Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Federated Learning by Balancing Communication Trade-Offs

Federated Learning (FL) has recently received a lot of attention for large-scale privacy-preserving machine learning. However, high communication overheads due to frequent gradient transmissions decelerate FL. To mitigate the communication overheads, two main techniques have been studied: (i) local update of weights characterizing the trade-off between communication and computation and (ii) gradient compression characterizing the trade-off between communication and precision. To the best of our knowledge, studying and balancing those two trade-offs jointly and dynamically while considering their impacts on convergence has remained unresolved even though it promises significantly faster FL. In this paper, we first formulate our problem to minimize learning error with respect to two variables: local update coefficients and sparsity budgets of gradient compression who characterize trade-offs between communication and computation/precision, respectively. We then derive an upper bound of the learning error in a given wall-clock time considering the interdependency between the two variables. Based on this theoretical analysis, we propose an enhanced FL scheme, namely Fast FL (FFL), that jointly and dynamically adjusts the two variables to minimize the learning error. We demonstrate that FFL consistently achieves higher accuracies faster than similar schemes existing in the literature.
Counter-Epidemiological Projections of e-Coaching

This paper considers e-coaching at times of pandemic. It utilizes the Emergency Management Cycle (EMC), a core doctrine for managing disasters. The EMC dimensions provide a useful taxonomical view for the development and application of e-coaching systems, emphasizing technological and societal issues. Typical pandemic symptoms such as anxiety, panic, avoidance, and stress, if properly detected, can be mitigated using the e-coaching tactic and strategy. In this work, we focus on a stress monitoring assistant developed upon machine learning techniques. We provide the results of an experimental study of a prototype of such an assistant. Our study leads to the conclusion that stress monitoring shall become a valuable component of e-coaching at all EMC phases.
Rethinking Global Context in Crowd Counting

This paper investigates the role of global context for crowd counting. Specifically, a pure transformer is used to extract features with global information from overlapping image patches. Inspired by classification, we add a context token to the input sequence, to facilitate information exchange with tokens corresponding to image patches throughout transformer layers. Due to the fact that transformers do not explicitly model the tried-and-true channel-wise interactions, we propose a token-attention module (TAM) to recalibrate encoded features through channel-wise attention informed by the context token. Beyond that, it is adopted to predict the total person count of the image through regression-token module (RTM). Extensive experiments on various datasets, including ShanghaiTech, UCF-QNRF, JHU-CROWD++ and NWPU, demonstrate that the proposed context extraction techniques can significantly improve the performance over the baselines.
A spectral approach to quenched linear and higher-order response for partially hyperbolic dynamics

For smooth random dynamical systems we consider the quenched linear and higher-order response of equivariant physical measures to perturbations of the random dynamics. We show that the spectral perturbation theory of Gouëzel, Keller and Liverani [28, 33], which has been applied to deterministic systems with great success, may be adapted to study random systems that possess good mixing properties. As a consequence, we obtain general linear and higher-order response results, as well as the differentiability of the variance in quenched central limit theorems (CLTs), for random dynamical systems (RDSs) that we then apply to random Anosov diffeomorphisms and random U(1) extensions of expanding maps. We emphasize that our results apply to random dynamical systems over a general ergodic base map, and are obtained without resorting to infinite-dimensional multiplicative ergodic theory.
Dimensions of Kleinian orbital sets

Given a non-empty bounded subset of hyperbolic space and a Kleinian group acting on that space, the orbital set is the orbit of the given set under the action of the group. We may view orbital sets as bounded (often fractal) subsets of Euclidean space. We prove that the upper box dimension of an orbital set is given by the maximum of three quantities: the upper box dimension of the given set; the Poincar\'e exponent of the Kleinian group; and the upper box dimension of the limit set of the Kleinian group. Since we do not make any assumptions about the Kleinian group, none of the terms in the maximum can be removed in general. We show by constructing an explicit example that the (hyperbolic) boundedness assumption on $C$ cannot be removed in general.
Large-Scale Attribute-Object Compositions

We study the problem of learning how to predict attribute-object compositions from images, and its generalization to unseen compositions missing from the training data. To the best of our knowledge, this is a first large-scale study of this problem, involving hundreds of thousands of compositions. We train our framework with images from Instagram using hashtags as noisy weak supervision. We make careful design choices for data collection and modeling, in order to handle noisy annotations and unseen compositions. Finally, extensive evaluations show that learning to compose classifiers outperforms late fusion of individual attribute and object predictions, especially in the case of unseen attribute-object pairs.
Estimating leverage scores via rank revealing methods and randomization

