Miracle-dz/UniFAR-FTUs huggingface dataset:
CAMAL: Context-Aware Multi-layer Attention framework for Lightweight Environment Invariant Visual Place Recognition.

In the last few years, Deep Convolutional Neural Networks (D-CNNs) have shown state-of-the-art (SOTA) performance for Visual Place Recognition (VPR), a pivotal component of long-term intelligent robotic vision (vision-aware localization and navigation systems). The prestigious generalization power of D-CNNs gained upon training on large scale places datasets and learned persistent image regions which are found to be robust for specific place recognition under changing conditions and camera viewpoints. However, against the computation and power intensive D-CNNs based VPR algorithms that are employed to determine the approximate location of resource-constrained mobile robots, lightweight VPR techniques are preferred. This paper presents a computation- and energy-efficient CAMAL framework that captures place-specific multi-layer convolutional attentions efficient for environment invariant-VPR. At 4x lesser power consumption, evaluating the proposed VPR framework on challenging benchmark place recognition datasets reveal better and comparable Area under Precision-Recall (AUC-PR) curves with approximately 4x improved image retrieval performance over the contemporary VPR methodologies.
AdaIN-Switchable CycleGAN for Efficient Unsupervised Low-Dose CT Denoising

Recently, deep learning approaches have been extensively studied for low-dose CT denoising thanks to its superior performance despite the fast computational time. In particular, cycleGAN has been demonstrated as a powerful unsupervised learning scheme to improve the low-dose CT image quality without requiring matched high-dose reference data. Unfortunately, one of the main limitations of the cycleGAN approach is that it requires two deep neural network generators at the training phase, although only one of them is used at the inference phase. The secondary auxiliary generator is needed to enforce the cycle-consistency, but the additional memory requirement and increases of the learnable parameters are the main huddles for cycleGAN training. To address this issue, here we propose a novel cycleGAN architecture using a single switchable generator. In particular, a single generator is implemented using adaptive instance normalization (AdaIN) layers so that the baseline generator converting a low-dose CT image to a routine-dose CT image can be switched to a generator converting high-dose to low-dose by simply changing the AdaIN code. Thanks to the shared baseline network, the additional memory requirement and weight increases are minimized, and the training can be done more stably even with small training data. Experimental results show that the proposed method outperforms the previous cycleGAN approaches while using only about half the parameters.
Inset Edges Effect and Average Distance of Trees

An added edge to a graph is called an inset edge. Predicting k inset edges which minimize the average distance of a graph is known to be NP-Hard. When k = 1 the complexity of the problem is polynomial. In this paper, we further find the single inset edge(s) of a tree with the closest change on the average distance to a given input. To do that we may require the effect of each inset edge for the set of inset edges. For this, we propose an algorithm with the time complexity between O(m) and O(m/m) and an average of less than O( m.log(m)), where m stands for the number of possible inset edges. Then it takes up to O(log(m)) to find the target inset edges for a custom change on the average distance. Using theoretical tools, the algorithm strictly avoids recalculating the distances with no changes, after adding a new edge to a tree. Then reduces the time complexity of calculating remaining distances using some matrix tools which first introduced in [8] with one additional technique. This gives us a dynamic time complexity and absolutely depends on the input tree which is proportion to the Wiener index of the input tree.
Kinetic Simulation of Nonrelativistic Perpendicular Shocks of Young Supernova Remnants. IV. Electron Heating

High-Mach-number collisionless shocks are found in planetary systems and supernova remnants (SNRs). Electrons are heated at these shocks to temperatures well above the Rankine–Hugoniot prediction. However, the processes responsible for causing the electron heating are still not well understood. We use a set of large-scale particle-in-cell simulations of nonrelativistic shocks in the high-Mach-number regime to clarify the electron heating processes. The physical behavior of these shocks is defined by ion reflection at the shock ramp. Further interactions between the reflected ions and the upstream plasma excites electrostatic Buneman and two-stream ion–ion Weibel instabilities. Electrons are heated via shock surfing acceleration, the shock potential, magnetic reconnection, stochastic Fermi scattering, and shock compression. The main contributor is the shock potential. The magnetic field lines become tangled due to the Weibel instability, which allows for parallel electron heating by the shock potential. The constrained model of electron heating predicts an ion-to-electron temperature ratio within observed values at SNR shocks and in Saturn’s bow shock.
Changes in Tree Leaf Quality and Growth Performance of Lepidopteran Larvae

