Miracle-dz/UniFAR-FTUs huggingface dataset:
STULL: Unbiased Online Sampling for Visual Exploration of Large Spatiotemporal Data

Online sampling-supported visual analytics is increasingly important, as it allows users to explore large datasets with acceptable approximate answers at interactive rates. However, existing online spatiotemporal sampling techniques are often biased, as most researchers have primarily focused on reducing computational latency. Biased sampling approaches select data with unequal probabilities and produce results that do not match the exact data distribution, leading end users to incorrect interpretations. In this paper, we propose a novel approach to perform unbiased online sampling of large spatiotemporal data. The proposed approach ensures the same probability of selection to every point that qualifies the specifications of a user’s multidimensional query. To achieve unbiased sampling for accurate representative interactive visualizations, we design a novel data index and an associated sample retrieval plan. Our proposed sampling approach is suitable for a wide variety of visual analytics tasks, e.g., tasks that run aggregate queries of spatiotemporal data. Extensive experiments confirm the superiority of our approach over a state-of-the-art spatial online sampling technique, demonstrating that within the same computational time, data samples generated in our approach are at least 50% more accurate in representing the actual spatial distribution of the data and enable approximate visualizations to present closer visual appearances to the exact ones.
Testing the ALP-photon coupling with polarization measurements of Sagittarius A⋆

Ultra-light bosons such as axions or axion-like particles (ALPs), are promising candidates to solve the dark matter problem. A unique way to detect such ALPs is to search for the periodic oscillation feature of the position angles of linearly polarized photons emitted from the galaxy center. In this work, we use the high-resolution polarimetric measurements of the radiation near the super-massive black hole (SMBH) in the center of the Milky Way, i.e., Sagittarius A⋆ (Sgr A⋆), by a sub-array of the Event Horizon Telescope to search for the ultra-light ALPs. We derive upper limits on the ALP-photon coupling of ∼ 10-12 GeV-1 for ALP masses of ma∼ (10-19-10-18) eV, with a solitonic core + NFW dark matter density profile. Our results are stronger than that derived from the observations of SN1987A and a population of supernovae in the mass window of (10-19-10-17) eV. Improved polarimetric measurements with the full Event Horizon Telescope can further strengthen the constraints.
Fifer: Tackling Underutilization in the Serverless Era

Datacenters are witnessing a rapid surge in the adoption of serverless functions for microservices-based applications. A vast majority of these microservices typically span less than a second, have strict SLO requirements, and are chained together as per the requirements of an application. The aforementioned characteristics introduce a new set of challenges, especially in terms of container provisioning and management, as the state-of-the-art resource management frameworks, employed in serverless platforms, tend to look at microservice-based applications similar to conventional monolithic applications. Hence, these frameworks suffer from microservice-agnostic scheduling and colossal container over-provisioning, especially during workload fluctuations, thereby resulting in poor resource utilization. In this work, we quantify the above shortcomings using a variety of workloads on a multi-node cluster managed by Kubernetes and Brigade serverless framework. To address them, we propose \emph{Fifer} -- an adaptive resource management framework to efficiently manage function-chains on serverless platforms. The key idea is to make \emph{Fifer} (i) utilization conscious by efficiently bin packing jobs to fewer containers using function-aware container scaling and intelligent request batching, and (ii) at the same time, SLO-compliant by proactively spawning containers to avoid cold-starts, thus minimizing the overall response latency. Combining these benefits, \emph{Fifer} improves container utilization and cluster-wide energy consumption by 4x and 31%, respectively, without compromising on SLO's, when compared to the state-of-the-art schedulers employed by serverless platforms.
Introduction to Numerical Relativity

