Miracle-dz/UniFAR-FTUs huggingface dataset:
ION ACCELERATION IN NON-RELATIVISTIC ASTROPHYSICAL SHOCKS

We explore the physics of shock evolution and particle acceleration in non-relativistic collisionless shocks using hybrid simulations. We analyze a wide range of physical parameters relevant to the acceleration of cosmic rays (CRs) in astrophysical shock scenarios. We show that there are fundamental differences between high and low Mach number shocks in terms of the electromagnetic turbulence generated in the pre-shock zone; dominant modes are resonant with the streaming CRs in the low Mach number regime, while both resonant and non-resonant modes are present for high Mach numbers. Energetic power-law tails for ions in the downstream plasma account for up to 15% of the incoming upstream flow energy, distributed over ∼5% of the particles in a power law with slope −2 ± 0.2 in energy. Quasi-parallel shocks with θ ⩽ 45° are good ion accelerators, while power laws are greatly suppressed for quasi-perpendicular shocks, θ > 45°. The efficiency of conversion of flow energy into the energy of accelerated particles peaks at θ = 15°–30° and MA = 6, and decreases for higher Mach numbers, down to ∼2% for MA = 31. Accelerated particles are produced by diffusive shock acceleration (DSA) and by shock drift acceleration (SDA) mechanisms, with the SDA contribution to the overall energy gain increasing with magnetic inclination. We also present a direct comparison between hybrid and fully kinetic particle-in-cell results at early times. In supernova remnant (SNR) shocks, particle acceleration will be significant for low Mach number quasi-parallel flows (MA < 30, θ < 45). This finding underscores the need for an effective magnetic amplification mechanism in SNR shocks.
UAV Energy Awareness based on Network Communication Optimization and Power Efficient Trajectories

The purpose of energy-efficient Coverage Path Planning (CPP) methods is to minimize energy consumption using multiple Unmanned Aerial Vehicles (UAVs) of the coverage area. In multiple UAVs systems, the network configuration plays a crucial role in the network’s survivability and mission execution. However, the network’s survivability and stability depend on the network’s resources optimization. This paper presents a review of single or multiple UAV energy-efficient CPP methods. Furthermore, we discuss the network configurations of multiple UAVs systems. Likewise, we aim to present networks’ energy optimization approaches and directions for future research.
Measurement of the Modulation Bandwidth of High-Power Chip-on-Board LEDs for VLC systems

During the last decade, the LED technology has developed lamps up to 90% more efficient in terms of energy and brightness in comparison with the use of halogen technology. Indeed, the manufacture of halogen lamps are currently banned in the European Union and it is forecasted that most of the halogen lamps will leave no trace before 2024. In this context, visible light communications (VLC) have been proposed to exploit the LED technology for providing both illumination and data transmission. VLC is particularly attractive for industrial environments where RF transmission is not efficient such as tunnel construction, mining, or oil&gas plants. However, the use of commercial LEDs is required to scale the implementation of the VLC technology so that the current deployment of LEDs can be exploited for providing connectivity. In this work, the use of high-power chip-on-board (CoB) LEDs typically deployed in industrial environments is analyzed. To do that, a VLC communication link is implemented using a CoB high-power LED, and a novel measurement methodology is applied to determine the parameters that hamper the achievement of high-data rates. The obtained results show that CoB LEDs are useful for medium-data rates applications while they are not suitable for high-data rate solutions.
SIMILARITIES IN POPULATIONS OF STAR CLUSTERS

