Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning based Antenna Selection and CSI Extrapolation in Massive MIMO Systems

A critical bottleneck of massive multiple-input multiple-output (MIMO) system is the huge training overhead caused by downlink transmission, like channel estimation, downlink beamforming and covariance observation. In this paper, we propose to use the channel state information (CSI) of a small number of antennas to extrapolate the CSI of the other antennas and reduce the training overhead. Specifically, we design a deep neural network that we call an antenna domain extrapolation network (ADEN) that can exploit the correlation function among antennas. We then propose a deep learning (DL) based antenna selection network (ASN) that can select a limited antennas for optimizing the extrapolation, which is conventionally a type of combinatorial optimization and is difficult to solve. We trickly designed a constrained degradation algorithm to generate a differentiable approximation of the discrete antenna selection vector such that the back-propagation of the neural network can be guaranteed. Numerical results show that the proposed ADEN outperforms the traditional fully connected one, and the antenna selection scheme learned by ASN is much better than the trivially used uniform selection.
On the detection and identification of edge disconnections in a multi-agent consensus network

In this paper we investigate the problem of the sudden disconnection of an edge in a discrete-time multi-agent consensus network. If the graph remains strongly connected, the multi-agent system still achieves consensus, but in general, unless the information exchange between each pair of agents is symmetric, the agents' states converge to a drifted value of the original consensus value. Consequently the edge disconnection can go unnoticed. In this paper the problems of detecting an edge disconnection and of identifying in a finite number of steps the exact edge that got disconnected are investigated. Necessary and sufficient conditions for both problems to be solvable are presented, both in case all the agents' states are available and in case only a subset of the agents' states is measured. Finally, an example of a network of 7 agents is provided, to illustrate some of the theoretical results derived in the paper.
Spatial self-organization of vegetation in water-limited systems: mechanistic causes, empirical tests, and ecosystem-level consequences

Self-organized spatial patterns of vegetation are frequent in water-limited regions and have been suggested as important ecosystem health indicators. However, the mechanisms underlying their formation remain unclear. It has been hypothesized that patterns could emerge from a water-mediated scale-dependent feedback (SDF), whereby interactions favoring plant growth dominate at short distances while growth-inhibitory interactions dominate in the long range. As precipitation declines, this framework predicts a sequential change from gapped to labyrinthine to spotted spatial patterns. However, we know little about how net plant-to-plant interactions may shift from positive to negative as a function of inter-individual distance, and in the absence of strong empirical support, the relevance of SDF for vegetation pattern formation remains disputed. Alternative theories show that the same sequence of patterns could emerge even if net interactions between plants are always inhibitory, provided that their intensity decays sharply enough with inter-individual distance. Importantly, although these alternative hypotheses lead to visually indistinguishable spatial distributions of plants, the two different frameworks predict different ecosystem-level consequences for these resulting patterns, thus limiting their potential use as ecosystem-state indicators. Moreover, the interaction of vegetation with other ecosystem components can alter the dynamics of the pattern or even introduce additional spatio-temporal scales. Therefore, to make reliable ecological predictions, models need to accurately capture the mechanisms at play in the systems of interest. Here, we review existing theories for vegetation self-organization and their conflicting ecosystem-level predictions. We further discuss possible ways for reconciling these predictions. We focus on the mechanistic differences among models, which can provide valuable information to help researchers decide which model to use for a particular system and/or whether it requires modification.
Bladder segmentation based on deep learning approaches: current limitations and lessons

