Miracle-dz/UniFAR-FTUs huggingface dataset:
Kinematics of the ionized and molecular gas in nearby luminous infrared interacting galaxies

We have observed three luminous infrared galaxy systems (LIRGS) which are pairs of interacting galaxies, with the Galaxy H$\alpha$ Fabry-Perot system (GH$\alpha$FaS) mounted on the 4.2m William Herschel Telescope at the Roque de los Muchachos Observatory, and combined the observations with the Atacama Large Millimeter Array (ALMA) observations of these systems in CO emission to compare the physical properties of the star formation regions and the molecular gas clouds, and specifically the internal kinematics of the star forming regions. We identified 88 star forming regions in the H$\alpha$ emission data-cubes, and 27 molecular cloud complexes in the CO emission data-cubes. The surface densities of the star formation rate and the molecular gas are significantly higher in these systems than in non-interacting galaxies and the Galaxy, and are closer to the surface densities of the star formation rate and the molecular gas of extreme star forming galaxies at higher redshifts. The large values of the velocity dispersion also show the enhanced gas surface density. The HII regions are situated on the ${\rm{SFR}}-\sigma_v$ envelope, and so are also in virial equilibrium. Since the virial parameter decreases with the surface densities of both the star formation rate and the molecular gas, we claim that the clouds presented here are gravitationally dominated rather than being in equilibrium with the external pressure.
The Realistic 5G New Radio Coverage Analysis for Urban Area

A cellular network connection is one of the most evolving areas. It provides a lot of comfort in a regular life, business, seamless connection for industry, especially for Internet of Things and other smart or wearable devices. The radio coverage planning process is important for any wireless networks, especially for cellular networks using high frequency range. The 0.5 GHz – 100 GHz spectrum is used for 5G New Radio network deployment. The problem is to accurately predict a cell range for various planning scenarios. The most significant part is to predict a cell radius for Urban or Sub-Urban areas. The more a number of obstacles and buildings the more a radio signal power degradation, reflection of shadowing, which causes a significant Quality of Service degradation. The article is aimed to automate a radio planning process for 5G New Radio networks for realistic Urban area scenario. Authors have developed a software solution capable of data collection from real map and 3D terrain and buildings height visualization. It provides a possibility to calculate more accurate signal distribution through the air, buildings, relief, etc.
Filter Design Using Double Extracted Zeros

In [1] a single transmission zero was used to enhance the lower stopband of an all-pole bandpass filter, using a capacitively loaded coaxial stub inductively coupled to the first and/or last resonator of the bandpass circuit. An improvement was presented in recent publications [2], [3], [4], showing that it is possible to produce in-line bandpass designs with a transmission zero near the passband, using the same physical resonator structure as employed in the bandpass portion. This has been accomplished using shunt resonators weakly coupled (using a virtual inductive coupling) to the first and/or last bandpass resonator, resulting in a configuration that locates the zero near a passband edge, with the concomitant pole located far from the passband. Both the original and the improved circuits have thus been called “Extracted Zero”. Use of the same physical resonator structure simplifies the physical implementation. In this paper, the “Double Extracted Zero” is introduced. Similar in physical structure to the single zero in [1], the double zero is used to provide additional depth or width to the desired zero frequency region, and can be located at either or both ends of a given passband.
Experimental Assessment of Passive UHF-RFID Sensor Tags for Environment and Kinematic Data

Radio Frequency IDentification (RFID) technology and low power electronic sensors may be combined to build wireless passive sensors which might cover many applications ranging from Internet of Things to Healthcare. This work presents a multi-perspective overview of RFID-based sensor tags and then proceed to experimentally characterize the sensing capabilities of two commercial fully-passive RFID sensor tags for acceleration and relative humidity data acquisition, respectively. Measurements are compared with data acquired through commercial traditional non-RFID wired sensors
Visualization of Machine Learning Dynamics in Tsetlin Machines

