Miracle-dz/UniFAR-FTUs huggingface dataset:
Diba: A Re-configurable Stream Processor

Stream processing acceleration is driven by the continuously increasing volume and velocity of data generated on the Web and the limitations of storage, computation, and power consumption. Hardware solutions provide better performance and power consumption, but they are hindered by the high research and development costs and the long time to market. In this work, we propose our re-configurable stream processor (Diba), a complete rethinking of a previously proposed customized and flexible query processor that targets real-time stream processing. Diba uses a unidirectional dataflow not dedicated to any specific type of query (operator) on streams, allowing a straightforward placement of processing components on a general data path that facilitates query mapping. In Diba, the concepts of the distribution network and processing components are implemented as two separate entities connected using generic interfaces. This approach allows the adoption of a versatile architecture for a family of queries rather than forcing a rigid chain of processing components to implement such queries. Our experimental evaluations of representative queries from TPC-H yielded processing times of 300, 1220, and 3520 milliseconds for data streams with scale factor sizes of one, four, and ten gigabytes, respectively.
The implications of large dust masses at high redshifts: a first look at galactic evolution in the submillimetre waveband

We show that despite the large uncertainty in the absolute dust masses of galaxies, it is possible to calculate the ratio of the dust masses of galaxies at different redshifts with acceptable accuracy as long as the dust masses are estimated from submillimetre fluxes measured at the same rest-frame wavelength. If one assumes that the same fraction of the metals in a galaxy is always incorporated in dust, for which there is some observational evidence, then it is possible to use standard chemical evolution models to estimate how the mass of dust in a galaxy should change with time. Our models show that dust mass is a poor 'galactic clock', because the variation with time depends critically on whether galactic evolution is best represented by a closed-box, inflow or outflow model. Nevertheless, the high dust masses of the high-redshift objects from which submillimetre dust emission has recently been observed do place significant limits on galactic evolution/cosmology. In an no = 1 universe it is possible to reproduce the high dust masses only with the outflow models and with the high-redshift objects being the ancestors of the most massive galaxies in the local Universe. If we live in a low-density universe, it is not possible to reproduce the dust masses with our dust-evolution models, and either one or more of our assumptions must be radically wrong, or gravitational lensing must be amplifying the submillimetre fluxes, and hence the dust masses, or there must be a component of dust in galaxies which is hot at high redshift but which in nearby galaxies is so cold that its presence cannot be detected even at submillimetre wavelengths. We also show that submillimetre observations of galaxies at both high and low redshifts are beginning to put interesting limits on the processes that form dust.
A Decentralized Primal Dual Algorithm with Quasi-Newton Tracking

This paper considers the decentralized optimization problem of minimizing a finite sum of strongly convex and twice continuously differentiable functions over a fixed connected undirected network. A fully decentralized primal dual method (DPDM) is proposed. In DPDM, both primal and dual updates use second order information while Hessian approximations are constructed by simple algebraic calculations and at most matrix-vector products. Additionally, on each node in the network the local direction asymptotically approaches the centralized quasi-Newton direction. Under some mild assumptions, the proposed algorithm is showed to have global linear convergence rate for solving strongly convex decentralized optimization problems. Numerical results are also provided for demonstrating the effectiveness of this new algorithm.
Contributions à l'analyse numérique de méthodes de volumes finis, à la modélisation et au calcul en électrocardiologie.

L'etude mathematique des modeles et des methodes de calcul en electrophysiologie des tissus cardiaques constitue la principale motivation de mes travaux de recherche en mathematiques appliquees. Ces travaux ont trouve des applications en imagerie medicale et en bioingenierie grâce aux simulations numeriques que nous avons rendues possibles. Les equations d'electrocardiologie, de type reaction-diffusion degeneree, peuvent etre discretisees efficacement par des methodes de volumes finis. Ce memoire synthetise l'ensemble des resultats de mes travaux dans ces domaines, c'est a dire : analyse des equations aux derivees partielles d'electrocardiologie, experimentation et applications numeriques d'une part; introduction de nouveaux schemas et analyse numerique de methodes de volumes finis pour des problemes de diffusion anisotrope, de convection-diffusion et des systemes hyperboliques lineaires d'autre part. Ces travaux visent une meilleure comprehension scientifique des equations de l'electrophysiologie et plus generalement du fonctionnement electrique d'un tissu cardiaque ou du coeur entier.
MCTrans++: A 0-D Model for Centrifugal Mirrors

