Miracle-dz/UniFAR-FTUs huggingface dataset:
RecSal : Deep Recursive Supervision for Visual Saliency Prediction

State-of-the-art saliency prediction methods develop upon model architectures or loss functions; while training to generate one target saliency map. However, publicly available saliency prediction datasets can be utilized to create more information for each stimulus than just a final aggregate saliency map. This information when utilized in a biologically inspired fashion can contribute in better prediction performance without the use of models with huge number of parameters. In this light, we propose to extract and use the statistics of (a) region specific saliency and (b) temporal order of fixations, to provide additional context to our network. We show that extra supervision using spatially or temporally sequenced fixations results in achieving better performance in saliency prediction. Further, we also design novel architectures for utilizing this extra information and show that it achieves superior performance over a base model which is devoid of extra supervision. We show that our best method outperforms previous state-of-the-art methods with 50-80% fewer parameters. We also show that our models perform consistently well across all evaluation metrics unlike prior methods.
Common misconceptions about 'transonic' screw dislocations

A number of recent Molecular Dynamics (MD) simulations have demonstrated that screw dislocations in face centered cubic (fcc) metals can achieve stable steady state motion above the lowest shear wave speed ($v_\text{shear}$) which is parallel to their direction of motion (often referred to as transonic motion). This is in direct contrast to classical continuum analyses which predict a divergence in the elastic energy of the host material at $v_\text{shear}$. Within this work, we first demonstrate through analytic analyses that the elastic energy of the host material diverges at a dislocation velocity ($v_\text{crit}$) which is greater than $v_\text{shear}$, i.e. $v_\text{crit} > v_\text{shear}$. We argue that it is this latter derived velocity ($v_\text{crit}$) which separates 'subsonic' and 'supersonic' regimes of dislocation motion in the analytic solution. In support of our analyses, we show MD simulation results of stable steady state screw dislocation motion above $v_\text{shear}$ and below $v_\text{crit}$ for several fcc materials at room temperature, which are in agreement with several earlier studies. Both our independent MD simulations, and the earlier works suggest that screw dislocation motion is always unstable above our derived $v_\text{crit}$ at room temperature. Despite these findings, in real-world polycrystalline materials $v_\text{crit}$ cannot be interpreted as a hard limit for subsonic dislocation motion. In fact, at very low temperatures we find that screw dislocations in Cu can truly overcome $v_\text{crit}$: Our MD simulations of Cu at 10 Kelvin confirm a recent claim in the literature that true 'supersonic' screw dislocations with dislocation velocities $v>v_\text{crit}$ are possible at very low temperatures.
Alternating minimization algorithms for graph regularized tensor completion

We consider a low-rank tensor completion (LRTC) problem which aims to recover a tensor from incomplete observations. LRTC plays an important role in many applications such as signal processing, computer vision, machine learning, and neuroscience. A widely used approach is to combine the tensor completion data fitting term with a regularizer based on a convex relaxation of the multilinear ranks of the tensor. For the data fitting function, we model the tensor variable by using the Canonical Polyadic (CP) decomposition and for the low-rank promoting regularization function, we consider a graph Laplacian-based function which exploits correlations between the rows of the matrix unfoldings. For solving our LRTC model, we propose an efficient alternating minimization algorithm. Furthermore, based on the Kurdyka-Łojasiewicz property, we show that the sequence generated by the proposed algorithm globally converges to a critical point of the objective function. Besides, an alternating direction method of multipliers algorithm is also developed for the LRTC model. Extensive numerical experiments on synthetic and real data indicate that the proposed algorithms are effective and efficient.
Trait Evolution on Phylogenetic Networks