Numerical Relativity is a multidisciplinary field including relativity, magneto-hydrodynamics, astrophysics and computational methods, among others, with the aim of solving numerically highly-dynamical, strong-gravity scenarios where no other approximations are available. Here we describe some of the foundations of the field, starting from the covariant Einstein equations and how to write them as a well-posed system of evolution equations, discussing the different formalisms, coordinate conditions, and numerical methods commonly employed nowadays for the modeling of gravitational wave sources.
Accelerating Sparse DNN Models without Hardware-Support via Tile-Wise Sparsity

Network pruning can reduce the high computation cost of deep neural network (DNN) models. However, to maintain their accuracies, sparse models often carry randomly-distributed weights, leading to irregular computations. Consequently, sparse models cannot achieve meaningful speedup on commodity hardware (e.g., GPU) built for dense matrix computations. As such, prior works usually modify or design completely new sparsity-optimized architectures for exploiting sparsity. We propose an algorithm-software co-designed pruning method that achieves latency speedups on existing dense architectures. Our work builds upon the insight that the matrix multiplication generally breaks the large matrix into multiple smaller tiles for parallel execution. We propose a tiling-friendly “tile-wise” sparsity pattern, which maintains a regular pattern at the tile level for efficient execution but allows for irregular, arbitrary pruning at the global scale to maintain the high accuracy. We implement and evaluate the sparsity pattern on GPU tensor core, achieving a 1.95$\times$ speedup over the dense model.
LIMSI_UPV at SemEval-2020 Task 9: Recurrent Convolutional Neural Network for Code-mixed Sentiment Analysis

This paper describes the participation of LIMSI_UPV team in SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text. The proposed approach competed in SentiMix HindiEnglish subtask, that addresses the problem of predicting the sentiment of a given Hindi-English code-mixed tweet. We propose Recurrent Convolutional Neural Network that combines both the recurrent neural network and the convolutional network to better capture the semantics of the text, for code-mixed sentiment analysis. The proposed system obtained 0.69 (best run) in terms of F1 score on the given test data and achieved the 9th place (Codalab username: somban) in the SentiMix Hindi-English subtask.
Supernova Interaction with a Dense Detached Shell in SN 2001em

We carry out a comprehensive analysis of supernova SN 2001em covering a period of 19 yr since discovery. SN 2001em is the oldest supernova known to have undergone a metamorphosis from a stripped envelope to an interacting supernova. An early spectrum indicates it exploded as a Type Ib supernova. Later, the ejecta caught up with a dense circumstellar H-shell, ejected a few thousand years before the explosion, triggering interaction between the supernova ejecta and the dense shell, producing radio, X-ray, and Hα emission. We use archival data with the Very Large Array in radio bands and with Chandra, XMM-Newton, and Swift-XRT in X-ray bands, along with published Hα measurements. We combine these data with our low radio frequency observations with the Giant Metrewave Radio Telescope at two epochs covering three frequencies. While the observations missed the phase when the shock entered the dense shell, the X-rays indicate that the shock came out of the dense shell at around 1750 days. The data suggest a forward shock origin of the X-ray emission. Radio data show a spectral inversion at late epochs ( days) at around 3 GHz, which mimics the properties of the central absorbed component seen in SN 1986J. A possible explanation for this component is that the progenitor of SN 2001em was a massive binary system that had undergone a period of common-envelope evolution. The hydrogen envelope from the SN 2001em progenitor may have been lost as a result of binary interaction.
Random Batch Algorithms for Quantum Monte Carlo simulations

Random batch algorithms are constructed for quantum Monte Carlo simulations. The main objective is to alleviate the computational cost associated with the calculations of two-body interactions, including the pairwise interactions in the potential energy, and the two-body terms in the Jastrow factor. In the framework of variational Monte Carlo methods, the random batch algorithm is constructed based on the over-damped Langevin dynamics, so that updating the position of each particle in an $N$-particle system only requires $\mathcal{O}(1)$ operations, thus for each time step the computational cost for $N$ particles is reduced from $\mathcal{O}(N^2)$ to $\mathcal{O}(N)$. For diffusion Monte Carlo methods, the random batch algorithm uses an energy decomposition to avoid the computation of the total energy in the branching step. The effectiveness of the random batch method is demonstrated using a system of liquid ${}^4$He atoms interacting with a graphite surface.
Designing Narrative-Focused Role-Playing Games for Visualization Literacy in Young Children

