Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiwavelength Detection of Cluster AGN

Abstract We present the initial results of a multiwavelength study to detect AGN in several galaxy clusters at moderate redshifts (0.5 < z < 0.9). By using multiple epochs of HST data, we identify 10–15 optically variable galactic nuclei in each cluster. The variable and non-variable galaxies are compared with X-ray point sources.
Positive realization of difference equations

The problem treated here is that of realization of an nthorder linear difference equation d(D)y = 0 describing free responses of a physical system in the form x(k + 1)=Ax(k), y(k)=c'x(k) , where the elements of matrix A and vector c are restricted to be nonnegative to reflect physical constraints. The specific problem treated here are realizability conditions, and characterizations of minimal realizations. These problems are discussed in detail through a geometric approach, specifically through the convex analysis. It is shown that the necessary and sufficient condition for realizability and the minimal dimension are completely characterized by a convex cone derived from the difference equation. A matrix equation generating all possible realizations is obtained, and then the canonical structure of minimal realizations is derived.
Quantile Autoregression for Censored Data

Quantile autoregression (QAR) is particularly attractive for censored data. However, unlike the standard regression models, the autoregressive models must take account of censoring on both response and regressors. In this article, we show that the existing censored quantile regression methods produce consistent estimators for QAR models when using only the fully observed regressors. A new algorithm is proposed to provide a censored QAR estimator by adopting imputation methods. The algorithm redistributes probability mass of censored points appropriately and iterates towards self‐consistent solutions. Monte Carlo simulations and empirical applications are conducted to demonstrate merits of the proposed method.
Cogging torque and acoustic noise reduction in permanent magnet motors by teeth pairing

This paper investigates reduction of cogging torque and acoustic noise in permanent magnet motors with larger stator slot opening. Using an energy method with Fourier series expansion, cogging torque is analytically determined with airgap MMF function and airgap permeance function. It shows that there exist several tooth widths which minimize the cogging torque. It also proposes a teeth pairing with two different types of tooth width which can effectively reduce the cogging torque and thus the acoustic noise. Experimental results show that the proposed teeth pairing reduces the cogging torque by 85% and the acoustic noise by 3.1 dB.
Circle Detection Using Its Maximal Symmetry Property

Circle detection has long been studied as one of fundamental image processing applications. It is used in divers areas including industrial inspection, medial image analysis, radio astronomy data analysis, and other object recognition applications. The most widely used class of circle detection techniques is the circle Hough transform and its variants. Management of 3 dimensional parameter histogram used in these methods brings about spatial and temporal overheads, and a lot of studies have dealt the problem. This paper proposes a robust circle detection method using maximal symmetry property of circle. The basic idea is that if perpendicular bisectors of pairs of edges are accumulated in image space, center of circle is determined to be the location of highest accumulation. However, directly implementing the idea in image space requires a lot of calculations. The method of this paper reduces the number of calculations by mapping the perpendicular bisectors into parameter space, selecting small number of parameters, and mapping them inversely into image space. Test on 22 images shows the calculations of the proposed method is 0.056% calculations of the basic idea. The test images include simple circles, multiple circles with various sizes, concentric circles, and partially occluded circles. The proposed method detected circles in various situations successfully.
Modified Montgomery modular multiplication and RSA exponentiation techniques

Modified Montgomery multiplication and associated RSA modular exponentiation algorithms and circuit architectures are presented. These modified multipliers use carry save adders (CSAs) to perform large word length additions. These have the attraction that, when repeatedly used to perform RSA modular exponentiation, the (carry save) format of the output words is compatible with that required by the multiplier inputs. This avoids the repeated interim output/input format conversion, needed when previously reported Montgomery multipliers are used for RSA modular exponentiation. Thus, the lengthy and costly conventional additions required at each stage are avoided. As a consequence, the critical path delay and, hence, the data throughput rate of the resulting Montgomery multiplier architectures are also word length independent. The approach presented is based on a reformulation of the solution to modular multiplication within the context of RSA exponentiation. Two algorithmic variants are presented, one based on a five-to-two CSA and the other on a four-to-two CSA plus multiplexer. The practical application of the approach has been demonstrated by using this to design special purpose RSA processing units with 512-bit and 1024-bit key sizes. The resulting RSA units exhibit the highest data rates reported in the literature to date, reflecting the very low and word length independent critical path delay achieved.
Block-Wise Pseudo-Marginal Metropolis-Hastings

