Miracle-dz/UniFAR-FTUs huggingface dataset:
Feedback by AGN Jets and Wide-angle Winds on a Galactic Scale

To investigate the differences in mechanical feedback from radio-loud and radio-quiet active galactic nuclei on the host galaxy, we perform 3D AMR hydrodynamic simulations of wide-angle, radio-quiet winds with different inclinations on a single, massive, gas-rich disk galaxy at a redshift of 2–3. We compare our results to hydrodynamic simulations of the same galaxy but with a jet. The jet has an inclination of 0° (perpendicular to the galactic plane), and the winds have inclinations of 0°, 45°, and 90°. We analyze the impact on the host’s gas, star formation, and circumgalactic medium. We find that jet feedback is energy-driven and wind feedback is momentum-driven. In all the simulations, the jet or wind creates a cavity mostly devoid of dense gas in the nuclear region where star formation is then quenched, but we find strong positive feedback in all the simulations at radii greater than 3 kpc. All four simulations have similar SFRs and stellar velocities with large radial and vertical components. However, the wind at an inclination of 90° creates the highest density regions through ram pressure and generates the highest rates of star formation due to its ongoing strong interaction with the dense gas of the galactic plane. With increased wind inclination, we find greater asymmetry in gas distribution and resulting star formation. Our model generates an expanding ring of triggered star formation with typical velocities of the order of 1/3 of the circular velocity, superimposed on the older stellar population. This should result in a potentially detectable blue asymmetry in stellar absorption features at kiloparsec scales.
Assessment of Network & Processor Virtualization in Cloud Computing

Cloud Computing is an emerging field in applied computer science. Cloud computing depends on virtualization, where a sole physical resource is virtualized into numerous virtual resources. Resources that are being virtualized include physical storage, processing units, volatile memories, network, and further physical resources. Processor and network virtualization offer several advantages like lessening hardware cost, energy consumption, and the human struggle for administration and management of resources. In this article, the primary concentration was on the evaluation and assessment of network virtualization between different cloud computing hypervisors and to contrast their evaluated results. This comparison is based on a few experiments performed on Citrix XenServer, Microsoft Hyper-V, and VMware vSphere hypervisors. XenServer uses Open vSwitch for its network virtualization whereas Hyper-V and vSphere use Microsoft network virtualization and vSphere switch for managing their network virtualization respectively. We have found that virtual networks are further scalable and secure than physical networks. We can assign multiple virtual interfaces or connections to a single virtual machine. In light of various experiments, in which we have compared non-virtualized with virtualized scenarios, for all these above-mentioned hypervisors, we found out that we can use virtual networks with and without VLAN Tags or VLAN Ids. Hyper-V provides further flexibility and options regarding VLAN Tag in comparison with XenServer or vSphere. We have also found that XenServer and vSphere give us extra network throughput in comparison with Hyper-V in terms of TCP and UDP whereas Round Trip Time (RTT) in the virtual network of all hypervisors are high in comparison with the non-virtual environment. Network CPU usage is also measured and found that vSphere and XenServer together consume more CPU during network activity in comparison with Hyper-V. We also found that processor virtualization did not majorly result from the average schedule time in comparison with a non-virtualized machine. We consequently conclude that in general a small reduction in the performance, in the case of virtualization is insignificant as compared with the advantages we get from virtualization when using cloud hypervisors. For the application of virtualization, this work will motivate to set up of data centers using a virtualized environment.
InFit: Combination Movement Recognition for Intensive Fitness Assistant via Wi-Fi

