Miracle-dz/UniFAR-FTUs huggingface dataset:
Stimulated Hawking emission from electromagnetic analogue black hole: Theory and observation

In this paper we consider possible analogue Hawking radiation from a normal dielectric and metamaterial composite, having an analogue horizon where the dielectric parameters vanish and change sign upon crossing this transition zone. We follow a complex path analysis to show the presence of an analogue Hawking temperature at the horizon and subsequent photon production from the ambient electromagnetic field. Possibility of experimental observation is also commented upon.
Subdivisions of maximal 3-degenerate graphs of order $d+1$ in graphs of minimum degree $d$

We prove that every graph of minimum degree at least $d \ge 1$ contains a subdivision of some maximal 3-degenerate graph of order $d+1$. This generalizes the classic results of Dirac ($d=3$) and Pelikan ($d=4$). We conjecture that for any planar maximal 3-degenerate graph $H$ of order $d+1$ and any graph $G$ of minimum degree at least $d$, $G$ contains a subdivision of $H$. We verify this in the case $H$ is $P_6^3$ and $P_7^3$
Knowledge-Based Visual Question Answering in Videos

We propose a novel video understanding task by fusing knowledge-based and video question answering. First, we introduce KnowIT VQA, a video dataset with 24,282 human-generated question-answer pairs about a popular sitcom. The dataset combines visual, textual and temporal coherence reasoning together with knowledge-based questions, which need of the experience obtained from the viewing of the series to be answered. Second, we propose a video understanding model by combining the visual and textual video content with specific knowledge about the show. Our main findings are: (i) the incorporation of knowledge produces outstanding improvements for VQA in video, and (ii) the performance on KnowIT VQA still lags well behind human accuracy, indicating its usefulness for studying current video modelling limitations.
Thermodynamics and weak cosmic censorship conjecture of 4D Gauss-Bonnet-Maxwell black holes via charged particle absorption

Recently, the non-trivial solutions for 4-dimensional black holes of Einstein-Gauss-Bonnet gravity had been discovered. In this paper, considering a charged particle entering into a 4-dimensional Gauss-Bonnet-Maxwell black hole, we calculate the black hole thermodynamic properties using the Hamilton-Jacobi equation. In the normal phase space, the cosmological constant and Gauss-Bonnet parameter are fixed, the black hole satisfies the first and second laws of thermodynamics, and the weak cosmic censorship conjecture (WCCC) is valid. On the other hand, in the case of extended phase space, the cosmological constant and Gauss-Bonnet parameter are treated as thermodynamic variables. The black hole also satisfies the first law of thermodynamics. However, the increase or decrease in the black hole's entropy depends on some specific conditions. Finally, we observe that the WCCC is violated for the near-extremal black holes in the extended phase space.
A Day in the Life of a Meme.

A DAY IN THE LIFE OF A MEME Liane Gabora Center for the Study of the Evolution and Origin of Life University of California, Los Angeles Los Angeles, CA 90095-1567 liane@cs.ucla. edu 1. CONCEPTUAL CHANGE: A SECOND FORM OF EVOLUTION Knowing everything there is to know about how concepts or ideas can get represented in the mind of an individual would not take us far toward an account of why individuals understand the world the way they do, or explain why their understanding differs from that of their ancestors. While some ideas fade into obscurity, others rise to prominence and spread horizontally through society, and vertically from one generation to another, getting progressively refined and embellished along the way. Ideas, like the strands of DNA that encode instructions for building and maintaining living organisms, seem to undergo a process analogous to biological evolution. Understanding this process is a vital part of unraveling our cognitive make-up. There has been a slow but steady effort to map the concept of evolution onto the dynamics of culture. Popper (1963) and Campbell (1987) alerted us to the evolutionary flavor of epistemology. Dawkins (1976) introduced the notion of a meme--a replicator of cultural information analogous to the gene. In his words: "Just as genes propagate themselves in the gene pool by leaping from body to body via sperm or eggs, so do memes propagate themselves in the meme pool by leaping from brain to brain. " Others have drawn from mathematical models of population genetics and epidemiology to model the spread of ideas (Cavalli-Sforza & Feldman 1981; Lumsden & Wilson 1981; Schuster & Sigmund 1983; Boyd & Richerson 1985; Hofbauer & Sigmund 1988). These works point toward the possibility that memetics constitutes a second form of evolution, distinct from yet intertwined with biological evolution, with the potential to provide the kind of overarching framework for the social and cognitive sciences that the first form provides for the biological sciences. However thus far memetics has not lived up to this potential, a situation that seems unfortunate given the success of the biological precedent. Although much was known about living things before Darwin, his theory of how life evolves through natural selection united previously disparate phenomena and paved the way for further biological inquiry.
On the renewal theorem for maxima on trees