We study algorithms for estimating the statistical leverage scores of rectangular dense or sparse matrices of arbitrary rank. Our approach is based on combining rank revealing methods with compositions of dense and sparse randomized dimensionality reduction transforms. We first develop a set of fast novel algorithms for rank estimation, column subset selection and least squares preconditioning. We then describe the design and implementation of leverage score estimators based on these primitives. These estimators are also effective for rank deficient input, which is frequently the case in data analytics applications. We provide detailed complexity analyses for all algorithms as well as meaningful approximation bounds and comparisons with the state-of-the-art. We conduct extensive numerical experiments to evaluate our algorithms and to illustrate their properties and performance using synthetic and real world data sets.
Topological Early Universe Cosmology

The early history of the universe might be described by a topological phase followed by a standard second phase of Einstein gravity. To study this scenario in its full generality, we consider a four-manifold of Euclidean signature in the topological phase, which shares a common boundary with a corresponding manifold of Lorentzian signature in the Einstein phase. We find that the boundary should have vanishing extrinsic curvature, whereas the manifold in the topological phase should have zero Euler number. In addition, we show that the second phase must be characterized by an initial vanishing Weyl tensor and that the standard cosmological flatness problem is not automatically solved unless a conformal invariant boundary term is added. We also characterize the scalar perturbations in the standard Einstein phase. We show that they must contain an initial non-vanishing shear component inherited from the topological phase and we estimate the non-Gaussian parameters. Finally, we argue that the topological early universe cosmology shares common features of previous ideas, such as the so-called Weyl curvature hypothesis, the universe’s creation out of nothing and the no-boundary proposal. ar X iv :2 10 5. 10 66 9v 1 [ he pth ] 2 2 M ay 2 02 1
Uncertainty quantification for distributed regression

The ever-growing size of the datasets renders well-studied learning techniques, such as Kernel Ridge Regression, inapplicable, posing a serious computational challenge. Divide-and-conquer is a common remedy, suggesting to split the dataset into disjoint partitions, obtain the local estimates and average them, it allows to scale-up an otherwise ineffective base approach. In the current study we suggest a fully data-driven approach to quantify uncertainty of the averaged estimator. Namely, we construct simultaneous element-wise confidence bands for the predictions yielded by the averaged estimator on a given deterministic prediction set. The novel approach features rigorous theoretical guaranties for a wide class of base learners with Kernel Ridge regression being a special case. As a by-product of our analysis we also obtain a sup-norm consistency result for the divide-and-conquer Kernel Ridge Regression. The simulation study supports the theoretical findings.
Stellar Mass and Star Formation Rate within a Billion Light-years

To develop galaxy-targeting approaches, the gravitational-wave community built a catalog of stellar mass in the local universe based on the Two Micron All Sky Survey (2MASS) spectroscopic and photometric redshift surveys. By cleaning and supplementing this catalog, the present work aims to establish a near-infrared flux-limited sample to map both stellar mass and star formation rate (SFR) over the full sky. The 2MASS spectroscopic and photometric redshift surveys are crossmatched with the HyperLEDA database and the Local Volume sample at d < 11 Mpc, providing a flux-limited sample with revised distance estimates and corrections for incompleteness out to 350 Mpc. Scaling relations with stellar mass as a function of morphology are used to construct an SFR cosmography in the local universe. Stellar-mass and SFR densities converge toward values compatible with deep-field observations beyond 100 Mpc. The 3D distribution of these two tracers is consistent with the distribution of matter deduced from cosmic flows. With spectroscopic redshifts available for about half of the ∼400,000 galaxies within 350 Mpc and photometric distances with a 12% uncertainty available for the other half, the present sample may find applications in both cosmology and astroparticle physics. The present work provides, in particular, new bases for modeling the large- and intermediate-scale anisotropies observed at ultra-high energies. The distribution of magnetic fields at megaparsec scales, which can be deduced from the 3D distribution of matter, is inferred to be crucial in shaping the ultra-high-energy sky.
Gravitational lensing 
H0
 tension from ultralight axion galactic cores

Gravitational lensing time delays offer an avenue to measure the Hubble parameter $H_0$, with some analyses suggesting a tension with early-type probes of $H_0$. The lensing measurements must mitigate systematic uncertainties due to the mass modelling of lens galaxies. In particular, a core component in the lens density profile would form an approximate local mass sheet degeneracy and could bias $H_0$ in the right direction to solve the lensing tension. We consider ultralight dark matter as a possible mechanism to generate such galactic cores. We show that cores of roughly the required properties could arise naturally if an ultralight axion of mass $m\sim10^{-25}$ eV makes up a fraction of order ten percent of the total cosmological dark matter density. A relic abundance of this order of magnitude could come from vacuum misalignment. Stellar kinematics measurements of well-resolved massive galaxies (including the Milky Way) may offer a way to test the scenario. Kinematics analyses aiming to test the core hypothesis in massive elliptical lens galaxies should not, in general, adopt the perfect mass sheet limit, as ignoring the finite extent of an actual physical core could lead to significant systematic errors.
Heuristic Weakly Supervised 3D Human Pose Estimation in Novel Contexts without Any 3D Pose Ground Truth

