Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial Distribution of Distortion due to Nonlinear Power Amplification in Distributed Massive MIMO

Due to the nonlinearity of power amplifiers (PAs), the transmit signal is distorted. Previous works have studied the spatial distribution of this distortion for a central massive multiple input multiple output (MIMO) array. In this work, we extend the analysis for distributed massive MIMO in a line-of-sight (LoS) scenario. We show that the distortion is not always uniformly distributed in space. In the single-user case, it coherently combines at the user location. In the few users case, the signals will add up at the user locations plus several others locations. As the number of users increases, it becomes close to uniformly distributed. As a comparison with a central massive MIMO system, having the same total number of antennas, the distortion in distributed massive MIMO is considerably more uniformly distributed in space. Moreover, the potential coherent combining is contained in a zone rather than in generic directions, i.e., in a beamspot. Furthermore, a small-scale fading effect is observed at unintended locations due to the non-coherent combining of the signals. As a result, one can expect that going towards distributed systems allows working closer to saturation, increasing the PA operating efficiency and/or using low-cost PAs.
The ensemble Kalman filter for rare event estimation

We present a novel sampling-based method for estimating probabilities of rare or failure events. Our approach is founded on the Ensemble Kalman filter (EnKF) for inverse problems. Therefore, we reformulate the rare event problem as an inverse problem and apply the EnKF to generate failure samples. To estimate the probability of failure, we use the final EnKF samples to fit a distribution model and apply Importance Sampling with respect to the fitted distribution. This leads to an unbiased estimator if the density of the fitted distribution admits positive values within the whole failure domain. To handle multi-modal failure domains, we localise the covariance matrices in the EnKF update step around each particle and fit a mixture distribution model in the Importance Sampling step. For affine linear limit-state functions, we investigate the continuous-time limit and large time properties of the EnKF update. We prove that the mean of the particles converges to a convex combination of the most likely failure point and the mean of the optimal Importance Sampling density if the EnKF is applied without noise. We provide numerical experiments to compare the performance of the EnKF with Sequential Importance Sampling.
Wide stochastic networks: Gaussian limit and PAC-Bayesian training

The limit of infinite width allows for substantial simplifications in the analytical study of over-parameterised neural networks. With a suitable random initialisation, an extremely large network exhibits an approximately Gaussian behaviour. In the present work, we establish a similar result for a simple stochastic architecture whose parameters are random variables, holding both before and during training. The explicit evaluation of the output distribution allows for a PAC-Bayesian training procedure that directly optimises the generalisation bound. For a large but finite-width network, we show empirically on MNIST that this training approach can outperform standard PAC-Bayesian methods.
Electronic and Magnetic Properties of Building Blocks of Mn and Fe Atomic Chains on Nb(110)

We present results for the electronic and magnetic structure of Mn and Fe clusters on Nb(110) surface, focusing on building blocks of atomic chains as possible realizations of topological superconductivity. The magnetic ground states of the atomic dimers and most of the monatomic chains are determined by the nearest-neighbor isotropic interaction. To gain physical insight, the dependence on the crystallographic direction as well as on the atomic coordination number is analyzed via an orbital decomposition of this isotropic interaction based on the spin-cluster expansion and the difference in the local density of states between ferromagnetic and antiferromagnetic configurations. A spin-spiral ground state is obtained for Fe chains along the [11¯0] direction as a consequence of the frustration of the isotropic interactions. Here, a flat spin-spiral dispersion relation is identified, which can stabilize spin spirals with various wave vectors together with the magnetic anisotropy. This may lead to the observation of spin spirals of different wave vectors and chiralities in longer chains instead of a unique ground state.
Goal-Directed Planning by Reinforcement Learning and Active Inference

What is the difference between goal-directed and habitual behavior? We propose a novel computational framework of decision making with Bayesian inference, in which everything is integrated as an entire neural network model. The model learns to predict environmental state transitions by self-exploration and generating motor actions by sampling stochastic internal states ${z}$. Habitual behavior, which is obtained from the prior distribution of ${z}$, is acquired by reinforcement learning. Goal-directed behavior is determined from the posterior distribution of ${z}$ by planning, using active inference which optimizes the past, current and future ${z}$ by minimizing the variational free energy for the desired future observation constrained by the observed sensory sequence. We demonstrate the effectiveness of the proposed framework by experiments in a sensorimotor navigation task with camera observations and continuous motor actions.
Synthetic COVID-19 Chest X-ray Dataset for Computer-Aided Diagnosis