We consider the distributional fixed-point equation: $$R \stackrel{\mathcal{D}}{=} Q \vee \left( \bigvee_{i=1}^N C_i R_i \right),$$ where the $\{R_i\}$ are i.i. d. ~copies of $R$, independent of the vector $(Q, N, \{C_i\})$, where $N \in \mathbb{N}$, $Q, \{C_i\} \geq 0$ and $P(Q > 0) > 0$. By setting $W = \log R$, $X_i = \log C_i$, $Y = \log Q$ it is equivalent to the high-order Lindley equation $$W \stackrel{\mathcal{D}}{=} \max\left\{ Y, \, \max_{1 \leq i \leq N} (X_i + W_i) \right\}. $$ It is known that under Kesten assumptions, $$P(W > t) \sim H e^{-\alpha t}, \qquad t \to \infty,$$ where $\alpha>0$ solves the Cramer-Lundberg equation $E \left[ \sum_{j=1}^N C_i ^\alpha \right] = E\left[ \sum_{i=1}^N e^{\alpha X_i} \right] = 1$. The main goal of this paper is to provide an explicit representation of the constant $H$ which can be directly connected to the underlying weighted branching process where $W$ is constructed. The main tool in our analysis is Alsmeyer's Markov renewal theorem, which we apply after a suitable change of measure. Furthermore, our new explicit approach for deriving $H$ will also provide an unbiased and strongly efficient importance sampling estimator for the rare event probability $P(W > t)$. We provide numerical examples illustrating the use of this new algorithm.
SOS: Socially omitting selfishness in IoT for smart and connected communities

Smart and connected communities (SCC) is an emerging field of internet of things (IoT), and it has potential applications to improve human life. The improvement may be in terms of preservation, revitalization, livability, and sustainability of a community. The resources of the nodes and devices in the SCC have certain constraints that may not allow the devices and nodes to cooperate to save their resources such as memory, energy, and buffer, or simply maximize their performance. Thus, to stimulate the nodes to avoid selfish behavior, SSC needs a novel and well‐organized solution to motivate nodes for cooperation. This article aims to resolve the issue of selfish behaviors in SCC and to encourage the nodes for cooperation. A novel mechanism, socially omitting selfishness (SOS), has been proposed to manage/eradicate selfishness using a socially oriented election process. The election process elects different heads based on weight and cooperation (using Vickrey, Clarke, and Groves model). The election of heads and incentive mechanism encourages the nodes to show participation and behave as highly cooperative members of the community. Furthermore, an extended version of the Dempster Shafer model has been used to discourage the selfish behavior of the participating nodes in the SOS scheme. It uses different monitoring and gateway nodes to efficiently employ the proposed scheme. A mathematical model has been developed for the aforementioned aspects and simulated through NS2 simulation environment to analyze the performance of SOS. The results of the proposed scheme outperform the contemporary schemes in terms of average delivery delay, packet delivery ratio, throughput, and average energy.
Moire Image Restoration using Multi Level Hyper Vision Net

