Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy regularized models for logarithmic SPDEs and their numerical approximations

Understanding the properties of the stochastic phase field models is crucial to model processes in several practical applications, such as soft matters and phase separation in random environments. To describe such random evolution, this work proposes and studies two mathematical models and their numerical approximations for parabolic stochastic partial differential equation (SPDE) with a logarithmic Flory--Huggins energy potential. These multiscale models are built based on a regularized energy technique and thus avoid possible singularities of coefficients. According to the large deviation principle, we show that the limit of the proposed models with small noise naturally recovers the classical dynamics in deterministic case. Moreover, when the driving noise is multiplicative, the Stampacchia maximum principle holds which indicates the robustness of the proposed model. One of the main advantages of the proposed models is that they can admit the energy evolution law and asymptotically preserve the Stampacchia maximum bound of the original problem. To numerically capture these asymptotic behaviors, we investigate the semi-implicit discretizations for regularized logrithmic SPDEs. Several numerical results are presented to verify our theoretical findings.
MMCosine: Multi-Modal Cosine Loss Towards Balanced Audio-Visual Fine-Grained Learning

Audio-visual learning helps to comprehensively under-stand the world by fusing practical information from multiple modalities. However, recent studies show that the imbalanced optimization of uni-modal encoders in a joint-learning model is a bottleneck to enhancing the model’s performance. We further find that the up-to-date imbalance-mitigating methods fail on some audio-visual fine-grained tasks, which have a higher demand for distinguishable feature distribution. Fueled by the success of cosine loss that builds hyperspherical feature spaces and achieves lower intra-class angular variability, this paper proposes Multi-Modal Cosine loss, MMCosine. It performs a modality-wise L2 normalization to features and weights towards balanced and better multi-modal fine-grained learning. We demonstrate that our method can alleviate the imbalanced optimization from the perspective of weight norm and fully exploit the discriminability of the cosine metric. Extensive experiments prove the effectiveness of our method and the versatility with advanced multi-modal fusion strategies and up-to-date imbalance-mitigating methods. The project page is https://gewu-lab.github. io/MMCosine/.
EVOLIN Benchmark: Evaluation of Line Detection and Association

Lines are interesting geometrical features commonly seen in indoor and urban environments. There is missing a complete benchmark where one can evaluate lines from a sequential stream of images in all its stages: Line detection, Line Association and Pose error. To do so, we present a complete and exhaustive benchmark for visual lines in a SLAM front-end, both for RGB and RGBD, by providing a plethora of complementary metrics. We have also labeled data from well-known SLAM datasets in order to have all in one poses and accurately annotated lines. In particular, we have evaluated 17 line detection algorithms, 5 line associations methods and the resultant pose error for aligning a pair of frames with several combinations of detector-association. We have packaged all methods and evaluations metrics and made them publicly available on web-page33https://prime-slam.github. io/evolin/.
Natural Gradient Methods: Perspectives, Efficient-Scalable Approximations, and Analysis

Natural Gradient Descent, a second-degree optimization method motivated by the information geometry, makes use of the Fisher Information Matrix instead of the Hessian which is typically used. However, in many cases, the Fisher Information Matrix is equivalent to the Generalized Gauss-Newton Method, that both approximate the Hessian. It is an appealing method to be used as an alternative to stochastic gradient descent, potentially leading to faster convergence. However, being a second-order method makes it infeasible to be used directly in problems with a huge number of parameters and data. This is evident from the community of deep learning sticking with the stochastic gradient descent method since the beginning. In this paper, we look at the different perspectives on the natural gradient method, study the current developments on its efficient-scalable empirical approximations, and finally examine their performance with extensive experiments.
Fast estimation of symmetrical components in real time

Methods for online calculation of the phasors of symmetrical components from the complex space-phasor in three phase power systems are presented. In this way the number of numerical operations can be cut by one third. The methods combine the suppression of higher frequencies and the separation of symmetrical components. Transfer functions obtained from the developed filter algorithms for the complex input and output signals are investigated and compared. The proposed approach with a variable sampling window enables a faster estimation of the desired components under transient conditions compared to the method using a constant sampling window. < >
SSL2: self-supervised learning meets semi-supervised learning: multiple sclerosis segmentation in 7T-MRI from large-scale 3T-MRI