The pseudo-marginal Metropolis-Hastings approach is increasingly used for Bayesian inference in statistical models where the likelihood is analytically intractable but can be estimated unbiasedly, such as random effects models and state-space models, or for data subsampling in big data settings. In a seminal paper, Deligiannidis et al. (2015) show how the pseudo-marginal Metropolis-Hastings (PMMH) approach can be made much more e cient by correlating the underlying random numbers used to form the estimate of the likelihood at the current and proposed values of the unknown parameters. Their proposed approach greatly speeds up the standard PMMH algorithm, as it requires a much smaller number of particles to form the optimal likelihood estimate. We present a closely related alternative PMMH approach that divides the underlying random numbers mentioned above into blocks so that the likelihood estimates for the proposed and current values of the likelihood only di er by the random numbers in one block. Our approach is less general than that of Deligiannidis et al. (2015), but has the following advantages. First, it provides a more direct way to control the correlation between the logarithms of the estimates of the likelihood at the current and proposed values of the parameters. Second, the mathematical properties of the method are simplified and made more transparent compared to the treatment in Deligiannidis et al. (2015). Third, blocking is shown to be a natural way to carry out PMMH in, for example, panel data models and subsampling problems. We obtain theory and guidelines for selecting the optimal number of particles, and document large speed-ups in a panel data example and a subsampling problem.
Open string field theory, unstable D-branes and open-closed duality

In this thesis we explore different aspects of unstable D-brane dynamics and open-closed dualities. The first chapters focus on the properties of the so-called ”tachyon vacuum” for unstable D-branes, that is conjectured to describe the complete decay of the D-brane, using the tool of Open String Field Theory. The first chapter discusses a numerical analysis of this vacuum state, the second discusses and motivates a conjecture on the structure of String Field Theory around the vacuum state and the third presents in more detail some important analytic tools that appeared in chapter two. We shift the focus in chapter four to a time-dependent version of the tachyon decay process and to the duality between some configurations of D-branes in the imaginary time direction and some purely closed wave-like backgrounds. Similar ideas have an application in noncritical string theories in the context of the identification of D-branes as the basic degrees of freedom of the theory. In chapter five we discuss one particular aspect of this identification in the context of the (2, 1) noncritical string (2d topological gravity): we reduce Open String Field Theory on the stable D-branes of the (2, 1) model to the Kontsevich matrix integral, that is known to encode the whole closed string theory for the (2, 1) model.
An Age-Dependent Prognostic Model for Nonlinear Degrading Devices Based on Diffusion Process

Degradation modeling-based remaining useful life (RUL) predicting has become a significant basis of prognotics and system health management (PHM) and has drawn much favor of both scholars and engineers in the field of reliability. Based on a purely time-dependent diffusion process, this article presents a nonlinear degradation model with a special focus on the proportional relationship between the age-dependent expectation and variance of the degradation processes of the concerned devices. Exact probability density function (pdf) and cumulative distribution function (cdf) of lifetime and RUL in explicit forms are derived under the concept of first hitting time (FHT), incorporating the item-to-item variability. A framework of maximizing the likelihood function has been proposed to estimate the unknown parameters utilizing condition monitoring (CM) data of the devices. Case studies of fatigue crack length data of alloy and capacity data of electrolytic capacitors have been presented to illustrate the proposed prognostics model. The results show that the proposed degradation model can be well fitted to the nonlinear degradation data and provide an accurate prediction of RUL, and thus demonstrate the effectiveness of the proposed model.
The ‘Perfect Technology Syndrome’: How to Solve the Technology Dominance in Technology-Program Projects

Technology has kept its position as a key factor in technology-intensive product innovation. The prize of the first-mover advantage is a unique position in the market without competition and a wide time margin to exploit the technological breakthrough (Lieberman &Montgomery, 1988). Where the pioneer position is no longer available, one alternative policy is to aim for the ultimatum reach in the technology – to attain the perfect technology. Technology challenges, developers try to and overcome all perceived technological limitations, without necessarily paying adequate attention to the commercial demands of user-friendliness, branding or customer preferences (Ernst &Soll, 2003). Aspirations towards ‘perfect technology’ AbstrAct
Progressive Content-aware Coded Hyperspectral Compressive Imaging

