Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Coherent NOMA With Massive MIMO

By accommodating overlapping transmissions over non-orthogonal resources, non-orthogonal multiple access (NOMA) integrated with massive multiple-input multiple-output (MIMO) can boost spectral efficiency and obtain vast diversity for wireless multiuser systems. However, NOMA requires channel state information (CSI) at the receiver for coherent detection, resulting in unaffordable complexity in the channel estimation as the number of users goes large. Add to that, the introduction of massive MIMO makes the channel estimation even more challenging. In this letter, we propose a blind belief propagation (BP) detection for non-coherent NOMA with massive MIMO, where the transmitter of each user first performs differential modulation on PSK symbols, and then spreads its symbols using low-density spreading (LDS); the receiver of base station (BS) employs differential demodulation and then detects all users’ symbols using a blind BP detection without knowledge of CSI. Simulation results show that the proposed blind BP detection can not only support the overloaded users, but also achieve BER performance very close to that of point-to-point systems.
Relational Dose-Response Modeling for Cancer Drug Studies.

Exploratory cancer drug studies test multiple tumor cell lines against multiple candidate drugs. The goal in each paired (cell line, drug) experiment is to map out the dose-response curve of the cell line as the dose level of the drug increases. The level of natural variation and technical noise in these experiments is high, even when multiple replicates are run. Further, running all possible combinations of cell lines and drugs may be prohibitively expensive, leading to missing data. Thus, estimating the dose-response curve is a denoising and imputation task. We cast this task as a functional matrix factorization problem: finding low-dimensional structure in a matrix where every entry is a noisy function evaluated at a set of discrete points. We propose Bayesian Tensor Filtering (BTF), a hierarchical Bayesian model of matrices of functions. BTF captures the smoothness in each individual function while also being locally adaptive to sharp discontinuities. The BTF model can incorporate many types of likelihoods, making it flexible enough to handle a wide variety of data. We derive efficient Gibbs samplers for three classes of likelihoods: (i) Gaussian, for which updates are fully conjugate; (ii) binomial and related likelihoods, for which updates are conditionally conjugate through Polya-Gamma augmentation; and (iii) non-conjugate likelihoods, for which we develop an analytic truncated elliptical slice sampling routine. We compare BTF against a state-of-the-art method for dynamic Poisson matrix factorization, showing BTF better reconstructs held out data in synthetic experiments. Finally, we build a dose-response model around BTF and apply it to real data from two multi-sample, multi-drug cancer studies. We show that the BTF-based dose-response model outperforms the current standard approach in biology. Code is available at this https URL.
Multicolor High-Speed Tracking of Single Biomolecules with Silver, Gold, and Silver–Gold Alloy Nanoparticles

Gold nanoparticles have been used as imaging probes to track the motions of single biomolecules. To investigate behaviors of various biomolecules simultaneously, increase of the color palette is necessary. Here we developed a multicolor high-speed single-particle tracking system using silver, gold, and silver–gold alloy (5:5 composition ratio) nanoparticles. The peak wavelengths of the plasmon resonances for 30 nm silver, 30 nm silver–gold alloy, and 40 nm gold nanoparticles were around 410, 460, and 530 nm, respectively, and we constructed multicolor total internal reflection dark-field microscope with multiple lasers at 404 nm for silver, 473 nm for silver–gold alloy, and 561 nm for gold nanoparticles. By the use of a spectrophotometer in the detection optics, scattering images at each wavelength were projected onto different portions of a single two-dimensional detector. High-contrast images of silver, silver–gold alloy, and gold nanoparticles were simultaneously obtained in different color channels. A.
Classification of Human Activity Based on Radar Signal Using 1-D Convolutional Neural Network

