Miracle-dz/UniFAR-FTUs huggingface dataset:
‘Dirac–Fock + core-polarization’ calculations of E1 transitions in the francium isoelectronic sequence

Some E1 transitions in the francium isoelectronic sequence are computed in the ‘Dirac–Fock + core-polarization’ approximation, where core–valence electron correlation is treated in a semiclassical core-polarization picture. The obtained ionization energies and oscillator strengths are tested versus very accurate many-body perturbation treatment (MBPT) theoretical results published recently as well as versus available experimental data. The role of core–valence correlation (core polarization) is carefully studied for both ionization energies and oscillator strengths along the spectral series and isoelectronic sequence. Profound anomalies in oscillator strength fine structure components for the principal series of neutral francium are predicted and await experimental confirmation.
Latency-Constrained Task Distribution in Multi-Access Edge Computing Systems

Multi-Access Edge Computing (MEC) paradigm has been widely studied as a potential solution to cope with the challenges emerging from new generations of mobile networks. By processing applications’ data closer to the users, service providers are able to offload origin servers and their underlying network infrastructure, which consequently reduces users’ experienced latency. In this paper, we consider internet-based applications with strict latency tolerance which are primarily enabled by the MEC architecture, e.g., Virtual Reality (VR) control systems, cloud gaming, and smart healthcare. Moreover, nodes at the edge may host application-related tasks as well as assist in their provision. We introduce the Task Distribution (TD) problem, where the objective is to maximize the overall Quality of Service (QoS) while ensuring that tasks’ latency requirements are satisfied. The TD problem is modeled as an Integer Programming problem, taking into account three components: (i) tasks’ priority assignment, (ii) placement and (iii) routing through the MEC network. We propose to approach the TD problem through an approximation scheme, called Shortest-Flow Approximation (SFA), which considers pre-determined network flows selected according to a Betweenness-based criterion. In our experiments, we first extract useful insights on the TD problem that help characterize its solution. Then, we evaluate SFA’s performance empirically across different experimental settings, showing numerical results confirming its proximity to the optimal solution.
A Least-Square-based RMRAC for Grid-tied Voltage Source Inverters with LCL Filter

Renewable energy generation systems commonly use LCL filters as interfaces between them and the grid, aiming to reduce the harmonic content of Voltage Source Inverters. However, the LCL filter presents a relevant resonance peak that can lead the closed-loop to instability. Therefore, aiming to mitigate it, and provide an adequate current injection into the grid, in this paper is presented a Least-Squares-based Robust Model Reference Adaptive Controller. The proposed control strategy can track reference currents fast, while rejecting exogenous disturbances without needing additional control loops. Besides, the controller is robust enough to consider the LCL filter as a first order transfer function during its design, which allows it to use a first order reference model, reducing the complexity inherent to adaptive control strategies. Simulation results are presented to discuss the feasibility of a proposed control strategy for grid-injected current control.
The Influence of Training on the Construction of Evoked Potentials during Visual Stimulation

The present study compared evoked potentials in two groups of people with visual stimulation using a bistable image (Necker cube). The first group consisted of people who had seen this bistable image many times in various previous tests. The second group consisted of people passing the test with a bistable image for the first time. During the experiment, the subject was shown a large number of cubes with different intensities of the inner faces. For each cube, the subject had to determine its orientation (left or right) and press the corresponding button on the remote control. For the processing of evoked potentials, a new method of automatic labeling by amplitudes and latency times of the evoked potential components was used. The features of the formation of evoked potentials for these two experimental groups were shown.
How Do Algorithmic Fairness Metrics Align with Human Judgement? A Mixed-Initiative System for Contextualized Fairness Assessment

Fairness evaluation presents a challenging problem in machine learning, and is usually restricted to the exploration of various metrics that attempt to quantify algorithmic fairness. However, due to cultural and perceptual biases, such metrics are often not powerful enough to accurately capture what people perceive as fair or unfair. To close the gap between human judgement and automated fairness evaluation, we develop a mixed-initiative system named FairAlign, where laypeople assess the fairness of different classification models by analyzing expressive and interactive visualizations of data. Using the aggregated qualitative feedback, data scientists and machine learning experts can examine the similarities and the differences between predefined fairness metrics and human judgement in a contextualized setting. To validate the utility of our system, we conducted a small study on a socially relevant classification task, where six people were asked to assess the fairness of multiple prediction models using the provided visualizations. The results show that our platform is able to give valuable guidance for model evaluation in case of otherwise contradicting and indecisive metrics for algorithmic fairness.
Knowledge Transfer in Multi-Agent Reinforcement Learning Using Decision Trees

