Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloud Deployment Tradeoffs for the Analysis of Spatially-Distributed Systems of Internet-of-Things

Internet-enabled things and devices operating in the physical world are increasingly integrated in modern distributed systems, supporting functionalities that require assurances that certain critical requirements are satisfied by the overall system. We focus here on spatially-distributed Internet-of-Things systems such as smart environments, where the dynamics of spatial distribution of entities in the system is crucial to requirements satisfaction. Analysis techniques need to be in place while systems operate to ensure that requirements are fulfilled. This may be achieved by keeping a model of the system at runtime, monitoring events that lead to changes in the spatial environment, and performing analysis. This computationally-intensive runtime assurance method cannot be supported by resource-constrained devices that populate the space and must be offloaded to the cloud. However, challenges arise regarding resource allocation and cost, especially when the workload is unknown at the system's design time. As such, it may be difficult or even impossible to guarantee application service level agreements, e.g., on response times. To this end, we instantiate spatial verification processes, integrating them to the service layer of an IoT-cloud architecture based on microservices. We propose several cloud deployments for such an architecture for assurance of spatial requirements -- based on virtual machines, containers, and the recent Functions-as-a-Service paradigm. Then, we assess deployments' tradeoffs in terms of elasticity, performance and cost by using a workload scenario from a known dataset of taxis roaming in Beijing. We argue that the approach can be replicated in the design process of similar kinds of spatially distributed Internet-of-Things systems.
Statistics and properties of emission-line regions in the local volume dwarf galaxies

We used the $H\alpha$ images from a large sample of nearby late-type dwarf galaxies to investigate properties of their emission structure. The sample consists of three hundred galaxies of the irregular (Irr), Magellanic irregular (Im), blue compact dwarf (BCD), and transition (Tr) types situated within a distance of 11 Mpc. In each galaxy, we indicated: the number of compact HII-regions, the presence of bubble-like or filament-like structures, the presence of a faint diffuse emission, and a sign of the global burst. The larger luminosity of a galaxy, the greater number of compact HII-sources in it. The integral and specific star-formation rates of the dwarf increase steeply with the increase of the number of HII-regions showing the evidence of the epidemic character of star-formation process. The dwarf galaxies with emission-line bubbles, or filaments, or signs of the global star-formation burst have approximately the same hydrogen-mass-to-luminosity ratio as that of the whole sample objects. However, their mean star-formation rate is significantly higher than that of other galaxies in the sample. Emission bubble-like structures are found in the nearby dwarfs with a frequency of 1 case per 4-5 galaxies. Their linear diameters are close to those expected for supernova remnants. The mean specific SFR for the nearby late-type dwarfs is close to the Hubble parameter, $H_0 = -10.14$ dex (yr)$^{-1}$, consistent with the sluggish cosmic star-formation history of galaxies of this kind.
Cutoff Dependence and Complexity of the CFT$_2$ Ground State

We present the vacuum of a two-dimensional conformal field theory (CFT$_2$) as a network of Wilson lines in $SL(2,\mathbb{R}) \times SL(2,\mathbb{R})$ Chern-Simons theory, which is conventionally used to study gravity in three-dimensional anti-de Sitter space (AdS$_3$). The position and shape of the network encode the cutoff scale at which the ground state density operator is defined. A general argument suggests identifying the `density of complexity' of this network with the extrinsic curvature of the cutoff surface in AdS$_3$, which by the Gauss-Bonnet theorem agrees with the holographic Complexity = Volume proposal.
EMPRESS. III. Morphology, Stellar Population, and Dynamics of Extremely Metal-poor Galaxies (EMPGs): Are EMPGs Local Analogs of High-z Young Galaxies?

We present the morphology and stellar population of 27 extremely metal-poor galaxies (EMPGs) at z ∼ 0 with metallicities of 0.01–0.1 Z ⊙. We conduct multicomponent surface brightness (SB) profile fitting for the deep Subaru/Hyper Suprime-Cam i-band images of the EMPGs with the Galfit software, carefully removing the SB contributions of tails. We find that the EMPGs with a median stellar mass of log(M*/M⊙)=6.0 have a median Sérsic index of n = 1.1 and a median effective radius of r e = 200 pc, suggesting that typical EMPGs have a very compact disk. We compare the EMPGs with z ∼ 6 galaxies and local galaxies on the size–mass (r e –M *) diagram, and identify that the majority of the EMPGs have an r e –M * relation similar to z ∼ 0 star-forming galaxies rather than z ∼ 6 galaxies. Not every EMPG is a local analog of high-z young galaxies in the r e –M * relation. A spectrum of one pair of EMPG and tail, so far available, indicates that the tail is dynamically related to the EMPG with a median velocity difference of ΔV = 101 ± 32 km s−1. This moderately large ΔV cannot be explained by the dynamics of the tail, but likely by the infall on the tail. For the first time, we may identify the metal-poor star-forming system just now infalling into the tail.
YCB-M: A Multi-Camera RGB-D Dataset for Object Recognition and 6DoF Pose Estimation