A moire pattern in the images is resulting from high frequency patterns captured by the image sensor (colour filter array) that appear after demosaicing. These Moire patterns would appear in natural images of scenes with high frequency content. The Moire pattern can also vary intensely due to a minimal change in the camera direction/positioning. Thus the Moire pattern depreciates the quality of photographs. An important issue in demoireing pattern is that the Moireing patterns have dynamic structure with varying colors and forms. These challenges makes the demoireing more difficult than many other image restoration tasks. Inspired by these challenges in demoireing, a multilevel hyper vision net is proposed to remove the Moire pattern to improve the quality of the images. As a key aspect, in this network we involved residual channel attention block that can be used to extract and adaptively fuse hierarchical features from all the layers efficiently. The proposed algorithms has been tested with the NTIRE 2020 challenge dataset and thus achieved 36.85 and 0.98 Peak to Signal Noise Ratio (PSNR) and Structural Similarity (SSIM) Index respectively.
A Hybrid Bayesian Approach Towards Clock Offset and Skew Estimation in 5G Networks

In this work, we propose a hybrid Bayesian approach towards clock offset and skew estimation, thereby synchronizing large scale networks. In particular, we demonstrate the advantage of Bayesian Recursive Filtering (BRF) in alleviating time-stamping errors for pairwise synchronization. Moreover, we indicate the benefit of Factor Graph (FG), along with Belief Propagation (BP) algorithm in achieving high precision end-to-end network synchronization. Finally, we reveal the merit of hybrid synchronization, where a large-scale network is divided into local synchronization domains, for each of which a suitable synchronization algorithm (BP- or BRF-based) is utilized. The simulation results show that, despite the simplifications in the hybrid approach, the Root Mean Square Errors (RMSEs) of clock offset and skew estimation remain below 5 ns and 0.3 ppm, respectively.
Boundary amenability and measure equivalence rigidity among two-dimensional Artin groups of hyperbolic type

We study 2-dimensional Artin groups of hyperbolic type from the viewpoint of measure equivalence, and establish strong rigidity statements. We first prove that they are boundary amenable -- as is more generally every group acting discretely by isometries on a connected piecewise hyperbolic CAT(-1) simplicial complex with countably many simplices in finitely many isometry types, under the assumption that vertex stabilizers are boundary amenable. As a byproduct, these groups satisfy the Novikov conjecture. We find an infinite collection $\mathcal{C}$ of Artin groups (already considered by Crisp in his work on abstract commensurators) which are superrigid for measure equivalence: every countable group which is measure equivalent to a group $G$ in the class $\mathcal{C}$, is almost isomorphic to $G$. We also get measure equivalence classification results within the class of Artin groups. More precisely, we prove that whenever two 2-dimensional Artin groups of hyperbolic type are measure equivalent, then there is an isomorphism between their fixed set graphs -- these graphs were introduced by Crisp and constitute a natural analogue of the curve graph in the realm of Artin groups. This can be reformulated in terms of orbit equivalence rigidity of actions of 2-dimensional Artin groups of hyperbolic type. As many of these groups are properly proximal in the sense of Boutonnet, Ioana and Peterson, this also yields strong rigidity results for von Neumann algebras associated to their ergodic actions. In particular, we build a new infinite class of examples of groups all of whose ergodic probability measure-preserving actions are $W^*$-superrigid. Other applications of our work include a superrigidity statement for cocycles from higher-rank lattices to 2-dimensional Artin groups of hyperbolic type, and a rigidity statement regarding possible lattice embeddings of certain Artin groups.
On some properties of the electromagnetic field and its interaction with a charged particle