Previously, the 2-D convolutional neural networks (2-D-CNNs) have been introduced to classify the human activity based on micro-Doppler radar. Whereas these methods can achieve high accuracy, their application is limited by their high computational complexity. In this letter, an end-to-end 1-D convolutional neural network (1-D-CNN) is first proposed for radar-based sensors for human activity classification. In the proposed 1-D-CNN, the inception densely block (ID-Block) tailored for the 1-D-CNN is proposed. The ID-Block incorporated the three techniques: inception module, dense network, and network-in-network techniques. With these techniques, the proposed network not only achieve a high classification accuracy but also keep the computational complexity at a low level. The experiments results show that the classification accuracy of the proposed method is 96.1% for human activity classification that is higher than that of existing state-of-art 2-D-CNN methods while the computational speed of forward propagation is increased by about (<inline-formula> <tex-math notation="LaTeX">$2.71\times $ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$29.68\times $ </tex-math></inline-formula>) of the existing 2-D-CNN methods.
The Relative Contributions of Impulsivity Facets to Drinking Outcomes

Objectives:Alcohol use continues to have a significant negative health and economic impact in the United States. Understanding factors that contribute to alcohol use and its related consequences remains an important goal of psychological research. One personality trait consistently shown to contribute to alcohol use and problems is impulsivity. The relationship between impulsivity and drinking is complex because impulsivity is a multifaceted construct, composed of urgency (rash responding to negative affect), lack of premeditation (action without planning), lack of perseverance (inability to persist in a task), and sensation seeking (pursuit of excitement). Previous research on the relation between the different facets of impulsivity and drinking outcomes has been limited by the problem of multicollinearity of predictors (ie, the intercorrelations among the impulsivity facets make their relative contributions to drinking outcomes difficult to interpret). Methods:The current study overcame this problem by analyzing the relationship between impulsivity and drinking outcomes using relative weights analysis, a technique that determines the proportion of variance explained in a dependent variable that is accounted for by each independent variable after taking into account multicollinearity of predictors. Results:Results revealed that (1) sensation seeking is the best predictor of alcohol use across the impulsivity facets, (2) urgency is the best predictor of alcohol-related consequences, (3) premeditation is important to the prediction of all alcohol use outcomes, and (4) perseverance may not uniquely explain alcohol use outcomes. Conclusions:It is concluded that facets of impulsivity differentially predict alcohol use outcomes.
XNORCONV: CNNs accelerator implemented on FPGA using a hybrid CNNs structure and an inter-layer pipeline method

Nowadays, convolutional neural networks (CNNs) have become a research hotspot because of their high performance in computer vision and pattern recognition. However, as the high energy consumption of traditional graphic processing units-based CNNs, it is difficult to deploy them into portable devices. To deal with this problem, a hybrid CNN structure (XNORCONV) was proposed and implemented on field-programmable gate array (FPGA) in this study. Two improvements have been applied in XNORCONV. Firstly, the multiplications in the convolutional layer (CONV) were replaced by XNOR operations to save the multiplier and reduce computational complexity. Secondly, an inter-layer pipeline was designed to further accelerate the calculation. XNORCONV was implemented on Xilinx Zynq-7000 xc7z020clg400-1 under the clock frequency of 150 MHz and tested with MNIST dataset. The results of the experiment show that XNORCONV can classify each picture from MNIST in , and achieve 98.4% recognition accuracy. Compared with traditional Lenet-5 on different platforms, XNORCONV reduced multiplication by 85.6% with only 0.4% accuracy loss.
Note on response dimension reduction for multivariate regression

Response dimension reduction in a su ﬃ cient dimension reduction (SDR) context has been widely ignored until Yoo and Cook ( Computational Statistics and Data Analysis , 53 , 334–343, 2008) founded theories for it and developed an estimation approach. Recent research in SDR shows that a semi-parametric approach can outperform conventional non-parametric SDR methods. Yoo ( Statistics: A Journal of Theoretical and Applied Statistics , 52 , 409–425, 2018) developed a semi-parametric approach for response reduction in Yoo and Cook (2008) context, and Yoo ( Journal of the Korean Statistical Society , 2019) completes the semi-parametric approach by proposing an unstructured method. This paper theoretically discusses and provides insightful remarks on three versions of semi-parametric approaches that can be useful for statistical practitioners. It is also possible to avoid numerical instability by presenting the results for an orthogonal transformation of the response variables.
A high‐gain circularly polarized Fabry‐Perot antenna with chiral metamaterial‐based circular polarizer