Wi-Fi technology is becoming a promising enabler of device-free fitness tracking to provide reviews and recommendations for effective homely exercise. State-of-the-art Wi-Fi fitness assistants succeed in recognizing the simple meta-movements (e.g., Push-Up and Squat) with discrete and repeatable patterns. Unfortunately, these prior attempts can hardly scale to the combination movements of ever-growing interests in intensive fitness programs. Combination movements are composed of meta-movements that are mutually concatenated or inserted. They have a compound characteristic that inherits from the diversity of combination orders and continuity of meta-movements. The compound characteristic causes substantial training data collection costs and a challenge of combination decomposition that is a prerequisite for providing fine-grained fitness assessment. To this end, we propose InFit, a Wi-Fi-based device-free fitness assistant system for combination movements. First, we design a novel data augmentation method, namely Stitching-based Virtual Sample Generation (SVSG), to reduce the training data collection costs by generating virtual combination movements. Second, a 2-stage combination movement recognition model is designed to learn temporal dependencies between movements and decompose combination movements. From its outputs, we can tell whether a combination movement is standard. Extensive experimental results show that InFit can achieve an average recognition accuracy of 94%. With zero training samples of combination movements, the average accuracy is 40% higher than the baselines. In addition, SVSG can provide a general enhancement on multiple competing schemes with similar sensing tasks.
Black Hole Chemistry

The mass of a black hole has traditionally been identified with its energy. We describe a new perspective on black hole thermodynamics, one that identifies the mass of a black hole with chemical enthalpy, and the cosmological constant as thermodynamic pressure. This leads to an understanding of black holes from the viewpoint of chemistry, in terms of concepts such as Van der Waals fluids, reentrant phase transitions, and triple points. Both charged and rotating black holes exhibit novel chemical-type phase behaviour, hitherto unseen.
Morpho-kinematical modelling of Nova Eridani 2009 (KT Eri)

Modelling the morphology of a nova outburst provides valuable information on the shaping mechanism in operation at early stages following the outburst. We performed morphokinematical studies, using shape, of the evolution of the Hα line profile following the outburst of the nova KT Eridani. We applied a series of geometries in order to determine the morphology of the system. The best fit morphology was that of a dumbbell structure with a ratio between the major to minor axis of 4:1, with an inclination angle of 58+6−7 degrees and a maximum expansion velocity of 2800±200 km s−1. Although, we found that it is possible to define the overall structure of the system, the radial density profile of the ejecta is much more difficult to disentangle. Furthermore, morphology implied here may also be consistent with the presence of an evolved secondary as suggested by various authors.
Efficient Gaussian Mixture Modeling of Long-Range 2-D Radar Measurements

In some practical radar systems of interest, the standard EKF linearization approach applied to a nonlinear measurement transformation is ineffective. In particular, if measurements are performed in polar coordinates and the crossrange error is much greater than the range error, then the true error region in Cartesian space is no longer well approximated by a Gaussian distribution. This effect is known as the “contact lens” problem, and various approaches have been proposed to alleviate it, including particle and Gaussian mixture (GM) filters. In this work, a method is presented for modeling Cartesian converted measurement distributions, which suffer from the contact lens effect using Maximum Likelihood (ML) GM parameters. In order to allow an efficient implementation of this process in a GM Kalman filter, a normalization of the ML parameters is introduced so that parameters can be computed offline and efficiently stored in a lookup table. Simulation results are presented to illustrate the method and confirm the results.
Thermoelastic Properties of Olivine and Wadsleyite [Fe_x,Mg_(1-x)]_2SiO_4: Their Relationship to the 410 km Seismic Discontinuity

We combine density functional theory (DFT) within the local density approximation (LDA), the quasiharmonic approximation (QHA), and a model of vibrational density of states (VDoS) to calculate elastic moduli and sound velocities of alpha- and beta-[Fe_x,Mg_(1-x)]_2SiO_4 (olivine and wadsleyite), the most abundant minerals of the Earth's upper mantle (UM) and upper transition zone (TZ). Comparison with experimental values at room-temperature and high pressure or ambient-pressure and high temperature show good agreement with our first-principles findings. Using our results, we investigate the discontinuities in elastic moduli and velocities associated with the alpha-to-beta-[Fe_x,Mg_(1-x)]_2SiO_4 transformation at pressures and temperatures relevant to the 410 km seismic discontinuity. We find the compressional velocity contrast to be smaller than the shear velocity contrast, in agreement with the preliminary reference Earth model (PREM).
An Investigation of the Constructional Design Components Affecting the Mechanical Response and Cellular Activity of Electrospun Vascular Grafts