Precise determination and assessment of bladder cancer (BC) extent of muscle invasion involvement guides proper risk stratification and personalized therapy selection. In this context, segmentation of both bladder walls and cancer are of pivotal importance, as it provides invaluable information to stage the primary tumor. Hence, multiregion segmentation on patients presenting with symptoms of bladder tumors using deep learning heralds a new level of staging accuracy and prediction of the biologic behavior of the tumor. Nevertheless, despite the success of these models in other medical problems, progress in multiregion bladder segmentation is still at a nascent stage, with just a handful of works tackling a multiregion scenario. Furthermore, most existing approaches systematically follow prior literature in other clinical problems, without casting a doubt on the validity of these methods on bladder segmentation, which may present different challenges. Inspired by this, we provide an in-depth look at bladder cancer segmentation using deep learning models. The critical determinants for accurate differentiation of muscle invasive disease, current status of deep learning based bladder segmentation, lessons and limitations of prior work are highlighted.
Free Lunch for Few-shot Learning: Distribution Calibration

Learning from a limited number of samples is challenging since the learned model can easily become overfitted based on the biased distribution formed by only a few training examples. In this paper, we calibrate the distribution of these few-sample classes by transferring statistics from the classes with sufficient examples, then an adequate number of examples can be sampled from the calibrated distribution to expand the inputs to the classifier. We assume every dimension in the feature representation follows a Gaussian distribution so that the mean and the variance of the distribution can borrow from that of similar classes whose statistics are better estimated with an adequate number of samples. Our method can be built on top of off-the-shelf pretrained feature extractors and classification models without extra parameters. We show that a simple logistic regression classifier trained using the features sampled from our calibrated distribution can outperform the state-of-the-art accuracy on two datasets (~5% improvement on miniImageNet compared to the next best). The visualization of these generated features demonstrates that our calibrated distribution is an accurate estimation.
Anomalous separations of the CMB temperature angular power spectrum

In this article, we propose a novel technique to test for anomalous features in the CMB. We analyse separations of the observed CMB angular power spectrum (C`) using temperature anisotropy data from WMAP 9 year ILC and 2018 Planck maps of Commander, NILC and SMICA. We estimate the minimum, maximum, average separations and ratios of the maximum to minimum separations between consecutive multipoles of the weighted spectrum, f(`)C`. We see that such f(`)’s with higher multipole powers mitigate the parity asymmetry anomaly. For anomalous separations, we find that data exhibits anomalous ranges of multipoles defined by different `max and `min values, specifically for the entire range of multipoles from 2 − 31 of this work. Without parity based distinctions, most significantly, the maximum separation of the range 8 ≤ ` ≤ 31 is seen to be anomalously low at the 99.93% confidence level for f(`) = ` (WMAP), `(`+1) 2π (Planck NILC), the latter indicating a strong deviation from the Sachs-Wolfe plateau for maximum separations among low multipoles. The analysis is repeated for odd and even multipoles taken separately, in the same multipole ranges. Most noticeably, the even multipoles are seen to have anomalously low maximum and average separations relative to their odd counterparts, the most outstanding among which is the anomalously low maximum separation for even multipoles in the range 6 ≤ ` ≤ 31 for f(`) = ` (WMAP), at the 99.77% confidence level. For separation ratios, the multipole ranges are similar to those which turn up as anomalous when only separations are considered. Keywords— cosmic background radiation – inflation – methods: data analysis – cosmology: miscellaneous
Verifiable Failure Localization in Smart Grid under Cyber-Physical Attacks

Cyber-physical attacks impose a significant threat to the smart grid, as the cyber attack makes it difficult to identify the actual damage caused by the physical attack. To defend against such attacks, various inference-based solutions have been proposed to estimate the states of grid elements (e.g., transmission lines) from measurements outside the attacked area, out of which a few have provided theoretical conditions for guaranteed accuracy. However, these conditions are usually based on the ground truth states and thus not verifiable in practice. To solve this problem, we develop (i) verifiable conditions that can be tested based on only observable information, and (ii) efficient algorithms for verifying the states of links (i.e., transmission lines) within the attacked area based on these conditions. Our numerical evaluations based on the Polish power grid and IEEE 300-bus system demonstrate that the proposed algorithms are highly successful in verifying the states of truly failed links, and can thus greatly help in prioritizing repairs during the recovery process.
Non-convex weakly smooth Langevin Monte Carlo using regularization