A high‐gain circularly polarized Fabry‐Perot (FP) antenna with chiral metamaterial (CMM)‐based circular polarizer is presented in this paper. It consists of two cavities. One is a U‐shaped slot microstrip antenna with a partially reflecting surface (PRS), acting as an FP cavity. The other is composed of PRS and CMM plate which act as a circular polarizer. According to the experimental results, within the impedance bandwidth of 0.63 GHz from 8.71 to 9.34 GHz, the axial ratio and reflection coefficient S11 of the antenna are less than 3 and −10 dB, respectively. At 9 GHz, the maximum gains of simulation and measurement are 18.5 and 18.3 dB, respectively. The measurement results are according well with the simulation results. Similar methods can be extended to other bands to design high‐gain circularly polarized antennas.
Global dynamics of competition models with nonsymmetric nonlocal dispersals when one diffusion rate is small

In this paper, we study the global dynamics of a general \begin{document}$ 2\times 2 $\end{document} competition models with nonsymmetric nonlocal dispersal operators. Our results indicate that local stability implies global stability provided that one of the diffusion rates is sufficiently small. This paper extends the work in [ 3 ], where Lotka-Volterra competition models with symmetric nonlocal operators are considered, to more general competition models with nonsymmetric operators.
Improved radiation detection algorithm using wavelet packet decomposition

Easy-to-pilot drones are now readily available off the shelf, which have caused many security risks in public due to the unauthorised drones. However, limited works can be achieved to detect the drones, especially under strong interferences. This work considers the detection of drones via utilising a remote sensing algorithm developed in this work to extract the communication signals of the drone. The clutters including the static and/or non-static noises in the collected communication signals are eliminated based on the effective background suppression algorithm and linear trend suppression technique. Then, principal parts extraction method is utilised to remove the low-value parts of the radio-frequency signals, which can also improve via the automatic gain control method. Gaussian noise is suppressed using wavelet transform. Three errors of the acquired estimated frequency estimates from the above algorithms can be utilised to judge whether there are unauthorised drones or not in the monitoring airspace. The obtained detection results indicate the better performance and reliability of the detection algorithm developed in this study.
Accelerated Convergence of Saddle-Point Dynamics

In this thesis, a second-order continuous-time saddle-point dynamics is introduced that mimics Nesterov’s accelerated gradient flow dynamics. We study the convergence properties of this dynamics using a family of time-varying Lyapunov functions. In particular, we study the convergence rate of the dynamics for classes of strongly convex-strongly concave functions. For a class of quadratic strongly convex-strongly concave functions and under appropriate assumptions, this dynamics achieves global asymptotic convergence; in fact, further conditions lead to an accelerated convergence rate. We also provide conditions for both local asymptotic convergence and local accelerated convergence of general strongly convex-strongly concave functions.
Investigation of a 256‐Monopole Transmit Antenna Array for Over‐the‐Horizon Radar in Canada

A 256‐monopole planar array is investigated as a potential transmit antenna covering the entire 3‐ to 18‐MHz band in an over‐the‐horizon radar (OTHR) operating at high latitudes in Canada. This array is relatively inexpensive, simple to build, and offers the azimuth and elevation resolutions necessary to defeat the auroral clutter which has traditionally limited the operation of OTHR at high latitudes. It is demonstrated that excellent clutter rejection ratios are possible with this antenna array and accurate calibration, but significant performance degradation occurs with calibration errors in particular in the lower portion of the operating frequency band. Various calibration approaches and error mechanisms are studied. A new approach combining multiple‐input‐multiple‐output (MIMO) radar techniques at lower frequencies and conventional transmit null steering at higher frequencies is proposed and evaluated. This approach enhances the robustness of the array to calibration errors while preserving the radar unambiguous range‐Doppler space across the operating frequency band.
Desarrollo de un sistema modular de regulación de procesos genéticos y metabólicos vía CRISPRi para el control del comportamiento celular