We compare the observed mass functions and age distributions of star clusters in six well-studied galaxies: the Milky Way, Magellanic Clouds, M83, M51, and Antennae. In combination, these distributions span wide ranges of mass and age: 102 ≲ M/M☉ ≲ 106 and 106 ≲ τ/yr ≲ 109. We confirm that the distributions are well represented by power laws: dN/dM∝Mβ with β ≈ −1.9 and dN/dτ∝τγ with γ ≈ −0.8. The mass and age distributions are approximately independent of each other, ruling out simple models of mass-dependent disruption. As expected, there are minor differences among the exponents at a level close to the true uncertainties, ϵβ ∼ ϵγ ∼ 0.1–0.2. However, the overwhelming impression is the similarity of the mass functions and age distributions of clusters in these different galaxies, including giant and dwarf, quiescent and interacting galaxies. This is an important empirical result, justifying terms such as “universal” or “quasi-universal. ” We provide a partial theoretical explanation for these observations in terms of physical processes operating during the formation and disruption of the clusters, including star formation and feedback, subsequent stellar mass loss, and tidal interactions with passing molecular clouds. A full explanation will require additional information about the molecular clumps and star clusters in galaxies beyond the Milky Way.
Intelligent Façade Skinning System Based on Crowd-Sensing and Air-Quality Monitoring

In this paper, a real-time testbed for a cognitive morphing façade is proposed based on crowd-sensing and air-quality monitoring. It is one of the smart systems that employ the concept of the Internet of Things (IoT). Sensor nodes are used to connect the entire physical infrastructure with intelligent technologies for monitoring and control purposes. The proposed system includes two types of IoT nodes: indoor sensor node and outdoor actuator node. The environmental sensing and remote actuation algorithm (ESRAA) is proposed to enable the building to breathe like a lung. While the intelligent façade skinning system (IFSS) operates as “muscles” allowing the buildings to place their profiles into the required configuration. In other words, the façade of the building can be transformed by learning the indoor environment according to indoor air-quality standards. An artificial neural network (ANN)-based model is developed to classify the indoor air-quality and morph the building facade according to various conditions. The obtained results showed that the developed ANN model successfully classified the air-quality with an accuracy of 100% when utilizing an exhaustive search (ES) algorithm to select the optimum number of neurons in the hidden layer of the introduced ANN model.
IoT Botnets: Characteristics, Exploits, Attack Capabilities, and Targets

In recent years, the world has witnessed a significant increase in the number of IoT devices, with a global and continuous rise in the demand for their multi-purpose applications. However, malicious use of IoT devices began to emerge among cybercriminals. IoT-enabled cyberattacks and botnets, such as the Mirai botnet and its variants and imitators, demonstrate that the industry needs to better secure IoT devices and networks; otherwise, there will be higher risks of exposing the Internet’s infrastructure and services to increasingly disruptive DDoS attacks. This paper presents the results of a study of IoT botnets. We focus on their distinctive characteristics, exploits used, and cyberattack capabilities. In total, we have reviewed and compared 46 recent IoT botnets. We also present details of the main CPU architectures targeted by these IoT botnets. We illustrate that IoT botnets pose a significant threat to private individuals and enterprises by employing effective evasion mechanisms, encrypted communication, and targeting a wide range of systems and networks.
Estimating Bounded Uncertain Model for Stability-Certified Reinforcement Learning

This article presents the methods of identifying uncertain plants used for stability-certified reinforcement learning (RL). The uncertain plant is the interconnection of a perturbation and a nominal plant represented by a state-space model. By assuming that the perturbation is bounded, two types of methods are proposed to identify the uncertain plant. The first one is an optimization-based approach proposed for known nonlinear systems. The state-space model is given by solving an optimization problem of seeking matrices satisfying the bounded condition. The second method is a learning-based approach proposed for unknown systems. The state-space model is estimated by interacting with environments, which means that the second method is suitable for conjunction with stability-certified RL. In the numerical experiments, the identified uncertain models are used for stability analysis of feedback systems with a neural network controller. The results show that both feedback systems provide enough stability with similar regions of attractions (ROAs). This means that the learning-based method enables us to discuss the stability of neural network controllers even for unknown systems.
Clarification of the people the robot is talking to using projection