Cardiovascular disease is anticipated to remain the leading cause of death globally. Due to the current problems connected with using autologous arteries for bypass surgery, researchers are developing tissue-engineered vascular grafts (TEVGs). The major goal of vascular tissue engineering is to construct prostheses that closely resemble native blood vessels in terms of morphological, mechanical, and biological features so that these scaffolds can satisfy the functional requirements of the native tissue. In this setting, morphology and cellular investigation are usually prioritized, while mechanical qualities are generally addressed superficially. However, producing grafts with good mechanical properties similar to native vessels is crucial for enhancing the clinical performance of vascular grafts, exposing physiological forces, and preventing graft failure caused by intimal hyperplasia, thrombosis, aneurysm, blood leakage, and occlusion. The scaffold’s design and composition play a significant role in determining its mechanical characteristics, including suturability, compliance, tensile strength, burst pressure, and blood permeability. Electrospun prostheses offer various models that can be customized to resemble the extracellular matrix. This review aims to provide a comprehensive and comparative review of recent studies on the mechanical properties of fibrous vascular grafts, emphasizing the influence of structural parameters on mechanical behavior. Additionally, this review provides an overview of permeability and cell growth in electrospun membranes for vascular grafts. This work intends to shed light on the design parameters required to maintain the mechanical stability of vascular grafts placed in the body to produce a temporary backbone and to be biodegraded when necessary, allowing an autologous vessel to take its place.
A Methodical Approach for PCB PDN Decoupling Minimizing Overdesign with Genetic Algorithm Optimization

An optimization routine is applied for the decoupling capacitor placement on Power Distribution Networks to identify the limit beyond which the placement of additional decaps is no longer effective, thus leading to wasting layout area and components, and to a cost increase. A specific test example from a real design is used together with the required target impedance and frequency band of interest for the PDN design. The effectiveness of the decap placement while selecting different layers of the stack-up, and while moving the upper limit of the PDN design band is analyzed. Such analysis leads to helpful insights based on the progression of the input impedance during the optimization process, and to develop useful guidelines for avoiding over-design of the PDN.
Model of Dark Matter and Dark Energy Based on Gravitational Polarization

A model of dark matter and dark energy based on the concept of gravitational polarization is investigated. We propose an action in standard general relativity for describing, at some effective or phenomenological level, the dynamics of a dipolar medium, i.e. one endowed with a dipole moment vector, and polarizable in a gravitational field. Using first-order cosmological perturbations, we show that the dipolar fluid is undistinguishable from standard dark energy (a cosmological constant �) plus standard dark matter (a pressureless perfect fluid), and therefore benefits from the successes of the �-CDM (�-cold dark matter) scenario at cosmological scales. Invoking an argument of “weak clusterisation” of the mass distribution of dipole moments, we find that the dipolar dark matter reproduces the phenomenology of the modified Newtonian dynamics (MOND) at galactic scales. The dipolar medium action naturally contains a cosmological constant, and we show that if the
HiddenPose: Non-Line-of-Sight 3D Human Pose Estimation

Nearly all existing human pose estimation techniques address the problem under the line-of-sight (LOS) setting. Many real-life applications such as rescue missions and autonomous driving, in contrast, require estimating the pose of hidden subjects. In this paper, we present a non-line-of-sight (NLOS) pose estimator, which produces a skeletal representation of hidden human poses. A brute-force approach would first conduct albedo reconstruction of a hidden subject and then apply LOS pose estimation. We show that such an implementation does not effectively exploit features unique to NLOS and subsequently yields artifacts such as missing joints. We instead first generate a comprehensive NLOS human pose dataset of 19 subjects under 9 motions. We then present a spatially aware deep learning technique based on convolutional neural networks that explicitly employ NLOS features. Comprehensive experiments on both synthetic and real data show that our new estimator is both effective and robust and can be seamlessly integrated into learning-based NLOS scene reconstruction. Our HiddenPose transient dataset contains synthetic transients with ground-truths of the volumes and the joints and real-world transients captured from our NLOS imaging system. Extensive assessments demonstrate that the HiddenPose transient dataset is valuable for effective NLOS research. We will make our data and code publicly available.
Oxford SWIFT IFS and multi-wavelength observations of the Eagle galaxy at z=0.77

