Miracle-dz/UniFAR-FTUs huggingface dataset:
Variance matrix priors for Dirichlet process mixture models with Gaussian kernels

The Dirichlet Process Mixture Model (DPMM) is a Bayesian non-parametric approach widely used for density estimation and clustering. In this manuscript, we study the choice of prior for the variance or precision matrix when Gaussian kernels are adopted. Typically, in the relevant literature, the assessment of mixture models is done by considering observations in a space of only a handful of dimensions. Instead, we are concerned with more realistic problems of higher dimensionality, in a space of up to 20 dimensions. We observe that the choice of prior is increasingly important as the dimensionality of the problem increases. After identifying certain undesirable properties of standard priors in problems of higher dimensionality, we review and implement possible alternative priors. The most promising priors are identified, as well as other factors that affect the convergence of MCMC samplers. Our results show that the choice of prior is critical for deriving reliable posterior inferences. This manuscript offers a thorough overview and comparative investigation into possible priors, with detailed guidelines for their implementation. Although our work focuses on the use of the DPMM in clustering, it is also applicable to density estimation.
Extended Object Tracking in Curvilinear Road Coordinates for Autonomous Driving

In literature, Extended Object Tracking (EOT) algorithms developed for autonomous driving predominantly provide obstacles state estimation in cartesian coordinates in the Vehicle Reference Frame. However, in many scenarios, state representation in road-aligned curvilinear coordinates is preferred when implementing autonomous driving subsystems like cruise control, lane-keeping assist, platooning, etc. This paper proposes a Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter with an Unscented Kalman Filter (UKF) estimator that provides obstacle state estimates in curvilinear road coordinates. We employ a hybrid sensor fusion architecture between Lidar and Radar sensors to obtain rich measurement point representations for EOT. The measurement model for the UKF estimator is developed with the integration of coordinate conversion from curvilinear road coordinates to cartesian coordinates by using cubic hermit spline road model. The proposed algorithm is validated through Matlab Driving Scenario Designer simulation and experimental data collected at Monza Eni Circuit. The Experimental Dataset will be made publicly available upon the paper acceptance.
Residual Aligned: Gradient Optimization for Non-Negative Image Synthesis

In this work, we address an important problem of optical see through (OST) augmented reality: non-negative image synthesis. Most of the image generation methods fail under this condition, since they assume full control over each pixel and cannot create darker pixels by adding light. In order to solve the non-negative image generation problem in AR image synthesis, prior works have attempted to utilize optical illusion to simulate human vision but fail to preserve lightness constancy well under situations such as high dynamic range. In our paper, we instead propose a method that is able to preserve lightness constancy at a local level, thus capturing high frequency details. Compared with existing work, our method shows strong performance in image-to-image translation tasks, particularly in scenarios such as large scale images, high resolution images, and high dynamic range image transfer.
An Exploration of a New Group of Channel Symmetries

—We study a certain symmetry group associated to any given communication channel, which can informally be viewed as the set of transformations of the set of inputs that “commute” with the action of the channel. In a general setting, we show that the distribution over the inputs that maximizes the mutual information between the input and output of a given channel is a “ﬁxed point” of the action of the channel’s group. We consider as examples the groups associated with the binary symmetric channel and the binary deletion channel. We show that the group of the binary symmetric channel is extremely large (it contains a number of elements that grows faster than any exponential function of n ), and we give empirical evidence that the group of the binary deletion channel is extremely small (it contains a number of elements constant in n ). This serves as some formal justiﬁcation for why the analysis of the binary deletion channel has proved much more difﬁcult than its memoryless counterparts.
A model of virus infection with immune responses supports boosting CTL response to balance antibody response

We analyze a within-host model of virus infection with antibody and CD8 cytotoxic T lymphocyte (CTL) responses proposed by Schwartz et al. (2013). The goal of this work is to gain an overview of the stability of the biologically-relevant equilibria as a function of the model’s immune response parameters. We show that the equilibria undergo at most two forward transcritical bifurcations. The model is also explored numerically and results are applied to equine infectious anemia virus infection. In order to arrive at stability of the biologically-relevant endemic equilibrium characterized by coexistence of antibody and CTL responses, the parameters promoting CTL responses need to be boosted over parameters promoting antibody production. This result may seem counter-intuitive (in that a weaker antibody response is better) but can be understood in terms of a balance between CTL and antibody responses that is needed to permit existence of CTLs. In conclusion, an intervention such as a vaccine that is intended to control a persistent viral infection with both immune responses should moderate the antibody response to allow for stimulation of the CTL response.
Linear Time Kernel Matrix Approximation via Hyperspherical Harmonics