While a great variety of 3D cameras have been introduced in recent years, most publicly available datasets for object recognition and pose estimation focus on one single camera. In this work, we present a dataset of 32 scenes that have been captured by 7 different 3D cameras, totaling 49,294 frames. This allows evaluating the sensitivity of pose estimation algorithms to the specifics of the used camera and the development of more robust algorithms that are more independent of the camera model. Vice versa, our dataset enables researchers to perform a quantitative comparison of the data from several different cameras and depth sensing technologies and evaluate their algorithms before selecting a camera for their specific task. The scenes in our dataset contain 20 different objects from the common benchmark YCB object and model set [1], [2]. We provide full ground truth 6DoF poses for each object, per-pixel segmentation, 2D and 3D bounding boxes and a measure of the amount of occlusion of each object. We have also performed an initial evaluation of the cameras using our dataset on a state-of-the-art object recognition and pose estimation system [3].
The effects of endotracheal intubation via McGRATH Videolaryngoscope on intraocular pressure: A randomized clinical trial -

In this study; we aimed to compare the effects of endotracheal intubation via direct laryngoscope and McGRATH videolaryngoscope (VL) on intraocular pressure. Total of 50 ASA (American Society of Anesthesiologist) Grade 1-2, Mallampati score 1 or 2, age between 18 to 65 patients planned to undergo nonophthalmic surgery were included to study. Patients were divided randomly into 2 groups as direct laryngoscopic (Macintosh)(n=25) and videolaryngoscopic (McGRATH)(n=25) intubation group. The mean arterial blood pressure (MAP) and heart rate (HR) recorded by anesthesiologist and intraocular pressure (IOP) measured by ophthalmologist with tonopen device were recorded pre-induction (basal), pre-intubation, 1th,3rd,5th minutes of intubation, respectively. Study terminated after 5th minute values taken. There were no statistically significant differences in distirubiton of sex, weight, age, height, and ASA between groups. Duration of intubation in McGRATH group was 32 ± 2 s and statistically significantly longer than Macintosh group (23.8 ± 2.9 s)(p
Robust Transmission Design for Intelligent Reflecting Surface-Aided Secure Communication Systems With Imperfect Cascaded CSI

In this paper, we investigate the design of robust and secure transmission in intelligent reflecting surface (IRS) aided wireless communication systems. In particular, a multi-antenna access point (AP) communicates with a single-antenna legitimate receiver in the presence of multiple single-antenna eavesdroppers, where the artificial noise (AN) is transmitted to enhance the security performance. Besides, we assume that the cascaded AP-IRS-user channels are imperfect due to the channel estimation error. To minimize the transmit power, the beamforming vector at the transmitter, the AN covariance matrix, and the IRS phase shifts are jointly optimized subject to the outage rate probability constraints under the statistical cascaded channel state information (CSI) error model. To handle the resulting non-convex optimization problem, we first approximate the outage rate probability constraints by using the Bernstein-type inequality. Then, we develop a suboptimal algorithm based on alternating optimization, the penalty-based and semidefinite relaxation methods. Simulation results reveal that the proposed scheme significantly reduces the transmit power compared to other benchmark schemes.
Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows

Abstract We present a new data reconstruction method with supervised machine learning techniques inspired by super resolution and inbetweening to recover high-resolution turbulent flows from grossly coarse flow data in space and time. For the present machine-learning-based data reconstruction, we use the downsampled skip-connection/multiscale model based on a convolutional neural network, incorporating the multiscale nature of fluid flows into its network structure. As an initial example, the model is applied to the two-dimensional cylinder wake at $Re_D = 100$. The reconstructed flow fields by the present method show great agreement with the reference data obtained by direct numerical simulation. Next, we apply the current model to a two-dimensional decaying homogeneous isotropic turbulence. The machine-learned model is able to track the decaying evolution from spatial and temporal coarse input data. The proposed concept is further applied to a complex turbulent channel flow over a three-dimensional domain at $Re_{\tau }=180$. The present model reconstructs high-resolved turbulent flows from very coarse input data in space, and also reproduces the temporal evolution for appropriately chosen time interval. The dependence on the number of training snapshots and duration between the first and last frames based on a temporal two-point correlation coefficient are also assessed to reveal the capability and robustness of spatio-temporal super resolution reconstruction. These results suggest that the present method can perform a range of flow reconstructions in support of computational and experimental efforts.
The Spectrum of Gravitational Waves, Their Overproduction in Quintessential Inflation and Its Influence in the Reheating Temperature