Multi-agent reinforcement learning has achieved promising progress in a range of real-world tasks. However, learning a model from scratch for every new task largely struggles for the optimal policy requiring more learning processes. In this paper, we propose a new method that realizes robust coordination knowledge transfer by extracting the knowledge from a single agent and guiding the agent executes the action during the early stage. First, we train a single agent to obtain an expert policy with a deep reinforcement learning algorithm. Then, after getting the expert policy, we utilize the expert policy to generate the trajectories experience to extract the transfer of domain control knowledge. Finally, we utilize the extracted knowledge to guide the multi-agents quickly adapt the complex tasks. The experimental results demonstrate that our proposed transfer knowledge method is effective, and the efficiency of multi-agent reinforcement learning has been considerably improved.
Routing with Graph Convolutional Networks and Multi-Agent Deep Reinforcement Learning

The computer networking community has been steadily increasing investigations into machine learning to help solve tasks such as routing, traffic prediction, and resource management. In particular, due to the recent successes in other applications, Reinforcement Learning (RL) has seen steady growth in network management and, more recently, in routing. However, changes in the network topology prevent RL-based routing approaches from being employed in real environments due to the need for retraining. In this paper, we approach routing as an RL problem with two novel twists: minimizing flow set collisions and dealing with routing in dynamic network conditions without retraining. We compare this approach to other routing protocols, including multi-agent learning, to various Quality-of-Service metrics, and we report our lesson learned.
IoT Course Construction in General Education against the Background of China's University-Industry Collaboration

This study describes the IoT course design in general education and contributes to the understanding of the influence of university-industry collaboration on higher education. The rapid development of IoT has made a significant impact on education by changing the traditional teaching method as well as putting forward a new demand for the introduction of relevant knowledge to students. Against the background of China's government encouraging university-industry collaboration, we analyzed the necessity and feasibility of introducing the IoT course as a general elective course for undergraduates. A curriculum that adopts the Niagara framework as a lab platform is described. The empirical study at Nankai university illustrated that through university-industry collaboration, cutting-edge technology such as IoT could be effectively integrated into universities' general education and benefit students of various majors.
Transcutaneous Electrical Nerve Stimulation along the Base of the Finger to Modify the Location of Tactile Sensation at the Finger

We propose transcutaneous electrical nerve stimulation along the finger's base to achieve induction of tactile sensation at the finger. Unlike many other methods, our method can superimpose tactile sensation on natural perception through interaction with real objects because the finger is left uncovered with a device. We conducted a psychophysical experiment to demonstrate that the location of the induced tactile sensation could be modified by altering the position of stimulation along the base of the finger. Our method solves the problem of inducing tactile sensation in various locations without covering the targeted position.
Graph Convolutional Networks-based Label Distribution Learning for Image Classification

The one-hot vector employed for true label representation has been widely applied for image classification. However, the one-hot representation assumes that a single label is only associated with one instance, which is not reasonable because labels are generally not completely independent and instances may relate to multiple labels for the real scenarios. Such one-hot representation may ignore the relevance among labels that provide more supervision information for model training. To capture and explore such significant relevance in image classification, we propose, in this paper, GCNLDL, a Graph Convolutional Network based Label Distribution Learning approach for classification. GCNLDL builds a directed graph over the images, where each node (sample) is represented by the embedding of an image, where GCN learns the relevance between an input image and multiple training images from different classes to obtain the label distribution vector. The resulting vector is further combined with the one-hot label to recover a realistic label distribution of the input image, which is employed to train the state-of-the-art classification models. Furthermore, a multilayer perception is proposed to learn an effective label correlation matrix to guide information propagation among the nodes in GCN. GCNLDL is capable of capturing the relevance among labels by representation learning graph structure among image samples during training process and produces a better label distribution to guide the training of the state-of-the-art image classification models, resulting in a performance improvement of image classification. Rigorous experimental results on four public image classification datasets show that GCNLDL outperforms other approaches and effectively improves the performance of deep learning classification based models.
Design of an Innovative Campus Remote Seat Booking System for Smart Learning Environment

This paper presents a project-based research study that tackles a real-world problem at the University of Nottingham Ningbo China. The project was performed by recruiting student interns from two different campuses. The project investigated how a remote seat booking system could be designed and used in the library or other study rooms across the campus. A wireless sensor network was created using Zigbee wireless standards. A prototype was implemented using a pressure sensor integrated into the CC2530-based wireless sensor node. A WeChat mini program was designed as a front-end for a student user to search and book the available seat. The proposed prototype in this paper aims at cost reduction and flexibility enhancement. The study recommends that a comprehensive system based on the proposed prototype could be part of the campus digital transformation in the future.
Explore and Interpret the Correlations Among VR Applications

