolmogorov complexity of a unit vector in a finite-dimensional Hilbert space, which is based on "qubit complexity" of Berthiaume, W. Van Dam and S. Laplante. Then, the Kolmogorov complexity of quantum density operator is naturally defined as the Kolmogorov complexity of the corresponding purified state. Inspired by the quantum mechanical relation between the unitary evolution and the Hermitian operator of a quantum system, we introduce a mapping from the unitary transformation to a Hermitian operator first and, subsequently, we map the Hermitian operator to a unit vector in a Hilbert space. Thus, for every unitary transformation, we construct a corresponding unit vector. Then, we define the Kolmogorov complexity of the unitary transformation via the Kolmogorov complexity of the constructed vector and obtain a simple upper bound. Therefore, we establish the duality of unitary transformations and unit vectors and shall measure the descriptional (i.e, Kolmogorov) complexity of a unitary transformation in terms of the unit vector complexity.
Hitting times for second-order random walks

A second-order random walk on a graph or network is a random walk where transition probabilities depend not only on the present node but also on the previous one. A notable example is the non-backtracking random walk, where the walker is not allowed to revisit a node in one step. Second-order random walks can model physical diffusion phenomena in a more realistic way than traditional random walks and have been very successfully used in various network mining and machine learning settings. However, numerous questions are still open for this type of stochastic processes. In this work, we extend well-known results concerning mean hitting and return times of standard random walks to the second-order case. In particular, we provide simple formulas that allow us to compute these numbers by solving suitable systems of linear equations. Moreover, by introducing the ‘pullback’ first-order stochastic process of a second-order random walk, we provide second-order versions of the renowned Kac’s and Random Target Lemmas.