Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum impurity model for anyons

One of the hallmarks of quantum statistics, tightly entwined with the concept of topological phases of matter, is the prediction of anyons. Although anyons are predicted to be realized in certain fractional quantum Hall systems, they have not yet been unambiguously detected in experiment. Here we introduce a quantum impurity model, where bosonic (or fermionic) impurities turn into anyons as a consequence of their interaction with the surrounding many-particle bath. A cloud of phonons dresses each impurity in such a way that it effectively attaches fluxes/vortices to it and thereby converts it into an Abelian anyon. The corresponding quantum impurity model, first, provides a new approach to the numerical solution of the many-anyon problem, along with a new concrete perspective of anyons as emergent quasiparticles built from composite bosons or fermions. More importantly, the model paves the way towards realizing anyons using impurities in crystal lattices as well as ultracold gases. In particular, we consider two heavy electrons interacting with a two-dimensional lattice crystal in a magnetic field, and show that they behave as anyons when the impurity-bath system is rotated at the cyclotron frequency. A possible experimental realization is proposed by identifying the statistics parameter in terms of the mean square distance of the impurities and the magnetization of the impurity-bath system, both of which are accessible to experiment. Another proposed application are impurities immersed in a two-dimensional weakly interacting Bose gas.
Flash Ionization Signatures in the Type Ibn Supernova SN 2019uo

We present photometric and spectroscopic observations of the type Ibn supernova (SN) 2019uo, the second ever SN Ibn with flash ionization (He ii, C iii, N iii) features in its early spectra. SN 2019uo displays a rapid post-peak luminosity decline of 0.1 mag day−1 similar to most of the SNe Ibn, but is fainter ( mag) than a typical SN Ibn and shows a color evolution that places it between SNe Ib and the most extreme SNe Ibn. SN 2019uo shows P-cygni He i features in the early spectra which gradually evolve and become emission dominated post peak. It also shows faster evolution in line velocities as compared to most other members of the type Ibn subclass. The bolometric light curve is fairly well described by a 56Ni + circumstellar interaction model.
Effect of Electromagnetic Interaction on Galactic Center Flare Components

Recently, near-infrared GRAVITY@ESO observations at 2.2 μm announced the detection of three bright “flares” in the vicinity of the Galactic center supermassive black hole (SMBH) that exhibited orbital motion at a distance of about 6–11 gravitational radii from an ∼4 × 106 M⊙ black hole. There are indications of the presence of a large-scale, organized component of the magnetic field at the Galactic center. Electromagnetic effects on the flare dynamics were previously not taken into account despite the relativistic motion of a plasma in magnetic field leading to the charge separation and nonnegligible net charge density in the plasma. Applying various approaches, we find the net charge number density of the flare components of the order of 10−3–10−4 cm−3, while the particles’ total number density is of the order of 106–108 cm−3. However, even such a tiny excess of charged particles in the quasi-neutral plasma can significantly affect the dynamics of flare components, which can then lead to the degeneracy in the measurements of spin of the SMBH. Analyzing the dynamics of recent flares in the case of the rapidly rotating black hole, we also constrain the inclination angle between the magnetic field and spin axis to α < 50°, as for larger angles, the motion of the hot spot is strongly chaotic.
Pairwise Feedback for Data Programming

The scalability of the labeling process and the attainable quality of labels have become limiting factors for many applications of machine learning. The programmatic creation of labeled datasets via the synthesis of noisy heuristics provides a promising avenue to address this problem. We propose to improve modeling of latent class variables in the programmatic creation of labeled datasets by incorporating pairwise feedback into the process. We discuss the ease with which such pairwise feedback can be obtained or generated in many application domains. Our experiments show that even a small number of sources of pairwise feedback can substantially improve the quality of the posterior estimate of the latent class variable.
Consensus seeking gradient descent flows on boundaries of convex sets

Consensus on nonlinear spaces is of use in many control applications. This paper proposes a gradient descent flow algorithm for consensus on hypersurfaces. We show that if an inequality holds, then the system converges for almost all initial conditions and all connected graphs. The inequality involves the hypersurface Gauss map and the gradient and Hessian of the implicit equation. Moreover, for the inequality to hold, it is necessary that the manifold is the boundary of a convex set. The literature already contains an algorithm for consensus on hypersurfaces. That algorithm on any ellipsoid is equivalent to our algorithm on the unit sphere. In particular, that algorithm achieves almost global synchronization on ellipsoids. These findings suggest that strong convergence results for consensus seeking gradient descent flows may be established on manifolds that are the boundaries of convex sets.
A Unified Framework for Speech Separation