Digital technologies significantly improved people's lives, and result in the concept of digital economy. Recently, virtual reality technology has been developed continuously, which helps to promote the digital economy. In this context, there have been growing online e-commerce platforms that are designed for VR merchants, e.g., Oculus1. On these platforms, merchants can present their VR applications for attracting consumers to purchase. In this paper, we aim at exploring and interpreting the relationships among VR applications in these e-commerce platforms. To this end, we collect a dataset from the Oculus and use a classical topic model, namely Link-LDA, to jointly model user purchase records and user reviews for the VR applications. Through extensive experiments, we find that the Link-LDA not only identifies the associations among VR applications well but also provides an explanation for these associations. Our research provides insights into VR consumer behavior analysis and VR application recommendations on e-commerce platforms.
Load-Store Queue Sizing for Efficient Dataflow Circuits

Dataflow circuits implement dynamic scheduling and have recently been explored as an alternative to standard, statically scheduled high-level synthesis (HLS) solutions. In contrast to static HLS, dataflow circuits resolve memory dependencies during runtime by employing load-store queues (LSQs) at the memory interface. However, LSQs are extremely resource-expensive to implement in a spatial system and may cause notable frequency degradation. Therefore, there is a clear need to minimize their size and complexity, while still allowing the circuit to achieve a high computational rate. So far, designers resorted to manually tuning the LSQ depth (i.e., number of queue entries) to trade off area and performance; yet, this approach is evidently time-consuming and unfeasible for complex designs. In this work, we develop a strategy to automatically determine the most affordable LSQ depths in dataflow circuits while maintaining the best possible circuit throughput. We demonstrate our technique on benchmarks obtained from C code with different memory access patterns and show that it can effectively produce the desired Pareto-optimal design points.
Unraveling the burial and exhumation history of foreland basins using the spread of apatite (U-Th-Sm)/He single grain ages

Reconstructing the evolution of foreland basins that experienced late exhumation is challenging due to an incomplete sedimentary record. Thermochronometry has been applied successfully to reconstruct basin evolution, but the 15 method is subject to uncertainties. For the Swiss Molasse Basin, a wide range of exhumation magnitude and timing has been proposed based on thermochronometry. We aim to reduce uncertainty by dating larger numbers of grains and samples, to obtain statistically more robust data. New apatite (U-Th-Sm)/He (AHe) data from a single borehole shows ages of 4 to 30 Ma in the upper 500 meters and ages of 3 to 80 Ma below 1300 meters. This is counterintuitive as a total reset is expected at depths exceeding approximately 600 m. To arrive at a single consistent thermal history including our and previously 20 published data, we conduct thermal modeling with different software. In particular we test the influence of different provenance histories and distinguish between cooling associated with changes in heat flow vs changes in exhumation. We determine 1050 m +/100 m of exhumation, starting slowly at 13 Ma and accelerating at 9 Ma. Coinciding with exhumation, heat flow begins to rise sharply, causing heating until 5 Ma, despite ongoing exhumation. We show that this discrepancy between start of exhumation and start of cooling is the main reason for differing estimates for the burial and 25 exhumation history of the basin. We suggest that the remaining misfit between modeled and measured Molasse AHe ages can be explained by post-Miocene hydrothermal flux in the Neogene sediment fill above a sealing layer, potentially the Middle Jurassic claystones or Triassic evaporites. In summary, we show that a single consistent model for basin exhumation relies on large sets of grains and samples, as well 30 as inclusion of provenance ages in the models. With timing of the main exhumation phase constrained to start at 9 Ma, we can rule out a 5 Ma climatic event as exhumation driver. As the region is not affected by extensive faulting, deep seated processes related to mantle dynamics remain as exhumation driving process. https://doi.org/10.5194/egusphere-2022-1323 Preprint. Discussion started: 5 December 2022 c © Author(s) 2022. CC BY 4.0 License.
Representation of sound localization transfer function and psychoacoustical evaluation

A method is proposed for determining representatives of the sound localization transfer function (SLTF) aiming at a compromise between feasibility and localization accuracy. This method is based on principal components analysis (PCA). The magnitudes of SLTF measured at both ears of 56 subjects for 24 targets were modelled by linear combination of six principal components (PC). For each target and ear, the representative was determined to be the SLTF whose weights of PC contributing to the magnitude best approximated the average over subjects. The effectiveness of the representatives was evaluated both statistically and psychoacoustically. The statistical analysis indicates that the individual distribution of the weights of PC is assumed to be a six-dimensional normal distribution that is densest around the average. The discrepancy of the representatives from the average is within 1.5 percent of the individual accumulation. The psychoacoustical evaluation for 28 of the subjects indicates that the increase in the front/back confusion rate in the use of the representatives is significant for frontal targets, but not for other targets. The lateral judgement is robust against SLTFs for all targets tested. However, considerable individual dispersion of the front/back confusion rate hindered from determining the allowance of the discrepancy between individual SLTFs and the representatives for acceptable localization accuracy.
On magnetospheric interchange instability

