Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Gameplay in MOBA Games Under Different Network Conditions

This work aims to analyze the influence that different network conditions can have on the performance of a player in a multiplayer game of the MOBA genre, according to scenarios where transmission rate, latency and packet loss are used as factors. The scoring data of the players is collected at the end of each scenario and a quantitative analysis is performed. For this purpose, the DOTA 2 game was chosen and the evaluation metrics were KDA (Kills, Deaths and Assists), along with the farm and the gold collected. This allowed the analysis of how the factors influenced the defined metrics. To create the scenarios, the Clumsy 0.2 network condition emulation tool was used, thus controlling how the network would behave in each scenario. Finally, it can be concluded that for games of the MOBA genre the players have a significant decrease in their performance when submitted to networks of lower quality. The latency has the greatest influence on the player's performance, whereas packet losses of up 5% are almost insignificant if not associated with high latency and the transmission rate is insignificant for the DOTA 2 game since it uses a very low transmission rate, which is typical of this game genre.
Dangers in autonomous vehicles

Nowadays, one of the biggest expectation of people of this era is the moment when self-driving cars finally become quotidian on the roads, just like in sci-fi movies. Car factories and software developers have been working tirelessly for years to achieve this goal, and these vehicles are passing their tests successfully, in some cases even preventing accidents that would have been the driver's fault. However, there are also reports of failed tests as the technology is still being tweaked. The aim of this article is to shed light on some of the factors that negatively affect different stages of development and to examine the functionality of these vehicles, paying close attention to software related issues that include information protection, security breaches, certain sensory malfunctions that may occur due to faulty road conditions, and even deliberate attempts to damage the vehicle.
Stability Analysis of Multi-Paralleled Grid-Connected Inverters with LCL-Filter in the Weak Grid

In this paper, the stability problem of multi-paralleled grid-connected inverter system in the weak grid is investigated. Due to the existence of grid impedance, the inverters in the system have coupling problems with each other. This may cause resonance and lead the system to be unstable. This paper focuses on the general system of inverters with different parameters, and then establishes the Norton equivalent model of the system. Based on the equivalent transformation of the Norton model, the grid impedance is equivalently allocated to each inverter, and a stability criterion for a multi-paralleled grid-connected inverter system is proposed. In addition, considering the influence of the digital-control-delay on system stability, a phase-lead compensator is added to the capacitor-current-feedback path to improve the system robustness against grid impedance variation. The effectiveness of the theoretical analysis is verified by experimental results.
Reconfigurable Surface Design for Electromagnetic Wave Control

A reconfigurable surface, for electromagnetic (EM) wave manipulation, utilizing frequency selective unit-cells integrated with PIN diodes are introduced. By digitally controlling the reconfigurable surface, several functionalities such as focusing, linear polarization conversion and scattering are obtained on the same surface. For the scattering purposes, three arrangement methods of alternative periodic columns, chessboard-like structure and coding surface optimized by GSO algorithm are analyzed. For the coded surface, 10-dB RCS reduction from 15.5 GHz to 22.5 GHz is obtained with a bandwidth of 37%. All the results show that the introduced reconfigurable surface has more potential for future applications.
±535kV High Voltage DC Circuit Breaker Insulation Test Analysis

This paper introduces insulation tests of a ±535kV mechanical high voltage DC circuit breaker, which is used for Zhangbei flexile DC transmission project. First, the load characteristics of each branch were analyzed, including main branch, transfer branch, energy dissipation branch and buffer branch, and the specific test items were proposed. Then, the test circuit and arrangement of lightning impulse withstand voltage were analyzed, switching overvoltage and test waveform of DCCB were also discussed. At last, long-term DC partial discharge measurement and characteristics were analyzed. This article also discussed some problems and solutions encountered in specific tests.
Maximal amenability with asymptotic orthogonality in amalgamated free products

We investigate the use of Popa's asymptotic orthogonality to establish maximal amenability for amalgamated free product von Neumann algebras. Although new techniques have recently been developed to consider amalgamated free products, we find that the technique developed by Popa in the 1980s can be used to demonstrate maximal amenability for a certain family of amalgamated free product von Neumann algebras.
Prioritized Unit Propagation with Periodic Resetting is (Almost) All You Need for Random SAT Solving

We propose prioritized unit propagation with periodic resetting, which is a simple but surprisingly effective algorithm for solving random SAT instances that are meant to be hard. In particular, an evaluation on the Random Track of the 2017 and 2018 SAT competitions shows that a basic prototype of this simple idea already ranks at second place in both years. We share this observation in the hope that it helps the SAT community better understand the hardness of random instances used in competitions and inspire other interesting ideas on SAT solving.
Parallel Restarted SPIDER - Communication Efficient Distributed Nonconvex Optimization with Optimal Computation Complexity