Speech separation refers to extracting each individual speech source in a given mixed signal. Recent advancements in speech separation and ongoing research in this area, have made these approaches as promising techniques for pre-processing of naturalistic audio streams. After incorporating deep learning techniques into speech separation, performance on these systems is improving faster. The initial solutions introduced for deep learning based speech separation analyzed the speech signals into time-frequency domain with STFT; and then encoded mixed signals were fed into a deep neural network based separator. Most recently, new methods are introduced to separate waveform of the mixed signal directly without analyzing them using STFT. Here, we introduce a unified framework to include both spectrogram and waveform separations into a single structure, while being only different in the kernel function used to encode and decode the data; where, both can achieve competitive performance. This new framework provides flexibility; in addition, depending on the characteristics of the data, or limitations of the memory and latency can set the hyper-parameters to flow in a pipeline of the framework which fits the task properly. We extend single-channel speech separation into multi-channel framework with end-to-end training of the network while optimizing the speech separation criterion (i.e., Si-SNR) directly. We emphasize on how tied kernel functions for calculating spatial features, encoder, and decoder in multi-channel framework can be effective. We simulate spatialized reverberate data for both WSJ0 and LibriSpeech corpora here, and while these two sets of data are different in the matter of size and duration, the effect of capturing shorter and longer dependencies of previous/+future samples are studied in detail. We report SDR, Si-SNR and PESQ to evaluate the performance of developed solutions.
Changing reference measure in Bayes spaces with applications to functional data analysis

Probability density functions (PDFs) can be understood as continuous compositions by the theory of Bayes spaces. The origin of a Bayes space is determined by a given reference measure. This can be easily changed through the well-known chain rule which has an impact on the geometry of the Bayes space. This work provides a mathematical framework for setting a reference measure. It is used to develop a weighting scheme on the bounded domain of distributional data. The impact on statistical analysis is shown from the perspective of simplicial functional principal component analysis. Moreover, a novel centered log-ratio transformation is proposed to map a weighted Bayes spaces into an unweighted $L^2$ space, enabling to use most tools developed in functional data analysis (e.g. clustering, regression analysis, etc.) while accounting for the weighting strategy. The potential of our proposal is shown through simulation and on a real case study using Italian income data.
Weibull Analysis with Sequential Order Statistics Under a Power Trend Model for Hazard Rates with Application in Aircraft Data Analysis

In engineering systems, it is usually assumed that lifetimes of components are independent and identically distributed (iid). But, the failure of a component results in a higher load on the remaining components and hence causes the distribution of the surviving components change. For modeling this kind of systems, the theory of sequential order statistics (SOS) can be used. Assuming Weibull distribution for lifetimes of components and conditionally proportional hazard rates model as a special case of the SOS theory, the maximum likelihood estimates of the unknown parameters are obtained in different cases. A new model, denoted by PTCPHM, as a generalization of the iid case is proposed, and then statistical inferential methods including point and interval estimation as well as hypothesis tests under PTCPHM are then developed. Finally, a real data on failure times of aircraft components, due to Mann and Fertig (1973), is analyzed to illustrate the model and inferential methods developed here.
Towards Smart Radio Environment for Wireless Communications via Intelligent Reflecting Surfaces: A Comprehensive Survey

This paper presents a comprehensive literature review on applications and design aspects of the intelligent reflecting surface (IRS) in the future wireless networks. Conventionally, the network optimization has been limited to transmission control at two endpoints, i.e., end users and network controller. The fading wireless channel is uncontrollable and becomes one of the main limiting factors for performance improvement. The IRS is composed of a large array of scattering elements, which can be individually configured to generate additional phase shifts to the signal reflections. Hence, it can actively control the signal propagation properties in favor of signal reception, and thus realize the notion of a smart radio environment. As such, the IRS's phase control combined with the conventional transmission control can potentially bring performance gain compared to the conventional wireless networks without using the IRS. In this survey, we first introduce basic concepts of the IRS and the realizations of its reconfigurability. Then, we focus on applications of the IRS in wireless communications. We overview different performance metrics and analytical approaches to characterize the performance improvement of IRS-assisted wireless networks. To exploit the performance gain, we discuss the joint optimization of the IRS's phase control and the transceivers' transmission control in different network design problems, e.g., rate maximization and power minimization problems. Furthermore, we extend the discussion of IRS-assisted wireless networks to some emerging wireless applications. Finally, we highlight important practical challenges and future research directions of realizing IRS-assisted wireless communications in beyond 5G networks.
Is multiphase gas cloudy or misty?