One of the most important issues in an inflationary theory as standard or quintessential inflation is the mechanism to reheat the universe after the end of the inflationary period in order to match with the Hot Big Bang universe. In quintessential inflation two mechanisms are frequently used, namely the reheating via gravitational particle production which is, as we will see, very efficient when the phase transition from the end of inflation to a kinetic regime (all the energy of the inflaton field is kinetic) is very abrupt, and the so-called instant preheating which is used for a very smooth phase transition because in that case the gravitational particle production is very inefficient. In the present work, a detailed study of these mechanisms is done, obtaining bounds for the reheating temperature and the range of the parameters involved in each reheating mechanism in order that the Gravitational Waves (GWs) produced at the beginning of kination do not disturb the Big Bang Nucleosynthesis (BBN) success.
Perturbation theory for operational quantum non-Markovianity

The definition of memory in operational approaches to quantum non-Markovianity depends on the statistical properties of different sets of outcomes related to successive measurement processes performed over the system of interest. Using projectors techniques we develop a perturbation theory that enables to expressing both joint probabilities and outcome correlations in terms of the unperturbed system density matrix propagator. This object defines the open system dynamics in absence of measurement processes. Successive series terms, which are scaled by the system-environment interaction strength, consist in a convolution structure involving system propagators weighted by higher order bath correlations. The formalism is corroborated by studying different dynamics that admit an exact description. Using the perturbative approach, unusual memory effects induced by the interplay between the system-environment interaction and measurement processes are found in finite temperature reservoirs.
Statistical Study of GOES X-Ray Quasi-periodic Pulsations in Solar Flares

Small-amplitude quasi-periodic pulsations (QPPs) detected in soft X-ray emission are commonplace in many flares. To date, the underpinning processes resulting in the QPPs are unknown. In this paper, we attempt to constrain the prevalence of stationary QPPs in the largest statistical study to date, including a study of the relationship of QPP periods to the properties of the flaring active region, flare ribbons, and coronal mass ejection (CME) affiliation. We build upon the work of Inglis et al. and use a model comparison test to search for significant power in the Fourier spectra of lightcurves of the GOES 1–8 Å channel. We analyze all X-, M- and C-class flares of the past solar cycle, a total of 5519 flares, and search for periodicity in the 6–300 s timescale range. Approximately 46% of X-class, 29% of M-class, and 7% of C-class flares show evidence of stationary QPPs, with periods that follow a log-normal distribution peaked at 20 s. The QPP periods were found to be independent of flare magnitude; however, a positive correlation was found between QPP period and flare duration. No dependence of the QPP periods on the global active region properties was identified. A positive correlation was found between QPPs and ribbon properties, including unsigned magnetic flux, ribbon area, and ribbon separation distance. We found that both flares with and without an associated CME can host QPPs. Furthermore, we demonstrate that for X- and M-class flares, decay-phase QPPs have statistically longer periods than impulsive-phase QPPs.
Continuous Deep Hierarchical Reinforcement Learning for Ground-Air Swarm Shepherding

The control and guidance of multi-robots (swarm) is a non-trivial problem due to the complexity inherent in the coupled interaction among the group. Whether the swarm is cooperative or non-cooperative, lessons can be learnt from sheepdogs herding sheep. Biomimicry of shepherding offers computational methods for swarm control with the potential to generalize and scale in different environments. However, learning to shepherd is complex due to the large search space that a machine learner is faced with. We present a deep hierarchical reinforcement learning approach for shepherding, whereby an unmanned aerial vehicle (UAV) learns to act as an aerial sheepdog to control and guide a swarm of unmanned ground vehicles (UGVs). The approach extends our previous work on machine education to decompose the search space into a hierarchically organized curriculum. Each lesson in the curriculum is learnt by a deep reinforcement learning model. The hierarchy is formed by fusing the outputs of the model. The approach is demonstrated first in a high-fidelity robotic-operating-system (ROS)-based simulation environment, then with physical UGVs and a UAV in an in-door testing facility. We investigate the ability of the method to generalize as the models move from simulation to the real-world and as the models move from one scale to another.
A New Optimized Quasihelically SymmetricStellarator