Discretization of continuous-time diffusion processes is a widely recognized method for sampling. However, the canonical Euler Maruyama discretization of the Langevin diffusion process, referred as Langevin Monte Carlo (LMC), studied mostly in the context of smooth (gradient Lipschitz) and strongly log-concave densities, is a considerable hindrance for its deployment in many sciences, including computational statistics and statistical learning. In this paper, we establish several theoretical contributions to the literature on such sampling methods for weakly smooth and non-convex densities. Particularly, we use convexiﬁcation of nonconvex domain (Ma et al., 2019) in combination with regularization to prove convergence in Kullback-Leibler (KL) divergence with the number of iterations to reach ε − neighborhood of a target distribution in only polynomial dependence on the dimension. We relax the conditions of (Vempala and Wibisono, 2019) and prove convergence guarantees under isoperimetry, degenerated convex and non strongly convex at inﬁnity.
Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach

Semantic segmentation of 3D point clouds relies on training deep models with a large amount of labeled data. However, labeling 3D point clouds is expensive, thus smart approach towards data annotation, a.k. a. active learning is essential to label-efficient point cloud segmentation. In this work, we first propose a more realistic annotation counting scheme so that a fair benchmark is possible. To better exploit labeling budget, we adopt a super-point based active learning strategy where we make use of manifold defined on the point cloud geometry. We further propose active learning strategy to encourage shape level diversity and local spatial consistency constraint. Experiments on two benchmark datasets demonstrate the efficacy of our proposed active learning strategy for label-efficient semantic segmentation of point clouds. Notably, we achieve significant improvement at all levels of annotation budgets and outperform the state-of-the-art methods under the same level of annotation cost.
Magnetization switching induced by spin–orbit torque from Co2MnGa magnetic Weyl semimetal thin films

This study reports the magnetization switching induced by spin-orbit torque (SOT) from the spin current generated in Co2MnGa magnetic Weyl semimetal (WSM) thin films. We deposited epitaxial Co2MnGa thin films with highly B2-ordered structure on MgO(001) substrates. The SOT was characterized by harmonic Hall measurements in a Co2MnGa/Ti/CoFeB heterostructure and a relatively large spin Hall efficiency of -7.8% was obtained.The SOT-induced magnetization switching of the perpendicularly magnetized CoFeB layer was further demonstrated using the structure. The symmetry of second harmonic signals, thickness dependence of spin Hall efficiency, and shift of anomalous Hall loops under applied currents were also investigated. This study not only contributes to the understanding of the mechanisms of spin-current generation from magnetic-WSM-based heterostructures, but also paves a way for the applications of magnetic WSMs in spintronic devices.
New Low Rank Optimization Model and Convex Approach for Robust Spectral Compressed Sensing

This paper investigates recovery of an undamped spectrally sparse signal and its spectral components from a set of regularly spaced samples within the framework of spectral compressed sensing and super-resolution. We show that the existing Hankel-based optimization methods suffer from the fundamental limitation that the prior of undampedness cannot be exploited. We propose a new low rank optimization model partially inspired by forward-backward processing for line spectral estimation and show its capability in restricting the spectral poles on the unit circle. We present convex relaxation approaches with the model and show their provable accuracy and robustness to bounded and sparse noise. All our results are generalized from the 1-D to arbitrary-dimensional spectral compressed sensing. Numerical simulations are provided that corroborate our analysis and show efficiency of our model and advantageous performance of our approach in improved accuracy and resolution as compared to the state-of-the-art Hankel and atomic norm methods.
Deep Cox Mixtures for Survival Regression