Automated segmentation of multiple sclerosis (MS) lesions from MRI scans is important to quantify disease progression. In recent years, convolutional neural networks (CNNs) have shown top performance for this task when a large amount of labeled data is available. However, the accuracy of CNNs suffers when dealing with few and/or sparsely labeled datasets. A potential solution is to leverage the information available in large public datasets in conjunction with a target dataset which only has limited labeled data. In this paper, we propose a training framework, SSL2 (self-supervised-semi-supervised), for multi-modality MS lesion segmentation with limited supervision. We adopt self-supervised learning to leverage the knowledge from large public 3T datasets to tackle the limitations of a small 7T target dataset. To leverage the information from unlabeled 7T data, we also evaluate state-of-the-art semi-supervised methods for other limited annotation settings, such as small labeled training size and sparse annotations. We use the shifted-window (Swin) transformer1 as our backbone network. The effectiveness of self-supervised and semi-supervised training strategies is evaluated in our in-house 7T MRI dataset. The results indicate that each strategy improves lesion segmentation for both limited training data size and for sparse labeling scenarios. The combined overall framework further improves the performance substantially compared to either of its components alone. Our proposed framework thus provides a promising solution for future data/label-hungry 7T MS studies.
Blackwell's Approachability with Time-Dependent Outcome Functions and Dot Products. Application to the Big Match

Blackwell's approachability is a very general sequential decision framework where a Decision Maker obtains vector-valued outcomes, and aims at the convergence of the average outcome to a given"target"set. Blackwell gave a sufficient condition for the decision maker having a strategy guaranteeing such a convergence against an adversarial environment, as well as what we now call the Blackwell's algorithm, which then ensures convergence. Blackwell's approachability has since been applied to numerous problems, in online learning and game theory, in particular. We extend this framework by allowing the outcome function and the dot product to be time-dependent. We establish a general guarantee for the natural extension to this framework of Blackwell's algorithm. In the case where the target set is an orthant, we present a family of time-dependent dot products which yields different convergence speeds for each coordinate of the average outcome. We apply this framework to the Big Match (one of the most important toy examples of stochastic games) where an $\epsilon$-uniformly optimal strategy for Player I is given by Blackwell's algorithm in a well-chosen auxiliary approachability problem.
The joint node degree distribution in the Erd\H{o}s-R\'enyi network

The Erd\H{o}s-R\'enyi random graph is the simplest model for node degree distribution, and it is one of the most widely studied. In this model, pairs of $n$ vertices are selected and connected uniformly at random with probability $p$, consequently, the degrees for a given vertex follow the binomial distribution. If the number of vertices is large, the binomial can be approximated by Normal using the Central Limit Theorem, which is often allowed when $\min (np, n(1-p))>5$. This is true for every node independently. However, due to the fact that the degrees of nodes in a graph are not independent, we aim in this paper to test whether the degrees of per node collectively in the Erd\H{o}s-R\'enyi graph have a multivariate normal distribution MVN. A chi square goodness of fit test for the hypothesis that binomial is a distribution for the whole set of nodes is rejected because of the dependence between degrees. Before testing MVN we show that the covariance and correlation between the degrees of any pair of nodes in the graph are $p(1-p)$ and $1/(n-1)$, respectively. We test MVN considering two assumptions: independent and dependent degrees, and we obtain our results based on the percentages of rejected statistics of chi square, the $p$-values of Anderson Darling test, and a CDF comparison. We always achieve a good fit of multivariate normal distribution with large values of $n$ and $p$, and very poor fit when $n$ or $p$ are very small. The approximation seems valid when $np \geq 10$. We also compare the maximum likelihood estimate of $p$ in MVN distribution where we assume independence and dependence. The estimators are assessed using bias, variance and mean square error.
Parallel Strong Connectivity Based on Faster Reachability

Computing strongly connected components (SCC) is among the most fundamental problems in graph analytics. Given the large size of today's real-world graphs, parallel SCC implementation is increasingly important. SCC is challenging in the parallel setting and is particularly hard on large-diameter graphs. Many existing parallel SCC implementations can be even slower than Tarjan's sequential algorithm on large-diameter graphs. To tackle this challenge, we propose an efficient parallel SCC implementation using a new parallel reachability approach. Our solution is based on a novel idea referred to as vertical granularity control (VGC). It breaks the synchronization barriers to increase parallelism and hide scheduling overhead. To use VGC in our SCC algorithm, we also design an efficient data structure called the parallel hash bag. It uses parallel dynamic resizing to avoid redundant work in maintaining frontiers (vertices processed in a round). We implement the parallel SCC algorithm by Blelloch et al. (J. ACM, 2020) using our new parallel reachability approach. We compare our implementation to the state-of-the-art systems, including GBBS, iSpan, Multi-step, and our highly optimized Tarjan's (sequential) algorithm, on 18 graphs, including social, web, k-NN, and lattice graphs. On a machine with 96 cores, our implementation is the fastest on 16 out of 18 graphs. On average (geometric means) over all graphs, our SCC is 6.0× faster than the best previous parallel code (GBBS), 12.8× faster than Tarjan's sequential algorithms, and 2.7× faster than the best existing implementation on each graph. We believe that our techniques are of independent interest. We also apply our parallel hash bag and VGC scheme to other graph problems, including connectivity and least-element lists (LE-lists). Our implementations improve the performance of the state-of-the-art parallel implementations for these two problems.
Thermodynamic, dynamic and structural properties of liquid and supercritical matter