Species may evolve on a reticulate network due to hybridization or other gene flow rather than on a strictly bifurcating tree, but comparative methods to deal with trait evolution on a network are lacking. We create such a method, which uses a Brownian motion model. Our method seeks to separately or jointly detect a bias in trait value coming from hybridization (β) and a burst of variation at the time of hybridization (vH) associated with the hybridization event, as well as traditional Brownian motion parameters of ancestral state (μ) and rate of evolution (σ2) of Brownian motion, as well as measurement error of the tips (SE). We test the method with extensive simulations. We also apply the model to two empirical examples, cichlid body size and Nicotiana drought tolerance, and find substantial measurement error and a hint that hybrids have greater drought tolerance in the latter case. The new methods are available in CRAN R package BMhyd.
An algorithm to locate the centers of baryon acoustic oscillations

Context. The cosmic structure formed from baryon acoustic oscillations (BAO) in the early universe is imprinted in the galaxy distribution observable in large-scale surveys and is used as a standard ruler in contemporary cosmology. Typically, BAOs are detected as a preferential length scale in two-point statistics, which gives little information about the location of the BAO structures in real space. Aims. The aim of the algorithm described in this paper is to find probable centers of BAOs in the cosmic matter distribution. Methods. The algorithm convolves the three-dimensional distribution of matter density with a spherical shell kernel of variable radius placed at different locations. The locations that correspond to the highest values of the convolution correspond to the probable centers of BAOs. This method is realized in an open-source, computationally efficient algorithm. Results. We describe the algorithm and present the results of applying it to the SDSS DR9 CMASS survey and associated mock catalogs. Conclusions. A detailed performance study demonstrates the ability of the algorithm to locate BAO centers and in doing so presents a novel detection of the BAO scale in galaxy surveys.
Instability of compact stars with a nonminimal scalar-derivative coupling

For a theory in which a scalar field ϕ has a nonminimal derivative coupling to the Einstein tensor Gμ ν of the form ϕ Gμ ν∇μ∇ν ϕ, it is known that there exists a branch of static and spherically-symmetric relativistic stars endowed with a scalar hair in their interiors. We study the stability of such hairy solutions with a radial field dependence ϕ(r) against odd- and even-parity perturbations. We show that, for the star compactness 𝒞 smaller than 1/3, they are prone to Laplacian instabilities of the even-parity perturbation associated with the scalar-field propagation along an angular direction. Even for 𝒞>1/3, the hairy star solutions are subject to ghost instabilities. We also find that even the other branch with a vanishing background field derivative is unstable for a positive perfect-fluid pressure, due to nonstandard propagation of the field perturbation δ ϕ inside the star. Thus, there are no stable star configurations in derivative coupling theory without a standard kinetic term, including both relativistic and nonrelativistic compact objects.
A3Ident: A Two-phased Approach to Identify the Leading Authors of Android Apps

Authorship identification is the process of identifying and classifying authors through given codes. Authorship identification can be used in a wide range of software domains, e.g., code authorship disputes, plagiarism detection, exposure of attackers’ identity. Besides the inherent challenges from legacy software development, framework programming and crowdsourcing mode in Android raise the difficulties of authorship identification significantly. More specifically, widespread third party libraries and inherited components (e.g., classes, methods, and variables) dilute the primary code within the entire Android app and blur the boundaries of code written by different authors. However, prior research has not well addressed these challenges.To this end, we design a two-phased approach to attribute the primary code of an Android app to the specific developer. In the first phase, we put forward three types of strategies to identify the relationships between Java packages in an app, which consist of context, semantic and structural relationships. A package aggregation algorithm is developed to cluster all packages that are of high probability written by the same authors. In the second phase, we develop three types of features to capture authors’ coding habits and code stylometry. Based on that, we generate fingerprints for an author from its developed Android apps and employ several machine learning algorithms for authorship classification. We evaluate our approach in three datasets that contain 15,666 apps from 257 distinct developers and achieve a 92.5% accuracy rate on average. Additionally, we test it on 2,900 obfuscated apps and our approach can classify apps with an accuracy rate of 80.4%.
Control of a Nature-inspired Scorpion using Reinforcement Learning

