Miracle-dz/UniFAR-FTUs huggingface dataset:
Cell-Free Massive MIMO with Channel Aging and Pilot Contamination

In this paper, we investigate the impact of channel aging on the performance of cell-free (CF) massive multiple-input multiple-output (MIMO) systems with pilot contamination. To take into account the channel aging effect due to user mobility, we first compute a channel estimate. We use it to derive novel closed-form expressions for the uplink spectral efficiency (SE) of CF massive MIMO systems with large-scale fading decoding and matched-filter receiver cooperation. The performance of a small-cell system is derived for comparison. It is found that CF massive MIMO systems achieve higher 95%-likely uplink SE in both low-and high-mobility conditions, and CF massive MIMO is more robust to channel aging. Fractional power control (FPC) is considered to compensate to limit the inter-user interference. The results show that, compared with full power transmission, the benefits of FPC are gradually weakened as the channel aging grows stronger.
Recovering the Initial Data of the Wave Equation from Neumann Traces

We study the problem of recovering the initial data (f, 0) of the standard wave equation from the Neumann trace (the normal derivative) of the solution on the boundary of convex domains in arbitrary spatial dimension. Among others, this problem is relevant for tomographic image reconstruction including photoacoustic tomography. We establish explicit inversion formulas of the back-projection type that recover the initial data up to an additive term defined by a smoothing integral operator. In the case that the boundary of the domain is an ellipsoid, the integral operator vanishes, and hence we obtain an analytic formula for recovering the initial data from Neumann traces of the wave equation on ellipsoids.
Compact Elliptical Galaxies in Different Local Environments: A Mixture of Galaxies with Different Origins?

We present the stellar populations of 138 compact elliptical galaxies (cEs) in the redshift range of z < 0.05 using the Sloan Digital Sky Survey (SDSS) DR12. Our cEs are divided into those with [cE(w)] and without [cE(w/o)] a bright (Mr < −21 mag) host galaxy. We investigated the stellar population properties of cEs based on the Lick line indices extracted from SDSS spectra. cE(w)s show [Z/H] and [α/Fe] distributions skewed toward higher values compared to those of the cE(w/o)s. No statistically significant difference in age distribution was found between the cE(w)s and cE(w/o)s. In the mass–metallicity distribution, cE(w)s deviate from the relation observed for early-type galaxies at a given stellar mass, whereas cE(w/o)s conform to the relation. Based on the different features in the stellar populations of cE(w)s and cE(w/o)s, we can propose two different cE formation channels tracing different original masses of the progenitors. cE(w)s would be the remnant cores of the massive progenitor galaxies whose outer parts are tidally stripped by a massive neighboring galaxy (i.e., a nurture origin). In contrast, cE(w/o)s are likely the faint end of early-type galaxies maintaining in situ evolution in an isolated environment with no massive galaxy nearby (i.e., a nature origin). Our results reinforce the propositions that cEs comprise a mixture of galaxies with two types of origins depending on their local environment.
Theory of microwave-optical conversion using rare-earth-ion dopants

We develop a theoretical description of a device for coherent conversion of microwave to optical photons. For the device, dopant ions in a crystal are used as three-level systems, and interact with the fields inside overlapping microwave and optical cavities. We develop a model for the cavity fields interacting with an ensemble of ions, and model the ions using an open quantum systems approach, while accounting for the effect of inhomogeneous broadening. Numerical methods are developed to allow us to accurately simulate the device. We also further develop a simplified model, applicable in the case of small cavity fields which is relevant to quantum information applications. This simplified model is used to predict the maximum conversion efficiency of the device. We investigate the effect of various parameters, and predict that conversion efficiency of above 80% should be possible with currently existing experimental setups inside a dilution refrigerator.
Frierson's 1907 Parameterization of Compound Magic Squares Extended to Orders 3^{l}, l=1,2,3,..., with Information Entropy.

Frierson used a powerful parameterization of the pattern of the order 3 associative magic square to construct a family of six related order 9 compound (or composite) magic squares, several of them ancient. Stimulated by Bellew's 1997 extension to order 27, we extend these ideas to all orders that are powers of 3, and in addition find simple formulae for the matrix spectra and entropic measures for all those orders. This construction is fractal and we give numerical results to order 243 which show an information entropy measure converging to a constant value of about 1.168. for the lowest entropy members. We also briefly consider compounding of an order 4 magic square with the lowest entropy, for which we find a similar trend to constant entropy.
A Graph Spectral Flow for Computing Nodal Deficiencies