We propose a new technique for constructing low-rank approximations of matrices that arise in kernel methods for machine learning. Our approach pairs a novel automatically constructed analytic expansion of the underlying kernel function with a data-dependent compression step to further optimize the approximation. This procedure works in linear time and is applicable to any isotropic kernel. Moreover, our method accepts the desired error tolerance as input, in contrast to prevalent methods which accept the rank as input. Experimental results show our approach compares favorably to the commonly used Nystrom method with respect to both accuracy for a given rank and computational time for a given accuracy across a variety of kernels, dimensions, and datasets. Notably, in many of these problem settings our approach produces near-optimal low-rank approximations. We provide an efficient open-source implementation of our new technique to complement our theoretical developments and experimental results.
MUSE sneaks a peek at extreme ram-pressure stripping events -- V. Towards a complete view of the galaxy cluster A1367

We present an analysis of the kinematics and ionization conditions in a sample composed of seven star-forming galaxies undergoing ram-pressure stripping in the A1367 cluster, and the galaxy ESO137-001 in the Norma cluster. MUSE observations of two new galaxies in this sample, CGCG097-073 and CGCG097-079, are also presented. This sample is characterized by homogeneous integral field spectroscopy with MUSE and by a consistent selection based on the presence of ionised gas tails. The ratio [OI]/Hα is consistently elevated in the tails of these objects compared to what observed in unperturbed galaxy disks, an ubiquitous feature which we attribute to shocks or turbulent phenomena in the stripped gas. Compact star-forming regions are observed in only ≈ 50% of the tails, implying that specific (currently unknown) conditions are needed to trigger star formation inside the stripped gas. Focusing on the interface regions between the interstellar and intracluster medium, we observe different line ratios that we associate to different stages of the stripping process, with galaxies at an early stage of perturbation showing more prominent signatures of elevated star formation. Our analysis thus demonstrates the power of a well selected and homogeneous sample to infer general properties arising from ram-pressure stripping inside local clusters.
Real-time disease prediction with local differential privacy in Internet of Medical Things

—The rapid development in Internet of Medical Things (IoMT) boosts the opportunity for real-time health monitoring using various data types such as electroencephalography (EEG) and electrocardiography (ECG). Security issues have signiﬁcantly impeded the e-healthcare system implementation. Three important challenges for privacy preserving system need to be addressed: accurate matching, privacy enhancement without compromising security, and computation efﬁciency. It is essential to guarantee prediction accuracy since disease diagnosis is strongly related to health and life. In this paper, we propose efﬁ-cient disease prediction that guarantees security against malicious clients and honest-but-curious server using matrix encryption technique. A biomedical signal provided by the client is diagnosed such that the server does not get any information about the signal as well as the ﬁnal result of the diagnosis while the client does not learn any information about the server’s medical data. Thorough security analysis illustrates the disclosure resilience of the proposed scheme and the encryption algorithm satisﬁes local differential privacy. After result decryption performed by the client’s device, performance is not degraded to perform prediction on encrypted data. The proposed scheme is efﬁcient to implement real-time health monitoring.
Counterfactual Multi-Token Fairness in Text Classification

The counterfactual token generation has been limited to perturbing only a single token in texts that are generally short and single sentences. These tokens are often associated with one of many sensitive attributes. With limited counterfactuals generated, the goal to achieve invariant nature for machine learning classification models towards any sensitive attribute gets bounded, and the formulation of Counterfactual Fairness gets narrowed. In this paper, we overcome these limitations by solving root problems and opening bigger domains for understanding. We have curated a resource of sensitive tokens and their corresponding perturbation tokens, even extending the support beyond traditionally used sensitive attributes like Age, Gender, Race to Nationality, Disability, and Religion. The concept of Counterfactual Generation has been extended to multi-token support valid over all forms of texts and documents. We define the method of generating counterfactuals by perturbing multiple sensitive tokens as Counterfactual Multi-token Generation. The method has been conceptualized to showcase significant performance improvement over single-token methods and validated over multiple benchmark datasets. The emendation in counterfactual generation propagates in achieving improved Counterfactual Multi-token Fairness.
Impact of Parameter Sparsity on Stochastic Gradient MCMC Methods for Bayesian Deep Learning