Despite the apparent low nutritional value of mature tree leaves, most temperate forest macrolepidopteran larvae feed on them during the summer or fall. This may occur in part because late-feeding larvae are nutritionally best adapted to exploit the nutrients in mature leaves. To test this hypothesis, larvae of the early-feeding species Malacosoma americanum and the late-feeding species Hyalophora cecropia were reared on spring-, summer-, or fall-collected leaves of Prunus serotina. Changes in leaf nutrient quality and larval growth performance with leaf maturity were monitored. Chemical analyses confirmed that, as expected, leaf nitrogen, water, sugar, and cyanide content de- creased while lignin, cellulose, and energy content increased with leaf maturity. There was no evidence for nutritional factors that produce proximate barriers to summer feeding by M. americanum or to spring feeding by H. cecropia. Larvae of both H. cecropia and M. americanum grew more rapidly and/or attained larger larval size when fed immature leaves than when fed mature leaves of P. serotina. However, the decrease in growth rate and/or larval size with leaf maturity was much greater for M. americanum than for H. cecropia, suggesting that H. cecropia is better adapted, relative to M. amer- icanum, for feeding on mature leaves. The adaptations for using mature leaves by H. cecropia larvae were both nutritional (higher net growth efficiency and nitrogen conversion efficiency) and develop- mental (nearly complete compensation for decreased growth rate by increased duration of the larval period).
Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach. Part II: Theoretical Analysis

This paper presents a detailed theoretical analysis of the three stochastic approximation proximal gradient algorithms proposed in our companion paper [49] to set regularization parameters by marginal maximum likelihood estimation. We prove the convergence of a more general stochastic approximation scheme that includes the three algorithms of [49] as special cases. This includes asymptotic and non-asymptotic convergence results with natural and easily verifiable conditions, as well as explicit bounds on the convergence rates. Importantly, the theory is also general in that it can be applied to other intractable optimisation problems. A main novelty of the work is that the stochastic gradient estimates of our scheme are constructed from inexact proximal Markov chain Monte Carlo samplers. This allows the use of samplers that scale efficiently to large problems and for which we have precise theoretical guarantees.
Facial Expression Recognition Under Partial Occlusion from Virtual Reality Headsets based on Transfer Learning

Facial expressions of emotion are a major channel in our daily communications, and it has been subject of intense research in recent years. To automatically infer facial expressions, convolutional neural network based approaches has become widely adopted due to their proven applicability to Facial Expression Recognition (FER) task.On the other hand Virtual Reality (VR) has gained popularity as an immersive multimedia platform, where FER can provide enriched media experiences. However, recognizing facial expression while wearing a head-mounted VR headset is a challenging task due to the upper half of the face being completely occluded. In this paper we attempt to overcome these issues and focus on facial expression recognition in presence of a severe occlusion where the user is wearing a head-mounted display in a VR setting. We propose a geometric model to simulate occlusion resulting from a Samsung Gear VR headset that can be applied to existing FER datasets. Then, we adopt a transfer learning approach, starting from two pretrained networks, namely VGG and ResNet. We further fine-tune the networks on FER+ and RAF-DB datasets. Experimental results show that our approach achieves comparable results to existing methods while training on three modified benchmark datasets that adhere to realistic occlusion resulting from wearing a commodity VR headset. Code for this paper is available at: https://github.com/bita-github/MRP-FER
End-to-end Contextual Perception and Prediction with Interaction Transformer

In this paper, we tackle the problem of detecting objects in 3D and forecasting their future motion in the context of self-driving. Towards this goal, we design a novel approach that explicitly takes into account the interactions between actors. To capture their spatial-temporal dependencies, we propose a recurrent neural network with a novel Transformer [1] architecture, which we call the Interaction Transformer. Importantly, our model can be trained end-to-end, and runs in real-time. We validate our approach on two challenging real-world datasets: ATG4D [2] and nuScenes [3]. We show that our approach can outperform the state-of-the-art on both datasets. In particular, we significantly improve the social compliance between the estimated future trajectories, resulting in far fewer collisions between the predicted actors.
Orbital Distribution of Infalling Satellite Halos across Cosmic Time

The initial orbits of infalling subhalos largely determine the subsequent evolution of the subhalos and satellite galaxies therein and shed light on the assembly of their hosts. Using a large set of cosmological simulations of various resolutions, we quantify the orbital distribution of subhalos at infall time and its mass and redshift dependence in a large dynamic range. We further provide a unified and accurate model validated across cosmic time, which can serve as the initial condition for semianalytic models. We find that the infall velocity v follows a nearly universal distribution peaked near the host virial velocity Vh for any subhalo mass or redshift, while the infall orbit is most radially biased when v ∼ Vh. Moreover, subhalos that have a higher host mass or a higher sub-to-host ratio tend to move along a more radial direction with a relatively smaller angular momentum than their low host mass or low sub-to-host ratio counterparts, though they share the same normalized orbital energy. These relations are nearly independent of the redshift when using the density peak height as the proxy for host halo mass. The above trends are consistent with the scenario where the dynamical environment is relatively colder for more massive structures because their own gravity is more likely to dominate the local potentials. Based on this understanding, the more massive or isolated halos are expected to have higher velocity anisotropy.
Meta Learning MPC using Finite-Dimensional Gaussian Process Approximations