In this paper we propose a spectral flow for graph Laplacians, and prove that it counts the number of nodal domains for a given Laplace eigenvector. This extends work done for Laplacians on $\mathbb{R}^n$ to the graph setting. We mention some open problems relating the topology of a graph to the analytic behaviour of its Laplace eigenvectors, and include numerical examples illustrating our flow.
Power-law spectra from stochastic acceleration

Numerical simulations of particle acceleration in magnetized turbulence have recently observed powerlaw spectra where pile-up distributions were rather expected. We interpret this as evidence for particle segregation based on acceleration rate, which is likely related to a non-trivial dependence of this acceleration efficiency on phase space variables other than the momentum. We describe the corresponding transport in momentum space using continuous-time random walks, in which the time between two consecutive momentum jumps becomes a random variable. We show that powerlaws indeed emerge when the experimental (simulation) timescale does not encompass the full extent of the distribution of waiting times. We provide analytical solutions, which reproduce dedicated numerical Monte Carlo realizations of the stochastic process, as well as analytical approximations. Our results can be readily extrapolated for applications to astrophysical phenomenology.
Two-Stream Networks for Lane-Change Prediction of Surrounding Vehicles

In highway scenarios, an alert human driver will typically anticipate early cut-in and cut-out maneuvers of surrounding vehicles using only visual cues. An automated system must anticipate these situations at an early stage too, to increase the safety and the efficiency of its performance. To deal with lane-change recognition and prediction of surrounding vehicles, we pose the problem as an action recognition/prediction problem by stacking visual cues from video cameras. Two video action recognition approaches are analyzed: two-stream convolutional networks and spatiotemporal multiplier networks. Different sizes of the regions around the vehicles are analyzed, evaluating the importance of the interaction between vehicles and the context information in the performance. In addition, different prediction horizons are evaluated. The obtained results demonstrate the potential of these methodologies to serve as robust predictors of future lane-changes of surrounding vehicles in time horizons between 1 and 2 seconds.
T0
 censorship of early dark energy and AdS vacua

Present-day temperature $T_0$ of cosmic microwave background has been precisely measured by the FIRAS experiment. We identify why the early dark energy (EDE) (non-negligible around matter-radiation equality) scenario is compatible with the FIRAS result, while lifting the Hubble constant $H_0$. We perform Monte Carlo Markov Chain analysis to confirm our observations. We also present an $\alpha$-attractor Anti-de Sitter (AdS) model of EDE. As expected, the existence of an AdS phase near recombination can effectively result in $H_0\sim 73$km/s/Mpc at 1$\sigma$ region in the bestfit model.
Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO

Machine learning (ML) has attracted a great research interest for physical layer design problems, such as channel estimation, thanks to its low complexity and robustness. Channel estimation via ML requires model training on a dataset, which usually includes the received pilot signals as input and channel data as output. In previous works, model training is mostly done via centralized learning (CL), where the whole training dataset is collected from the users at the base station (BS). This approach introduces huge communication overhead for data collection. In this paper, to address this challenge, we propose a federated learning (FL) framework for channel estimation. We design a convolutional neural network (CNN) trained on the local datasets of the users without sending them to the BS. We develop FL-based channel estimation schemes for both conventional and RIS (intelligent reflecting surface) assisted massive MIMO (multiple-input multiple-output) systems, where a single CNN is trained for two different datasets for both scenarios. We evaluate the performance for noisy and quantized model transmission and show that the proposed approach provides approximately 16 times lower overhead than CL, while maintaining satisfactory performance close to CL. Furthermore, the proposed architecture exhibits lower estimation error than the state-of-the-art ML-based schemes.
Fast Reaction Limit with Nonmonotone Reaction Function

We analyse the fast reaction limit in the reaction‐diffusion system with nonmonotone reaction function and one nondiffusing component. As speed of reaction tends to infinity, the concentration of the nondiffusing component exhibits fast oscillations. We identify precisely its Young measure which, as a by‐product, proves strong convergence of the diffusing component, a result that is not obvious from a priori estimates. Our work is based on an analysis of regularization for forward‐backward parabolic equations by Plotnikov. We rewrite his ideas in terms of kinetic functions which clarifies the method, brings new insights, relaxes assumptions on model functions, and provides a weak formulation for the evolution of the Young measure. © 2022 Wiley Periodicals LLC.
Image Colorization: A Survey and Dataset

Image colorization is an essential image processing and computer vision branch to colorize images and videos. Recently, deep learning techniques progressed notably for image colorization. This article presents a comprehensive survey of recent state-of-the-art colorization using deep learning algorithms, describing their fundamental block architectures in terms of skip connections, input etc. as well as optimizers, loss functions, training protocols, and training data etc. Generally, we can roughly categorize the existing colorization techniques into seven classes. Besides, we also provide some additional essential issues, such as benchmark datasets and evaluation metrics. We also introduce a new dataset specific to colorization and perform an experimental evaluation of the publicly available methods. In the last section, we discuss the limitations, possible solutions, and future research directions of the rapidly evolving topic of deep image colorization that the community should further address. Dataset and Codes for evaluation will be publicly available at this https URL
Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker-Planck Equation and Physics-informed Neural Networks