Gold has pointed out that the gas in the magnetosphere of the earth can take part in large-scale motions in which tubes of force of equal flux and the gas within them are interchanged. Such motions leave the magnetic field and its energy almost unchanged, and may therefore be driven by variation in the state of the gas. In this paper the stability of the magnetosphere against such motion occurring spontaneously is studied by investigation of the change in potential energy of the system during a hypothetical interchange of two tubes of force. For isothermal interchanges the energy criterion may be cast into a useful form without further physical assumptions. Our analysis shows that, except possibly at low latitudes, such motions are not of importance. The more realistic case of adiabatic interchanges is discussed in the two limits where the gravitational energy is much smaller or much larger, in absolute value, than the internal energy of the gas. Smaller gravitational energy applies to the outer tubes of force for which the energy content is dominated by the internal energy of the fully ionized hot gas residing far out in the geomagnetic field; spontaneous interchanges occur only if the energy content of the tubes of force decreases sufficiently rapidly in the outward direction. Larger gravitational energy applies to the inner tubes of force since the energy content of these tubes is dominated by the gravitational energy of the cold gas at the feet of the tubes of force, i.e. the ionospheric material. It is shown that spontaneous adiabatic interchanges may occur if the temperature in the upper ionosphere increases sufficiently rapidly with increasing latitude. It is also demonstrated that the temperature gradients in subauroral latitudes, caused by the heating of the ionosphere in the auroral zones by fast particles, may be sufficient to trigger this instability. Thus an inner convective zone may exist in the magnetosphere. The size and location of this zone are discussed.
Sigmoid Structure of an Emerging Flux Tube

We present the results from three-dimensional MHD simulations of a magnetic flux tube emerging through the solar photosphere. The simulation is initialized with a straight tube of twisted magnetic field located in the upper convection zone. Buoyancy effects drive an arched segment of the tube upward through the photospheric layer and into the corona. Matter drains from the coronal field, which thereafter undergoes a rapid expansion. The coronal magnetic field formed in this manner exhibits outer poloidal field lines that resemble a potential arcade and inner toroidal field lines that emerge after the tube axis, forming sigmoid structure. The simulations suggest that the neutral-line shear and sigmoidal field arise as a natural by-product of flux emergence.
Reflection from a one-dimensional, totally refracting random multilayer

The interplay of random scattering and total internal reflection is studied in the context of an idealized one-dimensional problem arising in acoustic scattering from ocean sediments. Appropriate scalings are introduced; the asymptotic analysis of the Fokker–Planck equation for the reflection coefficient is performed and subsequently compared with the results of numerical simulations.
Three-dimensional imaging of nanovoids in copper interconnects using incoherent bright field tomography

As integrated circuits have shrunk, conventional electron microscopies have proven inadequate for imaging complicated interconnect structures due to the overlap of features in projection. These techniques produce transmission functions with a nonmonotonic dependence of intensity on thickness for common microelectronic materials, making them unsuitable for tomography. We report the use of an incoherent bright field imaging technique in a scanning transmission electron microscope optimized for the three-dimensional reconstruction of thick copper microelectronic structures. Predictable behavior of the signal in samples up to ∼1μm thick allows us to reconstruct and quantify the shape and volume of stress voids within Ta-lined interconnects.
Human cadaver retina model for retinal heating during corneal surgery with a femtosecond laser

Femtosecond lasers are widely used in everyday clinical procedures to perform minimally invasive corneal refractive surgery. The intralase femtosecond laser (AMO Corp. Santa Ana, CA) is a common example of such a laser. In the present study a numerical simulation was developed to quantify the temperature rise in the retina during femtosecond intracorneal surgery. Also, ex-vivo retinal heating due to laser irradiation was measured with an infrared thermal camera (Fluke Corp. Everett, WA) as a validation of the simulation. A computer simulation was developed using Comsol Multiphysics to calculate the temperature rise in the cadaver retina during femtosecond laser corneal surgery. The simulation showed a temperature rise of less than 0.3 degrees for realistic pulse energies for the various repetition rates. Human cadaver retinas were irradiated with a 150 kHz Intralase femtosecond laser and the temperature rise was measured withan infrared thermal camera. Thermal camera measurements are in agreement with the simulation. During routine femtosecond laser corneal surgery with normal clinical parameters, the temperature rise is well beneath the threshold for retina damage. The simulation predictions are in agreement with thermal measurements providing a level of experimental validation.