Survival analysis is a challenging variation of regression modeling because of the presence of censoring, where the outcome measurement is only partially known, due to, for example, loss to follow up. Such problems come up frequently in medical applications, making survival analysis a key endeavor in biostatistics and machine learning for healthcare, with Cox regression models being amongst the most commonly employed models. We describe a new approach for survival analysis regression models, based on learning mixtures of Cox regressions to model individual survival distributions. We propose an approximation to the Expectation Maximization algorithm for this model that does hard assignments to mixture groups to make optimization efficient. In each group assignment, we fit the hazard ratios within each group using deep neural networks, and the baseline hazard for each mixture component non-parametrically. We perform experiments on multiple real world datasets, and look at the mortality rates of patients across ethnicity and gender. We emphasize the importance of calibration in healthcare settings and demonstrate that our approach outperforms classical and modern survival analysis baselines, both in terms of discriminative performance and calibration, with large gains in performance on the minority demographics.
On the efficient parallel computing of long term reliable trajectories for the Lorenz system

In this work, we propose an effi cient parallelization of multiple-precision Taylor series method with variable stepsize and fi xed order. For given level of accuracy the optimal variable stepsize determines higher order of the method than in the case of optimal fi xed stepsize. Although the used order of the method is greater than that in the case of fi xed stepsize, and hence the computational work per step is greater, the reduced number of steps gives less overall work. In addition, the greater order of the method is benefi cial in the sense that it increases the parallel effi ciency. As a model problem, we use the paradigmatic Lorenz system. With 256 CPU cores in Nestum cluster, Sofi a, Bulgaria, we succeed to obtain a correct reference solution in the rather long time interval – [0,11000]. To get this solution we perform two large computations: one computation with 4566 decimal digits of precision and 5240-th order method, and second computation for verifi cation – with 4778 decimal digits of precision and 5490-th order method.
CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote-Sensing Images

Object detection in optical remote-sensing images is an important and challenging task. In recent years, the methods based on convolutional neural networks (CNNs) have made good progress. However, due to the large variation in object scale, aspect ratio, as well as the arbitrary orientation, the detection performance is difficult to be further improved. In this article, we discuss the role of discriminative features in object detection, and then propose a critical feature capturing network (CFC-Net) to improve detection accuracy from three aspects: building powerful feature representation, refining preset anchors, and optimizing label assignment. Specifically, we first decouple the classification and regression features, and then construct robust critical features adapted to the respective tasks of classification and regression through the polarization attention module (PAM). With the extracted discriminative regression features, the rotation anchor refinement module (R-ARM) performs localization refinement on preset horizontal anchors to obtain superior rotation anchors. Next, the dynamic anchor learning (DAL) strategy is given to adaptively select high-quality anchors based on their ability to capture critical features. The proposed framework creates more powerful semantic representations for objects in remote-sensing images and achieves high-performance real-time object detection. Experimental results on three remote-sensing datasets including HRSC2016, DOTA, and UCAS-AOD show that our method achieves superior detection performance compared with many state-of-the-art approaches. Code and models are available at https://github.com/ming71/CFC-Net.
Optimal Reinsurance: A Ruin-Related Uncertain Programming Approach

We investigate the role of reinsurance in maximizing the wealth of an insurance company. We use Liu's uncertainty theory (B. Liu, 2007) for the problem modeling and follow-up computations. The uncertainty measure of ruin for the insurance company is considered as the optimization criterion. Since calculating the ruin index is very difficult, we introduce a simple computational method to identify the uncertain measure of ruin for an insurance company. Finally, a generalized model is presented, granting the model be more practical.
Water exchange on magnesium(II) in aqueous solution: a variable temperature and pressure 17O NMR study