We introduce a new dataset called Synthetic COVID-19 Chest X-ray Dataset for training machine learning models. The dataset consists of 21,295 synthetic COVID-19 chest X-ray images to be used for computer-aided diagnosis. These images, generated via an unsupervised domain adaptation approach, are of high quality. We find that the synthetic images not only improve performance of various deep learning architectures when used as additional training data under heavy imbalance conditions, but also detect the target class with high confidence. We also find that comparable performance can also be achieved when trained only on synthetic images. Further, salient features of the synthetic COVID-19 images indicate that the distribution is significantly different from Non-COVID-19 classes, enabling a proper decision boundary. We hope the availability of such high fidelity chest X-ray images of COVID-19 will encourage advances in the development of diagnostic and/or management tools.
Training or Architecture? How to Incorporate Invariance in Neural Networks

Many applications require the robustness, or ideally the invariance, of a neural network to certain transformations of input data. Most commonly, this requirement is addressed by either augmenting the training data, using adversarial training, or defining network architectures that include the desired invariance automatically. Unfortunately, the latter often relies on the ability to enlist all possible transformations, which make such approaches largely infeasible for infinite sets of transformations, such as arbitrary rotations or scaling. In this work, we propose a method for provably invariant network architectures with respect to group actions by choosing one element from a (possibly continuous) orbit based on a fixed criterion. In a nutshell, we intend to 'undo' any possible transformation before feeding the data into the actual network. We analyze properties of such approaches, extend them to equivariant networks, and demonstrate their advantages in terms of robustness as well as computational efficiency in several numerical examples. In particular, we investigate the robustness with respect to rotations of images (which can possibly hold up to discretization artifacts only) as well as the provable rotational and scaling invariance of 3D point cloud classification.
Sparse Linear Spectral Unmixing of Hyperspectral Images Using Expectation-Propagation

This article presents a novel Bayesian approach for hyperspectral image unmixing. The observed pixels are modeled by a linear combination of material signatures weighted by their corresponding abundances. A spike-and-slab abundance prior is adopted to promote sparse mixtures and an Ising prior model is used to capture spatial correlation of the mixture support across pixels. We approximate the posterior distribution of the abundances using the expectation-propagation (EP) method. We show that it can significantly reduce the computational complexity of the unmixing stage and meanwhile provide uncertainty measures, compared to expensive Monte Carlo strategies traditionally considered for uncertainty quantification. Moreover, many variational parameters within each EP factor can be updated in a parallel manner, which enables mapping of efficient algorithmic architectures based on graphics processing units (GPUs). Under the same approximate Bayesian framework, we then extend the proposed algorithm to semi-supervised unmixing, whereby the abundances are viewed as latent variables and the expectation-maximization (EM) algorithm is used to refine the endmember matrix. Experimental results on synthetic data and real hyperspectral data illustrate the benefits of the proposed framework over state-of-art linear unmixing methods.
Being Properly Improper

Properness for supervised losses stipulates that the loss function shapes the learning algorithm towards the true posterior of the data generating distribution. Unfortunately, data in modern machine learning can be corrupted or twisted in many ways. Hence, optimizing a proper loss function on twisted data could perilously lead the learning algorithm towards the twisted posterior, rather than to the desired clean posterior. Many papers cope with specific twists (e.g., label/feature/adversarial noise), but there is a growing need for a unified and actionable understanding atop properness. Our chief theoretical contribution is a generalization of the properness framework with a notion called twist-properness, which delineates loss functions with the ability to"untwist"the twisted posterior into the clean posterior. Notably, we show that a nontrivial extension of a loss function called $\alpha$-loss, which was first introduced in information theory, is twist-proper. We study the twist-proper $\alpha$-loss under a novel boosting algorithm, called PILBoost, and provide formal and experimental results for this algorithm. Our overarching practical conclusion is that the twist-proper $\alpha$-loss outperforms the proper $\log$-loss on several variants of twisted data.
The MURALES survey. IV. Searching for nuclear outflows in 3C radio galaxies at z < 0.3 with MUSE observations.