A procedure for solving the Maxwell equations in vacuum, under the additional requirement that both scalar invariants are equal to zero, is presented. Such a field is usually called a null electromagnetic field. Based on the complex Euler potentials that appear as arbitrary functions in the general solution, a vector potential for the null electromagnetic field is defined. This potential is called natural vector potential of the null electromagnetic field. An attempt is made to make the most of knowing the general solution. The properties of the field and the potential are studied without fixing a specific family of solutions. A equality, which is similar to the Dirac gauge condition, is found to be true for both null field and Lienard-Wiechert field. It turns out that the natural potential is a substantially complex vector, which is equivalent to two real potentials. A modification of the coupling term in the Dirac equation is proposed, that makes the equation work with both real potentials. A solution, that corresponds to the Volkov's solution for a Dirac particle in a linearly polarized plane electromagnetic wave, is found. The solution found is directly compared to Volkov's solution under the same conditions.
The life cycle of radio galaxies in the LOFAR Lockman Hole field

Radio galaxies are known to go through cycles of activity, where phases of apparent quiescence can be followed by repeated activity of the central supermassive black hole. A better understanding of this cycle is crucial for ascertaining the energetic impact that the jets have on the host galaxy, but little is known about it. We used deep LOFAR images at 150 MHz of the Lockman Hole extragalactic field to select a sample of 158 radio sources with sizes > 60″ in different phases of their jet life cycle. Using a variety of criteria (e.g. core prominence combined with low-surface brightness of the extended emission and steep spectrum of the central region) we selected a subsample of candidate restarted radio galaxies representing between 13% and 15% of the 158 sources of the main sample. We compare their properties to the rest of the sample, which consists of remnant candidates and active radio galaxies. Optical identifications and characterisations of the host galaxies indicate similar properties for candidate restarted, remnant, and active radio galaxies, suggesting that they all come from the same parent population. The fraction of restarted radio galaxies is slightly higher with respect to remnants, suggesting that the restarted phase can often follow after a relatively short remnant phase (the duration of the remnant phase being a few times 107 years). This confirms that the remnant and restarted phases are integral parts of the life cycle of massive elliptical galaxies. A preliminary investigation does not suggest a strong dependence of this cycle on the environment surrounding any given galaxy.
Model-Predictive Control via Cross-Entropy and Gradient-Based Optimization

Recent works in high-dimensional model-predictive control and model-based reinforcement learning with learned dynamics and reward models have resorted to population-based optimization methods, such as the Cross-Entropy Method (CEM), for planning a sequence of actions. To decide on an action to take, CEM conducts a search for the action sequence with the highest return according to the dynamics model and reward. Action sequences are typically randomly sampled from an unconditional Gaussian distribution and evaluated on the environment. This distribution is iteratively updated towards action sequences with higher returns. However, this planning method can be very inefficient, especially for high-dimensional action spaces. An alternative line of approaches optimize action sequences directly via gradient descent, but are prone to local optima. We propose a method to solve this planning problem by interleaving CEM and gradient descent steps in optimizing the action sequence. Our experiments show faster convergence of the proposed hybrid approach, even for high-dimensional action spaces, avoidance of local minima, and better or equal performance to CEM. Code accompanying the paper is available here this https URL.
Born sigma model for branes in exceptional geometry

In double field theory, the physical space has been understood as a subspace of the doubled space. Recently, the doubled space has been defined as the para-Hermitian manifold and the physical space is realized as a leaf of a foliation of the doubled space. This construction naturally introduces the fundamental 2-form, which plays an important role in a reformulation of string theory known as the Born sigma model. In this paper, we present the Born sigma model for $p$-branes in M-theory and type IIB theory by extending the fundamental 2-form into $U$-duality-covariant $(p+1)$-forms.
The effective impedances of infinite ladder networks and Dirichlet problem on graphs

We calculate effective impedances of infinite $LC$- and $CL$- ladder networks as limits of effective impedances of finite network approximations, using a new method, which involves precise mathematical concepts. These concepts are related to the classical differential operators on weighted graphs. As an auxiliary result, we solve a discrete boundary value Dirichlet problem on a finite ladder network.
A Subterranean Virtual Cave World for Gazebo based on the DARPA SubT Challenge