In this paper, we propose a distributed algorithm for stochastic smooth, non-convex optimization. We assume a worker-server architecture where $N$ nodes, each having $n$ (potentially infinite) number of samples, collaborate with the help of a central server to perform the optimization task. The global objective is to minimize the average of local cost functions available at individual nodes. The proposed approach is a non-trivial extension of the popular parallel-restarted SGD algorithm, incorporating the optimal variance-reduction based SPIDER gradient estimator into it. We prove convergence of our algorithm to a first-order stationary solution. The proposed approach achieves the best known communication complexity $O(\epsilon^{-1})$ along with the optimal computation complexity. For finite-sum problems (finite $n$), we achieve the optimal computation (IFO) complexity $O(\sqrt{Nn}\epsilon^{-1})$. For online problems ($n$ unknown or infinite), we achieve the optimal IFO complexity $O(\epsilon^{-3/2})$. In both the cases, we maintain the linear speedup achieved by existing methods. This is a massive improvement over the $O(\epsilon^{-2})$ IFO complexity of the existing approaches. Additionally, our algorithm is general enough to allow non-identical distributions of data across workers, as in the recently proposed federated learning paradigm.
On the formation of exotic, massive, stellar-remnant black holes at solar and sub-solar metallicities through evolution of massive binaries.

The recent inference of a 70M_sun black hole (BH) in the Galactic, detached binary LB-1 has sparked cross-disciplinary debate since a stellar remnant of such large mass is well above what can be expected from stellar-evolutionary theory, especially in an enriched environment like that of the Milky Way. This study focusses on the possibilities of formation of extraordinarily massive BHs at solar and globular cluster (GC)-like metallicities via evolution of massive stellar binaries. A population-synthesis approach is followed utilizing the recently updated BSE program. BHs in the mass range of 50M_sun-80M_sun could be formed at the solar metallicity only if a large fraction, >70%, of matter is allowed to accrete onto a low-mass BH, in a BH-star merger product (a "black hole Thorne-Zytkow object"; BH-TZO). Their counterparts at GC-like metallicities can reach 100 M_sun. Although post-accretion BHs can, generally, be expected to be of high spin parameter, they can potentially be of low spin in the case of a BH-TZO. This spin aspect remains speculative in this work and deserves detailed hydrodynamic studies.
Boundary layer in linear viscoelasticity

It is well known that a boundary layer develops along an infinite plate under oscillatory motion in a Newtonian fluid. In this work, this oscillatory boundary layer theory is generalized to the case of linear viscoelastic(LVE) flow. We demonstrate that the dynamics in LVE are generically different than those for flow of similar settings in Newtonian fluids, in several aspects. These new discoveries are expected to have consequences on related engineering applications. Mimicking the theory for Stokes oscillatory layers along an infinite plate in Newtonian flow, we derive a similar oscillatory boundary layer formula for the case of LVE. In fact, the new theory includes the Stokes layer theory as a special case. For the disturbance flow caused by particles undergoing oscillatory motion in linear viscoelasticity(LVE), a numerical investigation is necessary. A boundary integral method is developed for this purpose. We verify our numerical method by comparing its results to an existing analytic solution, in the simple case of a spherical particle. Then the numerical method is applied in case studies of more general geometries. Two geometries are considered because of their prevalence in applications: spheroids; dumbbells and biconcave disks.
STEERAGE: Synthesis of Neural Networks Using Architecture Search and Grow-and-Prune Methods

Neural networks (NNs) have been successfully deployed in many applications. However, architectural design of these models is still a challenging problem. Moreover, neural networks are known to have a lot of redundancy. This increases the computational cost of inference and poses an obstacle to deployment on Internet-of-Thing sensors and edge devices. To address these challenges, we propose the STEERAGE synthesis methodology. It consists of two complementary approaches: efficient architecture search, and grow-and-prune NN synthesis. The first step, covered in a global search module, uses an accuracy predictor to efficiently navigate the architectural search space. The predictor is built using boosted decision tree regression, iterative sampling, and efficient evolutionary search. The second step involves local search. By using various grow-and-prune methodologies for synthesizing convolutional and feed-forward NNs, it reduces the network redundancy, while boosting its performance. We have evaluated STEERAGE performance on various datasets, including MNIST and CIFAR-10. On MNIST dataset, our CNN architecture achieves an error rate of 0.66%, with 8.6x fewer parameters compared to the LeNet-5 baseline. For the CIFAR-10 dataset, we used the ResNet architectures as the baseline. Our STEERAGE-synthesized ResNet-18 has a 2.52% accuracy improvement over the original ResNet-18, 1.74% over ResNet-101, and 0.16% over ResNet-1001, while having comparable number of parameters and FLOPs to the original ResNet-18. This shows that instead of just increasing the number of layers to increase accuracy, an alternative is to use a better NN architecture with fewer layers. In addition, STEERAGE achieves an error rate of just 3.86% with a variant of ResNet architecture with 40 layers. To the best of our knowledge, this is the highest accuracy obtained by ResNet-based architectures on the CIFAR-10 dataset.
Graph Theory and Metro Traffic Modelling

