Miracle-dz/UniFAR-FTUs huggingface dataset:
Boundary Aware Multi-focus Image Fusion Using Deep Neural Network

Since it is usually difficult to capture an all-in-focus image of a 3D scene directly, various multi-focus image fusion methods are employed to generate it from several images focusing at different depths. However, the performance of existing methods is barely satisfactory and often degrades for areas near the focused/defocused boundary (FDB). In this paper, a boundary aware method using deep neural network is proposed to overcome this problem. (1) Aiming to acquire improved fusion images, a 2-channel deep network is proposed to better extract the relative defocus information of the two source images. (2) After analyzing the different situations for patches far away from and near the FDB, we use two networks to handle them respectively. (3) To simulate the reality more precisely, a new approach of dataset generation is designed. Experiments demonstrate that the proposed method outperforms the state-of-the-art methods, both qualitatively and quantitatively.
Optimization of MIMO Device-to-Device Networks via Matrix Fractional Programming: A Minorization–Maximization Approach

Interference management is a fundamental issue in device-to-device (D2D) communications whenever the transmitter-and-receiver pairs are located in close proximity and frequencies are fully reused, so active links may severely interfere with each other. This paper devises an optimization strategy named FPLinQ to coordinate the link scheduling decisions among the interfering links, along with power control and beamforming. The key enabler is a novel optimization method called matrix fractional programming (FP) that generalizes previous scalar and vector forms of FP in allowing multiple data streams per link. From a theoretical perspective, this paper provides a deeper understanding of FP by showing a connection to the minorization-maximization (MM) algorithm. From an application perspective, this paper shows that as compared to the existing methods for coordinating scheduling in the D2D network, such as FlashLinQ, ITLinQ, and ITLinQ+, the proposed FPLinQ approach is more general in allowing multiple antennas at both the transmitters and the receivers, and further in allowing arbitrary and multiple possible associations between the devices via matching. Numerical results show that FPLinQ significantly outperforms the previous state-of-the-art in a typical D2D communication environment.
Improvements on Sawyer type estimates for generalized maximal functions

In this paper we prove mixed inequalities for the maximal operator MΦ , for general Young functions Φ with certain additional properties, improving and generalizing some previous estimates for the Hardy–Littlewood maximal operator proved by E. Sawyer. We show that given r≥1 , if u,vr are weights belonging to the A1‐Muckenhoupt class and Φ is a Young function as above, then the inequality uvrx∈Rn:MΦ(fv)(x)v(x)>t≤C∫RnΦ|f(x)|tu(x)vr(x)dxholds for every positive t.
Multi-vision Attention Networks for On-line Red Jujube Grading

To solve the red jujube classification problem, this paper designs a convolutional neural network model with low computational cost and high classification accuracy. The architecture of the model is inspired by the multi-visual mechanism of the organism and DenseNet. To further improve our model, we add the attention mechanism of SE-Net. We also construct a dataset which contains 23,735 red jujube images captured by a jujube grading system. According to the appearance of the jujube and the characteristics of the grading system, the dataset is divided into four classes: invalid, rotten, wizened and normal. The numerical experiments show that the classification accuracy of our model reaches to 91.89%, which is comparable to DenseNet-121, InceptionV3, InceptionV4, and Inception-ResNet v2. However, our model has real-time performance.
Filling the gap: first record of the transparent-winged big-eared bat Histiotus diaphanopterus (Chiroptera: Vespertilionidae) in southwestern Brazil

Abstract Known only from five localities Histiotus diaphanopterus is the least known species of the genus, occurring in the dry diagonal vegetation across central-eastern South America. Herein, we provide two new records of the species based on voucher specimens and provide new data on its reproductive biology. Our record from Brazil represents the southernmost locality of H. diaphanopterus in the Cerrado, filling a gap in the geographic distribution of the species approximately 1200 km and 950 km to the records of northeast Brazil and central Bolivia, respectively. The reproductive data suggest a polyestrous seasonal bimodal reproductive pattern in the Cerrado biome.
Estimation of additive and dominance genetic variance components for female fertility traits in Iranian Holstein cows

