Miracle-dz/UniFAR-FTUs huggingface dataset:
Dimensional aspects of Lovelock-Lanczos gravity

There has recently been an increasing interest in regularizations of Lovelock-Lanczos gravity (LLG) in four dimensions, in which dimensional poles and possibly counter-terms are introduced to compensate the vanishing of the Lovelock field equations in critical and lower dimensions. In this paper, we review and extend some of these results. We first find a class of LLG theories whose perturbative expansion around a given (A)dS vacuum can be regularized up to arbitrary order, the simplest one being close to Lovelock gravities with a unique vacuum. If well-defined, these models might be interpreted as effective field theories of gravitons in four dimensions, or might be combined with other regularization approaches. Among those, we establish the general procedure to obtain $4$D covariant and background independent regularizations from metric transformations. In the conformal (and critical) case, we generalize previous results obtained for the Gauss-Bonnet theory to the full Lovelock series. Similarly, the regularization of Gauss-Bonnet gravity from the breaking of $4$D-covariance down to $3$D is generalized to arbitrary curvature order, seemingly resulting in new Minimally Modified gravities propagating solely the two degrees of freedom of the graviton. Finally, we present general results regarding the minisuperspace regularization of specific sectors of LLG. Non-perturbative (in curvature) regularized theories admitting non-singular black holes as well as non-singular past-dS$_4$ and cyclic closed cosmologies are found. We conclude with the non-uniqueness of these background regularizations by finding inequivalent regularizations of the Bianchi I sector of Lovelock-Lanczos gravity in four dimensions.
Simultaenous Sieves: A Deterministic Streaming Algorithm for Non-Monotone Submodular Maximization

In this work, we present a combinatorial, deterministic single-pass streaming algorithm for the problem of maximizing a submodular function, not necessarily monotone, with respect to a cardinality constraint (SMCC). In the case the function is monotone, our algorithm reduces to the optimal streaming algorithm of Badanidiyuru et al. (2014). In general, our algorithm achieves ratio $\alpha / (1 + \alpha) - \varepsilon$, for any $\varepsilon > 0$, where $\alpha$ is the ratio of an offline (deterministic) algorithm for SMCC used for post-processing. Thus, if exponential computation time is allowed, our algorithm deterministically achieves nearly the optimal $1/2$ ratio. These results nearly match those of a recently proposed, randomized streaming algorithm that achieves the same ratios in expectation. For a deterministic, single-pass streaming algorithm, our algorithm achieves in polynomial time an improvement of the best approximation factor from $1/9$ of previous literature to $\approx 0.2689$.
Enhanced Cyber-Physical Security Using Attack-resistant Cyber Nodes and Event-triggered Moving Target Defence

This paper outlines a cyber-physical authentication strategy to protect power system infrastructure against false data injection (FDI) attacks. We demonstrate that it is feasible to use small, low-cost, yet highly attack-resistant security chips as measurement nodes, enhanced with an event-triggered moving target defence (MTD), to offer effective cyber-physical security. At the cyber layer, the proposed solution is based on the MULTOS Trust-Anchor chip, using an authenticated encryption protocol, offering cryptographically protected and chained reports at up to 12/s. The availability of the trust-anchors, allows the grid controller to delegate aspects of passive anomaly detection, supporting local as well as central alarms. In this context, a distributed event-triggered MTD protocol is implemented at the physical layer to complement cyber side enhancement. This protocol applies a distributed anomaly detection scheme based on Holt-Winters seasonal forecasting in combination with MTD implemented via inductance perturbation. The scheme is shown to be effective at preventing or detecting a wide range of attacks against power system measurement system.
Long Short-Term Memory Neuron Equalizer.