x 1. INTRODUCCION . 1 1.1 Enfoques de la Biología Sintética . 1 1.2 Fabricación de ADN . 2 1.3 Sistema CRISPR . 4 1.3.1 CRISPR para edición de genomas . 4 1.3.2 CRISPRi para regulación de genes . 5 1.4 Violaceína . 6 1.5 Auxotrofía . 7 1.6 Regulación de procesos biológicos a la escala de sistemas multicelulares . 10 2. HIPOTESIS Y OBJETIVOS . 11 2.1 Hipótesis . 11 2.2 Objetivo General . 11 2.3 Objetivos específicos . 11 3. METODOLOGIA . 12 3.1 Vectores y partes genéticas . 12 3.2 Condiciones de crecimiento . 13 3.3 Selección y diseño de sgRNAs . 16 3.4 Transformaciones simples y Co-transformaciones . 16 3.5 Segregación de plásmidos en colonias . 17 3.6 Microscopía y fluorescencia . 18 4. RESULTADOS . 19 4.1 Sistema de construcción modular y combinatorial de vectores . 19 4.2 Regulación de genes mediante sistema CRISPRi/dCas9 . 20 4.3 Regulación de genes en subdominio específico mediante sistema CRISPRi/dCas9 . 22 4.4 Regulación de la vía metabólica de metionina mediante CRISPRi/dCas9 . 23 4.5 Auxotrofía inducible a metionina a través de CRISPRi/dCas9 . 27 4.6 Regulación del crecimiento en espacio y tiempo de poblaciones bacterianas . 31 5. CONCLUSIONES . 38 BIBLIOGRAFIA . 41 A N E X O S . 46 Anexo A: Artículo ISI . 47 Anexo B: Primers utilizados . 58
Comparison and optimisation of three-level neutral point clamped dual output inverter

Multi-level inverter gives low total harmonic distortion when compared to the two-level inverter. A huge number of switches are required to enhance the performance of multi-level inverters. Conventional neutral point clamped (NPC) three-level inverter needs 12 switches to control a single three-phase load. In this research, dual input and output reduced switch count three-level inverter topology with a minimum number of switches is proposed to reduce the system cost, weight and driver circuits. The sinusoidal pulse width modulation (SPWM) control techniques are developed to control NPC dual inverters. The dual inverter with constant frequency and variable frequency mode are simulated by MATLAB Simulink software and the results are discussed in detail. The analysis plan with 15 different combinations of input parameters is developed as per the Box-Behnken method and the results are compared and discussed. Also, the results such as output voltage, V THD , I THD and efficiency are analysed by regression analysis and the derived regression equations can be used to predict the output parameters. The combination of input parameters and dual inverter topology, which give optimum output values are identified by composite desirability analysis. This dual inverter reduced switch count topology can be used for renewable energy applications to reduce the inverter cost and space.
Principal points analysis via p-median problem for binary data

ABSTRACT Analysis with principal points is a useful statistical tool for summarizing large data. In this paper, we propose a subgradient-based algorithm to calculate a set of principal points for multivariate binary data by the formulating it as a p-median problem. This enables us to find a globally optimal set of principal points or an ε-optimal solution in the middle of the calculation by combining an upper bound found using the greedy method. This algorithm is an iterative procedure where each iteration can be calculated in an efficient manner. We investigate the applicability of the proposed framework with questionnaire data and arXiv co-authors data.
The Upper Limit of the Mass and Energy of the Relativistic Particles

It is shown that a relativistic particle has an upper limit of the mass and energy when it is accelerated to the speed of light. Now we can calculate the limiting parameters of the relativistic particles by the use of the normalized relativistic factor in Einstein's relativistic equations. For example, the maximum mass of the relativistic proton is a limited number. The state of the relativistic particle is described by a mass balance and an energy balance. The energy balance includes the maximum energy of a relativistic particle and her real energy and her hidden energy.
Quantum-optical analysis of high-order harmonic generation

