Miracle-dz/UniFAR-FTUs huggingface dataset:
Resource-Efficient Neural Networks for Embedded Systems

While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation, and the vision of the Internet of Things fuel the interest in resource-efficient approaches. These approaches aim for a carefully chosen trade-off between performance and resource consumption in terms of computation and energy. The development of such approaches is among the major challenges in current machine learning research and key to ensure a smooth transition of machine learning technology from a scientific environment with virtually unlimited computing resources into every day's applications. In this article, we provide an overview of the current state of the art of machine learning techniques facilitating these real-world requirements. In particular, we focus on deep neural networks (DNNs), the predominant machine learning models of the past decade. We give a comprehensive overview of the vast literature that can be mainly split into three non-mutually exclusive categories: (i) quantized neural networks, (ii) network pruning, and (iii) structural efficiency. These techniques can be applied during training or as post-processing, and they are widely used to reduce the computational demands in terms of memory footprint, inference speed, and energy efficiency. We substantiate our discussion with experiments on well-known benchmark data sets to showcase the difficulty of finding good trade-offs between resource-efficiency and predictive performance.
Information geometry in quantum field theory: lessons from simple examples

Motivated by the increasing connections between information theory and high-energy physics, particularly in the context of the AdS/CFT correspondence, we explore the information geometry associated to a variety of simple systems. By studying their Fisher metrics, we derive some general lessons that may have important implications for the application of information geometry in holography. We begin by demonstrating that the symmetries of the physical theory under study play a strong role in the resulting geometry, and that the appearance of an AdS metric is a relatively general feature. We then investigate what information the Fisher metric retains about the physics of the underlying theory by studying the geometry for both the classical 2d Ising model and the corresponding 1d free fermion theory, and find that the curvature diverges precisely at the phase transition on both sides. We discuss the differences that result from placing a metric on the space of theories vs.~states, using the example of coherent free fermion states. We compare the latter to the metric on the space of coherent free boson states and show that in both cases the metric is determined by the symmetries of the corresponding density matrix. We also clarify some misconceptions in the literature pertaining to different notions of flatness associated to metric and non-metric connections, with implications for how one interprets the curvature of the geometry. Our results indicate that in general, caution is needed when connecting the AdS geometry arising from certain models with the AdS/CFT correspondence, and seek to provide a useful collection of guidelines for future progress in this exciting area.
Merger rate distribution of primordial black hole binaries with electric charges

We consider gravitational radiation and electromagnetic radiation from point mass binary with electric charges in a Keplerian orbit, and calculate the merger rate distribution of primordial black hole binaries with charges and a general mass function by taking into account gravitational torque and electromagnetic torque by the nearest primordial black hole. We apply the formalism to the extremal charged case and find that $\alpha=-(m_i+m_j)^2\partial^2 \ln {\cal R}(m_i,m_j)/\partial m_i \partial m_j=12/11$, which is independent of the mass function.
A blazar as the likely counterpart to 4FGL J0647.7−4418 instead of a gamma-ray binary

The persistent gamma-ray source 4FGL J0647.7-4418 is tentatively associated in the latest Fermi catalogue with the sub-dwarf O-type X-ray binary HD 49798. However, an AGN candidate is also mentioned as an alternative identification in updated versions of the catalogue accompanying paper. If the first association were correct, this would add HD 49798 to the handful of currently known gamma-ray binaries and, therefore, represent a significant breakthrough not only because of a new member addition, but also because of the apparent white dwarf companion in this system. Despite these perspectives, here we show that the stellar association is likely wrong and that the proposed AGN object, well inside the Fermi 95% confidence ellipse, is a more conceivable counterpart candidate to the Fermi source due to its strong blazar similarities.
A Generalized Probabilistic Learning Approach for Multi-Fidelity Uncertainty Propagation in Complex Physical Simulations

Two of the most significant challenges in uncertainty propagation pertain to the high computational cost for the simulation of complex physical models and the high dimension of the random inputs. In applications of practical interest both of these problems are encountered and standard methods for uncertainty quantification either fail or are not feasible. To overcome the current limitations, we propose a probabilistic multi-fidelity framework that can exploit lower-fidelity model versions of the original problem in a small data regime. The approach circumvents the curse of dimensionality by learning dependencies between the outputs of high-fidelity models and lower-fidelity models instead of explicitly accounting for the high-dimensional inputs. We complement the information provided by a low-fidelity model with a low-dimensional set of informative features of the stochastic input, which are discovered by employing a combination of supervised and unsupervised dimensionality reduction techniques. The goal of our analysis is an efficient and accurate estimation of the full probabilistic response for a high-fidelity model. Despite the incomplete and noisy information that low-fidelity predictors provide, we demonstrate that accurate and certifiable estimates for the quantities of interest can be obtained in the small data regime, i.e., with significantly fewer high-fidelity model runs than state-of-the-art methods for uncertainty propagation. We illustrate our approach by applying it to challenging numerical examples such as Navier-Stokes flow simulations and monolithic fluid-structure interaction problems.
Weak cosmic censorship conjecture for a Kerr-Taub-NUT black hole with a test scalar field and particle