The Fokker-Planck (FP) equation governing the evolution of the probability density function (PDF) is applicable to many disciplines but it requires specification of the coefficients for each case, which can be functions of space-time and not just constants, hence requiring the development of a data-driven modeling approach. When the data available is directly on the PDF, then there exist methods for inverse problems that can be employed to infer the coefficients and thus determine the FP equation and subsequently obtain its solution. Herein, we address a more realistic scenario, where only sparse data are given on the particles' positions at a few time instants, which are not sufficient to accurately construct directly the PDF even at those times from existing methods, e.g., kernel estimation algorithms. To this end, we develop a general framework based on physics-informed neural networks (PINNs) that introduces a new loss function using the Kullback-Leibler divergence to connect the stochastic samples with the FP equation, to simultaneously learn the equation and infer the multi-dimensional PDF at all times. In particular, we consider two types of inverse problems, type I where the FP equation is known but the initial PDF is unknown, and type II in which, in addition to unknown initial PDF, the drift and diffusion terms are also unknown. In both cases, we investigate problems with either Brownian or Levy noise or a combination of both. We demonstrate the new PINN framework in detail in the one-dimensional case (1D) but we also provide results for up to 5D demonstrating that we can infer both the FP equation and} dynamics simultaneously at all times with high accuracy using only very few discrete observations of the particles.
Strategies for conservation of germplasm in endemic redwoods in the face of climate change: a review

This study reviews the various conservation strategies applied to the four redwood species, namely coast redwood (Sequoia sempervirens), Sierra redwood or giant sequoia (Sequoiadendron giganteum), dawn redwood (Metasequoia glyptostroboides) and South American redwood or alerce (Fitzroya cupressoides), which are endemic in the USA, China and South America, respectively. All four redwood genera belong to the family Cupressaceae; they are monospecific, share a number of common phenotypic traits, including red wood, and are threatened in their native ranges due to human activity and a changing climate. Therefore, the management objective should be to conserve representative populations of the native species with as much genetic diversity as possible for their future survival. Those representative populations exhibiting relatively high levels of genetic diversity should be selected for germplasm preservation and monitored during the conservation phase by using molecular markers. In situ and ex situ strategies for the preservation of germplasm of the redwoods are discussed in this study. A holistic in situ gene conservation strategy calls for the regeneration of a large number of diverse redwood genotypes that exhibit adequate levels of neutral and adaptive genetic variability, by generative and vegetative methods for their preservation and maintenance in their endemic locations. At the same time, it would be desirable to conserve the redwoods in new ex situ reserves, away from their endemic locations with similar as well as different environmental conditions for testing their growth and survival capacities. In addition, other ex situ strategies involving biotechnological approaches for preservation of seeds, tissues, pollen and DNA in genebanks should also be fully exploited in the face of global climate change.
How Ominous is the Future Global Warming Premonition

Global warming, the phenomenon of increasing global average temperature in the recent decades, is receiving wide attention due to its very significant adverse effects on climate. Whether global warming will continue even in the future, is a question that is most important to investigate. In this regard, the so-called general circulation models (GCMs) have attempted to project the future climate, and nearly all of them exhibit alarming rates of global temperature rise in the future. Although global warming in the current time frame is undeniable, it is important to assess the validity of the future predictions of the GCMs. In this article, we attempt such a study using our recently-developed Bayesian multiple testing paradigm for model selection in inverse regression problems. The model we assume for the global temperature time series is based on Gaussian process emulation of the black box scenario, realistically treating the dynamic evolution of the time series as unknown. We apply our ideas to datasets available from the Intergovernmental Panel on Climate Change (IPCC) website. The best GCM models selected by our method under different assumptions on future climate change scenarios do not convincingly support the present global warming pattern when only the future predictions are considered known. Using our Gaussian process idea, we also forecast the future temperature time series given the current one. Interestingly, our results do not support drastic future global warming predicted by almost all the GCM models.
The fairness‐accuracy Pareto front