—Monocular 3D human pose estimation from a single RGB image has received a lot attentions in the past few year. Pose inference models with competitive performance however require supervision with 3D pose ground truth data or at least known pose priors in their target domain. Yet, these data requirements in many real-world applications with data collection constraints may not be achievable. In this paper, we present a heuristic weakly supervised solution, called HW-HuP to estimate 3D human pose in contexts that no ground truth 3D data is accessible, even for ﬁne-tuning. HW-HuP learns partial pose priors from public 3D human pose datasets and uses easy-to-access observations from the target domain to iteratively estimate 3D human pose and shape in an optimization and regression hybrid cycle. In our design, depth data as an auxiliary information is employed as weak supervision during training, yet it is not needed for the inference. We evaluate HW-HuP performance qualitatively on datasets of both in-bed human and infant poses, where no ground truth 3D pose is provided neither any target prior. We also test HW-HuP performance quantitatively on a publicly available motion capture dataset against the 3D ground truth. HW-HuP is also able to be extended to other input modalities for pose estimation tasks especially under adverse vision conditions, such as occlusion or full darkness. On the Human3.6M benchmark, HW-HuP shows 104.1mm in MPJPE and 50.4mm in PA MPJPE, comparable to the existing state-of-the-art approaches that beneﬁt from full 3D pose supervision.
Cosmic rays and non-thermal emission in simulated galaxies − I. Electron and proton spectra compared to Voyager-1 data

Current-day cosmic ray (CR) propagation studies use static Milky-Way models and fit parametrized source distributions to data. Instead, we use three-dimensional magneto-hydrodynamical (MHD) simulations of isolated galaxies with the moving-mesh code Arepo that self-consistently accounts for hydrodynamic effects of CR protons. In post-processing, we calculate their steady-state spectra, taking into account all relevant loss processes. We show that this steady-state assumption is well justified in the disc and generally for regions that emit non-thermal radio and gamma rays. Additionally, we model the spectra of primary electrons, accelerated by supernova remnants, and secondary electrons and positrons produced in hadronic CR proton interactions with the gas. We find that proton spectra above 10 GeV only weakly depend on galactic radius, while they acquire a radial dependence at lower energies due to Coulomb interactions. Radiative losses steepen the spectra of primary CR electrons in the central galactic regions while diffusive losses dominate in the outskirts. Secondary electrons exhibit a steeper spectrum than primaries because they originate from the transported steeper CR proton spectra. Consistent with Voyager-1 and AMS-02 data, our models (i) show a turn-over of proton spectra below GeV energies due to Coulomb interactions so that electrons start to dominate the total particle spectra and (ii) match the shape of the positron fraction up to 10 GeV. We conclude that our steady-state CR modeling in MHD-CR galaxy simulations is sufficiently realistic to capture the dominant transport effects shaping their spectra, arguing for a full MHD treatment to accurately model CR transport in the future.
Distributed CNN Inference on Resource-Constrained UAVs for Surveillance Systems: Design and Optimization

Unmanned aerial vehicles (UAVs) have attracted great interest in the last few years owing to their ability to cover large areas and access difficult and hazardous target zones, which is not the case of traditional systems relying on direct observations obtained from fixed cameras and sensors. Furthermore, thanks to the advancements in computer vision and machine learning, UAVs are being adopted for a broad range of solutions and applications. However, deep neural networks (DNNs) are progressing toward deeper and complex models that prevent them from being executed onboard. In this article, we propose a DNN distribution methodology within UAVs to enable data classification in resource-constrained devices and avoid extra delays introduced by the server-based solutions due to data communication over air-to-ground links. The proposed method is formulated as an optimization problem that aims to minimize the latency between data collection and decision-making while considering the mobility model and the resource constraints of the UAVs as part of the air-to-air communication. We also introduce the mobility prediction to adapt our system to the dynamics of UAVs and the network variation. The simulation conducted to evaluate the performance and benchmark the proposed methods, namely, optimal UAV-based layer distribution (OULD) and OULD with mobility prediction (OULD-MP), was run in an HPC cluster. The obtained results show that our optimization solution outperforms the existing and heuristic-based approaches.
Targeted rescue of synaptic plasticity improves cognitive decline after severe systemic inflammation

