Miracle-dz/UniFAR-FTUs huggingface dataset:
Limitations of Weak Labels for Embedding and Tagging

Many datasets and approaches in ambient sound analysis use weakly labeled data. Weak labels are employed because annotating every data sample with a strong label is too expensive. Yet, their impact on the performance in comparison to strong labels remains unclear. Indeed, weak labels must often be dealt with at the same time as other challenges, namely multiple labels per sample, unbalanced classes and/or overlapping events. In this paper, we formulate a supervised learning problem which involves weak labels. We create a dataset that focuses on the difference between strong and weak labels as opposed to other challenges. We investigate the impact of weak labels when training an embedding or an end-to-end classifier. Different experimental scenarios are discussed to provide insights into which applications are most sensitive to weakly labeled data.
Light-by-light scattering and spacetime noncommutativity

The aim of this article is to explore a potential usability of a photon-photon self-interaction from the noncommutative quantum electrodynamics (NCQED) in the case of light-by-light scattering (γγ→γγ) in ultraperipheral Pb+Pb collisions, a reaction measured recently by ATLAS and planned for future experiments in hadron-hadron colliders. We compute the total cross section from both, the full one-loop standard model (SM) and the tree-level NCQED amplitudes, in the equivalent photon approximation with impact parameters, for various noncommutative scales, ΛNC, and incoming nuclear spin-energy combinations. We find that NCQED contribution to the cross section has considerable increase at diphoton invariant mass range higher than ΛNC, while the SM contribution is strongly suppressed in such region. Our results show that the current ATLAS sNN=5.02 TeV experiment can only probe ΛNC<100 GeV region. On the other hand, future hadron-hadron collider proposals could have the potential to extend to ΛNC≲300 GeV region, making the performance of PbPb→PbPbγγ scattering on testing space-time noncommutativity close to that of the previously proposed photon-photon mode of linear electron-positron collider.
Joint Power Allocation and Load Balancing Optimization for Energy-Efficient Cell-Free Massive MIMO Networks

Large-scale distributed antenna systems with many access points (APs) that serve the users by coherent joint transmission is being considered for 5G-and-beyond networks. The technology is called Cell-free Massive MIMO and can provide a more uniform service level to the users than a conventional cellular topology. For a given user set, only a subset of the APs is likely needed to satisfy the users’ performance demands, particularly outside the peak traffic hours. To find achieve an energy-efficient load balancing, we minimize the total downlink power consumption at the APs, considering both the transmit powers and hardware dissipation. APs can be temporarily turned off to reduce the latter part. The formulated optimization problem is non-convex but, nevertheless, a globally optimal solution is obtained by solving a mixed-integer second-order cone program. Since the computational complexity is prohibitive for real-time implementation, we also propose two low-complexity algorithms that exploit the inherent group-sparsity and the optimized transmit powers in the problem formulation. Numerical results manifest that our optimization algorithms can greatly reduce the power consumption compared to keeping all APs turned on and only minimizing the transmit powers. Moreover, the low-complexity algorithms can effectively handle the power allocation and AP activation for large-scale networks.
'Why not give this work to them?' Explaining AI-Moderated Task-Allocation Outcomes using Negotiation Trees

The problem of multi-agent task allocation arises in a variety of scenarios involving human teams. In many such settings, human teammates may act with selﬁsh motives and try to minimize their cost metrics. In the absence of (1) complete knowledge about the reward of other agents and (2) the team’s overall cost associated with a particular allocation outcome, distributed algorithms can only arrive at sub-optimal solutions within a reasonable amount of time. To address these challenges, we introduce the notion of an AI Task Allocator (AITA) that, with complete knowledge, comes up with fair allocations that strike a balance between the individual human costs and the team’s performance cost. To ensure that AITA is explicable to the humans, we allow each human agent to question AITA’s proposed allocation with counterfactual allocations. In response, we design AITA to provide a replay negotiation tree that acts as an explanation showing why the counterfactual allocation, with the correct costs, will eventually result in a sub-optimal allocation. This explanation also updates a human’s incomplete knowledge about their teammate’s and the team’s actual costs. We then investigate whether humans are (1) able to understand the explanations provided and (2) convinced by it using human factor studies. Finally, we show the effect of various kinds of incompleteness on the length of explanations. We conclude that underestimation of other’s costs often leads to the need for explanations and in turn, longer explanations on average.
Model-based design of riblets for turbulent drag reduction