Among three basic states of matter (solid, liquid, gas), liquids are least understood from the theoretical point of view. The perceived difficulty is that interactions in a liquid are both strong and system-specific, implying that the energy strongly depends on the liquid type and that, therefore, liquid energy can not be calculated in general form. In this thesis, a phonon theory of liquids is developed where this problem is avoided. Central to the thesis is a development an alternative to calculating liquid energy and heat capacity. The proposed phonon theory of liquids covers both classical and quantum regimes and accounts for the contribution of anharmonicity and thermal expansion to liquid energy and heat capacity. Within the framework of the phonon theory of liquids a good agreement of calculated and experimental heat capacity of liquids, including helium, noble, metallic, molecular and hydrogen-bonded network liquids in a wide range of temperature and pressure is demonstrated. It was also found that in the very wide pressure range 5 MPa-500 MPa liquid helium near melting temperature is both solid-like and quantum. The thermodynamic properties of the supercritical state are studied, which lead to discovery that specific heat shows a crossover between two different dynamic regimes of the low-temperature rigid liquid and high-temperature non-rigid supercritical fluid. A theory of heat capacity above the crossover is formulated, and good agreement between calculated and experimental data for rare-gas supercritical liquids is obtained. The relationship between power exponents of heat capacity and viscosity in the supercritical region is derived. The thermodynamic properties are explained by the temperature behaviour of the maximal length of the longitudinal phonons that can exist in the supercritical system and that is not sensitive to system details. We also introduce a new idea that enables a unified description of all three states of matter. A generic form of an interacting phonon Hamiltonian with ground state configurations minimising the potential is introduced. Symmetry breaking leads to emergence of energy gaps of shear excitations as a consequence of the Goldstone theorem, and readily results in the emergence of energy spectra of solid, liquid and gas phases.
The Bystander Affect Detection (BAD) Dataset for Failure Detection in HRI

For a robot to repair its own error, it must first know it has made a mistake. One way that people detect errors is from the implicit reactions from bystanders - their confusion, smirks, or giggles clue us in that something unexpected occurred. To enable robots to detect and act on bystander responses to task failures, we developed a novel method to elicit bystander responses to human and robot errors. Using 46 different stimulus videos featuring a variety of human and machine task failures, we collected a total of 2,452 webcam videos of human reactions from 54 participants. To test the viability of the collected data, we used the bystander reaction dataset as input to a deep-learning model, BADNet, to predict failure occurrence. We tested different data labeling methods and learned how they affect model performance, achieving precisions above 90%. We discuss strategies (manual labelling, failure-vs-control, and failure-time) used to model bystander reactions and predict failure, and how this approach can be used in real-world robotic deployments to detect errors and improve robot performance. As part of this work, we also contribute with the “Bystander Affect Detection” (BAD) dataset of bystander reactions, supporting the development of better prediction models.
High Fidelity Synthetic Face Generation for Rosacea Skin Condition from Limited Data

Similarly to the majority of deep learning applications, diagnosing skin diseases using computer vision and deep learning often requires a large volume of data. However, obtaining sufficient data for particular types of facial skin conditions can be difficult, due to privacy concerns. As a result, conditions like rosacea are often understudied in computer-aided diagnosis. The limited availability of data for facial skin conditions has led to the investigation of alternative methods of computer-aided diagnosis. In recent years, generative adversarial networks (GANs), mainly variants of StyleGANs, have demonstrated promising results in generating synthetic facial images. In this study, for the first time, a small dataset of rosacea with 300 full-face images was utilized to further investigate the possibility of generating synthetic data. Our experimentation demonstrated that the strength of R1 regularization is crucial for generating high-fidelity rosacea images using a few hundred images. This was complemented by various experimental settings to ensure model convergence. We successfully generated 300 high-quality synthetic images, significantly contributing to the limited pool of rosacea images for computer-aided diagnosis. Additionally, our qualitative evaluations by 3 expert dermatologists and 23 non-specialists highlighted the realistic portrayal of rosacea features in the synthetic images. We also provide a critical analysis of the quantitative evaluations and discuss the limitations of solely relying on validation metrics in the field of computer-aided clinical image diagnosis.
Frits Zernike, 1888-1966