Abstract The aim of the current study was to estimate additive and dominance genetic variance components for days from calving to first service (DFS), a number of services to conception (NSC) and days open (DO). Data consisted of 25 518 fertility records from first parity dairy cows collected from 15 large Holstein herds of Iran. To estimate the variance components, two models, one including only additive genetic effects and another fitting both additive and dominance genetic effects together, were used. The additive and dominance relationship matrices were constructed using pedigree data. The estimated heritability for DFS, NSC and DO were 0.068, 0.035 and 0.067, respectively. The differences between estimated heritability using the additive genetic and additive-dominance genetic models were negligible regardless of the trait under study. The estimated dominance variance was larger than the estimated additive genetic variance. The ratio of dominance variance to phenotypic variance was 0.260, 0.231 and 0.196 for DFS, NSC and DO, respectively. Akaike's information criteria indicated that the model fitting both additive and dominance genetic effects is the best model for analysing DFS, NSC and DO. Spearman's rank correlations between the predicted breeding values (BV) from additive and additive-dominance models were high (0.99). Therefore, ranking of the animals based on predicted BVs was the same in both models. The results of the current study confirmed the importance of taking dominance variance into account in the genetic evaluation of dairy cows.
ON NONUNIQUENESS FOR THE ANISOTROPIC CALDERÓN PROBLEM WITH PARTIAL DATA

We show that there is nonuniqueness for the Calderón problem with partial data for Riemannian metrics with Hölder continuous coefficients in dimension greater than or equal to three. We provide simple counterexamples in the case of cylindrical Riemannian manifolds with boundary having two ends. The coefficients of these metrics are smooth in the interior of the manifold and are only Hölder continuous of order $\unicode[STIX]{x1D70C}<1$ at the end where the measurements are made. More precisely, we construct a toroidal ring $(M,g)$ and we show that there exist in the conformal class of $g$ an infinite number of Riemannian metrics $\tilde{g}=c^{4}g$ such that their corresponding partial Dirichlet-to-Neumann maps at one end coincide. The corresponding smooth conformal factors are harmonic with respect to the metric $g$ and do not satisfy the unique continuation principle.
Self-Dual Skew Cyclic Codes over Fq+uFq

E-mail: ozen@sakarya.edu. tr (M. Özen), tugbaozzaim@gmail.com (N.T. Özzaim)2 E-mail: agullive@ece.uvic. ca (T. Aaron Gulliver)3 Abstract In this paper, we give conditions for the existence of Hermitian selfdual Θ−cyclic and Θ−negacyclic codes over the finite chain ring Fq + uFq. By defining a Gray map from R = Fq + uFq to F 2 q, we prove that the Gray images of skew cyclic codes of odd length n over R with even characteristic are equivalent to skew quasi-twisted codes of length 2n over Fq of index 2. We also extend an algorithm of Boucher and Ulmer [9] to construct self-dual skew cyclic codes based on the least common left multiples of non-commutative polynomials over Fq + uFq.
Unsupervised Contextual Anomaly Detection using Joint Deep Variational Generative Models

A method for unsupervised contextual anomaly detection is proposed using a cross-linked pair of Variational Auto-Encoders for assigning a normality score to an observation. The method enables a distinct separation of contextual from behavioral attributes and is robust to the presence of anomalous or novel contextual attributes. The method can be trained with data sets that contain anomalies without any special pre-processing.
Episodic Control as Meta-Reinforcement Learning

Recent research has placed episodic reinforcement learning (RL) alongside model-free and model-based RL on the list of processes centrally involved in human reward-based learning. In the present work, we extend the unified account of model-free and model-based RL developed by Wang et al. (2018) to further integrate episodic learning. In this account, a generic model-free “meta-learner” learns to deploy and coordinate among all of these learning algorithms. The meta-learner learns through brief encounters with many novel tasks, so that it learns to learn about new tasks. We show that when equipped with an episodic memory system inspired by theories of reinstatement and gating, the meta-learner learns to use the episodic and model-based learning algorithms observed in humans in a task designed to dissociate among the influences of various learning strategies. We discuss implications and predictions of the model.
UVA: A Universal Variational Framework for Continuous Age Analysis