Data availability has dramatically increased in recent years, driving model-based control methods to exploit learning techniques for improving the system description, and thus control performance. Two key factors that hinder the practical applicability of learning methods in control are their high computational complexity and limited generalization capabilities to unseen conditions. Meta-learning is a powerful tool that enables efficient learning across a finite set of related tasks, easing adaptation to new unseen tasks. This paper makes use of a meta-learning approach for adaptive model predictive control, by learning a system model that leverages data from previous related tasks, while enabling fast fine-tuning to the current task during closed-loop operation. The dynamics is modeled via Gaussian process regression and, building on the Karhunen-Lo{e}ve expansion, can be approximately reformulated as a finite linear combination of kernel eigenfunctions. Using data collected over a set of tasks, the eigenfunction hyperparameters are optimized in a meta-training phase by maximizing a variational bound for the log-marginal likelihood. During meta-testing, the eigenfunctions are fixed, so that only the linear parameters are adapted to the new unseen task in an online adaptive fashion via Bayesian linear regression, providing a simple and efficient inference scheme. Simulation results are provided for autonomous racing with miniature race cars adapting to unseen road conditions.
Sparse Coding Driven Deep Decision Tree Ensembles for Nucleus Segmentation in Digital Pathology Images

Automating generalized nucleus segmentation has proven to be non-trivial and challenging in digital pathology. Most existing techniques in the field rely either on deep neural networks or on shallow learning-based cascading models. The former lacks theoretical understanding and tends to degrade performance when only limited amounts of training data are available while the latter often suffers from limitations for generalization. To address these issues, we propose sparse coding driven deep decision tree ensembles (ScD2TE), an easily trained yet powerful representation learning approach with performance highly competitive to deep neural networks in the generalized nucleus segmentation task. We explore the possibility of stacking several layers based on fast convolutional sparse coding–decision tree ensemble pairwise modules and generate a layer-wise encoder–decoder architecture with intra-decoder and inter-encoder dense connectivity patterns. Under this architecture, all the encoders share the same assumption across the different layers to represent images and interact with their decoders to give fast convergence. Compared with deep neural networks, our proposed ScD2TE does not require back-propagation computation and depends on less hyper-parameters. ScD2TE is able to achieve a fast end-to-end pixel-wise training in a layer-wise manner. We demonstrated the superiority of our segmentation method by evaluating it on the multi-disease state and multi-organ dataset where consistently higher performances were obtained for comparison against other state-of-the-art deep learning techniques and cascading methods with various connectivity patterns.
Effect of High Temperature and Shading during the Developmental Stages of Inflorescence on Flower Bud Abortion in Gymnaster savatieri Kitamura

This experiment was conducted to study the combined effect of high temperature and shading on the flower bud abortion in Gymnaster savatieri Kitamura. The temperature treatments ranged from 30 to 35•Ž for 5 h from 10 am to 3 pm, for 0, 5, 10 and 15 days and shading was provided with two layers of black cheese cloth for 0, 5, 10 and 15 days. Their effect at eight specific developmental stages of inflorescence was considered. The abortion occurred from the early stage of floret formation to the budding stage. No abortion occurred 7 to 10 days after budding. The percentage of the abortion increased with the duration of high temperature and shading. Also, the percentage of the abortion became less as the developmental stages of inflorescence advanced. The occurrence of blindness (characterized by necrotic disk and ray florets) was severe from the early stage of floret formation to the middle stage of petal formation. The percentage of the blindness increased with the duration of high temperature and shading. The entire necrosis of disk florets (characterized by necrotic disk florets and undeveloped ray florets) occurred at the late stage of petal formation and the complete stage of petal formation. The percentage was high at high temperature and shading for 15 days. The partial necrosis of disk florets (characterized by necrotic central disk florets and welldeveloped ray florets) occurred from the late and complete stages of petal formation. The percentage was higher at the complete stage of petal formation. Also, the percentage of the partial necrosis of disk florets increased with the duration of high temperature and shading. But, shading alone, without high temperature, did not induce partial necrosis of disk florets even when the duration of shading was increased.
Adversarial Knowledge Transfer from Unlabeled Data