The black-box approach towards machine learning models is not helpful for creating fast and efficient implementations. optimization of these models requires a clear understanding of every aspect of the system, including the architecture, algorithms, and dynamics of the training process. This paper presents a range of visualizations for investigating the dynamics of the Tsetlin Machine, which is a new machine-learning algorithm with logic underpinning. These include static visualizations such as architecture and algorithm diagrams and dynamic visualizations such as plotting the evolution of the internal state of the machine. The workflow that supports the visualizations is generalized into an extendable open-source development kit that can be used with future generations of Tsetlin Machines. The example visualization diagrams from the MNIST dataset are discussed from the viewpoint of parallel implementation, hardware acceleration, and opportunities for architectural optimization.
Virtualization as a Modern Tool for Design and Implementation of Robotic Applications

The article is focused on presenting the possibilities of using modern approaches in the design and implementation of applications in the field of industrial robotics. From a set of modern approaches, the focus is on virtualization in the process of creating a robotic cell control system. Virtualization is part of the design stage of the control system in the offline programming of robotic manipulators as action elements of a given cell. Virtualization is also used in the implementation phase of the solution, namely in virtual commissioning. The use of hardware-in-the-loop and modified reality-in-the-loop approaches is described. The possibility of using virtualization as a tool for the Industry 4.0 concept application in real conditions, without the need of a large amount of investment in order to complex equipment upgrade, is emphasized.
Square Kilometre Array Science Data Challenge 1

The Square Kilometre Array (SKA, https://skatelescope.org) will be the world's largest radio telescope. SKA Science Data Challenges will be regularly issued to the community as part of the science preparatory activities. The purpose of these challenges is to inform the development of the data reduction workflows, to allow the science community to get familiar with the standard products the SKA will deliver, and optimise their analyses to extract science from them. These challenges may consist of real data from currently operating radio facilities or of simulated SKA data. The purpose of this document is to provide information on how the SKA Science data challenge #1 (SDC1) has been produced and to set the challenge for the community. For more information on how to take part in the challenge and to download the data see https://astronomers.skatelescope.org/ska-science-data-challenge-1/
Automatic Bug Inference via Deep Image Understanding

In mobile crowdsourced testing, crowdworkers are usually far from experts, and low-quality bug reports are submitted, in which the bug descriptions are usually poorly written. Thus, the bug descriptions are hard to read and helpless for bug inference and bug understanding. To ease the understanding of bug scenarios, we present a novel method called BIU (Bug Inference via Image Understanding), which employs image understanding techniques to help crowdworkers automatically infer bugs and generate bug descriptions using bug screenshots. In this way, the burden of crowdworkers will be lowered and their working efficiency and report quality will be greatly improved. According to our preliminary experiments, the accuracy of BIU can reach up to 90%. The demonstration video can be found at: https://youtu.be/ZBOIqtdRFaU.
Circularly Polarized Phased Array System with Pattern Optimization Algorithm for Low Side Lobe Level and Scanning Axial Ratio

This paper proposes the circularly polarized (CP) phased array system with a pattern optimization algorithm for low sidelobe level and scanning axial ratio. For mobile satellite communication, the low profile CP phased array system with a low axial ratio and low sidelobe level in the scanning region is necessary. However, the previously reported phased array system hardly has the required performances simultaneously. In this paper, we proposed the CP phased array system which can control the amplitude and phase of the received signal for low axial ratio and sidelobe level. The system employs the dual-polarized receiver and polarization control unit. In the proposed pattern optimization algorithm, low axial ratio and low sidelobe level are achieved simultaneously by controlling the gain of low noise amplifier and phase offset of the phase shifter of dual-polarization. In the simulation, the proposed system has under 1 dB of the axial ratio and under -20 dB of the sidelobe level at the 2-dimensional scanning region from -50° to 50°. For validation, the 1x8 CP phased array antenna system was implemented from 4.9 GHz to 6 GHz. The measured axial ratio in the frequency bandwidth is under 2 dB and the measured sidelobe level is under -20 dB at the elevation scanning region from -53° to 53°.
Adaptive Ramp-Rate Control of Hybrid Energy Storage System for PV Application