The `Eagle' galaxy at a redshift of 0.77 is studied with the Oxford Short Wavelength Integral Field Spectrograph (SWIFT) and multi-wavelength data from the All-wavelength Extended Groth strip International Survey (AEGIS). It was chosen from AEGIS because of the bright and extended emission in its slit spectrum. Three dimensional kinematic maps of the Eagle reveal a gradient in velocity dispersion which spans 35-75 +/- 10 km/s and a rotation velocity of 25 +/- 5 km/s uncorrected for inclination. Hubble Space Telescope images suggest it is close to face-on. In comparison with galaxies from AEGIS at similar redshifts, the Eagle is extremely bright and blue in the rest-frame optical, highly star-forming, dominated by unobscured star-formation, and has a low metallicity for its size. This is consistent with its selection. The Eagle is likely undergoing a major merger and is caught in the early stage of a star-burst when it has not yet experienced metal enrichment or formed the mass of dust typically found in star-forming galaxies.
Optimizing scheme of probabilistic remote preparation state

We propose a new generalized remote state preparation protocol for using non-maximally entangled state as a shared resource. Different from the previous schemes, the parameters of measurement basis depend on not only the state of preparation but also the state of quantum channel. The advantage of the present protocols is that, by constructing a set of appropriate mutually orthogonal measurement basis, the success probability is improved. It is worthwhile noticing that the probability of success is determined by Alice's measurement. If Alice's measurement is appropriate, the remote preparation can be successfully realized with the maximal probability.
Gamma-Band Modulation in Parietal Area as the Electroencephalographic Signature for Performance in Auditory–Verbal Working Memory: An Exploratory Pilot Study in Hearing and Unilateral Cochlear Implant Children

This pilot study investigates the neurophysiological patterns of visual and auditory verbal working memory (VWM) in unilateral cochlear implant users (UCIs). We compared the task-related electroencephalogram (EEG) power spectral density of 7- to 13-year-old UCIs (n = 7) with a hearing control group (HC, n = 10) during the execution of a three-level n-back task with auditory and visual verbal (letters) stimuli. Performances improved as memory load decreased regardless of sensory modality (SM) and group factors. Theta EEG activation over the frontal area was proportionally influenced by task level; the left hemisphere (LH) showed greater activation in the gamma band, suggesting lateralization of VWM function regardless of SM. However, HCs showed stronger activation patterns in the LH than UCIs regardless of SM and in the parietal area (PA) during the most challenging audio condition. Linear regressions for gamma activation in the PA suggest the presence of a pattern-supporting auditory VWM only in HCs. Our findings seem to recognize gamma activation in the PA as the signature of effective auditory VWM. These results, although preliminary, highlight this EEG pattern as a possible cause of the variability found in VWM outcomes in deaf children, opening up new possibilities for interdisciplinary research and rehabilitation intervention.
H 2 O 2 -responsive nanocarriers prepared by in situ Polymerization-Induced Self-Assembly for biomedical applications