Sepsis-associated encephalopathy (SAE) is a frequent complication in patients with severe systemic infection resulting in acute brain dysfunction and incapacitating long-term sequelae. SAE includes delirium, premature death, post-traumatic stress disorder, and major long-term cognitive impairment. The underlying pathophysiology of SAE is largely unresolved and specific treatment options are missing. We induced the peritoneal contamination and infection (PCI) sepsis model in 769 mice and compared these with 259 control mice. We found that experimental sepsis causes synaptic pathology in the brain characterized by severely disordered synaptic plasticity with reduced long-term potentiation, changes in CA1 pyramidal neuron dendritic spines, and behavioral abnormalities indicating cognitive dysfunction. Using electrophysiology, we found reduced frequency of quantal and spontaneous excitatory postsynaptic currents whereas amplitudes of miniature, spontaneous, and evoked excitatory currents were increased, pointing towards a homeostatic synaptic scaling mechanism. Corresponding to dysfunctional excitatory synaptic function we discovered downregulation of genes related to neuronal and synaptic signaling in the brain, including the gene for activity-regulated cytoskeleton-associated protein (Arc/Arg3.1), members of the transcription-regulatory EGR gene family, and the gene for dual-specificity phosphatase 6 (Dusp6). At the protein level, ARC expression and MAP kinase signaling in the brain were affected. For targeted rescue of dysfunctional synaptic signaling and plasticity, we overexpressed ARC in the hippocampus by microinjection of an adeno-associated virus containing a neuron-specific plasmid of the ARC transgene. Hereby we achieved recovery of defective synaptic plasticity in the hippocampal Schaffer collateral-CA1 pathway and improvement of memory dysfunction. Using a different rescue paradigm, PCI mice were subjected to enriched environment providing multiple activating stimuli. Enriched environment led to restoration of disordered long-term potentiation and memory, thus demonstrating the potential for activity-induced improvement. Together, we identified synaptic pathomechanisms of SAE after severe systemic infection and provide a conceptual approach to treat SAE-related disease mechanisms which may be applicable to patients afflicted with SAE.
D1- and D2-like receptors differentially mediate the effects of dopaminergic transmission on cost/benefit evaluation and motivation in monkeys

It has been widely accepted that dopamine (DA) plays a major role in motivation, yet the specific contribution of DA signaling at D1-like receptor (D1R) and D2-like receptor (D2R) to cost-benefit trade-off remains unclear. Here, by combining pharmacological manipulation of DA receptors (DARs) and positron emission tomography imaging, we assessed the relationship between the degree of D1R/D2R blockade and changes in benefit- and cost-based motivation for goal-directed behavior of macaque monkeys. We found that the degree of blockade of either D1R or D2R was associated with a reduction of the positive impact of reward amount and increasing delay-discounting. Workload-discounting was selectively increased by D2R antagonism. In addition, blocking both D1R and D2R had a synergistic effect on delay-discounting but an antagonist effect on workload-discounting. These results provide fundamental in-sight into the distinct mechanisms of DA action in the regulation of the cost and benefit-based motivation, which have important implications for motivational alterations in both neurological and psychiatric disorders.
Effect of Phosphate on the Sorption of Arsenite onto Aluminum– or Iron–Oxide Coated Light Expanded Clay Aggregate (LECA)

Based on human epidemiology, arsenic (As) is classified as a first group carcinogen and lots of efforts have been made to reduce its intake by humans. Nevertheless, more than 120 million people in Bangladesh and West Bengal are still exposed to groundwater where As concentration is above the WHO maximum permissible limit. In groundwater the conditions favor the occurrence of arsenite (As III ) that is a more toxic species than arsenate (As V ). Moreover, As III has a lower sorption tendency than aluminium (Al) and iron (Fe) oxides. The presence of phosphate in groundwater further reduces the sorption tendency of As III . This study was undertaken to unravel the ability of Light Expanded Clay Aggregate (LECA) without and with Al or Fe oxide coating to remove As III as well as the impact of phosphate on the removal efficiency. The oxide coatings enhanced decisively the sorption. The sorption of As III increased with elevated pH, at pH 7 the apparent maximum sorption (X m ) being in LECA 68 µg g -1 , in Al-LECA 114 µg g -1 and in Fe-LECA 96 µg g -1 . At pH 10, on the contrary, X m for Fe-LECA was greater than for Al-LECA. The competition by phosphate affected less the sorption onto LECA than onto the oxide-coated LECAs. At pH 7 the highest P addition (100 µg L -1 ) diminished the As III removal by LECA by 4 times, the corresponding decreases in Al-LECA and Fe-LECA being 6 – and 5 – fold, respectively. At pH 3 and 5, the competition by phosphate was more marked in Fe-LECA than in Al-LECA, while at pH 7 an opposite trend was detected. In general, the uncoated LECA showed to be superior to the oxide-coated LECAs in the As III removal from groundwater.