Cold $T \sim 10^{4}$K gas morphology could span a spectrum ranging from large discrete clouds to a fine `mist' in a hot medium. This has myriad implications, including dynamics and survival, radiative transfer, and resolution requirements for cosmological simulations. Here, we use 3D hydrodynamic simulations to study the pressure-driven fragmentation of cooling gas. This is a complex, multi-stage process, with an initial Rayleigh-Taylor unstable contraction phase which seeds perturbations, followed by a rapid, violent expansion leading to the dispersion of small cold gas `droplets' in the vicinity of the gas cloud. Finally, due to turbulent motions, and cooling, these droplets may coagulate. Our results show that a gas cloud `shatters' if it is sufficiently perturbed out of pressure balance ($\delta P/P\sim 1$), and has a large final overdensity $\chi_{\mathrm{f}}\gtrsim 300$, with only a weak dependence on the cloud size. Otherwise, the droplets reassemble back into larger pieces. We discuss our results in the context of thermal instability, and clouds embedded in a shock heated environment.
Blowups with log canonical singularities

We show that the minimum weight of a weighted blow-up of $\mathbf A^d$ with $\varepsilon$-log canonical singularities is bounded by a constant depending only on $\varepsilon $ and $d$. This was conjectured by Birkar. Using the recent classification of $4$-dimensional empty simplices by Iglesias-Vali\~no and Santos, we work out an explicit bound for blowups of $\mathbf A^4$ with terminal singularities: the smallest weight is always at most $32$, and at most $6$ in all but finitely many cases.
CAG: A Real-time Low-cost Enhanced-robustness High-transferability Content-aware Adversarial Attack Generator

Deep neural networks (DNNs) are vulnerable to adversarial attack despite their tremendous success in many artificial intelligence fields. Adversarial attack is a method that causes the intended misclassfication by adding imperceptible perturbations to legitimate inputs. To date, researchers have developed numerous types of adversarial attack methods. However, from the perspective of practical deployment, these methods suffer from several drawbacks such as long attack generating time, high memory cost, insufficient robustness and low transferability. To address the drawbacks, we propose a Content-aware Adversarial Attack Generator (CAG) to achieve real-time, low-cost, enhanced-robustness and high-transferability adversarial attack. First, as a type of generative model-based attack, CAG shows significant speedup (at least 500 times) in generating adversarial examples compared to the state-of-the-art attacks such as PGD and C&W. Furthermore, CAG only needs a single generative model to perform targeted attack to any targeted class. Because CAG encodes the label information into a trainable embedding layer, it differs from prior generative model-based adversarial attacks that use n different copies of generative models for n different targeted classes. As a result, CAG significantly reduces the required memory cost for generating adversarial examples. Moreover, CAG can generate adversarial perturbations that focus on the critical areas of input by integrating the class activation maps information in the training process, and hence improve the robustness of CAG attack against the state-of-art adversarial defenses. In addition, CAG exhibits high transferability across different DNN classifier models in black-box attack scenario by introducing random dropout in the process of generating perturbations. Extensive experiments on different datasets and DNN models have verified the real-time, low-cost, enhanced-robustness, and high-transferability benefits of CAG.
Multi-Task Deep Learning for Real-Time 3D Human Pose Estimation and Action Recognition

Human pose estimation and action recognition are related tasks since both problems are strongly dependent on the human body representation and analysis. Nonetheless, most recent methods in the literature handle the two problems separately. In this article, we propose a multi-task framework for jointly estimating 2D or 3D human poses from monocular color images and classifying human actions from video sequences. We show that a single architecture can be used to solve both problems in an efficient way and still achieves state-of-the-art or comparable results at each task while running with a throughput of more than 100 frames per second. The proposed method benefits from high parameters sharing between the two tasks by unifying still images and video clips processing in a single pipeline, allowing the model to be trained with data from different categories simultaneously and in a seamlessly way. Additionally, we provide important insights for end-to-end training the proposed multi-task model by decoupling key prediction parts, which consistently leads to better accuracy on both tasks. The reported results on four datasets (MPII, Human3.6M, Penn Action and NTU RGB+D) demonstrate the effectiveness of our method on the targeted tasks. Our source code and trained weights are publicly available at https://github.com/dluvizon/deephar.
A family of first-order accurate gradient schemes for finite volume methods