Subterranean environments with lots of obstacles, including narrow passages, large voids, rock falls and absence of illumination were always challenging for control, navigation, and perception of mobile robots. The limited availability and access to such environments restricts the development pace of capabilities for robotic platforms to autonomously accomplish tasks in such challenging areas. The Subterranean Challenge is a competition focusing on bringing robotic exploration a step closer to real life applications for man-made underground tunnels, urban areas and natural cave networks, envisioning advanced assistance tools for first responders and disaster relief agencies. The challenge offers a software-based virtual part to showcase technologies in autonomy perception, networking and mobility for such areas. Thus, the presented open-source virtual world aims to become a test-bed for evaluating the developed algorithms and software and to foster mobile robotics developments.
A fast semi-automatic method for classification and counting the number and types of blood cells in an image

A novel and fast semi-automatic method for segmentation, locating and counting blood cells in an image is proposed. In this method, thresholding is used to separate the nucleus from the other parts. We also use Hough transform for circles to locate the center of white cells. Locating and counting of red cells is performed using template matching. We make use of finding local maxima, labeling and mean value computation in order to shrink the areas obtained after applying Hough transform or template matching, to a single pixel as representative of location of each region. The proposed method is very fast and computes the number and location of white cells accurately. It is also capable of locating and counting the red cells with a small error.
On Eagleson's theorem in the non‐stationary setup

A classical result due to Eagleson states (in particular) that if appropriately normalized Birkhoff sums generated by a measurable function and an ergodic probability preserving transformation converge in distribution, then they also converge in distribution with respect to any probability measure which is absolutely continuous with respect to the invariant one. In this note, we prove several quantitative and infinite‐dimensional versions of Eagleson's theorem for some classes of non‐stationary stochastic processes which satisfy certain type of decay of correlations.
Robust covariance estimation for high‐dimensional compositional data with application to microbial communities analysis

Microbial communities analysis is drawing growing attention due to the rapid development fire of high‐throughput sequencing techniques nowadays. The observed data has the following typical characteristics: it is high‐dimensional, compositional (lying in a simplex) and even would be leptokurtic and highly skewed due to the existence of overly abundant taxa, which makes the conventional correlation analysis infeasible to study the co‐occurrence and co‐exclusion relationship between microbial taxa. In this article, we address the challenges of covariance estimation for this kind of data. Assuming the basis covariance matrix lying in a well‐recognized class of sparse covariance matrices, we adopt a proxy matrix known as centered log‐ratio covariance matrix in the literature. We construct a Median‐of‐Means estimator for the centered log‐ratio covariance matrix and propose a thresholding procedure that is adaptive to the variability of individual entries. By imposing a much weaker finite fourth moment condition compared with the sub‐Gaussianity condition in the literature, we derive the optimal rate of convergence under the spectral norm. In addition, we also provide theoretical guarantee on support recovery. The adaptive thresholding procedure of the MOM estimator is easy to implement and gains robustness when outliers or heavy‐tailedness exist. Thorough simulation studies are conducted to show the advantages of the proposed procedure over some state‐of‐the‐arts methods. At last, we apply the proposed method to analyze a microbiome dataset in human gut.
Robust Frequency-Based Structure Extraction

State of the art mapping algorithms can produce high-quality maps. However, they are still vulnerable to clutter and outliers which can affect map quality and in consequence hinder the performance of a robot, and further map processing for semantic understanding of the environment. This paper presents ROSE, a method for building-level structure detection in robotic maps. ROSE exploits the fact that indoor environments usually contain walls and straight-line elements along a limited set of orientations. Therefore metric maps often have a set of dominant directions. ROSE extracts these directions and uses this information to segment the map into structure and clutter through filtering the map in the frequency domain (an approach substantially underutilised in the mapping applications). Removing the clutter in this way makes wall detection (e.g. using the Hough transform) more robust. Our experiments demonstrate that (1) the application of ROSE for decluttering can substantially improve structural feature retrieval (e.g., walls) in cluttered environments, (2) ROSE can successfully distinguish between clutter and structure in the map even with substantial amount of noise and (3) ROSE can numerically assess the amount of structure in the map.