Abstract Both experiments and direct numerical simulations have been used to demonstrate that riblets can reduce turbulent drag by as much as $10\,\%$, but their systematic design remains an open challenge. In this paper we develop a model-based framework to quantify the effect of streamwise-aligned spanwise-periodic riblets on kinetic energy and skin-friction drag in turbulent channel flow. We model the effect of riblets as a volume penalization in the Navier–Stokes equations and use the statistical response of the eddy-viscosity-enhanced linearized equations to quantify the effect of background turbulence on the mean velocity and skin-friction drag. For triangular riblets, our simulation-free approach reliably predicts drag-reducing trends as well as mechanisms that lead to performance deterioration for large riblets. We investigate the effect of height and spacing on drag reduction and demonstrate a correlation between energy suppression and drag reduction for appropriately sized riblets. We also analyse the effect of riblets on drag-reduction mechanisms and turbulent flow structures including very large-scale motions. Our results demonstrate the utility of our approach in capturing the effect of riblets on turbulent flows using models that are tractable for analysis and optimization.
Pure Differentially Private Summation from Anonymous Messages

The shuffled (aka anonymous) model has recently generated significant interest as a candidate distributed privacy framework with trust assumptions better than the central model but with achievable errors smaller than the local model. We study pure differentially private (DP) protocols in the shuffled model for summation, a basic and widely used primitive: - For binary summation where each of n users holds a bit as an input, we give a pure $\epsilon$-DP protocol for estimating the number of ones held by the users up to an error of $O_\epsilon(1)$, and each user sends $O_\epsilon(\log n)$ messages each of 1 bit. This is the first pure protocol in the shuffled model with error $o(\sqrt{n})$ for constant $\epsilon$. Using this protocol, we give a pure $\epsilon$-DP protocol that performs summation of real numbers in $[0, 1]$ up to an error of $O_{\epsilon}(1)$, and where each user sends $O_{\epsilon}(\log^3 n)$ messages each of $O(\log\log n)$ bits. - In contrast, we show that for any pure $\epsilon$-DP protocol for binary summation in the shuffled model having absolute error $n^{0.5-\Omega(1)}$, the per user communication has to be at least $\Omega_{\epsilon}(\sqrt{\log n})$ bits. This implies the first separation between the (bounded-communication) multi-message shuffled model and the central model, and the first separation between pure and approximate DP protocols in the shuffled model. To prove our lower bound, we consider (a generalization of) the following question: given $\gamma$ in $(0, 1)$, what is the smallest m for which there are two random variables $X^0, X^1$ supported on $\{0, \dots ,m\}$ such that (i) the total variation distance between $X^0$ and $X^1$ is at least $1-\gamma$, and (ii) the moment generating functions of $X^0$ and $X^1$ are within a constant factor of each other everywhere? We show that the answer is $m = \Theta(\sqrt{\log(1/\gamma)})$.
Forecasts for detecting the gravitational-wave memory effect with Advanced LIGO and Virgo

The detection of gravitational waves (GWs) from binary black holes (BBHs) has allowed the theory of general relativity to be tested in a previously unstudied regime: that of strong curvature and high GW luminosities. One distinctive and measurable effect associated with this aspect of the theory is the nonlinear GW memory effect. The GW memory effect is characterized by its effect on freely falling observers: the proper distance between their locations differs before and after a burst of GWs passes by their locations. Gravitational-wave interferometers, like the LIGO and Virgo detectors, can measure features of this effect from a single BBH merger, but previous work has shown that it will require an event that is significantly more massive and closer than any previously detected GW event. Finding evidence for the GW memory effect within the entire population of BBH mergers detected by LIGO and Virgo is more likely to occur sooner. A prior study has shown that the GW memory effect could be detected in a population of BBHs consisting of binaries like the first GW150914 event after roughly one-hundred events. In this paper, we compute forecasts of the time it will take the advanced LIGO and Virgo detectors (when the detectors are operating at their design sensitivities) to find evidence for the GW memory effect in a population of BBHs that is consistent with the measured population of events in the first two observing runs of the LIGO detectors. We find that after five years of data collected by the advanced LIGO and Virgo detectors the signal-to-noise ratio for the nonlinear GW memory effect in the population will be about three (near a previously used threshold for detection). We point out that the different approximation methods used to compute the GW memory effect can lead to notably different signal-to-noise ratios.
Stellar population properties of ETGs in compact groups of galaxies

We present results on the study of the stellar population in Early-Type galaxies (ETGs) belonging to 151 Compact Groups (CGs). We also selected a field sample composed of 846 ETGs to investigate environmental effects on galaxy evolution. We find that the dependences of mean stellar ages, [Z/H] and [$\alpha$/Fe] on central stellar velocity dispersion are similar, regardless where the ETG resides, CGs or field. When compared to the sample of centrals and satellites from the literature, we find that ETGs in GCs behave similarly to centrals, especially those embedded in low-mass haloes ($M_{h} < 10^ {12.5}M_{\odot}$). Except for the low-mass limit, where field galaxies present a Starforming signature, not seen in CGs, the ionization agent of the gas in CG and field galaxies seem to be similar and due to hot, evolved low-mass stars. However, field ETGs present an excess of H$\alpha$ emission relative to ETGs in CGs. Additionally, we performed a dynamical analysis, which shows that CGs present a bimodality in the group velocity dispersion distribution - a high and low-$\sigma$ mode. Our results indicate that high-$\sigma$ groups have a smaller fraction of spirals, shorter crossing times, and a more luminous population of galaxies than the low $\sigma$ groups. It is important to emphasize that our findings point to a small environmental impact on galaxies located in CGs. The only evidence we find is the change in gas content, suggesting environmentally-driven gas loss.
Convergence analysis of particle swarm optimization using stochastic Lyapunov functions and quantifier elimination