Frits Zernike was born in Amsterdam on 16 July 1888, both his parents being school-teachers. He was initially trained at Amsterdam University as a chemist and at the age of 24 was awarded a University prize for work on the theory of critical opalescence in gases. Two years later, in 1914, he was responsible jointly with Örnstein for the derivation of the Örnstein-Zernike relation in critical-point theory. His doctorate soon followed, awarded for a theoretical dissertation on critical opalescence and the derivation of Avogadro’s number therefrom. His first appointment was as assistant at the University of Gronigen to the astronomer Kapteyn but he soon moved over as a junior theoretician to the physics department where in due course he became Professor of Theoretical Physics. Zernike possessed that very rare gift of being at one and the same time a fine theoretician and a first-rate experimentalist. His early appointment at Groningen was at first a little clouded by the fact that his chief, treating him purely as a theoretical physicist, gave him scant laboratory facilities, but a change in Directorate soon rectified this.
You Only Crash Once: Improved Object Detection for Real-Time, Sim-to-Real Hazardous Terrain Detection and Classification for Autonomous Planetary Landings

The detection of hazardous terrain during the planetary landing of spacecraft plays a critical role in assuring vehicle safety and mission success. A cheap and effective way of detecting hazardous terrain is through the use of visual cameras, which ensure operational ability from atmospheric entry through touchdown. Plagued by resource constraints and limited computational power, traditional techniques for visual hazardous terrain detection focus on template matching and registration to pre-built hazard maps. Although successful on previous missions, this approach is restricted to the specificity of the templates and limited by the fidelity of the underlying hazard map, which both require extensive pre-flight cost and effort to obtain and develop. Terrestrial systems that perform a similar task in applications such as autonomous driving utilize state-of-the-art deep learning techniques to successfully localize and classify navigation hazards. Advancements in spacecraft co-processors aimed at accelerating deep learning inference enable the application of these methods in space for the first time. In this work, we introduce You Only Crash Once (YOCO), a deep learning-based visual hazardous terrain detection and classification technique for autonomous spacecraft planetary landings. Through the use of unsupervised domain adaptation we tailor YOCO for training by simulation, removing the need for real-world annotated data and expensive mission surveying phases. We further improve the transfer of representative terrain knowledge between simulation and the real world through visual similarity clustering. We demonstrate the utility of YOCO through a series of terrestrial and extraterrestrial simulation-to-real experiments and show substantial improvements toward the ability to both detect and accurately classify instances of planetary terrain.
Knowledge-augmented Few-shot Visual Relation Detection

Visual Relation Detection (VRD) aims to detect relationships between objects for image understanding. Most existing VRD methods rely on thousands of training samples of each relationship to achieve satisfactory performance. Some recent papers tackle this problem by few-shot learning with elaborately designed pipelines and pre-trained word vectors. However, the performance of existing few-shot VRD models is severely hampered by the poor generalization capability, as they struggle to handle the vast semantic diversity of visual relationships. Nonetheless, humans have the ability to learn new relationships with just few examples based on their knowledge. Inspired by this, we devise a knowledge-augmented, few-shot VRD framework leveraging both textual knowledge and visual relation knowledge to improve the generalization ability of few-shot VRD. The textual knowledge and visual relation knowledge are acquired from a pre-trained language model and an automatically constructed visual relation knowledge graph, respectively. We extensively validate the effectiveness of our framework. Experiments conducted on three benchmarks from the commonly used Visual Genome dataset show that our performance surpasses existing state-of-the-art models with a large improvement.
Iteratively comparing gravitational-wave observations to the evolution of massive stellar binaries

Gravitational-wave observations have the capability to strongly differentiate between different assumptions for how binary compact objects form. In this work, we show how to carefully interpolate a marginal likelihood between choices of binary evolution model parameters. Using the StarTrack binary evolution code, we compare one- and four-dimensional binary evolution models to the compact binary mergers reported in recent gravitational-wave observing runs. We first consider a one-dimensional model, studying the effect of supernova kick velocity (drawn from a Maxwellian with dispersion {\sigma}_eff) on the simulated population of compact binary mergers, and find support for substantial SN recoil kicks. We follow this up with a four-dimensional study of {\sigma}_eff, mass transfer efficiency (fa) and the efficiency of angular momentum depletion from ejected material (\b{eta}) during Roche-lobe accretion, and an observation-driven reduction in the mass-loss rate estimated from stellar wind models (fwind1). We find that three of them ({\sigma}_eff, fa, and fwind1) can be efficiently limited by these observational comparisons. After initially sampling from a uniform prior in the space of these parameters, we refined our sampling by iteratively estimating a Bayesian likelihood for each simulation and fitting that likelihood to a parametric model (a truncated Gaussian) in order to propose new. Our maximum likelihood simulation (K0559) has parameters: {\sigma}_eff = 108.3 km/s (indicating substantial SN recoil kicks), fa = 0.922 (indicating efficient mass transfer), and fwind1 = 0.328 (indicating support for reduced wind-driven mass loss). Note that our estimates are only valid within one particular model of compact binary formation through isolated binary evolution and do not yet take into account the impact of other uncertain pieces of stellar physics and binary evolution.