With the increasing penetration of solar energy, a huge amount of fluctuating power is injected into the power grid, which has a negative influence on power quality (e.g., voltage fluctuation). To solve this problem, power ramp rate control has been introduced in grid codes. In recent years, hybrid energy storage systems have been widely used in many applications for limiting the ramp rate of the power, due to their control flexibility. Currently, the integration of supercapacitors in such hybrid systems is a promising solution to extend the battery lifespan and improve dynamic performance. In this paper, an adaptive ramp-rate control strategy is conducted by using low-pass filters, which can change the filter time constant to reduce the battery cycles. The results show that compared to the traditional two-stage strategy, the proposed strategy can effectively reduce the battery cycles by nearly 18% during days of fast-moving clouds.
Vulkan-Sim: A GPU Architecture Simulator for Ray Tracing

Ray tracing can generate photorealistic images with more convincing visual effects compared to rasterization. Recent hardware advances have enabled ray tracing to be applied in real-time. Current GPUs feature a dedicated ray tracing acceleration unit, and game developers have started to make use of ray tracing APIs to bring more realistic graphics to their players. Industry cooperatively contributed to Vulkan, which recently introduced an open-standard API for ray tracing. However, little has been disclosed about the mapping of this API to hardware. In this paper, we introduce Vulkan-Sim, a detailed cycle-level simulator for enabling architecture research for ray tracing. We extend GPGPU-Sim, integrating it with Mesa, an open-source graphics library to support the Vulkan API, and add dedicated ray traversal and intersection units. We also demonstrate an explicit mapping of the Vulkan ray tracing pipeline to a modern GPU using a technique we call delayed intersection and any-hit execution. Additionally we evaluate several ray tracing workloads with Vulkan-Sim, identifying bottlenecks and inefficiencies of the ray tracing hardware we model. To demonstrate the utility of Vulkan-Sim we conduct two case studies evaluating techniques recently proposed or deployed by industry targeting enhanced ray tracing performance.
Realizing Emotional Interactions to Learn User Experience and Guide Energy Optimization for Mobile Architectures

In the age of AI, mobile architectures such as smartphones are still “cold machines”; machines do not feel. If the architecture is able to feel users’ feelings and runtime user experience (UX), it will accordingly adapt performance/energy to find the optimal system-operating state that consumes the least energy to satisfy users. In this paper, we will utilize users’ facial expressions (FEs) to learn their runtime UX. We know that FEs are the natural and direct way for humans to convey their emotions and feelings. Our study reveals that FEs also reflect UX. Our research for the first time quantifies the link between FEs and UX. Leveraging this link, the architecture will be able to use the front camera to see FEs and feel users’ UX. Based on UX, the architecture can appropriately provision computing resources. We propose Vi-energy system to realize the above idea. Our evaluation shows that Vi-energy reduces energy consumption by 52.9% at maximum and secures UX.
Democratic (S)fermions and Lepton Flavor Violation

The democratic approach to account for fermion masses and mixing is known to be successful not only in the quark sector but also in the lepton sector. Here we extend this ansatz to supersymmetric standard models, in which the K\"ahler potential obeys the underlying ${S}_{3}$ flavor symmetries. The requirement of neutrino bi-large mixing angles constrains the form of the K\"ahler potential for left-handed lepton multiplets. We find that right-handed sleptons can have nondegenerate masses and flavor mixing, while left-handed sleptons are argued to have universal and hence flavor-blind masses. This mass pattern is testable in future collider experiments when superparticle masses will be measured precisely. Lepton flavor violation arises in this scenario. In particular, $\stackrel{\ensuremath{\rightarrow}}{\ensuremath{\mu}}e\ensuremath{\gamma}$ is expected to be observed in a planned future experiment if supersymmetry breaking scale is close to the weak scale.
Distraction from pain depends on task demands and motivation