Conventional methods for facial age analysis tend to utilize accurate age labels in a supervised way. However, existing age datasets lies in a limited range of ages, leading to a long-tailed distribution. To alleviate the problem, this paper proposes a Universal Variational Aging (UVA) framework to formulate facial age priors in a disentangling manner. Benefiting from the variational evidence lower bound, the facial images are encoded and disentangled into an age-irrelevant distribution and an age-related distribution in the latent space. A conditional introspective adversarial learning mechanism is introduced to boost the image quality. In this way, when manipulating the age-related distribution, UVA can achieve age translation with arbitrary ages. Further, by sampling noise from the age-irrelevant distribution, we can generate photorealistic facial images with a specific age. Moreover, given an input face image, the mean value of age-related distribution can be treated as an age estimator. These indicate that UVA can efficiently and accurately estimate the age-related distribution by a disentangling manner, even if the training dataset performs a long-tailed age distribution. UVA is the first attempt to achieve facial age analysis tasks, including age translation, age generation and age estimation, in a universal framework. The qualitative and quantitative experiments demonstrate the superiority of UVA on five popular datasets, including CACD2000, Morph, UTKFace, MegaAge-Asian and FG-NET.
You Look Twice: GaterNet for Dynamic Filter Selection in CNNs

The concept of conditional computation for deep nets has been proposed previously to improve model performance by selectively using only parts of the model conditioned on the sample it is processing. In this paper, we investigate input-dependent dynamic filter selection in deep convolutional neural networks (CNNs). The problem is interesting because the idea of forcing different parts of the model to learn from different types of samples may help us acquire better filters in CNNs, improve the model generalization performance and potentially increase the interpretability of model behavior. We propose a novel yet simple framework called GaterNet, which involves a backbone and a gater network. The backbone network is a regular CNN that performs the major computation needed for making a prediction, while a global gater network is introduced to generate binary gates for selectively activating filters in the backbone network based on each input. Extensive experiments on CIFAR and ImageNet datasets show that our models consistently outperform the original models with a large margin. On CIFAR-10, our model also improves upon state-of-the-art results.
Spinor-helicity and the algebraic classification of higher-dimensional spacetimes

The spinor-helicity formalism is an essential technique of the amplitudes community. We draw on this method to construct a scheme for classifying higher-dimensional spacetimes in the style of the 4D Petrov classification and the Newman–Penrose formalism. We focus on the 5D case for concreteness. Our spinorial scheme naturally reproduces the full structure previously seen in both the CMPP and de Smet classifications, and resolves longstanding questions concerning the relationship between the two classifications.
Automorphisms of automatic shifts

In this paper we continue the study of automorphism groups of constant-length substitution shifts and also their topological factors. We show that, up to conjugacy, all roots of the identity map are letter-exchanging maps, and all other non-trivial automorphisms arise from twisted compressions of another constant-length substitution. We characterize the group of roots of the identity in both the measurable and topological setting. Finally, we show that any topological factor of a constant-length substitution shift is topologically conjugate to a constant-length substitution shift via a letter-to-letter code.
Searching for light WIMPs via their interaction with electrons

In the present work we examine the possibility of detecting light dark matter particles (WIMP) utilizing their possible interactions with the electrons. Employing reasonable theoretical models we evaluate the expected event rates in the following cases: i) For WIMPs in the meV region treating electrons as free and utilizing calorimetric detectors at low temperatures. ii) Detecting recoiling electrons with energy in the eV region ejected out of an atom in the case of WIMPs with a mass more than an order of magnitude heavier than the electron. iii) By observing atomic excitations in the range of 1 meV to 10 eV excitation energy induced by the electron spin interaction in a magnetic field.
Origin and Evolution of Long-period Comets