In this work we propose a neuromorphic hardware based signal equalizer by based on the deep learning implementation. The proposed neural equalizer is plasticity trainable equalizer which is different from traditional model designed based DFE. A trainable Long Short-Term memory neural network based DFE architecture is proposed for signal recovering and digital implementation is evaluated through FPGA implementation. Constructing with modelling based equalization methods, the proposed approach is compatible to multiple frequency signal equalization instead of single type signal equalization. We shows quantitatively that the neuronmorphic equalizer which is amenable both analog and digital implementation outperforms in different metrics in comparison with benchmarks approaches. The proposed method is adaptable both for general neuromorphic computing or ASIC instruments.
Let's Gamble: Uncovering the Impact of Visualization on Risk Perception and Decision-Making

Data visualizations are standard tools for assessing and communicating risks. However, it is not always clear which designs are optimal or how encoding choices might influence risk perception and decision-making. In this paper, we report the findings of a large-scale gambling game that immersed participants in an environment where their actions impacted their bonuses. Participants chose to either enter a draw or receive guaranteed monetary gains based on five common visualization designs. By measuring risk perception and observing decision-making, we showed that icon arrays tended to elicit economically sound behavior. We also found that people were more likely to gamble when presented area proportioned triangle and circle designs. Using our results, we model risk perception and decisions for each visualization and provide a ranking to improve visualization selection.
Hall effect for Dirac electrons in graphene exposed to an Abrikosov flux lattice

The proposals for realizing exotic particles through coupling of quantum Hall effect to superconductivity involve spatially non-uniform magnetic fields. As a step toward that goal, we study, both theoretically and experimentally, a system of Dirac electrons exposed to an Abrikosov flux lattice. We theoretically find that the non-uniform magnetic field causes a carrier-density–dependent reduction of the Hall conductivity. Our studies show that this reduction originates from a rather subtle effect: a levitation of the Berry curvature within Landau levels broadened by the non-uniform magnetic field. Experimentally, we measure the magneto-transport in a monolayer graphene-hexagonal boron nitride-niobium diselenide (NbSe2) heterostructure, and find a density-dependent reduction of the Hall resistivity of graphene as the temperature is lowered from above the superconducting critical temperature of NbSe2, when the magnetic field is uniform, to below, where the magnetic field bunches into an Abrikosov flux lattice.
Measuring Dark Matter in Galaxies: The Mass Fraction within Five Effective Radii

Large galaxies may contain an “atmosphere” of hot interstellar X-ray gas, and the temperature and radial density profile of this gas can be used to measure the total mass of the galaxy contained within a given radius r. We use this technique for 102 early-type galaxies with stellar masses M⋆ > 1010M⊙, to evaluate the mass fraction of dark matter (DM) within the fiducial radius r = 5re, denoted f5 = fDM(5re). On average, these systems have a median with a typical galaxy-to-galaxy scatter ±0.15. Comparisons with mass estimates made through the alternative techniques of satellite dynamics (e.g., velocity distributions of globular clusters, planetary nebulae, satellite dwarfs) as well as strong lensing show encouraging consistency over the same range of stellar mass. We find that many of the disk galaxies (S0/SA0/SB0) have a significantly higher mean f5 than do the pure ellipticals, by Δf5 ≃ 0.1. We suggest that this higher level may be a consequence of sparse stellar haloes and quieter histories with fewer major episodes of feedback or mergers. Comparisons are made with the Magneticum Pathfinder suite of simulations for both normal and centrally dominant “Brightest Cluster” galaxies. Though the observed data exhibit somewhat larger scatter at a given galaxy mass than do the simulations, the mean level of DM mass fraction for all classes of galaxies is in good first-order agreement with the simulations. Finally, we find that the group galaxies with stellar masses near M⋆ ∼ 1011M⊙ have relatively more outliers at low f5 than in other mass ranges, possibly the result of especially effective AGN feedback in that mass range leading to expansion of their DM halos.
Universal R\'enyi Entropy of Quasiparticle Excitations