This paper adds to the discussion about theoretical aspects of particle swarm stability by proposing to employ stochastic Lyapunov functions and to determine the convergence set by quantifier elimination. We present a computational procedure and show that this approach leads to reevaluation and extension of previously know stability regions for PSO using a Lyapunov approach under stagnation assumptions.
Context-Aware Recommendations for Televisions Using Deep Embeddings with Relaxed N-Pairs Loss Objective

This paper studies context-aware recommendations in the television domain by proposing a deep learning-based method for learning joint context-content embeddings (JCCE). The method builds on recent developments within recommendations using latent representations and deep metric learning, in order to effectively represent contextual settings of viewing situations as well as available content in a shared latent space. This embedding space is used for exploring relevant content in various viewing settings by applying an N -pairs loss objective as well as a relaxed variant introduced in this paper. Experiments on two datasets confirm the recommendation ability of JCCE, achieving improvements when compared to state-of-the-art methods. Further experiments display useful structures in the learned embeddings that can be used to gain valuable knowledge of underlying variables in the relationship between contextual settings and content properties.
Accelerating Object Detection by Erasing Background Activations

Recent advances in deep learning have enabled complex real-world use cases comprised of multiple vision tasks and detection tasks are being shifted to the edge side as a pre-processing step of the entire workload. However, since running a deep model on resource-constraint devices is challenging, the design of an efficient network is demanded. In this paper, we present an objectness-aware object detection method to accelerate detection speed by circumventing feature map computation on background regions where target objects don't exist. To accomplish this goal, we incorporate a light-weight objectness mask generation (OMG) network in front of an object detection (OD) network so that it can zero out background areas of an input image before being fed into the OD network. The inference speed, therefore, can be expedited with sparse convolution. By switching background areas to zeros for entire activations, the average number of zero values on MobileNetV2-SSDLite with ReLU activation is increased further, from 36% to 68% during inference step, which reduces 37.89\% MAC with negligible accuracy drop on MS-COCO. Moreover, experimental results also show similar trends in heavy networks such as VGG and RetinaNet with ResNet101, and an additional dataset, PASCAL VOC. The code will be released.
Multivariate normal approximation for traces of random unitary matrices

In this article, we obtain a super-exponential rate of convergence in total variation between the traces of the first $m$ powers of an $n\times n$ random unitary matrices and a $2m$-dimensional Gaussian random variable. This generalizes previous results in the scalar case to the multivariate setting, and we also give the precise dependence on the dimensions $m$ and $n$ in the estimate with explicit constants. We are especially interested in the regime where $m$ grows with $n$ and our main result basically states that if $m\ll \sqrt{n}$, then the rate of convergence in the Gaussian approximation is $\Gamma(\frac nm+1)^{-1}$ times a correction. We also show that the Gaussian approximation remains valid for all $m\ll n^{2/3}$ without a fast rate of convergence.
Characterising filaments in the SDSS volume from the galaxy distribution

Detecting the large-scale structure of the Universe based on the galaxy distribution and characterising its components is of fundamental importance in astrophysics but is also a difficult task to achieve. Wide-area spectroscopic redshift surveys are required to accurately measure galaxy positions in space that also need to cover large areas of the sky. It is also difficult to create algorithms that can extract cosmic web structures (e.g. filaments). Moreover, these detections will be affected by systematic uncertainties that stem from the characteristics of the survey used (e.g. its completeness and coverage) and from the unique properties of the specific method adopted to detect the cosmic web (i.e. the assumptions it relies on and the free parameters it may employ). For these reasons, the creation of new catalogues of cosmic web features on wide sky areas is important, as this allows users to have at their disposal a well-understood sample of structures whose systematic uncertainties have been thoroughly investigated. In this paper we present the filament catalogues created using the discrete persistent structure extractor tool in the Sloan Digital Sky Survey (SDSS), and we fully characterise them in terms of their dependence on the choice of parameters pertaining to the algorithm, and with respect to several systematic issues that may arise in the skeleton as a result of the properties of the galaxy distribution (such as Finger-of-God redshift distortions and defects of the density field that are due to the boundaries of the survey).
TPPO: A Novel Trajectory Predictor with Pseudo Oracle