We develop an evolutionary model of the long-period comet (LPC) population, starting from their birthplace in a massive trans-Neptunian disk that was dispersed by migrating giant planets. Most comets that remain bound to the solar system are stored in the Oort cloud. Galactic tides and passing stars make some of these bodies evolve into observable comets in the inner solar system. Our approach models each step in a full-fledged numerical framework. Subsequent analysis consists of applying plausible fading models and computing the original orbits to compare with observations. Our results match the observed semimajor axis distribution of LPCs when Whipple’s power-law fading scheme with an exponent is adopted. The cumulative perihelion (q) distribution is well fit by a linear increase plus a weak quadratic term. Beyond q = 15 au, however, the population increases steeply, and the isotropy of LPC orbital planes breaks. We find tentative evidence from the perihelion distribution of LPCs that the returning comets are depleted in supervolatiles and become active due to water ice sublimation for q ≤ 3 au. Using an independent calibration of the population of the initial disk, our predicted LPC flux is smaller than observations suggest by a factor of ≃2. Current data only characterize comets from the outer Oort cloud (semimajor axes ≳104 au). A true boost in understanding the Oort cloud’s structure should result from future surveys when they detect LPCs with perihelia beyond 15 au. Our results provide observational predictions of what can be expected from these new data.
Tidal heating and the habitability of the TRAPPIST-1 exoplanets

Context. New estimates of the masses and radii of the seven planets orbiting the ultracool M-dwarf TRAPPIST-1 star permit improved modelling of their compositions, heating by tidal dissipation, and removal of tidal heat by solid-state convection. Aims. Here we compute the heat flux due to insolation and tidal heating for the inner four planets. Methods. We apply a Maxwell viscoelastic rheology to compute the tidal response of the planets using the volume-weighted average of the viscosities and rigidities of the metal, rock, high-pressure ice, and liquid water/ice I layers. Results. We show that TRAPPIST-1d and e can avoid entering a runaway greenhouse state. Planet e is the most likely to support a habitable environment, with Earth-like surface temperatures and possibly liquid water oceans. Planet d also avoids a runaway greenhouse, if its surface reflectance is at least as high as that of the Earth. Planets b and c, closer to the star, have heat fluxes high enough to trigger a runaway greenhouse and to support volcanism on the surfaces of their rock layers, rendering them too warm for life. Planets f, g, and h are too far from the star to experience significant tidal heating, and likely have solid ice surfaces with possible subsurface liquid water oceans.
On the inexistence of solitons in Einstein–Maxwell-scalar models

Three non-existence results are established for self-gravitating solitons in Einstein–Maxwell-scalar models, wherein the scalar field is, generically, non-minimally coupled to the Maxwell field via a scalar function . Firstly, a trivial Maxwell field is considered, which yields a consistent truncation of the full model. In this case, using a scaling (Derrick-type) argument, it is established that no stationary and axisymmetric self-gravitating scalar solitons exist, unless the scalar potential energy is somewhere negative in spacetime. This generalises previous results for the static and strictly stationary cases. Thus, rotation alone cannot support self-gravitating scalar solitons in this class of models. Secondly, constant sign couplings are considered. Generalising a previous argument by Heusler for electro-vacuum, it is established that no static self-gravitating electromagnetic-scalar solitons exist. Thus, a varying (but constant sign) electric permittivity alone cannot support static Einstein–Maxwell-scalar solitons. Finally, the second result is generalised for strictly stationary, but not necessarily static, spacetimes, using a Lichnerowicz-type argument, generalising previous results in models where the scalar and Maxwell fields are not directly coupled. The scope of validity of each of these results points out the possible paths to circumvent them, in order to obtain self-gravitating solitons in Einstein–Maxwell-scalar models.
Stochastic Proximal Gradient Methods for Non-smooth Non-Convex Regularized Problems.