Motivated by the recent research of black holes with NUT charge, we investigate the validity of the weak cosmic censorship conjecture for the Kerr-Taub-NUT black hole with a test massive scalar field and a test particle, respectively. For the scalar field scattering gedanken experiment, we consider an infinitesimal time interval process. The result shows that both extremal and near-extremal Kerr-Taub-NUT black holes cannot be overspun. For the test particle thought experiment, the study suggests that the extremal Kerr-Taub-NUT black hole cannot be overspun; while the near-extremal Kerr-Taub-NUT black hole can be overspun. By comparing the two methods, the results indicate the time interval for particles crossing the black hole horizon might be important for consideration of the weak cosmic censorship conjecture.
Achieving Competitiveness in Online Problems

In the setting of online algorithms, the input is initially not present but rather arrive one-by-one over time and after each input, the algorithm has to make a decision. Depending on the formulation of the problem, the algorithm might be allowed to change its previous decisions or not at a later time. We analyze two problems to show that it is possible for an online algorithm to become more competitive by changing its former decisions. We first consider the online edge orientation in which the edges arrive one-by-one to an empty graph and the aim is to orient them in a way such that the maximum in-degree is minimized. We then consider the online bipartite b-matching. In this problem, we are given a bipartite graph where one side of the graph is initially present and where the other side arrive online. The goal is to maintain a matching set such that the maximum degree in the set is minimized. For both of the problems, the best achievable competitive ratio is $\Theta(\log n)$ over n input arrivals when decisions are irreversible. We study three algorithms for these problems, two for the former and one for the latter, that achieve O(1) competitive ratio by changing O(n) of their decisions over n arrivals. In addition to that, we analyze one of the algorithms, the shortest path algorithm, against an adversary. Through that, we prove some new results about algorithms performance.
An Emerging Coding Paradigm Vcm: A Scalable Coding Approach Beyond Feature And Signal

In this paper, we study a new problem arising from the emerging MPEG standardization effort Video Coding for Machine (VCM)1, which aims to bridge the gap between visual feature compression and classical video coding. VCM is committed to address the requirement of compact signal representation for both machine and human vision in a more or less scalable way. To this end, we make endeavors in leveraging the strength of predictive and generative models to support advanced compression techniques for both machine and human vision tasks simultaneously, in which visual features serve as a bridge to connect signal-level and task-level compact representations in a scalable manner. Specifically, we employ a conditional deep generation network to reconstruct video frames with the guidance of learned motion pattern. By learning to extract sparse motion pattern via a predictive model, the network elegantly leverages the feature representation to generate the appearance of to-be-coded frames via a generative model, relying on the appearance of the coded key frames. Meanwhile, the sparse motion pattern is compact and highly effective for high-level vision tasks, e.g. action recognition. Experimental results demonstrate that our method yields much better reconstruction quality compared with the traditional video codecs (0.0063 gain in SSIM), as well as state-of-the-art action recognition performance over highly compressed videos (9.4% gain in recognition accuracy), which showcases a promising paradigm of coding signal for both human and machine vision.
Optical polarization properties of AGNs with significant VLBI–Gaia offsets

Significant positional offsets of the value from 1 mas to more than 10 mas were found previously between radio (VLBI) and optical (Gaia) positions of active galactic nuclei (AGN). They happen preferentially parallel to the parsec-scale jet direction. AGN with VLBI-to-Gaia offsets pointed downstream the jet are found to have favourably higher optical polarization, as expected if extended optical jets dominate in the emission and shift the Gaia centroid away from the physical nucleus of the source. Upstream offsets with the suggested domination of accretion disks manifest themselves through the observed low optical polarization. Direction of linear optical polarization is confirmed to preferentially align with parsec-scale jets in AGN with dominant jets consistent with a toroidal magnetic field structure. Our findings support the disk-jet interpretation of the observed positional offsets. These results call on an intensification of AGN optical polarization monitoring programs in order to collect precious observational data. Taken together with the continued VLBI and Gaia observations, they will allow researchers to reconstruct detailed models of the disk-jet system in AGN on parsec scales.
Benchmarking In-Hand Manipulation