Fast water exchange rate constants on diamagnetic metal ions can be measured using either a paramagnetic chemical shift agent or a paramagnetic relaxation agent. An approximate analytical equation was developed for the chemical shift agent method, valid in the limit of rapid exchange. The limits of validity of these methods are discussed. These were applied to the study of water exchange on [Mg(H2O)]62+ over a large temperature range (252–350 K) and up to a pressure of 200 MPa by 17O NMR, leading to the following kinetic parameters: kMg298=6.7±0.2×105 s‐1; ΔHMg‡=49.1±0.7 kJ mol‐1; ΔSMg‡=+31.1±2.2 J K‐1; ΔVMg‡=+6.7±0.7 cm3 mol‐1. From these data, it is concluded that this process has an Id mechanism with strong d character, without excluding a limiting D mechanism. © 1997 John Wiley & Sons, Ltd.
Multi-view Data Visualisation via Manifold Learning

Non-linear dimensionality reduction can be performed by \textit{manifold learning} approaches, such as Stochastic Neighbour Embedding (SNE), Locally Linear Embedding (LLE) and Isometric Feature Mapping (ISOMAP). These methods aim to produce two or three latent embeddings, primarily to visualise the data in intelligible representations. This manuscript proposes extensions of Student's t-distributed SNE (t-SNE), LLE and ISOMAP, for dimensionality reduction and visualisation of multi-view data. Multi-view data refers to multiple types of data generated from the same samples. The proposed multi-view approaches provide more comprehensible projections of the samples compared to the ones obtained by visualising each data-view separately. Commonly visualisation is used for identifying underlying patterns within the samples. By incorporating the obtained low-dimensional embeddings from the multi-view manifold approaches into the K-means clustering algorithm, it is shown that clusters of the samples are accurately identified. Through the analysis of real and synthetic data the proposed multi-SNE approach is found to have the best performance. We further illustrate the applicability of the multi-SNE approach for the analysis of multi-omics single-cell data, where the aim is to visualise and identify cell heterogeneity and cell types in biological tissues relevant to health and disease.
Photodissociation of HNCO in the Vacuum Ultraviolet; Production of NCO A 2Σ and NH(A 3π, πc1)

Strong emission bands originating from NCO A2Σ and NH (A 3π, c 1π) were found in the photodissociation of HNCO in the vacuum ultraviolet. In addition, weak bands coming from NCO B 2Π were observed. Threshold energies of incident photons to produce these bands were measured, from which the following bond dissociation energies and heats of formation were obtained: D(H–NCO) = 4.90 ± 0.01 eV, D(HN–CO) = 3.38 ± 0.1 eV, D(N–CO) = 2.14 ± 0.15 eV, ΔHf(HNCO) = − 1.0 ± 0.14 eV, ΔHf(NCO) = 1.6 ± 0.15 eV. The emission spectrum, NCO A 2Σ → X 2π, produced from the photolysis at the Xe and Kr lines shows that NCO A 2Σ is highly excited in bending vibration, indicating that the upper state of HNCO from which NCO A 2Σ predissociates has a bent NCO configuration. NH c1Π formed is highly rotationally excited. Experimental evidence is presented to show that NH A 3π is produced primarily by a secondary process, CO a 3π + NH X 3Σ → CO X 1Σ + NH A 3π. Primary processes in the near‐ultraviolet photolysis of HNCO are discussed on.
An Embedded Multichannel Sound Acquisition System for Drone Audition

Microphone array techniques can improve the acoustic sensing performance of drones, compared with the use of a single microphone. However, multichannel sound acquisition systems are not available in current commercial drone platforms. We present an embedded multichannel sound acquisition and recording system with eight microphones mounted on a quadcopter. The system is developed based on Bela, an embedded computing system for audio processing. The system can record the sound from multiple microphones simultaneously, can store the data locally for on-device processing, and can transmit the multichannel audio via wireless communication to a ground terminal for remote processing. We disclose the technical details of the hardware and software design and development of the system. We implement two setups that place the microphone array at different locations on the drone body. We present experimental results obtained by the state-of-the-art drone audition algorithms applied to the sound recorded by the embedded system flying with a drone. It is shown that the ego-noise reduction performance achieved by the microphone array varies depending on the array placement and the location of the target sound. This observation provides valuable insights into hardware development for drone audition.