Supplemental Digital Content is Available in the Text. Distraction is effective at reducing pain when the task is both cognitively demanding and there is sufficient motivation to invest effort into the task. Abstract Introduction: Pain captures attention automatically, yet we can inhibit pain when we are motivated to perform other tasks. Previous studies show that engaging in a cognitively demanding task reduces pain compared with a task that is minimally demanding, yet the effects of motivation on this pain-reducing effect remain largely unexplored. Objectives: In this study, we hypothesized that motivating people to engage in a task with high demands would lead to more cognitive resources directed toward the task, thereby amplifying its pain-reducing effects. Methods: On different trials, participants performed an easy (left–right arrow discrimination) or demanding (2-back) cognitive task while receiving nonpainful or painful heat stimuli. In half of the trials, monetary rewards were offered to motivate participants to engage and perform well in the task. Results: Results showed an interaction between task demands and rewards, whereby offering rewards strengthened the pain-reducing effect of a distracting task when demands were high. This effect was reinforced by increased 2-back performance when rewards were offered, indicating that both task demands and motivation are necessary to inhibit pain. Conclusions: When task demands are low, motivation to engage in the task will have little impact on pain because performance cannot further increase. When motivation is low, participants will spend minimal effort to perform well in the task, thus hindering the pain-reducing effects of higher task demands. These findings suggest that the pain-reducing properties of distraction can be optimized by carefully calibrating the demands and motivational value of the task.
Interactive Patch Filtering via Test Generation

Automatic program repair (APR), which aims to fix software bugs without human intervention, is getting in-creasing attention from academic and industrial communities. Although promising outcomes regarding correctly-fixed bugs have been achieved recently, existing APR tools still suffer from the low accuracy of generated patches. In fact, it is fundamentally difficult to avoid generating incorrect patches due to the weak available test suite. In this paper, to improve the accuracy of patches generated by APR tools, we propose a novel HUman-machine interactive patch filterinG apprOach (HUGO) to help developers identify correct patches by generating additional test cases. We also implement the approach as an Eclipse plugin and evaluate the effectiveness and usefulness of the implementation. The results on the Defects4J dataset show that the proposed method can filter out 82.61 % of the incorrect patches, and improve the accuracy of patches by 25%.
Method of Static Synthesis of Aperture in Tasks of Remote Sensing and Non-Destructive Testing

Using the phenomenological approach, the concept of a coherent image of a surface that is based on the diffraction theory, Kirchhoff and Rayleigh-Sommerfeld theorems, and Huygens-Fresnel principle is defined. The basic operations of restoration of primary coherent images and possibilities of increasing their spatial resolutions in synthetic aperture radars are shown. The structure of coherent image in static synthesis of aperture in the Fraunhofer zone with telescopic view is determined. The resolution of the statistical aperture synthesis method in surface observation along the trajectory of cross form is studied.
Optimum Core Configurations of a BLDC Motor Prototype with Cost Consideration

This paper discusses the optimization of BLDC motor cores to obtain core configurations which result in technical output as high as possible but the total manufacturing cost is as low as possible. Several cores with different materials, thicknesses, stacking factors, and slot numbers are tested. For each configuration, the output power, efficiency, and cost are extracted and compared. The optimum degree of each model is then assessed. The results show that configurations with mild steel-made sheets, 1.5 mm thickness, 0.9 stacking factor, and slot numbers of 24, 54, and 48 results three most optimum output and cost under 85% minimum efficiency requirement. Meanwhile, for 87.75% minimum efficiency condition, the top two optimum configurations are with 24 slots, sheet thickness of 1 mm, and stacking factors of 0.9 and 0.94 respectively. In both minimum efficiency scenario, some configurations result in negative optimum degrees, meaning more unit needs to be produced.
Combating Stealthy Thermal Covert Channel Attack With Its Thermal Signal Transmitted in Direct Sequence Spread Spectrum