When a mobile robot provides various services in daily life spaces, it is important for the robot to speak naturally to people. However, non-humanoid robots have difficulty using nonverbal information such as gestures and body orientation like humans do, and especially in environments with multiple people, it is difficult to know who is speaking to whom, causing problems such as ignoring the other person or making the surrounding people feel lost or awkward. Here, this paper proposes a method for a robot that does not have a human-like body to present the area where the robot interacts using projection, and a method for clarifying who is speaking to whom. In the experiment, when the robot speaks to some people in an environment with multiple people and guides them, we verified whether the information is accurately conveyed to the people who speak to the robot, compared to the conventional method in which the robot turns toward the other person. As a result, all participants were correctly guided by the proposed method, whereas some participants were incorrectly guided by the method using the robot's orientation. The questionnaire evaluation confirmed that the proposed method has a good tendency in terms of comfort.
A Study of Management to Develop Adaptable AI-enabled Robots toward Inclusive Society

This paper reports on the management of developing an adaptable AI-enabled robot that aims to realize an inclusive society. The concepts of the AI robots are proposed to coexist with users and enhance their sense of self-efficacy. An application to care support that focuses on self-efficacy is exemplified. A management framework to develop the AI robots ethically is demonstrated.
Optimal Practical Design and Reduced Complexity of 6G sub-Terahertz Wireless Backhaul Networks

With the 6G being required to deliver data rates exceeding 100 Gbps for a single user, suitable wireless backhaul networks should be properly designed to carry these data to the backhaul network. In this paper, the complexity and practical aspects of designing these links in the sub-terahertz band with hardware power limitations, frequency selectivity, severe path loss, beam misalignment, coupling and noise figure losses are addressed with optimal power allocation where a new reduced complexity power allocation algorithm is proposed to realize faster data processing and lower computational delay and we investigate the effect of these factors on the system performance. The bit-rate achieved is expected to exceed 1 Tbps for 1 km using practical antennas and practically realized power sources from the literature.
A12CO,13CO, and C18O Survey of Infrared Dark Clouds

Infrared dark clouds (IRDCs) are extinction features against the Galactic infrared background, mainly in the mid-infrared band. Recently they were proposed to be potential sites of massive star formation. In this work we have made a12CO,13CO, and C18O (J = 1→ 0) survey of 61 IRDCs, 52 of which are in the first Galactic quadrant, selected from a catalog given by Simon and coworkers, while the others are in the outer Galaxy, selected by visually inspecting the MSX images. Detection rates in the three CO lines are 90%, 71%, and 62%, respectively. The distribution of IRDCs in the first Galactic quadrant is consistent with the 5 kpc molecular ring picture, while a slight trace of a spiral pattern is also noticeable, and needs to be further examined. The IRDCs have a typical excitation temperature of 10 K and typical column density of several 1022 cm−2. Their typical physical size is estimated to be several parsecs using angular sizes from the Simon catalog. Typical volume density and typical LTE mass are ~5000 cm−3 and ~5000 M☉, respectively. The IRDCs are in or near virial equilibrium. The properties of IRDCs are similar to those of star-forming molecular clumps, and they seem to be intermediate between giant molecular clouds and Bok globules; thus they may represent early stages of massive star formation.
Grad-Lidar-SLAM: Fully Differentiable Global SLAM for Lidar with Pose-Graph Optimization

—While Lidar-based SLAM systems are critical for autonomous vehicles, most existing methods are non-differentiable. This limits their use with deep neural networks to learn valuable representations. In this work, we propose Grad-Lidar-SLAM, a novel, fully differentiable SLAM framework for Lidar. We show its effectiveness by using it to solve point cloud completion task. We also show that when adapted for Lidar-based SLAM, existing differentiable baselines fail to perform well on real-world datasets. We address this problem by proposing a novel differentiable pose graph optimization framework. Our experiments on real-world datasets show that our proposed approaches outperform the baselines in static and dynamic environments
Cold planar horizons are floppy