A new discretisation scheme for the gradient operator, suitable for use in second-order accurate Finite Volume Methods (FVMs), is proposed. The derivation of this scheme, which we call the Taylor-Gauss (TG) gradient, is similar to that of the least-squares (LS) gradients, whereby the values of the differentiated variable at neighbouring cell centres are expanded in truncated Taylor series about the centre of the current cell, and the resulting equations are summed after being weighted by chosen vectors. Unlike in the LS gradients, the TG gradients use vectors aligned with the face normals, resembling the Green-Gauss (GG) gradients in this respect. Thus, the TG and LS gradients belong in a general unified framework, within which other gradients can also be derived. The similarity with the LS gradients allows us to try different weighting schemes (magnitudes of the weighting vectors) such as weighting by inverse distance or face area. The TG gradients are tested on a variety of grids such as structured, locally refined, randomly perturbed, and with high aspect ratio. They are shown to be at least first-order accurate in all cases, and are thus suitable for use in second-order accurate FVMs. In many cases they compare favourably over existing schemes.
Human Search in a Fitness Landscape: How to Assess the Difficulty of a Search Problem

Computational modeling is widely used to study how humans and organizations search and solve problems in fields such as economics, management, cultural evolution, and computer science. We argue that current computational modeling research on human problem-solving needs to address several fundamental issues in order to generate more meaningful and falsifiable contributions. Based on comparative simulations and a new type of visualization of how to assess the nature of the fitness landscape, we address two key assumptions that approaches such as the NK framework rely on: that the NK captures the continuum of the complexity of empirical fitness landscapes, and that search behavior is a distinct component, independent from the topology of the fitness landscape. We show the limitations of the most common approach to conceptualize how complex, or rugged, a landscape is, as well as how the nature of the fitness landscape is fundamentally intertwined with search behavior. Finally, we outline broader implications for how to simulate problem-solving.
Homology-Preserving Multi-Scale Graph Skeletonization Using Mapper on Graphs

Node-link diagrams are a popular method for representing graphs that capture relationships between individuals, businesses, proteins, and telecommunication endpoints. However, node-link diagrams may fail to convey insights regarding graph structures, even for moderately sized data of a few hundred nodes, due to visual clutter. We propose to apply the mapper construction-a popular tool in topological data analysis-to graph visualization, which provides a strong theoretical basis for summarizing the data while preserving their core structures. We develop a variation of the mapper construction targeting weighted, undirected graphs, called mapper on graphs, which generates homology-preserving skeletons of graphs. We further show how the adjustment of a single parameter enables multi-scale skeletonization of the input graph. We provide a software tool that enables interactive explorations of such skeletons and demonstrate the effectiveness of our method for synthetic and real-world data.
Vector-borne parasite invasion in communities across space and time

While vector-borne parasite transmission often operates via generalist-feeding vectors facilitating cross-species transmission in host communities, theory describing the relationship between host species diversity and parasite invasion in these systems is underdeveloped. Host community composition and abundance vary across space and time, generating opportunities for parasite invasion. To explore how host community variation can modify parasite invasion potential, we develop a model for vector-borne parasite transmission dynamics that includes a host community of arbitrary richness and species' abundance. To compare invasion potential across communities, we calculate the community basic reproductive ratio of the parasite. We compare communities comprising a set of host species to their subsets, which allows for flexible scenario building including the introduction of novel host species and species loss. We allow vector abundance to scale with, or be independent of, community size, capturing regulation by feeding opportunities and non-host effects such as limited oviposition sites. Motivated by equivocal data relating host species competency to abundance, we characterize plausible host communities via phenomenological relationships between host species abundance and competency. We identify an underappreciated mechanism whereby changes to communities simultaneously alter average competency and the vector to host ratio and demonstrate that the interaction can profoundly influence invasion potential.
Network analysis reveals underlying syntactic features in a vocally learnt mammalian display, humpback whale song

Vocal communication systems have a set of rules that govern the arrangement of acoustic signals, broadly defined as ‘syntax’. However, there is a limited understanding of potentially shared or analogous rules across vocal displays in different taxa. Recent work on songbirds has investigated syntax using network-based modelling. This technique quantifies features such as connectivity (adjacent signals in a sequence) and recurring patterns. Here, we apply network-based modelling to the complex, hierarchically structured songs of humpback whales (Megaptera novaeangliae) from east Australia. Given the song's annual evolving pattern and the cultural conformity of males within a population, network modelling captured the patterns of multiple song types over 13 consecutive years. Song arrangements in each year displayed clear ‘small-world’ network structure, characterized by clusters of highly connected sounds. Transitions between these connected sounds further suggested a combination of both structural stability and variability. Small-world network structure within humpback songs may facilitate the characteristic and persistent vocal learning observed. Similar small-world structures and transition patterns are found in several birdsong displays, indicating common syntactic patterns among vocal learning in multiple taxa. Understanding the syntactic rules governing vocal displays in multiple, independently evolving lineages may indicate what rules or structural features are important to the evolution of complex communication, including human language.