Miracle-dz/UniFAR-FTUs huggingface dataset:
Gravastar-like black hole solutions in q-theory

We present a stationary spherically symmetric solution of the Einstein equations, with a source generated by a scalar field of q-theory. In this theory Riemannian gravity, as described by the Einstein–Hilbert action, is coupled to a three—form field that describes the dynamical vacuum. Formally it behaves like a matter field with its own stress—energy tensor, equivalent to a scalar field minimally coupled to gravity. The asymptotically flat solutions obtained to the field equations represent black holes (BHs). For a sufficiently large horizon radius the energy density is localized within a thin spherical shell situated just outside of the horizon, analogous to a gravastar. The resulting solutions to the field equations, which admit this class of configurations, satisfy existence conditions that stem from the BH no—hair theorem, thanks to the presence of a region in space in which the energy density is negative.
Tuning path tracking controllers for autonomous cars using reinforcement learning

This article proposes an adaptable path tracking control system, based on reinforcement learning (RL), for autonomous cars. A four-parameter controller shapes the behaviour of the vehicle to navigate lane changes and roundabouts. The tuning of the tracker uses an ‘educated’ Q-Learning algorithm to minimize the lateral and steering trajectory errors, this being a key contribution of this article. The CARLA (CAR Learning to Act) simulator was used both for training and testing. The results show the vehicle is able to adapt its behaviour to the different types of reference trajectories, navigating safely with low tracking errors. The use of a robot operating system (ROS) bridge between CARLA and the tracker (i) results in a realistic system, and (ii) simplifies the replacement of CARLA by a real vehicle, as in a hardware-in-the-loop system. Another contribution of this article is the framework for the dependability of the overall architecture based on stability results of non-smooth systems, presented at the end of this article.
Propagation of plane sound waves of finite amplitude in inhomogeneous fluids

The problem of propagation of finite–amplitude waves in an inhomogeneous medium may be approached by considering the effect of inhomogeneity on the wavelet propagation speed c0 + βu. For a homogeneous medium this speech is constant. For an inhomogeneous medium, however, the speed varies because (1) the sound speed c0 generally varies, (2) the particle velocity u varies because of the changing impedance ρ0c0 of the medium, and (3) the coefficient β, which includes the parameter B/A characterizing the nonlinearity of the pressure‐density relation, may vary. For gases β = (γ + 1)/2, which is a constant because γ, the ratio of specific heats, is constant over a wide range of conditions. In the ocean, however, β varies because B/A depends on pressure, temperature, and salinity. It turns out that the solution for plane waves in an inhomogeneous medium is obtained by replacing x (distortion distance) and u in the solution for a homogeneous medium by the functions z = ∫(β/β)(c0/c0)5/2(ρ0/ρ0)1/2dx and w = (ρ0c.
The optical variability of PKS 2155 - 304

The BL Lacertae object, PKS 2155-304, has been photometrically monitored for the past decade (1979-1990). Optical variations have been observed on time scales ranging from several minutes to several years. The variations detected with time scales on the order of minutes are among the fastest optical variations detected for a BL Lacertae object. These rapid variations have provided constraints on the mass of the supermassive black hole, assumed to be present at the center of the object, of 5.5 ± 0.4 × 10 8 M ⊙ ≤ M ≤ 8.1 ± 1.0 × 10 9 M ⊙
Random-depth Quantum Amplitude Estimation

The maximum likelihood amplitude estimation algorithm (MLAE) is a practical solution to the quantum amplitude estimation problem with Heisenberg limit error convergence. We improve MLAE by using random depths to avoid the so-called critical points, and do numerical experiments to show that our algorithm is approximately unbiased compared to the original MLAE and approaches the Heisenberg limit better.
Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology

Recent genome-wide association studies (GWAS) have been successful in identifying associations between genetic variants and simple cardiac parameters derived from cardiac magnetic resonance (CMR) images. However, the emergence of big databases including genetic data linked to CMR, facilitates investigation of more nuanced patterns of shape variability. Here, we propose a new framework for gene discovery entitled Unsupervised Phenotype Ensembles (UPE). UPE builds a redundant yet highly expressive representation by pooling a set of phenotypes learned in an unsupervised manner, using deep learning models trained with different hyperparameters. These phenotypes are then analyzed via (GWAS), retaining only highly confident and stable associations across the ensemble. We apply our approach to the UK Biobank database to extract left-ventricular (LV) geometric features from image-derived three-dimensional meshes. We demonstrate that our approach greatly improves the discoverability of genes influencing LV shape, identifying 11 loci with study-wide significance and 8 with suggestive significance. We argue that our approach would enable more extensive discovery of gene associations with image-derived phenotypes for other organs or image modalities.
A generalization of the classical Euler and Korteweg fluids