We analyze VLT/MUSE observations of 37 radio galaxies from the Third Cambridge catalogue (3C) with redshift <0.3 searching for nuclear outflows of ionized gas. These observations are part of the MURALES project (a MUse RAdio Loud Emission line Snapshot survey), whose main goal is to explore the feedback process in the most powerful radio-loud AGN. We applied a nonparametric analysis to the [O III] λ5007 emission line, whose asymmetries and high-velocity wings reveal signatures of outflows. We find evidence of nuclear outflows in 21 sources, with velocities between ∼400–1000 km s−1, outflowing masses of ∼ 105 − 107 M , and a kinetic energy in the range ∼ 1053 − 1056 erg. In addition, evidence for extended outflows is found in the 2D gas velocity maps of 13 sources of the subclasses of high-excitation (HEG) and broad-line (BLO) radio galaxies, with sizes between 0.4 and 20 kpc. We estimate a mass outflow rate in the range 0.4–30 M yr−1 and an energy deposition rate of Ėkin ∼ 1042 − 1045 erg s−1. Comparing the jet power, the nuclear luminosity of the active galactic nucleus, and the outflow kinetic energy rate, we find that outflows of HEGs and BLOs are likely radiatively powered, while jets likely only play a dominant role in galaxies with low excitation. The low loading factors we measured suggest that these outflows are driven by momentum and not by energy. Based on the gas masses, velocities, and energetics involved, we conclude that the observed ionized outflows have a limited effect on the gas content or the star formation in the host. In order to obtain a complete view of the feedback process, observations exploring the complex multiphase structure of outflows are required.
A Spitzer survey for dust-obscured supernovae

Supernova (SN) rates serve as an important probe of star-formation models and initial mass functions. Near-infrared seeing-limited ground-based surveys typically discover a factor of 3–10 fewer SNe than predicted from far-infrared (FIR) luminosities owing to sensitivity limitations arising from both a variable point-spread function (PSF) and high dust extinction in the nuclear regions of star-forming galaxies. This inconsistency has potential implications for our understanding of star-formation rates and massive-star evolution, particularly at higher redshifts, where star-forming galaxies are more common. To resolve this inconsistency, a successful SN survey in the local universe must be conducted at longer wavelengths and with a space-based telescope, which has a stable PSF to reduce the necessity for any subtraction algorithms and thus residuals. Here we report on a two-year Spitzer/IRAC 3.6 μm survey for dust-extinguished SNe in the nuclear regions of forty luminous infrared galaxies (LIRGs) within 200 Mpc. The asymmetric Spitzer PSF results in worse than expected subtraction residuals when implementing standard template subtraction. Forward-modeling techniques improve our sensitivity by ∼ 1.5 magnitudes. We report the detection of 9 SNe, five of which were not discovered by optical surveys. After adjusting our predicted rates to account for the sensitivity of our survey, we find that the number of detections is consistent with the models. While this search is nonetheless hampered by a difficult-to-model PSF and the relatively poor resolution of Spitzer, it will benefit from future missions, such as Roman and JWST, with higher resolution and more symmetric PSFs.
Low Resource German ASR with Untranscribed Data Spoken by Non-native Children - INTERSPEECH 2021 Shared Task SPAPL System

This paper describes the SPAPL system for the INTERSPEECH 2021 Challenge: Shared Task on Automatic Speech Recognition for Non-Native Children's Speech in German. ~ 5 hours of transcribed data and ~ 60 hours of untranscribed data are provided to develop a German ASR system for children. For the training of the transcribed data, we propose a non-speech state discriminative loss (NSDL) to mitigate the influence of long-duration non-speech segments within speech utterances. In order to explore the use of the untranscribed data, various approaches are implemented and combined together to incrementally improve the system performance. First, bidirectional autoregressive predictive coding (Bi-APC) is used to learn initial parameters for acoustic modelling using the provided untranscribed data. Second, incremental semi-supervised learning is further used to iteratively generate pseudo-transcribed data. Third, different data augmentation schemes are used at different training stages to increase the variability and size of the training data. Finally, a recurrent neural network language model (RNNLM) is used for rescoring. Our system achieves a word error rate (WER) of 39.68% on the evaluation data, an approximately 12% relative improvement over the official baseline (45.21%).
Point-of-Interest Recommender Systems: A Survey from an Experimental Perspective

Point-of-Interest recommendation is an increasing research and developing area within the widely adopted technologies known as Recommender Systems. Among them, those that exploit information coming from Location-Based Social Networks (LBSNs) are very popular nowadays and could work with different information sources, which pose several challenges and research questions to the community as a whole. We present a systematic review focused on the research done in the last 10 years about this topic. We discuss and categorize the algorithms and evaluation methodologies used in these works and point out the opportunities and challenges that remain open in the field. More specifically, we report the leading recommendation techniques and information sources that have been exploited more often (such as the geographical signal and deep learning approaches) while we also alert about the lack of reproducibility in the field that may hinder real performance improvements
Boolean Matrix Factorization with SAT and MaxSAT

The Boolean matrix factorization problem consists in approximating a matrix by the Boolean product of two smaller Boolean matrices. To obtain optimal solutions when the matrices to be factorized are small, we propose SAT and MaxSAT encoding; however, when the matrices to be factorized are large, we propose a heuristic based on the search for maximal biclique edge cover. We experimentally demonstrate that our approaches allow a better factorization than existing approaches while keeping reasonable computation times. Our methods also allow the handling of incomplete matrices with missing entries.
Synchronising speech segments with musical beats in Mandarin and English singing