Hyperspectral imaging plays a pivotal role in a wide range of applications, like remote sensing, medicine, and cytology. By acquiring 3D hyperspectral images (HSIs) via 2D sensors, the coded aperture snapshot spectral imaging (CASSI) has achieved great success due to its hardware-friendly implementation and fast imaging speed. However, for some less spectrally sparse scenes, single snapshot and unreasonable coded aperture design tend to make HSI recovery more ill-posed and yield poor spatial and spectral fidelity. In this paper, we propose a novel Progressive Content-Aware CASSI framework, dubbed PCA-CASSI, which captures HSIs with multiple optimized content-aware coded apertures and fuses all the snapshots for reconstruction progressively. Simultaneously, by mapping the Range-Null space Decomposition (RND) into a deep network with several phases, an RND-HRNet is proposed for HSI recovery. Each recovery phase can fully exploit the hidden physical information in the coded apertures via explicit $\mathcal{R}$$-$$\mathcal{N}$ decomposition and explore the spatial-spectral correlation by dual transformer blocks. Our method is validated to surpass other state-of-the-art methods on both multiple- and single-shot HSI imaging tasks by large margins.
Rationality of the Gromov Boundary of Hyperbolic Groups

In [BBM21], Belk, Bleak and Matucci proved that hyperbolic groups can be seen as subgroups of the rational group. In order to do so, they associated a tree of atoms to each hyperbolic group. Not so many connections between this tree and the literature on hyperbolic groups were known. In this paper, we prove an atom-version of the fellow traveler property and exponential divergence, together with other similar results. These leads to several consequences: a bound from above of the topological dimension of the Gromov boundary, the definition of an augmented tree which is quasi-isometric to the Cayley graph and a synchronous recognizer which described the equivalence relation given by the quotient map defined from the end of the tree onto the Gromov boundary.
Nonlinearity parameter imaging in the frequency domain

Nonlinearity parameter tomography leads to the problem of identifying a coefficient in a nonlinear wave equation (such as the Westervelt equation) modeling ultrasound propagation. In this paper we transfer this into frequency domain, where the Westervelt equation gets replaced by a coupled system of Helmholtz equations with quadratic nonlinearities. For the case of the to-be-determined nonlinearity coefficient being a characteristic function of an unknown, not necessarily connected domain $D$, we devise and test a reconstruction algorithm based on weighted point source approximations combined with Newton's method. In a more abstract setting, convergence of a regularised Newton type method for this inverse problem is proven by verifying a range invariance condition of the forward operator and establishing injectivity of its linearisation.
On the Effect of Instrumentation on Test Flakiness

Test flakiness is a problem that affects testing and processes that rely on it. Several factors cause or influence the flakiness of test outcomes. Test execution order, randomness and concurrency are some of the more common and well-studied causes. Some studies mention code instrumentation as a factor that causes or affects test flakiness. However, evidence for this issue is scarce. In this study, we attempt to systematically collect evidence for the effects of instrumentation on test flakiness. We experiment with common types of instrumentation for Java programs—namely, application performance monitoring, coverage and profiling instrumentation. We then study the effects of instrumentation on a set of nine programs obtained from an existing dataset used to study test flakiness, consisting of popular GitHub projects written in Java. We observe cases where realworld instrumentation causes flakiness in a program. However, this effect is rare. We also discuss a related issue—how instrumentation may interfere with flakiness detection and prevention.
cito: An R package for training neural networks using torch

Deep Neural Networks (DNN) have become a central method in ecology. Most current deep learning (DL) applications rely on one of the major deep learning frameworks, in particular Torch or TensorFlow, to build and train DNN. Using these frameworks, however, requires substantially more experience and time than typical regression functions in the R environment. Here, we present 'cito', a user-friendly R package for DL that allows specifying DNNs in the familiar formula syntax used by many R packages. To fit the models, 'cito' uses 'torch', taking advantage of the numerically optimized torch library, including the ability to switch between training models on the CPU or the graphics processing unit (GPU) (which allows to efficiently train large DNN). Moreover, 'cito' includes many user-friendly functions for model plotting and analysis, including optional confidence intervals (CIs) based on bootstraps for predictions and explainable AI (xAI) metrics for effect sizes and variable importance with CIs and p-values. To showcase a typical analysis pipeline using 'cito', including its built-in xAI features to explore the trained DNN, we build a species distribution model of the African elephant. We hope that by providing a user-friendly R framework to specify, deploy and interpret DNN, 'cito' will make this interesting model class more accessible to ecological data analysis. A stable version of 'cito' can be installed from the comprehensive R archive network (CRAN).
Connected Trading Cycles

This paper studies one-sided matching with initial endowments and the social connections between participants are specifically considered (their social network). Different from the traditional setting, the matching starts with a group of initial participants, and the others need their invitation to join the matching game. Thus, the incentive compatibility (IC) notion here considers both reporting preferences and inviting others via their social connections. This is challenging because inviters and invitees might compete in the game. Besides IC, stability and optimality are two properties extensively studied in matching, but they both are incompatible with the new IC. In the new setting, compatible stability has been discussed, but no discussion about compatible optimality yet. We complete this and prove the theoretical boundaries regarding both stability and optimality in the new setting. We then propose a mechanism called Connected Trading Cycles to satisfy all the desirable properties for the first time. Unlike the previous solutions that add restrictions on participants' matching choices to achieve IC, we allow participants to choose anyone in the game, which in principle improves everyone's satisfiability in the matching. Finally, we give the first characterization of IC in the network setting to facilitate IC mechanism design.
Hamiltonicity of $1$-tough $(P_2\cup kP_1)$-free graphs