Extremal planar black holes of four dimensional Einstein-Maxwell theory with a negative cosmological constant have an AdS$_2 \times \R^2$ near horizon geometry. We show that this near horizon geometry admits a deformation to a two parameter family of extremal geometries with inhomogeneous, spatially periodic horizons. At a linear level, static inhomogeneous perturbations of AdS$_2 \times \R^2$ decay towards the horizon and thus appear irrelevant under the holographic RG flow. However we have found numerically that nonlinear effects lead to inhomogeneous near horizon geometries. A consequence of these observations is that an arbitrarily small periodic deformation of the boundary theory at nonzero charge density does not flow to AdS$_2 \times \R^2$ in the IR, but rather to an inhomogeneous horizon. These results shed light on existing numerical studies of low temperature periodically modulated black holes and also offer a new mechanism for holographic metal-insulator crossovers or transitions.
Quantum simulation of an artificial Abelian gauge field using nitrogen-vacancy-center ensembles coupled to superconducting resonators

We propose a potentially practical scheme to simulate artificial Abelian gauge field for polaritons using a hybrid quantum system consisting of nitrogen-vacancy center ensembles (NVEs) and superconducting transmission line resonators (TLR). In our case, the collective excitations of NVEs play the role of bosonic particles, and our multiport device tends to circulate polaritons in a behavior like a charged particle in an external magnetic field. We discuss the possibility of identifying signatures of the Hofstadter butterfly in the optical spectra of the resonators and analyze the ground state crossover for different gauge fields. Our work opens new perspectives in quantum simulation of condensed matter and many-body physics using a hybrid spin-ensemble circuit quantum electrodynamics system. The experimental feasibility and challenge are justified using currently available technology.
A multi-scale, multi-wavelength source extraction method: getsources

We present a multi-scale, multi-wavelength source extraction algorithm called getsources. Although it has been designed primarily for use in the far-infrared surveys of Galactic star-forming regions with Herschel, the method can be applied to many other astronomical images. Instead of the traditional approach of extracting sources in the observed images, the new method analyzes fine spatial decompositions of original images across a wide range of scales and across all wavebands. It cleans those single-scale images of noise and background, and constructs wavelength-independent single-scale detection images that preserve information in both spatial and wavelength dimensions. Sources are detected in the combined detection images by following the evolution of their segmentation masks across all spatial scales. Measurements of the source properties are done in the original background-subtracted images at each wavelength; the background is estimated by interpolation under the source footprints and overlapping sources are deblended in an iterative procedure. In addition to the main catalog of sources, various catalogs and images are produced that aid scientific exploitation of the extraction results. We illustrate the performance of getsources on Herschel images by extracting sources in sub-fields of the Aquila and Rosette star-forming regions. The source extraction code and validation images with a reference extraction catalog are freely available.
ImageNet-Cartoon and ImageNet-Drawing: Two domain shift datasets for ImageNet

Benchmarking the robustness to distribution shifts traditionally relies on dataset collection which is typically laborious and expensive, in particular for datasets with a large number of classes like ImageNet. An exception to this procedure is ImageNet-C (Hendrycks & Dietterich, 2019), a dataset created by applying common real-world corruptions at different levels of intensity to the (clean) ImageNet images. Inspired by this work, we introduce ImageNet-Cartoon and ImageNet-Drawing, two datasets constructed by converting ImageNet images into cartoons and colored pencil drawings, using a GAN framework (Wang & Yu, 2020) and simple image processing (Lu et al., 2012), respectively. Code is available at https://github.com/oberman-lab/ imagenet-shift .
An experimental study on turbulent-stripe structure in transitional channel flow

Turbulent stripe, which would occur in turbulent channel flows at transitional Reynolds numbers, was studied experimentally by flow visualization using reflective flake particles. In a range of bulk mean Reynolds number Re = 1700-2000, the turbulent stripe was observed to be inclined at angles of 20-30 degree against the streamwise direction, and its streamwise wave length was about 60 times of the channel half width ($\delta$). The longitudinal streaks with the spanwise spacing of 1.6$\delta$ were found in the quasi-laminar regions. The critical Reynolds number was Re = 1300. Time traces of the streamwise velocity were measured by laser Doppler velocimetry, revealing similarity to the equilibrium turbulent puff in the transitional pipe flow.
P NEG : Prompt-based Negative Response Generation for Robust Response Selection Model