The centrifugal mirror confinement scheme incorporates supersonic rotation of a plasma into a magnetic mirror device. This concept has been shown experimentally to drastically decrease parallel losses and increase plasma stability as compared to prior axisymmetric mirrors. MCTrans++ is a 0D scoping tool which rapidly models experimental operating points in the Centrifugal Mirror Fusion Experiment (CMFX) at the University of Maryland. In the low-collisionality regime, parallel losses can be modeled analytically. A confining potential is set up that is partially ambipolar and partially centrifugal. Due to the stabilizing effects of flow-shear, the perpendicular losses can be modeled as classical. Radiation losses such as Bremsstrahlung and cyclotron emission are taken into account. A neutrals model is included, and, in some circumstances, charge-exchange losses are found to exceed all other loss mechanisms. We use the SUNDIALS ARKODE library to solve the underlying equations of this model; the resulting software is suitable for scanning large parameter spaces, and can also be used to model time-dependent phenomena such as a capacitive discharge. MCTrans++ has been used to verify results from prior centrifugal mirrors, create an experimental plan for CMFX, and find configurations for future reactor-scale fusion devices.
Clustering Social Touch Gestures for Human-Robot Interaction

Social touch provides a rich non-verbal communication channel between humans and robots. Prior work has identified a set of touch gestures for human-robot interaction and described them with natural language labels (e.g., stroking, patting). Yet, no data exists on the semantic relationships between the touch gestures in users' minds. To endow robots with touch intelligence, we investigated how people perceive the similarities of social touch labels from the literature. In an online study, 45 participants grouped 36 social touch labels based on their perceived similarities and annotated their groupings with descriptive names. We derived quantitative similarities of the gestures from these groupings and analyzed the similarities using hierarchical clustering. The analysis resulted in 9 clusters of touch gestures formed around the social, emotional, and contact characteristics of the gestures. We discuss the implications of our results for designing and evaluating touch sensing and interactions with social robots.
Unified Emulation-Simulation Training Environment for Autonomous Cyber Agents

Autonomous cyber agents may be developed by applying reinforcement and deep reinforcement learning (RL/DRL), where agents are trained in a representative environment. The training environment must simulate with high-fidelity the network Cyber Operations (CyOp) that the agent aims to explore. Given the complexity of net-work CyOps, a good simulator is difficult to achieve. This work presents a systematic solution to automatically generate a high-fidelity simulator in the Cyber Gym for Intelligent Learning (CyGIL). Through representation learning and continuous learning, CyGIL provides a unified CyOp training environment where an emulated CyGIL-E automatically generates a simulated CyGIL-S. The simulator generation is integrated with the agent training process to further reduce the required agent training time. The agent trained in CyGIL-S is transferrable directly to CyGIL-E showing full transferability to the emulated"real"network. Experimental results are presented to demonstrate the CyGIL training performance. Enabling offline RL, the CyGIL solution presents a promising direction towards sim-to-real for leveraging RL agents in real-world cyber networks.
Parallel Quantum Chemistry at the Crossroads

Quantum chemistry was a compute intensive field from the beginning. It was also an early adopter of parallel computing, and hence, has more than twenty years of experience with parallelism. However, recently parallel computing has seen dramatic changes, such as the rise of multi-core architectures, hybrid computing, and the prospect of exa-scale machines requiring 1 billion concurrent threads. It is doubtful that current approaches can address the challenges ahead. As a result, the field finds itself at a crossroads, facing the challenge to successfully identify the way forward. This chapter tells a story in two parts. First, the achievements to date are considered, offering insights learned so far. Second, we look at paradigms based on directed acyclic graphs (DAG). The computer science community is strongly advocating this paradigm, but the quantum chemistry community has no experience with this approach. Therefore recent developments in that area will be discussed and their suitability for future parallel quantum chemistry computing demands considered.
Application and issues of chaos theory in hydroscience

The common methods characterizing chaotic system are briefly introduced in this paper. The most domestic and overseas advance in this filed are summarized, it is concluded that the main applications of chaos theory in hydroscience focused on hydrologic time series analysis and nonlinear prediction model. The most problems revealed in the past studies, such as delay time, data size, noise, are analyzed and discussed in this paper, so the hypothesis of chaotic character of hydroscience problems is reasonable. But as for any finite and noisy time series, it is almost impossible to determine strictly whether the phase-space is a determinate or stochastic system because whether pure stochastic or chaos is an ideal state of mathematics. Any observation achieved through single method may not be used as evidence of chaos in hydroscience problems, can only be regarded as chaotic character. Various ways should be employed to characterize chaos of hydroscience and define whichone, chaos or stochastic is dominating. Finally, we suggest that the fractal chaos dynamical system and the mean of combining chaos with neural net provide several new methods in the future studying.
Evolved massive stars at low-metallicity. V. Mass-loss rate of red supergiant stars in the Small Magellanic Cloud