Forecasting pedestrian trajectories in dynamic scenes remains a critical problem with various applications, such as autonomous driving and socially aware robots. Such forecasting is challenging due to human-human and human-object interactions and future uncertainties caused by human randomness. Generative model-based methods handle future uncertainties by sampling a latent variable. However, few previous studies carefully explored the generation of the latent variable. In this work, we propose the Trajectory Predictor with Pseudo Oracle (TPPO), which is a generative model-based trajectory predictor. The first pseudo oracle is pedestrians' moving directions, and the second one is the latent variable estimated from observed trajectories. A social attention module is used to aggregate neighbors' interactions on the basis of the correlation between pedestrians' moving directions and their future trajectories. This correlation is inspired by the fact that a pedestrian's future trajectory is often influenced by pedestrians in front. A latent variable predictor is proposed to estimate latent variable distributions from observed and ground-truth trajectories. Moreover, the gap between these two distributions is minimized during training. Therefore, the latent variable predictor can estimate the latent variable from observed trajectories to approximate that estimated from ground-truth trajectories. We compare the performance of TPPO with related methods on several public datasets. Results demonstrate that TPPO outperforms state-of-the-art methods with low average and final displacement errors. Besides, the ablation study shows that the prediction performance will not dramatically decrease as sampling times decline during tests.
A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum-L1-Norm Interpolated Classifiers

This paper establishes a precise high-dimensional asymptotic theory for boosting on separable data, taking statistical and computational perspectives. We consider a high-dimensional setting where the number of features (weak learners) $p$ scales with the sample size $n$, in an overparametrized regime. Under a class of statistical models, we provide an exact analysis of the generalization error of boosting when the algorithm interpolates the training data and maximizes the empirical $\ell_1$-margin. Further, we explicitly pin down the relation between the boosting test error and the optimal Bayes error, as well as the proportion of active features at interpolation (with zero initialization). In turn, these precise characterizations answer certain questions raised in \cite{breiman1999prediction, schapire1998boosting} surrounding boosting, under assumed data generating processes. At the heart of our theory lies an in-depth study of the maximum-$\ell_1$-margin, which can be accurately described by a new system of non-linear equations; to analyze this margin, we rely on Gaussian comparison techniques and develop a novel uniform deviation argument. Our statistical and computational arguments can handle (1) any finite-rank spiked covariance model for the feature distribution and (2) variants of boosting corresponding to general $\ell_q$-geometry, $q \in [1, 2]$. As a final component, via the Lindeberg principle, we establish a universality result showcasing that the scaled $\ell_1$-margin (asymptotically) remains the same, whether the covariates used for boosting arise from a non-linear random feature model or an appropriately linearized model with matching moments.
Observation of a cubic Rashba effect in the surface spin structure of rare-earth ternary materials

Spin-orbit interaction and structure inversion asymmetry in combination with magnetic ordering is a promising route to novel materials with highly mobile spin-polarized carriers at the surface. Spin-resolved measurements of the photoemission current from the Si-terminated surface of the antiferromagnet TbRh2Si2 and their analysis within an ab initio one-step theory unveil an unusual triple winding of the electron spin along the fourfold-symmetric constant energy contours of the surface states. A two-band k.p model is presented that yields the triple winding as a cubic Rashba effect. The curious in-plane spin-momentum locking is remarkably robust and remains intact across a paramagnetic-antiferromagnetic transition in spite of spin-orbit interaction on Rh atoms being considerably weaker than the out-of-plane exchange field due to the Tb 4f moments.
Axion and dark photon limits from Crab Nebula high-energy gamma rays

The observation of cosmic sub-PeV gamma rays from the Crab Nebula opens up the possibility of testing cosmic ray photon transparency at the multi-hundred-TeV scale. Assuming no deviation from a source gamma-ray emission due to accelerated electron inverse-Compton scattering, higher event energies can extend constraints on the effects of new physics; we consider oscillation between gamma rays and axions, plus attenuation effects from gamma-ray absorption in the case of dark photon dark matter. Combining the recent $\mathrm{AS}\ensuremath{\gamma}$ and HAWC sub-PeV data with earlier MAGIC and HEGRA data, axionlike particles are most constrained in the $2\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}7}--6\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}7}\text{ }\text{ }\mathrm{eV}$ mass range, where the coupling ${g}_{a\ensuremath{\gamma}\ensuremath{\gamma}}$ is constrained to be below $1.8\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}10}\text{ }\text{ }{\mathrm{GeV}}^{\ensuremath{-}1}$. Direct scattering from dark photon dark matter limits kinetic mixing $\ensuremath{\epsilon}\ensuremath{\lesssim}{10}^{\ensuremath{-}3}$ for masses between 0.01 and 1 eV.