Building on game design and education research, this paper introduces narrative-focused role-playing games as a way to promote visualization literacy in young children. Visualization literacy skills are vital in understanding the world around us and constructing meaningful visualizations, yet, how to better develop these skills at an early age remains largely overlooked and understudied. Only recently has the visualization community started to fill this gap, resulting in preliminary studies and development of educational tools for use in early education. We add to these efforts through the exploration of gamification to support learning, and identify an opportunity to apply role-playing game-based designs by leveraging the presence of narratives in data-related problems involving visualizations. We study the effects of including narrative elements on learning through a technology probe, grounded in a set of design considerations stemming from visualization, game design and education science. We create two versions of a game - one with narrative elements and one without - and evaluate our instances on 33 child participants between 11- to 13-years old using a between-subjects study design. Despite participants requiring double the amount of time to complete their game due to additional narrative elements, the inclusion of such elements were found to improve engagement without sacrificing learning; our results indicate no significant differences in development of graph-reading skills, but significant differences in engagement and overall enjoyment of the game. We report observations and qualitative feedback collected, and note areas for improvement and room for future work.
Tracking Single Particles Using Surface Plasmon Leakage Radiation Speckle

Label free tracking of small bio-particles such as proteins or viruses is of great utility in the study of biological processes, however such experiments are frequently hindered by weak signal strengths and a susceptibility to scattering impurities. To overcome these problems we here propose a novel technique leveraging the enhanced sensitivity of both interferometric detection and the strong field confinement of surface plasmons. Specifically, we show that interference between the field scattered by an analyte particle and a speckle reference field, derived from random scattering of surface plasmons propagating on a rough metal film, enables particle tracking with sub-wavelength accuracy. We present the analytic framework of our technique and verify its robustness to noise through Monte Carlo simulations.
Comments on Black Hole Entropy: A Closer Look

In a recent paper [Entropy 2020, 22(1), 17] C. Tsallis states that entropy -- as in Shannon's or Kullback-Leiber's definitions -- is inadequate to interpret black hole entropy and suggests a new non-additive functional that should take the role of entropy. Here we argue that Tsallis' motivations are unsound, caused by a misunderstanding of the concepts of additivity and extensivity. We also point out other problems in his analysis of black hole entropy.
On Communication for Distributed Babai Point Computation

We present a communication-efficient distributed protocol for computing the Babai point, an approximate nearest point for a random vector <inline-formula> <tex-math notation="LaTeX">${\mathbf{X}}\in \mathbb {R}^{n}$ </tex-math></inline-formula> in a given lattice. We show that the protocol is optimal in the sense that it minimizes the sum rate when the components of <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {X}$ </tex-math></inline-formula> are mutually independent. We then investigate the error probability, i.e. the probability that the Babai point does not coincide with the nearest lattice point, motivated by the fact that for some cases, a distributed algorithm for finding the Babai point is sufficient for finding the nearest lattice point itself. Two different probability models for <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {X}$ </tex-math></inline-formula> are considered—uniform and Gaussian. For the uniform model, in dimensions two and three, the error probability is seen to grow with the packing density, and we demonstrate that the densest lattice in dimension two presents the worst error probability. For higher dimensions, we develop probabilistic concentration bounds as well as bounds based on geometric arguments for the error probability. The probabilistic bounds lead to the conclusion that for lattices which generate suitably thin coverings of <inline-formula> <tex-math notation="LaTeX">$\mathbb {R}^{n}$ </tex-math></inline-formula> (which includes lattices that meet Rogers’ bound on the covering radius), the error probability goes to unity as <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> grows. Probabilistic and geometric bounds are also used to estimate the error probability under the uniform model for various lattices including the <inline-formula> <tex-math notation="LaTeX">$A_{n}$ </tex-math></inline-formula> family and the Leech lattice, <inline-formula> <tex-math notation="LaTeX">$\Lambda _{24}$ </tex-math></inline-formula>. On the other hand, for the Gaussian model, the error probability goes to zero as the lattice dimension tends to infinity, provided the noise variance is sufficiently small.
Learn by Observation: Imitation Learning for Drone Patrolling from Videos of A Human Navigator