While machine learning approaches to visual recognition offer great promise, most of the existing methods rely heavily on the availability of large quantities of labeled training data. However, in the vast majority of real-world settings, manually collecting such large labeled datasets is infeasible due to the cost of labeling data or the paucity of data in a given domain. In this paper, we present a novel Adversarial Knowledge Transfer (AKT) framework for transferring knowledge from internet-scale unlabeled data to improve the performance of a classifier on a given visual recognition task. The proposed adversarial learning framework aligns the feature space of the unlabeled source data with the labeled target data such that the target classifier can be used to predict pseudo labels on the source data. An important novel aspect of our method is that the unlabeled source data can be of different classes from those of the labeled target data, and there is no need to define a separate pretext task, unlike some existing approaches. Extensive experiments well demonstrate that models learned using our approach hold a lot of promise across a variety of visual recognition tasks on multiple standard datasets. Project page is at \texttthttps://agupt013.github. io/akt.html.
Swampland conjectures and single field inflation in modified cosmological scenarios

Swampland Conjectures have attracted quite some interest in the Cosmological Community. They have been shown to have wide ranging implications , like Constraints on Inflationary Models, Primordial Black Holes, Dark Energy to name a few. Particularly, their implications on Single Field Inflationary Models in General Relativity Based Cosmology has gathered huge attention. Swampland Conjectures in their usual form have been shown to be incompatible with these kind of Single Field Models, or have been shown to induce severe Fine Tuning in these Inflationary Models for them to be consistent with the Conjectures. In this work, we show that a Large Class of Single Field Inflationary Models can in fact bypass the problems faced by Inflationary Paradigms in GR Based Cosmology. We use the Exact Solution Approach to Inflation for the same purpose and show how String Theoretic Motivations of the Swampland Conjectures can be in perfect symphony with various Single Field Inflationary Models in Modified Cosmological Scenarios.
Process Discovery for Structured Program Synthesis

A core task in process mining is process discovery which aims to learn an accurate process model from event log data. In this paper, we propose to use (block-) structured programs directly as target process models so as to establish connections to the field of program synthesis and facilitate the translation from abstract process models to executable processes, e.g., for robotic process automation. Furthermore, we develop a novel bottom-up agglomerative approach to the discovery of such structured program process models. In comparison with the popular top-down recursive inductive miner, our proposed agglomerative miner enjoys the similar theoretical guarantee to produce sound process models (without deadlocks and other anomalies) while exhibiting some advantages like avoiding silent activities and accommodating duplicate activities. The proposed algorithm works by iteratively applying a few graph rewriting rules to the directly-follows-graph of activities. For real-world (sparse) directly-follows-graphs, the algorithm has quadratic computational complexity with respect to the number of distinct activities. To our knowledge, this is the first process discovery algorithm that is made for the purpose of program synthesis. Experiments on the BPI-Challenge 2020 dataset and the Karel programming dataset have demonstrated that our proposed algorithm can outperform the inductive miner not only according to the traditional process discovery metrics but also in terms of the effectiveness in finding out the true underlying structured program from a small number of its execution traces.
Black Magic in Deep Learning: How Human Skill Impacts Network Training

How does a user's prior experience with deep learning impact accuracy? We present an initial study based on 31 participants with different levels of experience. Their task is to perform hyperparameter optimization for a given deep learning architecture. The results show a strong positive correlation between the participant's experience and the final performance. They additionally indicate that an experienced participant finds better solutions using fewer resources on average. The data suggests furthermore that participants with no prior experience follow random strategies in their pursuit of optimal hyperparameters. Our study investigates the subjective human factor in comparisons of state of the art results and scientific reproducibility in deep learning.
Amniotic Membrane Transplantation in Ophthalmology: An Updated Perspective

Aim The aim of this paper is to provide a succinct literature review of the different clinical applications for AMT usage in an ophthalmic setting, ranging from commonly used applications to less mainstream approaches. The hope is that this review enables the reader to have a better understanding of the biological properties of amnion as well as the indications and scenarios in which AMT can be used, whilst presenting relevant evidence from within the literature which may be of interest. We also provide an update on the methods of preservation of amniotic membrane and the application methodologies. Methods Literature search. A PubMed search was performed using the search terms “amniotic membrane transplant”, “amnion AND cornea”, amnion AND ophthalmology”, “amnion AND ocular surface” and “Amnion AND eye”. A full review of the literature using the PubMed database was conducted up until 01/05/20. The articles used were written in English, with all articles accessed in full. Both review articles and original articles were used for this review. All full publications related to ophthalmology were considered.