The Renyi entropies of quasiparticle excitations in the many-body gapped systems show a remarkable universal picture which can be understood partially by combination of a semiclassical argument with the quantum effect of (in)distinguishability. It has been argued that the Renyi entropies are largely independent of the model, the quasiparticle momenta, and the connectedness of the subsystem. In this letter we calculate exactly the single-interval and double-interval Renyi entropies of quasiparticle excitations in the many-body gapped fermions, bosons, and XY chains. We find nontrivial additional terms to the universal Renyi entropy in the excited states with quasiparticles of different momenta. The additional terms are different in the fermionic and bosonic chains, depend on the momentum differences of the quasiparticles, and are different for the single interval and the double interval. We derive the analytical Renyi entropy in the extremely gapped limit, matching perfectly the numerical results as long as either the intrinsic correlation length of the model or the de Broglie wavelengths of the quasiparticles are small. When the momentum difference of any pair of distinct quasiparticles is small, the additional terms are non-negligible. On the contrary, when the difference of the momenta of each pair of distinct quasiparticles is large, the additional terms can be neglected. The universal single-interval Renyi entropy and its additional terms in the XY chain are the same as those in the fermionic chain, while the universal Renyi entropy of the double intervals and its additional terms are different, due to the fact that the local degrees of freedom of the XY chain are the Pauli matrices not the spinless fermions. We argue that the derived formulas have universal properties and can be applied for a wider range of models than those discussed here.
General higher-order majorization-minimization algorithms for (non)convex optimization.

Majorization-minimization algorithms consist of successively minimizing a sequence of upper bounds of the objective function so that along the iterations the objective function decreases. Such a simple principle allows to solve a large class of optimization problems, convex or nonconvex, smooth or nonsmooth. We propose a general higher-order majorization-minimization algorithm for minimizing an objective function that admits an approximation (surrogate) such that the corresponding error function has a higher-order Lipschitz continuous derivative. We present convergence guarantees for our new method for general optimization problems with convex or nonconvex, smooth or nonsmooth objective function. For convex (possibly nonsmooth) problems we provide global sublinear convergence rates, while for problems with uniformly convex objective function we obtain locally faster convergence rates, that is superlinear. We also prove global asymptotic stationary point guarantees for general nonconvex problems and under Kurdyka-Lojasiewicz property of the objective function we derive local convergence rates ranging from sublinear to superlinear for our majorization-minimization algorithm. Moreover, for composite (unconstrained) nonconvex problems we derive convergence rates in terms of first- (second)-order optimality conditions.
Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning

We identify an implicit under-parameterization phenomenon in value-based deep RL methods that use bootstrapping: when value functions, approximated using deep neural networks, are trained with gradient descent using iterated regression onto target values generated by previous instances of the value network, more gradient updates decrease the expressivity of the current value network. We characterize this loss of expressivity in terms of a drop in the rank of the learned value network features, and show that this corresponds to a drop in performance. We demonstrate this phenomenon on widely studies domains, including Atari and Gym benchmarks, in both offline and online RL settings. We formally analyze this phenomenon and show that it results from a pathological interaction between bootstrapping and gradient-based optimization. We further show that mitigating implicit under-parameterization by controlling rank collapse improves performance.
UAV-Assisted and Intelligent Reflecting Surfaces-Supported Terahertz Communications

In this letter, unmanned aerial vehicles (UAVs) and intelligent reflective surface (IRS) are utilized to support terahertz (THz) communications. To this end, the joint optimization of UAV’s trajectory, the phase shift of IRS, the allocation of THz sub-bands, and the power control are investigated to maximize the minimum average achievable rate of all users. An iteration algorithm based on successive Convex Approximation with the Rate constraint penalty (CAR) is developed to obtain UAV’s trajectory, and the IRS phase shift is formulated as a closed-form expression with introduced pricing factors. Simulation results show that the proposed scheme significantly enhances the rate performance of the whole system.
A novel variational form of the Schatten-p quasi-norm