The aim of this short paper is threefold. First, we develop an implicit generalization of a constitutive relation introduced by Korteweg (1901) that can describe the phenomenon of capillarity. Second, using a sub-class of the constitutive relations (implicit Euler equations), we show that even in that simple situation more than one of the members of the sub-class may be able to describe one or a set of experiments one is interested in describing, and we must determine which amongst these constitutive relations is the best by culling the class by systematically comparing against an increasing set of observations. (The implicit generalization developed in this paper is not a sub-class of the implicit generalization of the Navier-Stokes fluid developed by Rajagopal (2003), (2006) or the generalization due to Průša and Rajagopal (2012), as spatial gradients of the density appear in the constitutive relation developed by Korteweg (1901). ) Third, we introduce a challenging set of partial differential equations that would lead to new techniques in both analysis and numerical analysis to study such equations.
Mining Healthcare Procurement Data Using Text Mining and Natural Language Processing - Reflection From An Industrial Project

While text mining and NLP research has been established for decades, there remain gaps in the literature that reports the use of these techniques in building real-world applications. For example, they typically look at single and sometimes simplified tasks, and do not discuss in-depth data heterogeneity and inconsistency that is common in real-world problems or their implication on the development of their methods. Also, few prior work has focused on the healthcare domain. In this work, we describe an industry project that developed text mining and NLP solutions to mine millions of heterogeneous, multilingual procurement documents in the healthcare sector. We extract structured procurement contract data that is used to power a platform for dynamically assessing supplier risks. Our work makes unique contributions in a number of ways. First, we deal with highly heterogeneous, multilingual data and we document our approach to tackle these challenges. This is mainly based on a method that effectively uses domain knowledge and generalises to multiple text mining and NLP tasks and languages. Second, applying this method to mine millions of procurement documents, we develop the first structured procurement contract database that will help facilitate the tendering process. Second, Finally, we discuss lessons learned for practical text mining/NLP development, and make recommendations for future research and practice.
Reducing Datacenter Operational Carbon Emissions Effectively by Cooperating with the Grid

Facing growing concerns about power consumption and carbon emissions, cloud providers are adapting datacenter loads to reduce carbon emissions. With datacenters exceeding 100MW, they can affect grid dynamics, so doing this without damaging grid performance is difficult. We study power adaptation algorithms that use grid metrics and seek to reduce datacenter (DC) operational carbon emissions. First, we consider local, online adaptation. Second, to reduce grid disruption that can arise from adaptation, we consider using an external coordinator that limits aggregate impact. Finally, we study a novel cooperative scheme, where datacenters first create a full-day adapted power plan, based on day-ahead information, and then share it with the power grid. This novel approach is a partnership that reflects the shared responsibility between datacenter and the grid for carbon minimization. For each, we report DC carbon emissions reduction and grid impacts. Results show that PlanShare, the novel cooperative scheme is superior to all online approaches considered, achieving a net benefit of 12.6% reduction in datacenter operational carbon emissions. PlanShare achieves 26%–132% better performance than the best online approach, and far larger for many. This benefit arises from allowing the grid optimization with full future DC load information. Further it enables efficient DC resource management with a 24-hour capacity schedule. It also decreases power cost for the entire grid (datacenters and other customers).
Hadron-quark phase transition in neutron star by combining the relativistic Brueckner-Hartree-Fock theory and Dyson-Schwinger equation approach

Starting from the relativistic Brueckner-Hartree-Fock theory for nuclear matter and the Dyson-Schwinger equation approach for quark matter, the possible hadron-quark phase transition in the interior of a neutron star is explored. The first-order phase transition and crossover are studied by performing the Maxwell construction and three-window construction respectively. The mass-radius relation and the tidal deformability of the hybrid star are calculated and compared to the joint mass-radius observation of a neutron star and the constraints from gravitational wave detection. For the Maxwell construction, no stable quark core is found in the interior of a neutron star. For the three-window construction, the parameters of the smooth interpolation function are chosen in such a way to keep the thermodynamic stability and lead to a moderate crossover density region. To support a two-solar-mass neutron star under the three-window construction, the effective width of medium screening effects in quark matter should be around $0.35$ GeV.
Topic Modelling of Swedish Newspaper Articles about Coronavirus: a Case Study using Latent Dirichlet Allocation Method

Topic Modelling (TM) is a natural language processing (NLP) method for discovering topics in a collection of documents. Being an unsupervised method, it is a valuable tool when trying to summarise the main topics and topic changes in large quantities of data. In this study, we apply two prevalent topic modelling techniques - Latent Dirichlet Allocation (LDA) and BERTopic - to analyse the change of topics in the Swedish newspaper articles about COVID-19. We describe the corpus we created including 6515 articles, methods applied, and statistics on topic changes over approximately 1 year and two months period of time from 17th January 2020 to 13th March 2021. We hope this work can be an asset for grounding applications of topic modelling and can be inspiring for similar case studies in an era with pandemics, to support socio-economic impact research as well as clinical and healthcare analytics. Our data and source code is openly available at https://github.com/aaronlifenghan/Swed_Covid_TM.
Hardware Oriented Vision System of Logistics Robotics