Dialogue response selection models typically 001 predict an appropriate response relying on the 002 context-response content similarity. However, 003 the selection model with over-reliance only on 004 superficial features is vulnerable to adversar-005 ial responses that are semantically similar but 006 irrelevant to dialogue context. Recent studies 007 have shown that leveraging these adversarial 008 responses as negative training samples is useful 009 for improving the robustness of the selection 010 model. Nevertheless, existing methods often 011 require further fine-tuning for data creation or 012 have limited scalability. To overcome these 013 limitations, this paper proposes a simple but ef-014 fective method for generating adversarial nega-015 tive responses leveraging a large-scale language 016 model. Our method can generate realistic nega-017 tive responses only with a few human-written 018 examples and a prompt designed to optimize 019 generation quality. Experimental results on the 020 dialogue selection task show that our method 021 outperforms existing methods for creating neg-022 ative responses. Synthetic quality analyses and 023 ablation studies prove that our method is scal-024 able and can generate high-quality negative re-025 sponses. These results suggest that our method 026 can be an effective alternative to human anno-027 tators in generating adversarial responses. 028
An In Silico Study of the Interactions of Alkaloids from Cryptolepis sanguinolenta with Plasmodium falciparum Dihydrofolate Reductase and Dihydroorotate Dehydrogenase

The Plasmodium falciparum dihydrofolate reductase ( Pf DHFR) and dihydroorotate dehydrogenase ( Pf DHODH) are essential for Plasmodium falciparum growth and development, and have been validated as targets for the development of new antimalarial agents. Several alkaloids isolated from Cryptolepis sanguinolenta have been reported to have antiplasmodial activity, but their protein targets are unknown. Therefore, molecular docking and molecular dynamics simulations were used to investigate the interactions and stability of the alkaloids with Pf DHFR and Pf DHODH. Based on physicochemical characteristics, alkaloids were grouped as sterically bulky (sb) or planar (pg). Docking results revealed strong binding affinities ( − 6.0 to − 13.4kcal/mol) of the alkaloids against Pf DHODH and various strains of Pf DHFR while interacting with key residues such as Asp54 and Phe58 in Pf DHFR. The pg alkaloids had high binding affinity and preference for the inhibitor binding domain over the flavin mononucleotide (FMN) binding domain in Pf DHODH due to size considerations. From the molecular dynamics trajectories, protein-alkaloid complexes were stable throughout the simulation, with supporting evidence from root mean square deviations, root mean square fluctuations, radius of gyration, free binding energies, and other parameters. We report herein that biscryptolepine and cryptomisrine (sb class), as well as cryptolepinone, cryptoheptine, cryptolepine, and neocryptolepine (pg class), are capable of inhibiting Pf DHFR effectively in pyrimethamine sensitive and resistant cells. Also, our results show that alkaloids of the pg class can inhibit Pf DHODH as FMN decoys, as well as direct enzyme inhibitors, thereby halting crucial protein function. ligand (agonist or
Know the Star, Know the Planet

Abstract : The results of an adaptive optics survey of exoplanet host stars for stellar companions are presented. We used the Advanced Electro-Optical System telescope and its adaptive optics system to collect deep images of the stars in the I band. Sixty-two exoplanet host stars were observed and fifteen multiple star systems were resolved. Of these eight are known multiples, while seven are new candidate binaries. For all binaries, we measured the relative astrometry of the pair and the differential magnitude in the I band. We improved the orbits of HD 19994 and Boo. These observations will provide improved statistics on the duplicity of exoplanet host stars and provide an increased understanding of the dynamics of known binary star exoplanet hosts.