A unifying graph theoretic framework for the modelling of metro transportation networks is proposed. This is achieved by first introducing a basic graph framework for the modelling of the London underground system from a diffusion law point of view. This forms a basis for the analysis of both station importance and their vulnerability, whereby the concept of graph vertex centrality plays a key role. We next explore k-edge augmentation of a graph topology, and illustrate its usefulness both for improving the network robustness and as a planning tool. Upon establishing the graph theoretic attributes of the underlying graph topology, we proceed to introduce models for processing data on such a metro graph. Commuter movement is shown to obey the Fick's law of diffusion, where the graph Laplacian provides an analytical model for the diffusion process of commuter population dynamics. Finally, we also explore the application of modern deep learning models, such as graph neural networks and hyper-graph neural networks, as general purpose models for the modelling and forecasting of underground data, especially in the context of the morning and evening rush hours. Comprehensive simulations including the passenger in- and out-flows during the morning rush hour in London demonstrates the advantages of the graph models in metro planning and traffic management, a formal mathematical approach with wide economic implications.
Fundamental Limits of Lossless Data Compression With Side Information

The problem of lossless data compression with side information available to both the encoder and the decoder is considered. The finite-blocklength fundamental limits of the best achievable performance are defined, in two different versions of the problem: Reference-based compression, when a single side information string is used repeatedly in compressing different source messages, and pair-based compression, where a different side information string is used for each source message. General achievability and converse theorems are established for arbitrary source-side information pairs. Nonasymptotic normal approximation expansions are proved for the optimal rate in both the reference-based and pair-based settings, for memoryless sources. These are stated in terms of explicit, finite-blocklength bounds, that are tight up to third-order terms. Extensions that go significantly beyond the class of memoryless sources are obtained. The relevant source dispersion is identified and its relationship with the conditional varentropy rate is established. Interestingly, the dispersion is different in reference-based and pair-based compression, and it is proved that the reference-based dispersion is in general smaller.
staq—A full-stack quantum processing toolkit

We describe staq, a full-stack quantum processing toolkit written in standard C++. staq is a quantum compiler toolkit, comprising of tools that range from quantum optimizers and translators to physical mappers for quantum devices with restricted connectives. The design of staq is inspired from the UNIX philosophy of ‘less is more’, i.e. staq achieves complex functionality via combining (piping) small tools, each of which performs a single task using the most advanced current state-of-the-art methods. We also provide a set of illustrative benchmarks.
Deep Structured Implicit Functions

The goal of this project is to learn a 3D shape representation that enables accurate surface reconstruction, compact storage, efficient computation, consistency for similar shapes, generalization across diverse shape categories, and inference from depth camera observations. Towards this end, we introduce Deep Structured Implicit Functions (DSIF), a 3D shape representation that decomposes space into a structured set of local deep implicit functions. We provide networks that infer the space decomposition and local deep implicit functions from a 3D mesh or posed depth image. During experiments, we find that it provides 10.3 points higher surface reconstruction accuracy (F-Score) than the state-of-the-art (OccNet), while requiring fewer than 1 percent of the network parameters. Experiments on posed depth image completion and generalization to unseen classes show 15.8 and 17.8 point improvements over the state-of-the-art, while producing a structured 3D representation for each input with consistency across diverse shape collections. Please see our video at this https URL
Near-field analysis of strong coupling between localized surface plasmons and excitons

We simulate the near-field effects of strong coupling between molecular excitons and localized surface plasmons, supported by aluminum nanodisks. The simulations are done using a simple model of a two-level system, implemented in a commercial electromagnetic finite-difference time-domain solver. While the Rabi splitting is present in the near-field, its spectral gap is seen to be smaller than the one obtained in the far-field, although it follows a clear square root dependence on the molecular density as expected. Moreover, the energy exchange between the plasmonic mode and the excitonic material is evident in 'beats' within the electromagnetic near-field, which are out of phase with respect to the exciton population. Our results explicitly demonstrate the collective nature of strong coupling, which is expressed by the synchronized population oscillations at the collective Rabi frequency set by the number of molecules interacting with the plasmonic mode. This analysis sheds light on strong coupling effects in the near-field region using a versatile model, which provides a powerful tool to study strong coupling, near-field effects, and light-matter interactions in general.
PEEPLL: privacy-enhanced event pseudonymisation with limited linkability