We assemble the most complete and clean red supergiant (RSG) sample (2,121 targets) so far in the Small Magellanic Cloud (SMC) with 53 different bands of data to study the MLR of RSGs. In order to match the observed spectral energy distributions (SEDs), a theoretical grid of 17,820 Oxygen-rich models (``normal'' and ``dusty'' grids are half-and-half) is created by the radiatively-driven wind model of the DUSTY code, covering a wide range of dust parameters. We select the best model for each target by calculating the minimal modified chi-square and visual inspection. The resulting MLRs from DUSTY are converted to real MLRs based on the scaling relation, for which a total MLR of $6.16\times10^{-3}$ $M_\odot$ yr$^{-1}$ is measured (corresponding to a dust-production rate of $\sim6\times10^{-6}$ $M_\odot$ yr$^{-1}$), with a typical MLR of $\sim10^{-6}$ $M_\odot$ yr$^{-1}$ for the general population of the RSGs. The complexity of mass-loss estimation based on the SED is fully discussed for the first time, indicating large uncertainties based on the photometric data (potentially up to one order of magnitude or more). The Hertzsprung-Russell and luminosity versus median absolute deviation diagrams of the sample indicate the positive relation between luminosity and MLR. Meanwhile, the luminosity versus MLR diagrams show a ``knee-like'' shape with enhanced mass-loss occurring above $\log_{10}(L/L_\odot)\approx4.6$, which may be due to the degeneracy of luminosity, pulsation, low surface gravity, convection, and other factors. We derive our MLR relation by using a third-order polynomial to fit the sample and compare our result with previous empirical MLR prescriptions. Given that our MLR prescription is based on a much larger sample than previous determinations, it provides a more accurate relation at the cool and luminous region of the H-R diagram at low-metallicity compared to previous studies.
Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation Analysis

Classic and deep learning-based generalized canonical correlation analysis (GCCA) algorithms seek low-dimensional common representations of data entities from multiple “views” (e.g., audio and image) using linear transformations and neural networks, respectively. When the views are acquired and stored at different computing agents (e.g., organizations and edge devices) and data sharing is undesired due to privacy or communication cost considerations, federated learning-based GCCA is well-motivated. In federated learning, the views are kept locally at the agents and only derived, limited information exchange with a central server is allowed. However, applying existing GCCA algorithms onto such setting may incur prohibitively high communication overhead. This work puts forth a communication-efficient federated learning framework for both linear and deep GCCA under the maximum variance (MAX-VAR) formulation. The overhead issue is addressed by aggressively compressing (via quantization) the exchanging information between the computing agents and a central controller. Our synthetic and real-data experiments shows that the proposed algorithm enjoys a substantial reduction of communication overheads with virtually no loss in accuracy and convergence speed compared to the unquantized version. Rigorous convergence analyses are also presented, which is a nontrivial effort since generic federated optimization results do not cover the special problem structure of GCCA. Our result shows that the proposed algorithms for both linear and deep GCCA converge to critical points at a sublinear rate, even under heavy quantization and stochastic approximations. In addition, in the linear MAX-VAR case, the quantized algorithm approaches a global optimum in a geometric rate under reasonable conditions.
SM/VIO: Robust Underwater State Estimation Switching Between Model-based and Visual Inertial Odometry

This paper addresses the robustness problem of visual-inertial state estimation for underwater operations. Underwater robots operating in a challenging environment are required to know their pose at all times. All vision-based localization schemes are prone to failure due to poor visibility conditions, color loss, and lack of features. The proposed approach utilizes a model of the robot's kinematics together with proprioceptive sensors to maintain the pose estimate during visual-inertial odometry (VIO) failures. Furthermore, the trajectories from successful VIO and the ones from the model-driven odometry are integrated in a coherent set that maintains a consistent pose at all times. Health-monitoring tracks the VIO process ensuring timely switches between the two estimators. Finally, loop closure is implemented on the overall trajectory. The resulting framework is a robust estimator switching between model-based and visual-inertial odometry (SM/VIO). Experimental results from numerous deployments of the Aqua2 vehicle demonstrate the robustness of our approach over coral reefs and a shipwreck.
Optical detection of magnetic resonance in electron-irradiated GaN

Four new optically detected magnetic resonances are observed in photoluminescence bands at \ensuremath{\sim} 0.85 and \ensuremath{\sim}0.93 eV, which are produced in epitaxial wurtzite films of GaN by 2.5-MeV electron irradiation. One of these reveals a resolved hyperfine interaction with a single Ga nucleus, indicating a displaced Ga atom of some kind. It is tentatively identified as a complex involving interstitial ${\mathrm{Ga}}_{\mathrm{i}}^{2+}$.
QUANTITATIVE MEASURE FOR COMMUNITY DETECTION IN WEIGHTED NETWORKS

Detecting communities in weighted networks is becoming a challenging and interesting work. In this paper, a novel quantitative measure called weighted normalized modularity density is proposed and optimized to detect communities in weighted networks. Both theoretical certifications on simple schematic examples and numerical experiments on a suit of simulated networks and real-world networks show that the proposed quantitative measure not only improves the resolution limit in optimizing weighted modularity, but also avoids the emergence of negative communities in optimizing weighted modularity density.
Dielectrophoretic integration of single- and few-layer graphenes