A terrestrial robot that can maneuver rough terrain and scout places is very useful in mapping out unknown areas. It can also be used explore dangerous areas in place of humans. A terrestrial robot modeled after a scorpion will be able to traverse undetected and can be used for surveillance purposes. Therefore, this paper proposes modelling of a scorpion inspired robot and a reinforcement learning (RL) based controller for navigation. The robot scorpion uses serial four bar mechanisms for the legs movements. It also has an active tail and a movable claw. The controller is trained to navigate the robot scorpion to the target waypoint. The simulation results demonstrate efficient navigation of the robot scorpion.
Image Convolution-Based Method and Statistical Validation for Experimental Heights Estimations of Laminar to Transition-to-Turbulent Regime Diffusion Flames

This paper presents an experimental methodology to measure the height of the flame using convolution image processing and statistical analysis. The experimental setup employs a 4-nozzle burner in a radial distribution. Six different volumetric fuel flows were employed, and flame images were captured from three different visualization planes utilizing a three high-definition camera array, a thermal imaging camera and a proprietary image-processing algorithm. The flame height was measured indirectly using pixel quantification and conversion through a reference length. Whilst the fuel flow was the most significant factor, both visualization plane and image source were also found to be particularly relevant, since certain flame features were only perceivable depending on the approach. The measurements were compared to different existing theoretical correlations, yielding an overall adjustment ranging from 3.25 to 3.97cm. The present methodology yields an overall statistical tolerance of 1.27 cm and an expanded uncertainty of 0.599 cm. Furthermore, thermal imaging revealed a consistent difference in the overall luminous observable flame of 2.54 cm. Fitting curves for this particular burner configuration were developed by statistical modelling. The obtained curves explain the flame height fluctuations with an average setting of 97.23%.
Native Skywatchers and the Ojibwe Giizhig Anung Masinaaigan-Ojibwe Sky Star Map

The Native Skywatchers programming addresses the loss of Ojibwe and Dakota star knowledge. The goal is to help preserve indigenous astronomy and pass it on to future generations. The star knowledge will serve as a cornerstone inspiration for native communities and at the same time be influential to Native students interested in science and math. Presented here is the Ojibwe Giizhig Anung Masinaaigan-Ojibwe Sky Star Map created by Annette Lee and collaborators as part of the Native Skywatchers Middle School Teachers Workshop held June 2012.
CenterYou: A cloud-based Approach to Simplify Android Privacy Management

With mobile applications and associated services becoming increasingly popular, concerns are being raised about private data leakages have raised. Previous solutions to this well-known set of problems have approached it from the ground up but required rewriting the operating system which is unnecessary and burdensome. In this work, a framework we proposed to overcome these issues by applying a pseudo data technique and cloud-based decision-making system to identify potential privacy leakages.
Statistical Tree-based Population Seeding for Rolling Horizon EAs in General Video Game Playing

Multiple Artificial Intelligence (AI) methods have been proposed over recent years to create controllers to play multiple video games of different nature and complexity without revealing the specific mechanics of each of these games to the AI methods. In recent years, Evolutionary Algorithms (EAs) employing rolling horizon mechanisms have achieved extraordinary results in these type of problems. However, some limitations are present in Rolling Horizon EAs making it a grand challenge of AI. These limitations include the wasteful mechanism of creating a population and evolving it over a fraction of a second to propose an action to be executed by the game agent. Another limitation is to use a scalar value (fitness value) to direct evolutionary search instead of accounting for a mechanism that informs us how a particular agent behaves during the rolling horizon simulation. In this work, we address both of these issues. We introduce the use of a statistical tree that tackles the latter limitation. Furthermore, we tackle the former limitation by employing a mechanism that allows us to seed part of the population using Monte Carlo Tree Search, a method that has dominated multiple General Video Game AI competitions. We show how the proposed novel mechanism, called Statistical Tree-based Population Seeding, achieves better results compared to vanilla Rolling Horizon EAs in a set of 20 games, including 10 stochastic and 10 deterministic games.
Wray 15-906: a candidate luminous blue variable discovered with WISE, Herschel, and SALT