Many-core systems are susceptible to attacks launched by thermal covert channel (TCC) attacks. Detection of TCC attacks often relies on the use of threshold-based approaches or variants, and a countermeasure to thwart the channel can be applied only after an attack is deemed to be present. In this article, we describe a direct sequence spread spectrum (DSSS)-based TCC, where its thermal data are modulated by a pseudo-random bit sequence. Unfortunately, such DSSS-based TCC has an extremely low signal strength that the signal is nearly indistinguishable from the noise and thus cannot be detected by any existing threshold-based detection methods. To combat this stealthy TCC, we propose a novel detection scheme that lets the received signal pass through a differential filter where irrelevant frequency components occupied mainly by the noise gets eliminated and the filtered signal is next compared against a threshold for successful detection. Experimental results show that the DSSS-based TCC can effectively survive detection by the existing detection methods with its BER as low as 4%. In contrast, with the proposed detection and countermeasure applied, the detection accuracy jumps to 89%, and the BER of the DSSS-based TCC soars to 50%, which indicates that the TCC is practically shut down.
XRouting: Explainable Vehicle Rerouting for Urban Road Congestion Avoidance using Deep Reinforcement Learning

Rerouting vehicles for urban congestion avoidance is challenging as the decision has to be undertaken promptly with the consideration of traffic condition changes caused by other vehicles' routing plans. Existing solutions such as the on-board navigation systems (e.g., Google Maps) cannot meet these requirements which is prone to trigger the well-known routing oscillation problem. Though deep reinforcement learning (DRL) approaches are able to provide a high-quality solution and satisfy the real-time requirement, not only do they usually suffer the slow and instability issues for convergence, but the input information, like a picture for each time step, is also teeming with redundant information. In this paper, we propose XRouting model that uses policy-based DRL and the revised Gated Transformer (GTr) architecture to accelerate and stabilize the training convergence in solving dynamic routing problems. Our simulation study validates that compared with existing rerouting solutions, XRouting can achieve higher reductions in travel time, fuel consumption, CO2 emission, and the route length. More importantly, XRouting is capable of determining which features are predominant when vehicles conduct rerouting. This explainable ability of our model can further guide human drivers what features to consider when rerouting manually in real life.
Estimation of the Amplitude of a Sequence of Ultra-Wideband Quasi-Radio Signals

In this paper, an algorithm for estimation the amplitude of a sequence of ultra-wideband quasi-radio signals with unknown durations and initial phases observed with white Gaussian noise is studied. Among the many classes of UWB signals, there is a separate one – UWB quasi-radio signals. A quasi-likelihood estimation algorithm is synthesized, according to which, instead of an unknown duration, some predicted value is used, and instead of an unknown initial phase, the maximum likelihood estimate is used. It is assumed that the sequence is coherent, i.e. the initial phase and duration are the same for all pulses of the sequence. The structure and statistical properties of the algorithm for estimation the informative parameter of a sequence of ultra-wideband quasi-radio signals are found. The dependence of the variance on the signal-to-noise ratio is obtained for a different length of the sequence and their impact on the efficiency of algorithm for estimation is investigated.
Multi-frequency Power Transfer by Single-Phase Grid-Connected Converters

In this paper, the possibility of single-phase multi-frequency power transfer is investigated. This task is performed by using single-phase grid-connected converters. The active and reactive powers can be transferred separately and bidirectionally at different frequencies. The control system controls the powers and currents of the converters. For doing so, the frequencies are decoupled by using a MSOGI-FLL + HDN block for each input signal of the control system and they are converted to alpha-beta coordinates. Additionally, the PR current controller is used for each frequency of the signals so that they control each component of the converter currents independently.