The Schatten-$p$ quasi-norm with $p\in(0,1)$ has recently gained considerable attention in various low-rank matrix estimation problems offering significant benefits over relevant convex heuristics such as the nuclear norm. However, due to the nonconvexity of the Schatten-$p$ quasi-norm, minimization suffers from two major drawbacks: 1) the lack of theoretical guarantees and 2) the high computational cost which is demanded for the minimization task even for trivial tasks such as finding stationary points. In an attempt to reduce the high computational cost induced by Schatten-$p$ quasi-norm minimization, variational forms, which are defined over smaller-size matrix factors whose product equals the original matrix, have been proposed. Here, we propose and analyze a novel variational form of Schatten-$p$ quasi-norm which, for the first time in the literature, is defined for any continuous value of $p\in(0,1]$ and decouples along the columns of the factorized matrices. The proposed form can be considered as the natural generalization of the well-known variational form of the nuclear norm to the nonconvex case i.e., for $p\in(0,1)$. The resulting formulation gives way to SVD-free algorithms thus offering lower computational complexity than the one that is induced by the original definition of the Schatten-$p$ quasi-norm. A local optimality analysis is provided which shows~that we can arrive at a local minimum of the original Schatten-$p$ quasi-norm problem by reaching a local minimum of the matrix factorization based surrogate problem. In addition, for the case of the squared Frobenius loss with linear operators obeying the restricted isometry property (RIP), a rank-one update scheme is proposed, which offers a way to escape poor local minima. Finally, the efficiency of our approach is empirically shown on a matrix completion problem.
Learning Contextual Tag Embeddings for Cross-Modal Alignment of Audio and Tags

Self-supervised audio representation learning offers an attractive alternative for obtaining generic audio embeddings, capable to be employed into various downstream tasks. Published approaches that consider both audio and words/tags associated with audio do not employ text processing models that are capable to generalize to tags unknown during training. In this work we propose a method for learning audio representations using an audio autoencoder (AAE), a general word embed-dings model (WEM), and a multi-head self-attention (MHA) mechanism. MHA attends on the output of the WEM, pro-viding a contextualized representation of the tags associated with the audio, and we align the output of MHA with the out-put of the encoder of AAE using a contrastive loss. We jointly optimize AAE and MHA and we evaluate the audio representations (i.e. the output of the encoder of AAE) by utilizing them in three different downstream tasks, namely sound, music genre, and music instrument classification. Our results show that employing multi-head self-attention with multiple heads in the tag-based network can induce better learned audio representations.
Declining prevalence of antibody positivity to SARS-CoV-2: a community study of 365,000 adults

Background The prevalence and persistence of antibodies following a peak SARS-CoV-2 infection provides insights into its spread in the community, the likelihood of reinfection and potential for some level of population immunity. Methods Prevalence of antibody positivity in England, UK (REACT2) with three cross-sectional surveys between late June and September 2020. 365104 adults used a self-administered lateral flow immunoassay (LFIA) test for IgG. A laboratory comparison of LFIA results to neutralization activity in panel of sera was performed. Results There were 17,576 positive tests over the three rounds. Antibody prevalence, adjusted for test characteristics and weighted to the adult population of England, declined from 6.0% [5.8, 6.1], to 4.8% [4.7, 5.0] and 4.4% [4.3, 4.5], a fall of 26.5% [-29.0, -23.8] over the three months of the study. There was a decline between rounds 1 and 3 in all age groups, with the highest prevalence of a positive result and smallest overall decline in positivity in the youngest age group (18-24 years: -14.9% [-21.6, -8.1]), and lowest prevalence and largest decline in the oldest group (75+ years: -39.0% [-50.8, -27.2]); there was no change in antibody positivity between rounds 1 and 3 in healthcare workers (+3.45% [-5.7, +12.7]). The decline from rounds 1 to 3 was largest in those who did not report a history of COVID-19, (-64.0% [-75.6, -52.3]), compared to -22.3% ([-27.0, -17.7]) in those with SARS-CoV-2 infection confirmed on PCR. Discussion These findings provide evidence of variable waning in antibody positivity over time such that, at the start of the second wave of infection in England, only 4.4% of adults had detectable IgG antibodies using an LFIA. Antibody positivity was greater in those who reported a positive PCR and lower in older people and those with asymptomatic infection. These data suggest the possibility of decreasing population immunity and increasing risk of reinfection as detectable antibodies decline in the population.
DETECTION OF METHICILLIN RESISTANT STAPHYLOCOCCUS AUREUS IN CHICKEN CARCASSES AND LIVE BIRDS IN ZARIA, NIGERIA