We present an imitation learning method for autonomous drone patrolling based only on raw videos. Different from previous methods, we propose to let the drone learn patrolling in the air by observing and imitating how a human navigator does it on the ground. The observation process enables the automatic collection and annotation of data using inter-frame geometric consistency, resulting in less manual effort and high accuracy. Then a newly designed neural network is trained based on the annotated data to predict appropriate directions and translations for the drone to patrol in a lane-keeping manner as humans. Our method allows the drone to fly at a high altitude with a broad view and low risk. It can also detect all accessible directions at crossroads and further carry out the integration of available user instructions and autonomous patrolling control commands. Extensive experiments are conducted to demonstrate the accuracy of the proposed imitating learning process as well as the reliability of the holistic system for autonomous drone navigation. The codes, datasets as well as video demonstrations are available at https://vsislab.github. io/uavpatrol.
A Multisite, Report-Based, Centralized Infrastructure for Feedback and Monitoring of Radiology AI/ML Development and Clinical Deployment

An infrastructure for multisite, geographically-distributed creation and collection of diverse, high-quality, curated and labeled radiology image data is crucial for the successful automated development, deployment, monitoring and continuous improvement of Artificial Intelligence (AI)/Machine Learning (ML) solutions in the real world. An interactive radiology reporting approach that integrates image viewing, dictation, natural language processing (NLP) and creation of hyperlinks between image findings and the report, provides localized labels during routine interpretation. These images and labels can be captured and centralized in a cloud-based system. This method provides a practical and efficient mechanism with which to monitor algorithm performance. It also supplies feedback for iterative development and quality improvement of new and existing algorithmic models. Both feedback and monitoring are achieved without burdening the radiologist. The method addresses proposed regulatory requirements for post-marketing surveillance and external data. Comprehensive multi-site data collection assists in reducing bias. Resource requirements are greatly reduced compared to dedicated retrospective expert labeling.
AKHCRNet: Bengali Handwritten Character Recognition Using Deep Learning

I propose a state of the art deep neural architectural solution for handwritten character recognition for Bengali alphabets, compound characters as well as numerical digits that achieves state-of-the-art accuracy 96.8% in just 11 epochs. Similar work has been done before by Chatterjee, Swagato, et al. but they achieved 96.12% accuracy in about 47 epochs. The deep neural architecture used in that paper was fairly large considering the inclusion of the weights of the ResNet 50 model which is a 50 layer Residual Network. This proposed model achieves higher accuracy as compared to any previous work & in a little number of epochs. ResNet50 is a good model trained on the ImageNet dataset, but I propose an HCR network that is trained from the scratch on Bengali characters without the "Ensemble Learning" that can outperform previous architectures.
Statistically Robust, Risk-Averse Best Arm Identification in Multi-Armed Bandits

Traditional multi-armed bandit (MAB) formulations usually make certain assumptions about the underlying arms’ distributions, such as bounds on the support or their tail behaviour. Moreover, such parametric information is usually ‘baked’ into the algorithms. In this paper, we show that specialized algorithms that exploit such parametric information are prone to inconsistent learning performance when the parameter is misspecified. Our key contributions are twofold: (i) We establish fundamental performance limits of statistically robust MAB algorithms under the fixed-budget pure exploration setting, and (ii) We propose two classes of algorithms that are asymptotically near-optimal. Additionally, we consider a risk-aware criterion for best arm identification, where the objective associated with each arm is a linear combination of the mean and the conditional value at risk (CVaR). Throughout, we make a very mild ‘bounded moment’ assumption, which lets us work with both light-tailed and heavy-tailed distributions within a unified framework.
Improved Lite Audio-Visual Speech Enhancement