High-order harmonic generation is a highly nonlinear phenomenon in which ultraviolet photons are generated when an atom is subjected to a high-intensity infrared laser field. This process has been extensively studied, but most of the formulations employ a semiclassical approach in which the electron is treated quantum mechanically while the driving field is treated classically. Although some recent studies follow a quantum-optical approach, the existing descriptions do not offer much insight into the physical processes taking place, and are always limited to monochromatic driving fields. In this thesis, we construct a fully quantized framework for strong-field ionization and high-harmonic generation that allows for quantization of both the driving field and the ultraviolet emission. The presented formalism extends the Strong-Field Approximation theory to a quantum-optical framework, with which we can obtain an analytical expression for the electron and the driving field after the interaction.
Complex-Valued 3-D Convolutional Neural Network for PolSAR Image Classification

Recently, convolutional neural network (CNN) has been successfully utilized in the terrain classification of polarimetric synthetic aperture radar (PolSAR) images. However, most CNN-based models are currently limited to handle 2-D real-valued inputs, and therefore, the physical scattering mechanism contained in the complex-valued (CV) covariance/coherency matrix cannot be extracted effectively. For this reason, CV 3-D CNN (CV-3D-CNN) is proposed for PolSAR image classification. Compared with CNN, CV-3D-CNN simultaneously extracts hierarchical features in both the spatial and the scattering dimensions by performing 3-D CV convolutions, thereby capturing the physical property from polarimetric adjacent resolution cells. Experiments on real PolSAR images classification demonstrate the effectiveness and the superiorities of CV-3D-CNN and illustrate that CV-3D-CNN can deal with scattering characteristic in a more complete manner and achieve better performance in PolSAR image classification.
Near‐field analysis of discrete bowtie plasmonic nanoantennas

A discrete antenna is a discretized version of a traditional antenna design which enhances the antenna properties (such as field intensity, multi‐band, compact size, and low sidelobe levels) of its traditional counterpart. In this work, a classical bowtie antenna is discretized using circular elements of different sizes. Numerical simulations and near‐field optical scanning microscopy are performed on classical and discrete bowtie antennas in the infrared. Simulations show that discrete antennas have an increased bandwidth and an increased number of electric‐field hotspots and near‐field measurements confirm the increment of hotspots. These characteristics make discrete antennas potentially useful in applications where an extended region of localized electric field enhancements is of interest.
Graph Sampling for Matrix Completion Using Recurrent Gershgorin Disc Shift

Matrix completion algorithms fill missing entries in a large matrix given a subset of observed samples. The problem of how to pre-select a subset of entries for sampling to maximize the reconstructed matrix fidelity is largely unaddressed. In this paper, we propose two sampling algorithms to tackle this problem: (i) a fast base sampling algorithm on general single graphs, and (ii) an extended sampling algorithm from the base algorithm for active matrix completion. Assuming the double graph smoothness prior where rows / columns of the matrix signal are smooth with respect to row / column factor graphs, we first show that a matrix signal can be recovered from partial observations by solving a system of linear equations <inline-formula><tex-math notation="LaTeX">${\mathbf Q}{\mathbf x}={\mathbf y}$</tex-math></inline-formula>, where the sought matrix is vectorized as <inline-formula><tex-math notation="LaTeX">${\mathbf x}$</tex-math></inline-formula> and interpreted as a signal residing on a single graph. On this single graph, to promote small reconstruction error and stability of the linear system, we maximize the smallest eigenvalue <inline-formula><tex-math notation="LaTeX">$\lambda _{\min }$</tex-math></inline-formula> of <inline-formula><tex-math notation="LaTeX">${\mathbf Q}$</tex-math></inline-formula> by greedily selecting samples corresponding to the largest magnitude entries in the first eigenvectors of <inline-formula><tex-math notation="LaTeX">${\mathbf Q}$</tex-math></inline-formula>, based on a corollary of the Gershgorin circle theorem. Extending to the active matrix completion case, we alternately choose entries in the rows and columns corresponding to the largest magnitude entries in the first eigenvectors of the row / column coefficient matrices <inline-formula><tex-math notation="LaTeX">${\mathbf Q}_r$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">${\mathbf Q}_c$</tex-math></inline-formula>. Our algorithm benefits computationally from warm start as the first eigenvectors are computed recurrently for more samples. Extensive experiments on both synthetic and real-world datasets show that our extended graph sampling algorithm outperforms existing sampling schemes for matrix completion, when combined with a range of modern matrix completion algorithms.