Bayesian methods hold significant promise for improving the uncertainty quantification ability and robustness of deep neural network models. Recent research has seen the investigation of a number of approximate Bayesian inference methods for deep neural networks, building on both the variational Bayesian and Markov chain Monte Carlo (MCMC) frameworks. A fundamental issue with MCMC methods is that the improvements they enable are obtained at the expense of increased computation time and model storage costs. In this paper, we investigate the potential of sparse network structures to flexibly trade-off model storage costs and inference run time against predictive performance and uncertainty quantification ability. We use stochastic gradient MCMC methods as the core Bayesian inference method and consider a variety of approaches for selecting sparse network structures. Surprisingly, our results show that certain classes of randomly selected substructures can perform as well as substructures derived from state-of-the-art iterative pruning methods while drastically reducing model training times.
Computing the quasinormal modes and eigenfunctions for the Teukolsky equation using horizon penetrating, hyperboloidally compactified coordinates

We study the quasinormal mode eigenvalues and eigenfunctions for the Teukolsky equation in a horizon penetrating, hyperboloidally compactified coordinate system. Following earlier work by Zenginoğlu (2011 Phys. Rev. D 83 127502), we show that the quasinormal eigenfunctions (QNEs) for the Teukolsky equation are regular from the black hole horizon to future null infinity in these coordinates. We then present several example QNE solutions, and study some of their properties in the near-extremal Kerr limit.
Stein's method for steady-state diffusion approximation in Wasserstein distance

We provide a general steady-state diffusion approximation result which bounds the Wasserstein distance between the reversible measure μ of a diffusion process and the invariant measure ν of a Markov chain. Our result is obtained thanks to a generalization of a new approach to Stein’s method which may be of independent interest. As an application, we study the convergence of the invariant measure of the random walk on k-nearest neighbors graphs, providing a quantitative answer to a problem of interest to the machine learning community.
Assessing the Performance of Online Students -- New Data, New Approaches, Improved Accuracy

We consider the problem of assessing the changing performance levels of individual students as they go through online courses. This student performance (SP) modeling problem is a critical step for building adaptive online teaching systems. Specifically, we conduct a study of how to utilize various types and large amounts of student log data to train accurate machine learning (ML) models that predict the performance of future students. This study is the first to use four very large sets of student data made available recently from four distinct intelligent tutoring systems. Our results include a new ML approach that defines a new state of the art for logistic regression based SP modeling, improving over earlier methods in several ways: First, we achieve improved accuracy by introducing new features that can be easily computed from conventional question-response logs (e.g., the pattern in the student 's most recent answers). Second, we take advantage of features of the student history that go beyond question-response pairs (e.g., features such as which video segments the student watched, or skipped) as well as information about prerequisite structure in the curriculum. Third, we train multiple specialized SP models for different aspects of the curriculum (e.g., specializing in early versus later segments of the student history), then combine these specialized models to create a group prediction of the SP. Taken together, these innovations yield an average AUC score across these four datasets of 0.808 compared to the previous best logistic regression approach score of 0.767, and also outperforming state-of-the-art deep neural net approaches. Importantly, we observe consistent improvements from each of our three methodological innovations, in each dataset, suggesting that our methods are of general utility and likely to produce improvements for other online tutoring systems as well.
Fourier Representations for Black-Box Optimization over Categorical Variables

Optimization of real-world black-box functions defined over purely categorical variables is an active area of research. In particular, optimization and design of biological sequences with specific functional or structural properties have a profound impact in medicine, materials science, and biotechnology. Standalone search algorithms, such as simulated annealing (SA) and Monte Carlo tree search (MCTS), are typically used for such optimization problems. In order to improve the performance and sample efficiency of such algorithms, we propose to use existing methods in conjunction with a surrogate model for the black-box evaluations over purely categorical variables. To this end, we present two different representations, a group-theoretic Fourier expansion and an abridged one-hot encoded Boolean Fourier expansion. To learn such representations, we consider two different settings to update our surrogate model. First, we utilize an adversarial online regression setting where Fourier characters of each representation are considered as experts and their respective coefficients are updated via an exponential weight update rule each time the black box is evaluated. Second, we consider a Bayesian setting where queries are selected via Thompson sampling and the posterior is updated via a sparse Bayesian regression model (over our proposed representation) with a regularized horseshoe prior. Numerical experiments over synthetic benchmarks as well as real-world RNA sequence optimization and design problems demonstrate the representational power of the proposed methods, which achieve competitive or superior performance compared to state-of-the-art counterparts, while improving the computation cost and/or sample efficiency, substantially.
Contribution of directedness in graph spectra