Given a graph $H$, a graph $G$ is $H$-free if $G$ does not contain $H$ as an induced subgraph. For a positive real number $t$, a non-complete graph $G$ is said to be $t$-tough if for every vertex cut $S$ of $G$, the ratio of $|S|$ to the number of components of $G-S$ is at least $t$. A complete graph is said to be $t$-tough for any $t>0$. Chv\'{a}tal's toughness conjecture, stating that there exists a constant $t_0$ such that every $t_0$-tough graph with at least three vertices is Hamiltonian, is still open in general. Chv\'{a}tal and Erd\"{o}s \cite{CE} proved that, for any integer $k\ge 1$, every $\max\{2,k\}$-connected $(k+1)P_1$-free graph on at least three vertices is Hamiltonian. Along the Chv\'{a}tal-Erd\"{o}s theorem, Shi and Shan \cite{SS} proved that, for any integer $k\ge 4$, every $4$-tough $2k$-connected $(P_2\cup kP_1)$-free graph with at least three vertices is Hamiltonian, and furthermore, they proposed a conjecture that for any integer $k\ge 1$, any $1$-tough $2k$-connected $(P_2\cup kP_1)$-free graph is Hamiltonian. In this paper, we confirm the conjecture, and furthermore, we show that if $k\ge 3$, then the condition `$2k$-connected' may be weakened to be `$2(k-1)$-connected'. As an immediate consequence, for any integer $k\ge 3$, every $(k-1)$-tough $(P_2\cup kP_1)$-free graph is Hamiltonian. This improves the result of Hatfield and Grimm \cite{HG}, stating that every $3$-tough $(P_2\cup 3P_1)$-free graph is Hamiltonian.
Causal Discovery from Temporal Data: An Overview and New Perspectives

Temporal data, representing chronological observations of complex systems, has always been a typical data structure that can be widely generated by many domains, such as industry, medicine and finance. Analyzing this type of data is extremely valuable for various applications. Thus, different temporal data analysis tasks, eg, classification, clustering and prediction, have been proposed in the past decades. Among them, causal discovery, learning the causal relations from temporal data, is considered an interesting yet critical task and has attracted much research attention. Existing causal discovery works can be divided into two highly correlated categories according to whether the temporal data is calibrated, ie, multivariate time series causal discovery, and event sequence causal discovery. However, most previous surveys are only focused on the time series causal discovery and ignore the second category. In this paper, we specify the correlation between the two categories and provide a systematical overview of existing solutions. Furthermore, we provide public datasets, evaluation metrics and new perspectives for temporal data causal discovery.
Covariance properties under natural image transformations for the generalised Gaussian derivative model for visual receptive fields

The property of covariance, also referred to as equivariance, means that an image operator is well-behaved under image transformations, in the sense that the result of applying the image operator to a transformed input image gives essentially a similar result as applying the same image transformation to the output of applying the image operator to the original image. This paper presents a theory of geometric covariance properties in vision, developed for a generalised Gaussian derivative model of receptive fields in the primary visual cortex and the lateral geniculate nucleus, which, in turn, enable geometric invariance properties at higher levels in the visual hierarchy. It is shown how the studied generalised Gaussian derivative model for visual receptive fields obeys true covariance properties under spatial scaling transformations, spatial affine transformations, Galilean transformations and temporal scaling transformations. These covariance properties imply that a vision system, based on image and video measurements in terms of the receptive fields according to the generalised Gaussian derivative model, can, to first order of approximation, handle the image and video deformations between multiple views of objects delimited by smooth surfaces, as well as between multiple views of spatio-temporal events, under varying relative motions between the objects and events in the world and the observer. We conclude by describing implications of the presented theory for biological vision, regarding connections between the variabilities of the shapes of biological visual receptive fields and the variabilities of spatial and spatio-temporal image structures under natural image transformations. Specifically, we formulate experimentally testable biological hypotheses as well as needs for measuring population statistics of receptive field characteristics, originating from predictions from the presented theory, concerning the extent to which the shapes of the biological receptive fields in the primary visual cortex span the variabilities of spatial and spatio-temporal image structures induced by natural image transformations, based on geometric covariance properties.