Methicillin resistant Staphylococcus aureus (MRSA) associated with livestock is an emerging threat to public health. Poultry production represents a key interface for the spread of such novel zoonotic and antibiotic resistant pathogens. The occurrence of MRSA in chickens in two live bird markets and in chickens presented for clinical purposes to the Avian Unit of Ahmadu Bello University Veterinary Teaching Hospital, Zaria, Nigeria was investigated. One thousand four hundred samples in the form of swabs of trachea (400), cloaca (400), skin surface (400) and joint (200) were collected from 200 live chickens and 200 chicken carcasses and examined for the presence of Staphylococcus aureus. Thirteen (0.9%) of the samples, 7 from carcasses of slaughtered chickens and 6 from live birds in the live bird markets contained coagulase positive S. aureus. None was isolated from chickens in the avian clinic. The 13 S. aureus isolates were subjected to polymerase chain reaction (PCR) to detect the presence of mecA gene that codes for methicillin resistance. A PCR product of 533bp size consistent with the expected PCR product size of mecA gene from a positive control strain was detected in 1 of the 13 S. aureus isolates. The presence of MRSA in chicken carcasses and live birds in Zaria, Nigeria was demonstrated, highlighting the potential role of poultry production in the spread of MRSA. Keywords: Methicillin resistant Staphylococcus aureus, mecA gene, chickens
LEAD: Least-Action Dynamics for Min-Max Optimization

Adversarial formulations in machine learning have rekindled interest in differentiable games. The development of efficient optimization methods for two-player min-max games is an active area of research with a timely impact on adversarial formulations including generative adversarial networks (GANs). Existing methods for this type of problem typically employ intuitive, carefully hand-designed mechanisms for controlling the problematic rotational dynamics commonly encountered during optimization. In this work, we take a novel approach to address this issue by casting min-max optimization as a physical system. We propose LEAD (Least-Action Dynamics), a second-order optimizer that uses the principle of least-action from physics to discover an efficient optimizer for min-max games. We subsequently provide convergence analysis of our optimizer in quadratic min-max games using the Lyapunov theory. Finally, we empirically test our method on synthetic problems and GANs to demonstrate improvements over baseline methods.
Possible Transient Luminous Events Observed in Jupiter's Upper Atmosphere

Eleven transient bright flashes were detected in Jupiter's atmosphere using the ultraviolet spectrograph instrument on the Juno spacecraft. These bright flashes are only observed in a single spin of the spacecraft and their brightness decays exponentially with time, with a duration of ∼1.4 ms. The spectra are dominated by H2 Lyman band emission and based on the level of atmospheric absorption, we estimate a source altitude of 260 km above the 1‐bar level. Based on these characteristics, we suggest that these are observations of transient luminous events (TLEs) in Jupiter's upper atmosphere. In particular, we suggest that these are elves, sprites or sprite halos, three types of TLEs that occur in the Earth's upper atmosphere in response to tropospheric lightning strikes. This is supported by visible light imaging, which shows cloud features typical of lightning source regions at the locations of several of the bright flashes. TLEs have previously only been observed on Earth, although theoretical and experimental work has predicted that they should also be present on Jupiter.