The purpose of this benchmark is to evaluate the planning and control aspects of robotic in-hand manipulation systems. The goal is to assess the system's ability to change the pose of a hand-held object by either using the fingers, environment or a combination of both. Given an object surface mesh from the YCB data-set, we provide examples of initial and goal states (i.e. static object poses and fingertip locations) for various in-hand manipulation tasks. We further propose metrics that measure the error in reaching the goal state from a specific initial state, which, when aggregated across all tasks, also serves as a measure of the system's in-hand manipulation capability. We provide supporting software, task examples, and evaluation results associated with the benchmark.
Short-Range Audio Channels Security: Survey of Mechanisms, Applications, and Research Challenges

Short-range audio channels have appealing distinguishing characteristics: ease of use, low deployment costs, and easy to tune frequencies, to cite a few. Moreover, thanks to their seamless adaptability to the security context, many techniques and tools based on audio signals have been recently proposed. However, while the most promising solutions are turning into valuable commercial products, acoustic channels are also increasingly used to launch attacks against systems and devices, leading to security concerns that could thwart their adoption. To provide a rigorous, scientific, security-oriented review of the field, in this paper we survey and classify methods, applications, and use-cases rooted on short-range audio channels for the provisioning of security services—including Two-Factor Authentication techniques, pairing solutions, device authorization strategies, defense methodologies, and attack schemes. Moreover, we also point out the strengths and weaknesses deriving from the use of short-range audio channels. Finally, we provide open research issues in the context of short-range audio channels security, calling for contributions from both academia and industry.
Global Sensitivity Analysis in Load Modeling via Low-Rank Tensor

Growing model complexities in load modeling have created high dimensionality in parameter estimations, and thereby substantially increasing associated computational costs. In this letter, a tensor-based method is proposed for identifying composite load modeling (CLM) parameters and for conducting a global sensitivity analysis. Tensor format and Fokker-Planck equations are used to estimate the power output response of CLM in the context of simultaneously varying parameters under their full parameter distribution ranges. The proposed tensor structure is shown as effective for tackling high-dimensional parameter estimation and for improving computational performances in load modeling through global sensitivity analysis.
ALMA uncovers the [C ii] emission and warm dust continuum in a z = 8.31 Lyman break galaxy

We report on the detection of the [C ii] 157.7 μm emission from the Lyman break galaxy (LBG) MACS0416_Y1 at z = 8.3113, by using the Atacama Large Millimeter/submillimeter Array (ALMA). The luminosity ratio of [O iii] 88 μm (from previous campaigns) to [C ii] is 9.3 ± 2.6, indicative of hard interstellar radiation fields and/or a low covering fraction of photodissociation regions. The emission of [C ii] is cospatial to the 850 μm dust emission (90 μm rest frame, from previous campaigns), however the peak [C ii] emission does not agree with the peak [O iii] emission, suggesting that the lines originate from different conditions in the interstellar medium. We fail to detect continuum emission at 1.5 mm (160 μm rest frame) down to 18 μJy (3σ). This non-detection places a strong limits on the dust spectrum, considering the 137 ± 26 μJy continuum emission at 850 μm. This suggests an unusually warm dust component (T > 80 K, 90 per cent confidence limit), and/or a steep dust-emissivity index (βdust > 2), compared to galaxy-wide dust emission found at lower redshifts (typically T ∼ 30–50 K, βdust ∼ 1–2). If such temperatures are common, this would reduce the required dust mass and relax the dust production problem at the highest redshifts. We therefore warn against the use of only single-wavelength information to derive physical properties, recommend a more thorough examination of dust temperatures in the early Universe, and stress the need for instrumentation that probes the peak of warm dust in the Epoch of Reionization.
Optimal quantizer structure for binary discrete input continuous output channels under an arbitrary quantized-output constraint

Given a channel having binary input X = (x_1, x_2) having the probability distribution p_X = (p_{x_1}, p_{x_2}) that is corrupted by a continuous noise to produce a continuous output y \in Y = R. For a given conditional distribution p(y|x_1) = \phi_1(y) and p(y|x_2) = \phi_2(y), one wants to quantize the continuous output y back to the final discrete output Z = (z_1, z_2, ., z_N) with N \leq 2 such that the mutual information between input and quantized-output I(X; Z) is maximized while the probability of the quantized-output p_Z = (p_{z_1}, p_{z_2}, ., p_{z_N}) has to satisfy a certain constraint. Consider a new variable r_y=p_{x_1}\phi_1(y)/ (p_{x_1}\phi_1(y)+p_{x_2}\phi_2(y)), we show that the optimal quantizer has a structure of convex cells in the new variable r_y. Based on the convex cells property of the optimal quantizers, a fast algorithm is proposed to find the global optimal quantizer in a polynomial time complexity.
Energy Efficient Distributed Processing for IoT