In graph analyses, directed edges are often approximated to undirected ones so that the adjacency matrices may be symmetric. However, such a simpliﬁcation has not been thoroughly veriﬁed. In this study, we investigate how directedness aﬀects the graph spectra by introducing random directization, which is an opposite operation of neglecting edge directions. We analytically reveal that uniformly random directization typically conserves the relative spectral structure of the adjacency matrix in the perturbative regime. The result of random directization implies that the spectrum of the adjacency matrix can be conserved after the directedness is ignored.
Minimum-Time Quadrotor Waypoint Flight in Cluttered Environments

We tackle the problem of planning a minimum-time trajectory for a quadrotor over a sequence of specified waypoints in the presence of obstacles while exploiting the full quadrotor dynamics. This problem is crucial for autonomous search and rescue and drone racing scenarios but was, so far, unaddressed by the robotics community in its entirety due to the challenges of minimizing time in the presence of the non-convex constraints posed by collision avoidance. Early works relied on simplified dynamics or polynomial trajectory representations that did not exploit the full actuator potential of a quadrotor and, thus, did not aim at minimizing time. We address this challenging problem by using a hierarchical, sampling-based method with an incrementally more complex quadrotor model. Our method first finds paths in different topologies to guide subsequent trajectory search for a kinodynamic point-mass model. Then, it uses an asymptotically-optimal, kinodynamic sampling-based method based on a full quadrotor model on top of the point-mass solution to find a feasible trajectory with a time-optimal objective. The proposed method is shown to outperform all related baselines in cluttered environments and is further validated in real-world flights at over 60km/h in one of the world's largest motion capture systems. The source code will be released upon acceptance.
Understanding the Trustworthiness Management in the Social Internet of Things: A Survey

The next generation of the Internet of Things (IoT) facilitates the integration of the notion of social networking into smart objects (i.e., things) in a bid to establish the social network of interconnected objects. This integration has led to the evolution of a promising and emerging paradigm of Social Internet of Things (SIoT), wherein the smart objects act as social objects and intelligently impersonate the social behaviour similar to that of humans. These social objects are capable of establishing social relationships with the other objects in the network and can utilize these relationships for service discovery. Trust plays a significant role to achieve the common goal of trustworthy collaboration and cooperation among the objects and provide systems' credibility and reliability. In SIoT, an untrustworthy object can disrupt the basic functionality of a service by delivering malicious messages and adversely affect the quality and reliability of the service. In this survey, we present a holistic view of trustworthiness management for SIoT. The essence of trust in various disciplines has been discussed along with the Trust in SIoT followed by a detailed study on trust management components in SIoT. Furthermore, we analyzed and compared the trust management schemes by primarily categorizing them into four groups in terms of their strengths, limitations, trust management components employed in each of the referred trust management schemes, and the performance of these studies vis-a-vis numerous trust evaluation dimensions. Finally, we have discussed the future research directions of the emerging paradigm of SIoT, particularly for trustworthiness management in SIoT.
Interactions between a propagating detonation wave and water spray cloud in hydrogen/air mixture

Inhibition of hydrogen explosion is crucial to realize its wide applications and fine water spray is an ideal mitigant due to numerous advantages. In this work, interactions between a propagating hydrogen/air detonation wave and circular water cloud are numerically studied. Eulerian−Lagrangian method involving two-way gasdroplets coupling is applied, with a two-dimensional configuration. Different droplet (diameter, concentration) and cloud (diameter) properties are considered. Our results show that droplet size, concentration and cloud radius have significant effects on peak pressure trajectory of the detonation wave. After interacting with cloud, the detonation wave exhibits three propagation modes, including perturbed propagation, leeward re-detonation, and detonation extinction. Leeward re-detonation is analyzed from unsteady evolutions of gas and liquid droplet quantities. The refracted detonation wave inside the cloud is decoupled and propagates more slowly than the one outside the cloud. The detonation re-initiation is from a local hot spot, caused by shock focusing from upper and lower diffracted detonations. Disintegration of water droplets proceeds when the detonation wave crosses the cloud and multiphase interfacial instability is observed due to the difference in effective density of the two fluids. Furthermore, detonation extinction is observed when we consider various water cloud size. It is featured by quickly fading peak pressure trajectories when the detonation passes the cloud, and no local autoignition occurs in the shock focusing area. Evolutions of thermochemical structures from the shocked area in an extinction process are also studied. Moreover, parametric studies considering various droplet concentrations and cloud radii are performed. It is shown that the critical cloud size to quench a detonation decreases when the droplet concentration is increased. However, when the droplet concentration is beyond 0.84 kg/m, the critical cloud size is negligibly influenced.