We present the results of study of the Galactic candidate luminous blue variable Wray 15-906, revealed via detection of its infrared circumstellar shell (of \approx2 pc in diameter) with the Wide-field Infrared Survey Explorer and the Herschel Space Observatory. Using the stellar atmosphere code CMFGEN and the Gaia parallax, we found that Wray 15-906 is a relatively low-luminosity, log(L/Lsun)\approx5.4, star of temperature of 25\pm2 kK, with a mass-loss rate of \approx3\times10^{-5} Msun/yr, a wind velocity of 280\pm50 km/s, and a surface helium abundance of 65\pm2 per cent (by mass). In the framework of single star evolution, the obtained results suggest that Wray 15-906 is a post-red supergiant star with initial mass of \approx26\pm2 Msun and that before exploding as a supernova it could transform for a short time into a WN11h star. Our spectroscopic monitoring with the Southern African Large Telescope (SALT) does not reveal significant changes in the spectrum of Wray 15-906 during the last 8 yr, while the V-band light curve of this star over years 1999--2019 shows quasi-periodic variability with a period of \approx1700 d and an amplitude of \approx0.1 mag. We estimated the mass of the shell to be 2.9\pm0.5 Msun assuming the gas-to-dust mass ratio of 200. The presence of such a shell indicates that Wray 15-906 has suffered substantial mass loss in the recent past. We found that the open star cluster C1128-631 could be the birth place of Wray 15-906 provided that this star is a rejuvenated product of binary evolution (a blue straggler).
Small Quotient Minimal Log Discrepancies

We prove that for each positive integer $n$ there exists a positive number $\epsilon_n$ so that $n$-dimensional toric quotient singularities satisfy the ACC for mld's on the interval $(0,\epsilon_n)$. In the course of the proof, we will show a geometric Jordan property for finite automorphism groups of affine toric varieties.
Homological mirror symmetry of $\mathbb{C}P^n$ and their products via Morse homotopy

We propose a way of understanding homological mirror symmetry when a complex manifold is a smooth compact toric manifold. So far, in many example, the derived category $D^b(coh(X))$ of coherent sheaves on a toric manifold $X$ is compared with the Fukaya-Seidel category of the Milnor fiber of the corresponding Landau-Ginzburg potential. We instead consider the dual torus fibration $\pi:M \to B$ of the complement of the toric divisors in $X$, where $\bar{B}$ is the dual polytope of the toric manifold $X$. A natural formulation of homological mirror symmetry in this set-up is to define $Fuk(\bar{M})$ a variant of the Fukaya category and show the equivalence $D^b(coh(X)) \simeq D^b(Fuk(\bar{M}))$. As an intermediate step, we construct the category $Mo(P)$ of weighted Morse homotopy on $P:=\bar{B}$ as a natural generalization of the weighted Fukaya-Oh category proposed by Kontsevich-Soibelman. We then show a full subcategory $Mo_{\mathcal{E}}(P)$ of $Mo(P)$ generates $D^b(coh(X))$ for the cases $X$ is a complex projective space and their products.
VarifocalNet: An IoU-aware Dense Object Detector