In this paper, we propose and analyze stochastic proximal gradient methods for minimizing a non-convex objective that consists of a smooth non-convex loss and a non-smooth non-convex regularizer. Surprisingly, these methods are as simple as those proposed for handling convex regularizers, and enjoy the same complexities as those for solving convex regularized non-convex problems in terms of finding an approximate stationary point. Our results improve upon the-state-of-art results for solving non-smooth non-convex regularized problems in (Xu et al., 2018a; Metel and Takeda, 2019). In addition, we extend our results to stochastic proximal gradient with momentum methods such as heavy-ball and Nesterov's accelerated gradient.
Secure OTA Software Updates in Connected Vehicles: A survey

This survey highlights and discusses remote OTA software updates in the automotive sector, mainly from the security perspective. In particular, the major objective of this survey is to provide a comprehensive and structured outline of various research directions and approaches in OTA update technologies in vehicles. At first, we discuss the connected car technology and then integrate the relationship of remote OTA update features with the connected car. We also present the benefits of remote OTA updates for cars along with relevant statistics. Then, we emphasize on the security challenges and requirements of remote OTA updates along with use cases and standard road safety regulations followed in different countries. We also provide for a classification of the existing works in literature that deal with implementing different secured techniques for remote OTA updates in vehicles. We further provide an analytical discussion on the present scenario of remote OTA updates with respect to care manufacturers. Finally, we identify possible future research directions of remote OTA updates for automobiles, particularly in the area of security.
Modeling the Formation of the Family of the Dwarf Planet Haumea

The dwarf planet (136108) Haumea has an intriguing combination of unique physical properties: near-breakup spin, two regular satellites, and an unexpectedly compact family. While these properties point toward formation by a collision, there is no self-consistent and reasonably probable formation hypothesis that can connect the unusually rapid spin and low relative velocities of Haumea family members (“Haumeans”). We explore and test the proposed formation hypotheses (catastrophic collision, graze-and-merge, and satellite collision) in detail. We flexibly parameterize the properties of the collision (e.g., the collision location) and use simple models for the unique three-dimensional velocity ejection field expected from each model to generate simulated families. These are then compared to the observed Kuiper Belt objects using Bayesian parameter inference, including a mixture model that robustly allows for interlopers from the background population. After testing our methodology, we find that the best match to the observed Haumeans is an essentially isotropic ejection field with a typical velocity of 150 m s−1. The graze-and-merge formation hypothesis—in which Haumeans are shed due to excess angular momentum—is clearly disfavored because the observed Haumeans are not oriented in a plane. The satellite collision model is also disfavored. Including these new constraints, we present a detailed discussion of the formation hypotheses, including variations, some of which are tested. Some new hypotheses are proposed (a cratering collision and a collision where Haumea’s upper layers are “missing”) and scrutinized. We do not identify a satisfactory formation hypothesis, but we do propose several avenues of additional investigation. In the process of these analyses, we identify many new candidate Haumeans and dynamically confirm seven of them as consistent with the observed family. We also confirm that Haumeans have a shallow size distribution and discuss implications for the discovery and identification of new Haumeans.
Nonparametric density estimation for high‐dimensional data—Algorithms and applications

Density estimation is one of the central areas of statistics whose purpose is to estimate the probability density function underlying the observed data. It serves as a building block for many tasks in statistical inference, visualization, and machine learning. Density estimation is widely adopted in the domain of unsupervised learning especially for the application of clustering. As big data become pervasive in almost every area of data sciences, analyzing high‐dimensional data that have many features and variables appears to be a major focus in both academia and industry. High‐dimensional data pose challenges not only from the theoretical aspects of statistical inference, but also from the algorithmic/computational considerations of machine learning and data analytics. This paper reviews a collection of selected nonparametric density estimation algorithms for high‐dimensional data, some of them are recently published and provide interesting mathematical insights. The important application domain of nonparametric density estimation, such as modal clustering, is also included in this paper. Several research directions related to density estimation and high‐dimensional data analysis are suggested by the authors.