In the near future, the number of Internet connected objects is expected to be between 26 - 50 billion devices. This figure is expected to grow even further due to the production of miniaturized portable devices that are lightweight, energy, and cost-efficient. In this article, the entire IoT-fog-cloud architecture is modeled, the service placement problem is formulated using Mixed Integer Linear Programming (MILP) and the total power consumption is jointly minimized for processing and networking. We evaluate the distributed processing paradigm for both the un-capacitated and capacitated design settings in order to provide solutions for the long-term and short-term basis, respectively. Furthermore, four aspects of the IoT processing placement problem are examined: 1) IoT services with non-splittable tasks, 2) IoT services with splittable tasks, 3) impact of processing overheads needed for inter-service communication and 4) deployment of special-purpose data centers (SP-DCs) as opposed to the conventional general-purpose data center (GP-DC) in the core network. The results showed that for the capacitated problem, task splitting introduces power savings of up to 86% compared to 46% with non-splittable tasks. Moreover, it is observed that the overheads due to inter-service communication greatly impacts the total number of splits. However much insignificant the overhead factor, the results showed that this is not a trivial matter and hence much attention needs to be paid to this area to make the best use of the resources that are available at the edge of the network.
Study of the energy variation in many-body open quantum systems: Role of interactions in the weak and strong coupling regimes

We derive an expression for the rate of change of the energy of an interacting many-body system connected to macroscopic leads. We show that the energy variation is the sum of contributions from each different lead. Unlike the charge current each of this contribution can differ from the rate of change of the energy of the lead. We demonstrate that the discrepancy between the two is due to the direct exchange of energy among the considered lead and all other leads. We conclude that the microscopic mechanism behind it are virtual processes via the interacting central region. We also speculate on what are the implications of our findings in the calculation of the thermal conductance of an interacting system.
QCD phase diagram at finite isospin chemical potential and temperature in an IR-improved soft-wall AdS/QCD model

We study the phase transition between the pion condensed phase and normal phase, as well as chiral phase transition in a two flavor ( ) IR- improved soft-wall AdS/QCD model at finite isospin chemical potential and temperature T. By self-consistently solving the equations of motion, we obtain the phase diagram in the plane of and T. The pion condensation appears together with a massless Nambu-Goldstone boson , which is very likely to be a second-order phase transition with mean-field critical exponents in the small region. When , the critical isospin chemical potential approximates to vacuum pion mass . The pion condensed phase exists in an arched area, and the boundary of the chiral crossover intersects the pion condensed phase at a tri-critical point. Qualitatively, the results are in good agreement with previous studies on lattice simulations and model calculations.
Forming Compatible Teams in Signed Networks

The problem of team formation in a social network asks for a set of individuals who not only have the required skills to perform a task but who can also communicate effectively with each other. Existing work assumes that all links in a social network are positive, that is, they indicate friendship or collaboration between individuals. However, it is often the case that the network is signed, that is, it contains both positive and negative links, corresponding to friend and foe relationships. Building on the concept of structural balance, we provide definitions of compatibility between pairs of users in a signed network, and algorithms for computing it. We then define the team formation problem in signed networks, where we ask for a compatible team of individuals that can perform a task with small communication cost. We show that the problem is NP-hard even when there are no communication cost constraints, and we provide heuristic algorithms for solving it. We present experimental results with real data to investigate the properties of the different compatibility definitions, and the effectiveness of our algorithms.
Molding free-space light with guided wave–driven metasurfaces

A guided wave–driven metasurface directly extracts and molds light from a waveguide into arbitrarily complex free-space modes. Metasurfaces with unparalleled controllability of light have shown great potential to revolutionize conventional optics. However, they mainly require external light excitation, which makes it difficult to fully integrate them on-chip. On the other hand, integrated photonics enables packing optical components densely on a chip, but it has limited free-space light controllability. Here, by dressing metasurfaces onto waveguides, we molded guided waves into any desired free-space modes to achieve complex free-space functions, such as out-of-plane beam deflection and focusing. This metasurface also breaks the degeneracy of clockwise- and counterclockwise-propagating whispering gallery modes in an active microring resonator, leading to on-chip direct orbital angular momentum lasing. Our study shows a viable route toward complete control of light across integrated photonics and free-space platforms and paves a way for creating multifunctional photonic integrated devices with agile access to free space, which enables a plethora of applications in communications, remote sensing, displays, etc.