Accurately ranking the vast number of candidate detections is crucial for dense object detectors to achieve high performance. Prior work uses the classification score or a combination of classification and predicted localization scores to rank candidates. However, neither option results in a reliable ranking, thus degrading detection performance. In this paper, we propose to learn an Iou-Aware Classification Score (IACS) as a joint representation of object presence confidence and localization accuracy. We show that dense object detectors can achieve a more accurate ranking of candidate detections based on the IACS. We design a new loss function, named Varifocal Loss, to train a dense object detector to predict the IACS, and propose a new star-shaped bounding box feature representation for IACS prediction and bounding box refinement. Combining these two new components and a bounding box refinement branch, we build an IoU-aware dense object detector based on the FCOS+ATSS architecture, that we call VarifocalNet or VFNet for short. Extensive experiments on MS COCO show that our VFNet consistently surpasses the strong baseline by ~2.0 AP with different backbones. Our best model VFNet-X-1200 with Res2Net-101-DCN achieves a single-model single-scale AP of 55.1 on COCO test-dev, which is state-of-the-art among various object detectors. Code is available at: https://github.com/hyz-xmaster/VarifocalNet.
Detecting Generic Music Features with Single Layer Feedforward Network using Unsupervised Hebbian Computation

In this work, the authors extract information on distinct baseline features from a popular open-source music corpus and explore new recognition techniques by applying unsupervised Hebbian learning techniques on our single-layer neural network using the same dataset. They show the detailed empirical findings to simulate how such an algorithm can help a single layer feedforward network in training for music feature learning as patterns. The unsupervised training algorithm enhances the proposed neural network to achieve an accuracy of 90.36% for successful music feature detection. For comparative analysis against similar tasks, they put their results with the likes of several previous benchmark works. They further discuss the limitations and thorough error analysis of the work. They hope to discover and gather new information about this particular classification technique and performance, also further understand future potential directions that could improve the art of computational music feature recognition.
Dynamics of an acoustically trapped sphere in beating sound waves

A focused acoustic standing wave creates a Hookean potential well for a small sphere and can levitate it stably against gravity. Exposing the trapped sphere to a second transverse traveling sound wave imposes an additional acoustical force that drives the sphere away from its mechanical equilibrium. The driving force is shaped by interference between the standing trapping wave and the traveling driving. If, furthermore, the traveling wave is detuned from the standing wave, the driving force oscillates at the difference frequency. Far from behaving like a textbook driven harmonic oscillator, however, the wave-driven harmonic oscillator instead exhibits a remarkably rich variety of dynamical behaviors arising from the spatial dependence of the driving force. These include oscillations at both harmonics and subharmonics of the driving frequency, period-doubling routes to chaos and Fibonacci cascades. This model system therefore illustrates opportunities for dynamic acoustical manipulation based on spectral control of the sound field, rather than spatial control.
Geodesic Distance on Optimally Regularized Functional Connectomes Uncovers Individual Fingerprints

BACKGROUND Functional connectomes (FCs), have been shown to provide a reproducible individual fingerprint, which has opened the possibility of personalized medicine for neuro/psychiatric disorders. Thus, developing accurate ways to compare FCs is essential to establish associations with behavior and/or cognition at the individual-level. METHODS Canonically, FCs are compared using Pearson's correlation coefficient of the entire functional connectivity profiles. Recently, it has been proposed that the use of geodesic distance is a more accurate way of comparing functional connectomes, one which reflects the underlying non-Euclidean geometry of the data. Computing geodesic distance requires FCs to be positive-definite and hence invertible matrices. As this requirement depends on the fMRI scanning length and the parcellation used, it is not always attainable and sometimes a regularization procedure is required. RESULTS In the present work, we show that regularization is not only an algebraic operation for making FCs invertible, but also that an optimal magnitude of regularization leads to systematically higher fingerprints. We also show evidence that optimal regularization is dataset-dependent, and varies as a function of condition, parcellation, scanning length, and the number of frames used to compute the FCs. DISCUSSION We demonstrate that a universally fixed regularization does not fully uncover the potential of geodesic distance on individual fingerprinting, and indeed could severely diminish it. Thus, an optimal regularization must be estimated on each dataset to uncover the most differentiable across-subject and reproducible within-subject geodesic distances between FCs. The resulting pairwise geodesic distances at the optimal regularization level constitute a very reliable quantification of differences between subjects.