The dielectrophoretic integration of single- and few-layered graphenes from three distinct graphene suspensions is presented, enabling the parallel assembly of individual two-dimensional nanostructures at predefined locations. The first suspension is an aqueous solution of graphene oxide, the second is ultrasonically exfoliated pristine graphene in N-methyl-pyrrolidone (NMP), and the third is exfoliated graphene in surfactant-stabilized 1 wt % aqueous SDBS solutions. The most crucial aspect for the successful thin flake deposition is the solution quality of the exfoliated graphene. After dielectrophoresis, single-layer graphene oxide is placed between the electrodes, which, while initially insulating, recovers its electrical conductivity following thermal reduction. From the chemically unmodified graphene-NMP solutions, the directed assembly of electrically active few-layer graphene flakes is realized, with flake thicknesses in the range 8–30 nm. Liquid phase exfoliation in water-surfactant solutions yiel.
REFLECTION AND TRANSMISSION OF ACOUSTIC PLANE WAVES ON AN IMMERSED ORTHOTROPIC AND VISCOELASTIC SOLID LAYER

This paper presents a method of characterizing orthotropic and viscoelastic behavior of some composite materials. The purpose is to measure the nine coefficients of the complex stiffness matrix, by using ultrasonic waves transmitted through a plate-shaped sample immersed in water. From a study or propagation in principal planes that has been reported in a previous publication [B. Hosten et al., J. Acoust. Soc. Am. 82, 1763–1770 (1987)], this paper emphasizes heterogeneous mode conversion and propagation through a nonprincipal plane, inside the composite. A numerical method is proposed for computing reflection and transmission coefficients of plane waves for any incidence plane through an orthotropic, lossy solid layer. Numerical calculations of reflection and transmission are compared to experimental data: Amplitudes, wave vectors, and damping vectors are given for a unidirectional graphite/epoxy composite.
Investigating the Latent Structure of Executive Function in the Delis–Kaplan Executive Function System Using Cattell–Horn–Carroll Theory

Objective: To replicate a seven-factor model previously reported for the Delis–Kaplan Executive Function System (D-KEFS). Method: This study used the D-KEFS standardization sample including 1,750 non-clinical participants. Several seven-factor models previously reported for the D-KEFS were re-evaluated using confirmatory factor analysis (CFA). Previously published bi-factor models were also tested. These models were compared with a three-factor a priori model based on Cattell–Horn–Carroll (CHC) theory. Measurement invariance was examined across three age cohorts. Results: All previously reported models failed to converge when tested with CFA. None of the bi-factor models converged after large numbers of iterations, suggesting that bi-factor models are ill-suited to represent the D-KEFS scores as reported in the test manual. Although poor fit was initially observed for the three-factor CHC model, inspection of modification indices showed potential for improvement by including method effects via correlated residuals for scores derived from similar tests. The final CHC model showed good to excellent fit and strong metric measurement invariance across the three age cohorts with minor exceptions for a subset of Fluency parameters. Conclusions: CHC theory extends to the D-KEFS, supporting findings from previous studies that executive functions can be integrated into CHC theory.
Off Starburst Amacrine Cells in the Retina Trigger Looming-Evoked Fear Responses in Mice

Abstract A rapidly approaching dark object evokes an evolutionarily conserved fear response in both vertebrates and invertebrates, young to old. A looming visual stimulus mimics an approaching object and triggers a similarly robust fear response in mice, resulting in freeze and flight. However, the retinal neural pathway responsible for this innate response has not been fully understood. We first explored a variety of visual stimuli that reliably induced these innate responses, and found that a looming stimulus with 2-d acclimation consistently evoked fear responses. Because the fear responses were triggered by the looming stimulus with moving edges, but not by a screen flipping from light to dark, we targeted the starburst amacrine cells (SACs), crucial neurons for retinal motion detection. We used intraocular injection of diphtheria toxin (DT) in mutant mice expressing diphtheria toxin receptors (DTR) in SACs. The looming-evoked fear responses disappeared in half of the DT-injected mice, and the other mice still exhibited the fear responses. The optomotor responses (OMRs) were reduced or eliminated, which occurred independent of the disappearance of the fear responses. A histologic examination revealed that ON SACs were reduced in both mouse groups preserved or absent fear responses. In contrast, the number of OFF SACs was different among two groups. The OFF SACs were relatively preserved in mice exhibiting continued fear responses, whereas they were ablated in mice lacking fear response to looming stimulation. These results indicate that OFF SACs and the direction-selective pathway in the retina play a role in looming-induced fear behaviors.