A new optimized quasihelically symmetric configuration is described that has the desir-able properties of improved energetic particle confinement, reduced turbulent transportby 3D shaping, and non-resonant divertor capabilities. The configuration presented in thispaper is explicitly optimized for quasihelical symmetry, energetic particle confinement,neoclassical confinement, and stability near the axis. Post optimization, the configurationwas evaluated for its performance with regard to energetic particle transport, idealmagnetohydrodynamic (MHD) stability at various values of plasma pressure, and iontemperature gradient instability induced turbulent transport. The effect of discrete coilson various confinement figures of merit, including energetic particle confinement, aredetermined by generating single-filament coils for the configuration. Preliminary divertoranalysis shows that coils can be created that do not interfere with expansion of thevessel volume near the regions of outgoing heat flux, thus demonstrating the possibilityof operating a non-resonant divertor.
Correlations and lags between X-ray and radio emission of Cyg X-1

Cygnus X-1 is a Galactic black-hole binary and a microquasar. Its X-ray emission originates most likely in the accretion flow, while the radio emission is from a magnetized jet. We study long-term cross-correlations between its X-ray and radio fluxes, searching for lags of the peak of the correlation. We use soft X-ray data from the All Sky Monitor on board of Rossi X-ray Timing Explorer, hard X-ray data from the Burst Alert Telescope on board of Neil Gehrels Swift, and 15 GHz radio monitoring data from the Ryle and AMI telescopes, which light curves cover all of the spectral states of Cyg X-1. Previous studies of radio/X-ray correlations in black-hole binaries concentrated on the soft X-ray/radio correlation in the hard spectral state. In contrast, our strongest correlation is between the hard X-rays and radio in the entire data at a $\lesssim 1$-day lag. This indicates the jet formation from a hard X-ray emitting corona/hot accretion flow through all states of Cyg X-1. We also find evidence for a long lag of the radio with respect to the soft X-rays in the soft state. This indicates long-term advection of the magnetic field through the accretion disc from its high-mass donor, which is similar to the case of another microquasar with a high-mass donor, Cyg X-3.
Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response

Multimedia content in social media platforms provides significant information during disaster events. The types of information shared include reports of injured or deceased people, infrastructure damage, and missing or found people, among others. Although many studies have shown the usefulness of both text and image content for disaster response purposes, the research has been mostly focused on analyzing only the text modality in the past. In this paper, we propose to use both text and image modalities of social media data to learn a joint representation using state-of-the-art deep learning techniques. Specifically, we utilize convolutional neural networks to define a multimodal deep learning architecture with a modality-agnostic shared representation. Extensive experiments on real-world disaster datasets show that the proposed multimodal architecture yields better performance than models trained using a single modality (e.g., either text or image).
Deep 3D Portrait From a Single Image

In this paper, we present a learning-based approach for recovering the 3D geometry of human head from a single portrait image. Our method is learned in an unsupervised manner without any ground-truth 3D data. We represent the head geometry with a parametric 3D face model together with a depth map for other head regions including hair and ear. A two-step geometry learning scheme is proposed to learn 3D head reconstruction from in-the-wild face images, where we first learn face shape on single images using self-reconstruction and then learn hair and ear geometry using pairs of images in a stereo-matching fashion. The second step is based on the output of the first to not only improve the accuracy but also ensure the consistency of overall head geometry. We evaluate the accuracy of our method both in 3D and with pose manipulation tasks on 2D images. We alter pose based on the recovered geometry and apply a refinement network trained with adversarial learning to ameliorate the reprojected images and translate them to the real image domain. Extensive evaluations and comparison with previous methods show that our new method can produce high-fidelity 3D head geometry and head pose manipulation results.
Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty

Over the past decade, deep learning (DL) has been successfully applied to many industrial domain-specific tasks. However, the current state-of-the-art DL software still suffers from quality issues, which raises great concern especially in the context of safety- and security-critical scenarios. Adversarial examples (AEs) represent a typical and important type of defects needed to be urgently addressed, on which a DL software makes incorrect decisions. Such defects occur through either intentional attack or physical-world noise perceived by input sensors, potentially hindering further industry deployment. The intrinsic uncertainty nature of deep learning decisions can be a fundamental reason for its incorrect behavior. Although some testing, adversarial attack and defense techniques have been recently proposed, it still lacks a systematic study to uncover the relationship between AEs and DL uncertainty. In this paper, we conduct a large-scale study towards bridging this gap. We first investigate the capability of multiple uncertainty metrics in differentiating benign examples (BEs) and AEs, which enables to characterize the uncertainty patterns of input data. Then, we identify and categorize the uncertainty patterns of BEs and AEs, and find that while BEs and AEs generated by existing methods do follow common uncertainty patterns, some other uncertainty patterns are largely missed. Based on this, we propose an automated testing technique to generate multiple types of uncommon AEs and BEs that are largely missed by existing techniques. Our further evaluation reveals that the uncommon data generated by ourmethod is hard to be defended by the existing defense techniques with the average defense success rate reduced by 35%. Our results call for attention and necessity to generate more diverse data for evaluating quality assurance solutions of DL software.