Algorithmic fairness seeks to identify and correct sources of bias in machine learning algorithms. Confoundingly, ensuring fairness often comes at the cost of accuracy. We provide formal tools in this work for reconciling this fundamental tension in algorithm fairness. Specifically, we put to use the concept of Pareto optimality from multiobjective optimization and seek the fairness‐accuracy Pareto front of a neural network classifier. We demonstrate that many existing algorithmic fairness methods are performing the so‐called linear scalarization scheme, which has severe limitations in recovering Pareto optimal solutions. We instead apply the Chebyshev scalarization scheme which is provably superior theoretically and no more computationally burdensome at recovering Pareto optimal solutions compared to the linear scheme.
Conceptualized Representation Learning for Chinese Biomedical Text Mining

Biomedical text mining is becoming increasingly important as the number of biomedical documents and web data rapidly grows. Recently, word representation models such as BERT has gained popularity among researchers. However, it is difficult to estimate their performance on datasets containing biomedical texts as the word distributions of general and biomedical corpora are quite different. Moreover, the medical domain has long-tail concepts and terminologies that are difficult to be learned via language models. For the Chinese biomedical text, it is more difficult due to its complex structure and the variety of phrase combinations. In this paper, we investigate how the recently introduced pre-trained language model BERT can be adapted for Chinese biomedical corpora and propose a novel conceptualized representation learning approach. We also release a new Chinese Biomedical Language Understanding Evaluation benchmark (\textbf{ChineseBLUE}). We examine the effectiveness of Chinese pre-trained models: BERT, BERT-wwm, RoBERTa, and our approach. Experimental results on the benchmark show that our approach could bring significant gain. We release the pre-trained model on GitHub: this https URL.
Annual Variation in Reproductive Cycles of Scincid Lizards (Ctenotus) in Central Australia

Reproductive cycles of seven species of syntopic Ctenotus (Reptilia: Scincidae) were examined through mark-recapture studies of 2,727 lizards over three years (1985-88) on an arid spinifex-grassland site near Alice Springs in central Australia, supplemented with data from dissection of 1,465 museum specimens. Timing of reproduction based on dissection of museum samples varied only slightly between species. Ctenotus brooksi and C. pantherinus laid eggs three to four weeks earlier in spring (September) and summer (January) than did other species. A double-pulse of egg production (one clutch in November and one in January) suggested that some females may produce two clutches per activity season, but there was no direct evidence of multiple clutching by individual females from either the museum dissections or the mark-recapture field studies. Oviposition was spread over two months in spring and two months in summer so that at any one time the proportion of mature females that were reproductive was low. Adult females grew larger than males in most species, and the sex ratio was male-biased in all but one species (C. pantherinus). Reproductive output varied greatly between years in the field studies and correlated with hydric conditions of the environment. Generally, reproductive activity was low in the first and third years of the study (dry years) and high in the second year of the study after winter and spring rainfall. Ctenotus pantherinus did not follow this pattern and had a similar level of reproductive output in the first two years. Clutch and egg size increased with maternal size across most species. Clutch sizes of C. pantherinus were significantly larger than the clutch sizes of the other species, and egg sizes of C. leonhardii were larger than the egg sizes of other species, after correction for the relative body sizes. Age and size at maturity differed between species (and between years within a species). The largest species, C. pantherinus, grew fastest and matured within 11 months after hatching, as did the small-bodied species C. piankai. Other species matured at 12 to 18 months of age. Life-history variables and timing of reproduction were similar for both the field population and the broad patterns identified in the museum collections. Interspecific comparisons revealed one species (C. pantherinus) to be different from the other species in its life-history variables and responses to environmental conditions. Reproductive activity was greatly influenced by rainfall, and the low recruitment in average and dry years suggested that successful reproduction may be rare in central Australian Ctenotus.
A Critical Analysis of Patch Similarity Based Image Denoising Algorithms

Image denoising is a classical signal processing problem that has received significant interest within the image processing community during the past two decades. Most of the algorithms for image denoising has focused on the paradigm of non-local similarity, where image blocks in the neighborhood that are similar, are collected to build a basis for reconstruction. Through rigorous experimentation, this paper reviews multiple aspects of image denoising algorithm development based on non-local similarity. Firstly, the concept of non-local similarity as a foundational quality that exists in natural images has not received adequate attention. Secondly, the image denoising algorithms that are developed are a combination of multiple building blocks, making comparison among them a tedious task. Finally, most of the work surrounding image denoising presents performance results based on Peak-Signal-to-Noise Ratio (PSNR) between a denoised image and a reference image (which is perturbed with Additive White Gaussian Noise). This paper starts with a statistical analysis on non-local similarity and its effectiveness under various noise levels, followed by a theoretical comparison of different state-of-the-art image denoising algorithms. Finally, we argue for a methodological overhaul to incorporate no-reference image quality measures and unprocessed images (raw) during performance evaluation of image denoising algorithms.