Numerous studies have investigated the effectiveness of audio-visual multimodal learning for speech enhancement (AVSE) tasks, seeking a solution that uses visual data as auxiliary and complementary input to reduce the noise of noisy speech signals. Recently, we proposed a lite audio-visual speech enhancement (LAVSE) algorithm for a car-driving scenario. Compared to conventional AVSE systems, LAVSE requires less online computation and to some extent solves the user privacy problem on facial data. In this study, we extend LAVSE to improve its ability to address three practical issues often encountered in implementing AVSE systems, namely, the additional cost of processing visual data, audio-visual asynchronization, and low-quality visual data. The proposed system is termed improved LAVSE (iLAVSE), which uses a convolutional recurrent neural network architecture as the core AVSE model. We evaluate iLAVSE on the Taiwan Mandarin speech with video dataset. Experimental results confirm that compared to conventional AVSE systems, iLAVSE can effectively overcome the aforementioned three practical issues and can improve enhancement performance. The results also confirm that iLAVSE is suitable for real-world scenarios, where high-quality audio-visual sensors may not always be available.
V1460 Her: a fast spinning white dwarf accreting from an evolved donor star

We present time-resolved optical and ultraviolet (UV) spectroscopy and photometry of V1460 Her, an eclipsing cataclysmic variable with a 4.99-h orbital period and an overluminous K5-type donor star. The optical spectra show emission lines from an accretion disc along with absorption lines from the donor. We use these to measure radial velocities, which, together with constraints upon the orbital inclination from photometry, imply masses of $M_1=0.869\pm 0.006\, \mathrm{M}_\odot$ and $M_2=0.295\pm 0.004\, \mathrm{M}_\odot$ for the white dwarf and the donor. The radius of the donor, $R_2=0.43\pm 0.002\, \mathrm{\it R}_\odot$, is ≈50 per cent larger than expected given its mass, while its spectral type is much earlier than the M3.5 type that would be expected from a main-sequence star with a similar mass. Hubble Space Telescope (HST) spectra show strong N v 1240-Å emission but no C iv 1550-Å emission, evidence for CNO-processed material. The donor is therefore a bloated, overluminous remnant of a thermal time-scale stage of high mass transfer and has yet to reestablish thermal equilibrium. Remarkably, the HST UV data also show a strong 30 per cent peak-to-peak, $38.9\,$s pulsation that we explain as being due to the spin of the white dwarf, potentially putting V1460 Her in a similar category to the propeller system AE Aqr in terms of its spin frequency and evolutionary path. AE Aqr also features a post-thermal time-scale mass donor, and V1460 Her may therefore be its weak magnetic field analogue since the accretion disc is still present, with the white dwarf spin-up a result of a recent high accretion rate.
An Efficient Augmented Lagrangian Method with Semismooth Newton Solver for Total Generalized Variation

Total generalization variation (TGV) is a very powerful and important regularization for various inverse problems and computer vision tasks. In this paper, we propose a semismooth Newton based augmented Lagrangian method for solving this problem. The augmented Lagrangian method (also called as method of multipliers) is widely used for lots of smooth or nonsmooth variational problems. However, its efficiency heavily depends on solving the corresponding coupled and nonlinear system together and simultaneously. With efficient primal-dual semismooth Newton methods for the challenging and highly coupled nonlinear subproblems involving total generalized variation, we develop a highly efficient and competitive augmented Lagrangian method compared with some fast first-order method. With the analysis of the metric subregularities of the corresponding functions, we give both the global convergence and local linear convergence rate for the proposed augmented Lagrangian methods.