H 2 O 2 -sensitive block copolymer nanoparticles (NPs) composed of a hydrophilic macro-chain transfer agent (macro-CTA) and a hydrophobic thioether-bearing block were prepared by polymerization-induced self-assembly (PISA) approach. PISA process first involved a chain extension of poly((poly(ethylene glycol) methyl ether methacrylate)- co -(poly(ethylene glycol) methacrylate)) macro-CTA with a H 2 O 2 -responsive N-(2-(methylthio)ethyl)acrylamide monomer (MTEAM), which self-assembled into P((PEGMA-co -PEGMAOH)- b -PMTEAM) block copolymer NPs. The polymerization kinetics indicated the evolution of particle size and morphology from spherical micelles, fused micelles, to vesicles over time. Upon incubation with 0.1 to 10 mM H 2 O 2 , spheres were fragmented due to the oxidation of the PMTEAM cores, thus transforming the hydrophobic thioethers into hydrophilic sulfoxides, and disassociating the nanocarriers. 43 % of the hydrophobic Nile Red dye was encapsulated into the spherical micelles and demonstrated a controlled release in H 2 O 2 incubation. Spherical micelles and vesicles displayed no cytotoxicity in MCF-7, DU145 and 22Rv1 cells. Both spheres and vesicles were efficiently internalized into MCF-7 cells, with spheres showing higher level of uptake than vesicles due to the smaller sizes. In summary, PISA of P((PEGMA-co -PEGMAOH)- b - PMTEAM) allowed the direct formation and loading of hydrophobic dye into spherical micelles which showed a controlled drug release in H 2 O 2 .
Intelligent Dynamic Spectrum Allocation in MEC-Enabled Cognitive Networks: A Multiagent Reinforcement Learning Approach

Making effective use of scarce spectrum resources, along with efficient computational performance, is one of the key challenges for future wireless networks. To tackle this issue, in this paper, we focus on the intelligent dynamic spectrum allocation (DSA) in a mobile edge computing (MEC) enabled cognitive network. And our objective is to optimize the spectrum utilization and load balance among idle channels. Since users can only acquire part of environment information in a decentralized way, we model such a problem as decentralized partially observed Markov decision process (Dec-POMDP) and design the corresponding evaluating metric to encourage users sense and access spectrum properly. Then, we propose a QMIX-based DSA method with centralized training decentralized execution (CTDE) structure to tackle it. In the training phase, the users offload the computational tasks to the MEC server to obtain the optimal distributed DSA strategies, through which the users select the optimal channel locally in the execution phase. Simulation results show that, using the proposed algorithm, users can independently capture spectrum holes, and hence improve the spectrum utilization while balancing the load on available channels.
Image Classification and Text Extraction using Convolutional Neural Network

As luck would have its recent innovations in computer vision grant us to make considerable pace abate the data classification and analysis of enormous data in different organizations like training material, policy guides, and project documents that can be used internally. In addition, cloud service providers are rising in text detection techniques and offer many computer vision contributions included Google Vision, A WS Textract, and Azure OCR. In this paper image classification is achieved by CNN algorithm which is the top choice for image classification. Then using CNN, we extract text data from images. CNN has better performance rate on large datasets as it controls the problem of overfitting. Hence, the accuracy of these algorithms can be enhanced by increasing the epochs and integrating a large dataset. This approach used a methodology to resolve the number of convolution and pooling layers with the number of nodes in network. Lastly, CNN algorithms are better when data is appropriate if it has mortal features that can be analyzed and utilized by algorithm.
Efficient learning of robust quadruped bounding using pretrained neural networks

Bounding is one of the important gaits in quadrupedal locomotion for negotiating obstacles. The authors proposed an effective approach that can learn robust bounding gaits more efficiently despite its large variation in dynamic body movements. The authors first pretrained the neural network (NN) based on data from a robot operated by conventional model based controllers, and then further optimised the pretrained NN via deep reinforcement learning (DRL). In particular, the authors designed a reward function considering contact points and phases to enforce the gait symmetry and periodicity, which improved the bounding performance. The NN based feedback controller was learned in the simulation and directly deployed on the real quadruped robot Jueying Mini successfully. A variety of environments are presented both indoors and outdoors with the authors approach. The authors approach shows efficient computing and good locomotion results by the Jueying Mini quadrupedal robot bounding over uneven terrain.