Generating synthesised singing voice with models trained on speech data has many advantages due to the models' flexibility and controllability. However, since the information about the temporal relationship between segments and beats are lacking in speech training data, the synthesised singing may sound off-beat at times. Therefore, the availability of the information on the temporal relationship between speech segments and music beats is crucial. The current study investigated the segment-beat synchronisation in singing data, with hypotheses formed based on the linguistics theories of P-centre and sonority hierarchy. A Mandarin corpus and an English corpus of professional singing data were manually annotated and analysed. The results showed that the presence of musical beats was more dependent on segment duration than sonority. However, the sonority hierarchy and the P-centre theory were highly related to the location of beats. Mandarin and English demonstrated cross-linguistic variations despite exhibiting common patterns.
Residual Error: a New Performance Measure for Adversarial Robustness

Despite the significant advances in deep learning over the past decade, a major challenge that limits the wide-spread adoption of deep learning has been their fragility to adversarial attacks. This sensitivity to making erroneous predictions in the presence of adversarially perturbed data makes deep neural networks difficult to adopt for certain real-world, mission-critical applications. While much of the research focus has revolved around adversarial example creation and adversarial hardening, the area of performance measures for assessing adversarial robustness is not well explored. Motivated by this, this study presents the concept of residual error, a new performance measure for not only assessing the adversarial robustness of a deep neural network at the individual sample level, but also can be used to differentiate between adversarial and non-adversarial examples to facilitate for adversarial example detection. Furthermore, we introduce a hybrid model for approximating the residual error in a tractable manner. Experimental results using the case of image classification demonstrates the effectiveness and efficacy of the proposed residual error metric for assessing several well-known deep neural network architectures. These results thus illustrate that the proposed measure could be a useful tool for not only assessing the robustness of deep neural networks used in mission-critical scenarios, but also in the design of adversarially robust models.
Shipworm symbiosis ecology-guided discovery of an antibiotic that kills colistin-resistant Acinetobacter

Teredinibacter turnerae is an intracellular bacterial symbiont that lives in the gills of wood-eating shipworms, where it is proposed to use antibiotics to defend itself and its animal host. Several biosynthetic gene clusters are conserved in T. turnerae and in their host shipworms around the world, implying that they encode the important defensive antibiotics. Here, we describe the turnercyclamycins, lipopeptide antibiotics encoded in the genomes of all sequenced T. turnerae strains. Turnercyclamycins A and B are bactericidal against challenging Gram-negative pathogens, including Escherichia coli, Klebsiella pneumoniae, and Acinetobacter baumannii, at 1, 2, and 8 µg/mL, respectively. Additionally, these compounds kill colistin-resistant Acinetobacter strains, while lacking toxicity to mammalian cells. Phenotypic screening identified the outer membrane as the likely target. By exploring the inhabitants of environments that select for the properties we require, we can harvest the fruits of evolution to discover compounds with potential to target unmet health needs. Investigating the symbionts of animals, and shipworms in particular, is a powerful example of this principle.
Individual haplotyping of whale sharks from seawater environmental DNA

Population genetic data can provide valuable information on the demography of a species. For rare and elusive marine megafauna, samples for generating the data are traditionally obtained from tissue biopsies, which can be logistically difficult and expensive to collect and require invasive sampling techniques. Analysis of environmental DNA (eDNA) offers an alternative, minimally invasive approach to provide important genetic information. Although eDNA approaches have been studied extensively for species detection and biodiversity monitoring in metabarcoding studies, the potential for the technique to address population‐level questions remains largely unexplored. Here, we applied “eDNA haplotyping” to obtain estimates of the intraspecific genetic diversity of a whale shark (Rhincodon typus) aggregation at Ningaloo reef, Australia. Over 2 weeks, we collected seawater samples directly behind individual sharks prior to taking a tissue biopsy sample from the same animal. Our data showed a 100% match between mtDNA sequences recovered in the eDNA and tissue sample for all 28 individuals sampled. In the seawater samples, >97% of all reads were assigned to six dominant haplotypes, and a clear dominant signal (~99% of sample reads) was recovered in each sample. Our study demonstrates accurate individual‐level haplotyping from seawater eDNA. When DNA from one individual clearly dominates each eDNA sample, it provides many of the same opportunities for population genetic analyses as a tissue sample, potentially removing the need for tissue sampling. Our results show that eDNA approaches for population‐level analyses have the potential to supply critical demographic data for the conservation and management of marine megafauna.