Pseudonymisation provides the means to reduce the privacy impact of data collection and processing on individual subjects. Its application on data records, especially in an environment with additional constraints, like re-identification in the course of incident response, implies assumptions and privacy issues, which contradict the achievement of the desirable privacy level. Proceeding from two real-world scenarios, where personal and identifying data needs to be processed, we identify a system model for pseudonymisation and explicitly state the sustained privacy threats. With this system and threat model, we derive privacy protection goals together with possible technical realisations, which are integrated into our event pseudonymisation framework PEEPLL for the context of event processing, like auditing of user activities. Our framework provides privacy-friendly linkability in order to maintain the possibility for automatic event correlation and evaluation, while at the same time reduces the privacy impact on individuals. With this framework, privacy provided by event pseudonymisation can be enhanced by a more rigorous commitment to the concept of personal data minimisation.
Electrically Driven Hot-carrier Generation and Above-threshold Light Emission in Plasmonic Tunnel Junctions.

Above-threshold light emission from plasmonic tunnel junctions, when emitted photons have energies significantly higher than the energy scale of the incident electrons, has attracted much recent interest in nano-optics, while the underlying physical mechanism remains elusive. We examine above-threshold light emission in electromigrated tunnel junctions. Our measurements over a large ensemble of devices demonstrate a giant material dependence of photon yield (emitted photons per incident electrons), as large as four orders of magnitude. This dramatic effect cannot be explained only by the radiative field enhancement effect due to the localized plasmons in the tunneling gap. Emission is well described by a Boltzmann spectrum with an effective temperature exceeding 2000 K, coupled to a plasmon-modified photonic density of states. The effective temperature is approximately linear in the applied bias, consistent with a suggested theoretical model in which hot carriers are generated by non-radiative decay of electrically excited localized plasmons. Electrically driven hot-carrier generation and the associated non-traditional light emission could open new possibilities for active photochemistry, optoelectronics and quantum optics.
Speech-Driven Facial Animation Using Polynomial Fusion of Features

Speech-driven facial animation involves using a speech signal to generate realistic videos of talking faces. Recent deep learning approaches to facial synthesis rely on extracting low-dimensional representations and concatenating them, followed by a decoding step of the concatenated vector. This accounts for only first-order interactions of the features and ignores higher-order interactions. In this paper we propose a polynomial fusion layer that models the joint representation of the encodings by a higher-order polynomial, with the parameters modelled by a tensor decomposition. We demonstrate the suitability of this approach through experiments on generated videos evaluated on a range of metrics on video quality, audiovisual synchronisation and generation of blinks.
Terahertz Communications (TeraCom): Challenges and Impact on 6G Wireless Systems

Terahertz communications are envisioned as a key technology for 6G, which requires 100+ Gbps data rates, 1-millisecond latency, among other performance metrics. As a fundamental wireless infrastructure, the THz communication can boost abundant promising applications, including next-generation WLAN systems like Tera-WiFi, THz wireless backhaul, as well as other long-awaited novel communication paradigms. Serving as a basis of efficient wireless communication and networking design, this paper highlights the key THz channel features and recent advancements in device technologies. In light of these, impact and guidelines on 6G wireless communication and networking are elaborated. We believe the progress of THz technologies is helping finally close the so called THz Gap, and will realize THz communications as a pillar of 6G wireless systems.
Evaluation of Sequential Processing for the Extraction of Starch, Lipids, and Proteins From Wheat Bran

In line with the need to better utilize agricultural resources, and valorize underutilized fractions, we have developed protocols to increase the use of wheat bran, to improve utilization of this resource to additional products. Here, we report sequential processing for extraction of starch, lipids, and proteins from wheat brans with two different particle sizes leaving a rest-material enriched in dietary fiber. Mild water-based extraction of starch resulted in maximum 81.7 ± 0.67% yield. Supercritical fluid extraction of lipids by CO2 resulted in 55.2 ± 2.4% yield. This was lower than the corresponding yield using Soxhlet extraction, which was used as a reference method, but allowed a continued extraction sequence without denaturation of the proteins remaining in the raw-material. Alkaline extraction of non-degraded proteins resulted in a yield corresponding to one third of the total protein in the material, which was improved to reach 62 ± 8% by a combination of wheat bran enzymes activation followed by Osborne fractionation. The remaining proteins were extracted in degraded form, resulting in maximum 91.6 ± 1.6% yield of the total proteins content. The remaining material in both fine and coarse bran had a fiber content that on average corresponded to 73 ± 3%. The current work allows separation of several compounds, which is enabling valorization of the bran raw-material into several products.