A qualified logistics robot is required to locate and identify the target item properly. The vision system of the robot is the way it perceives the world which demands high precision and low latency. Using the state-of-art deep convolutional neural network model, we present a folder detector to locate and identify the file folder. We implement several classic CNN models in the Faster RCNN framework. The average precision of the ideal MobileNet model is up to 0.966 with the GPU inference time 59ms. Besides the model design, we present a hardware oriented layer adaptive quantization method. Using this method, we condense the model into low bitwidth fixed point arithmetic which is more efficient and hardware friendly than the GPU widely used 32 floating point arithmetic. We can condense the model into 8-bit fixed point arithmetic without the precision drop.
Coherent Control of Wave Beams Via Unidirectional Evanescent Modes Excitation

Conventional coherent absorption occurs only when two incident beams exhibit mirror symmetry with respect to the absorbing surface, i.e., the two beams have the same incident angles, phases, and amplitudes. This study proposes a more general metasurface paradigm for coherent perfect absorption with impinging waves from arbitrary asymmetric directions. By exploiting excitation of unidirectional evanescent waves, the output can be fixed at one reflection direction for any amplitude and phase of the control wave. It shows theoretically and confirm experimentally that the relative amplitude of the reflected wave can be tuned continuously from zero to unity by changing the phase difference between the two beams, i.e., switching from coherent perfect absorption to full reflection. It hopes that this study will open up promising possibilities for wave manipulation via evanescent waves engineering with applications in optical switches, optical computing, one‐side sensing, photovoltaics, and radar cross‐section control.
XDQN: Inherently Interpretable DQN through Mimicking

Although deep reinforcement learning (DRL) methods have been successfully applied in challenging tasks, their application in real-world operational settings is challenged by methods' limited ability to provide explanations. Among the paradigms for explainability in DRL is the interpretable box design paradigm, where interpretable models substitute inner constituent models of the DRL method, thus making the DRL method"inherently"interpretable. In this paper we explore this paradigm and we propose XDQN, an explainable variation of DQN, which uses an interpretable policy model trained through mimicking. XDQN is challenged in a complex, real-world operational multi-agent problem, where agents are independent learners solving congestion problems. Specifically, XDQN is evaluated in three MARL scenarios, pertaining to the demand-capacity balancing problem of air traffic management. XDQN achieves performance similar to that of DQN, while its abilities to provide global models' interpretations and interpretations of local decisions are demonstrated.
Revisiting the Distortion of Distributed Voting

We consider a setting with agents that have preferences over alternatives and are partitioned into disjoint districts. The goal is to choose one alternative as the winner using a mechanism which first decides a representative alternative for each district based on a local election with the agents therein as participants, and then chooses one of the district representatives as the winner. Previous work showed bounds on the distortion of a specific class of deterministic plurality-based mechanisms depending on the available information about the preferences of the agents in the districts. In this paper, we first consider the whole class of deterministic mechanisms and show asymptotically tight bounds on their distortion. We then initiate the study of the distortion of randomized mechanisms in distributed voting and show bounds based on several informational assumptions, which in many cases turn out to be tight. Finally, we also experimentally compare the distortion of many different mechanisms of interest using synthetic and real-world data.
Safehaul: Risk-Averse Learning for Reliable mmWave Self-Backhauling in 6G Networks

Wireless backhauling at millimeter-wave frequencies (mmWave) in static scenarios is a well-established practice in cellular networks. However, highly directional and adaptive beamforming in today’s mmWave systems have opened new possibilities for self-backhauling. Tapping into this potential, 3GPP has standardized Integrated Access and Backhaul (IAB) allowing the same base station to serve both access and backhaul traffic. Although much more cost-effective and flexible, resource allocation and path selection in IAB mmWave networks is a formidable task. To date, prior works have addressed this challenge through a plethora of classic optimization and learning methods, generally optimizing a Key Performance Indicator (KPI) such as throughput, latency, and fairness, and little attention has been paid to the reliability of the KPI. We propose Safehaul, a risk-averse learning-based solution for IAB mmWave networks. In addition to optimizing average performance, Safehaul ensures reliability by minimizing the losses in the tail of the performance distribution. We develop a novel simulator and show via extensive simulations that Safehaul not only reduces the latency by up to 43.2% compared to the benchmarks, but also exhibits significantly more reliable performance, e.g., 71.4% less variance in achieved latency.
On the Galactic Halos Rotation by Planck Data

As galactic halos are not directly visible, there are many ambiguities regarding their composition and rotational velocity. Though most of the dark matter is non-baryonic, some fraction is, and it can be used to trace the halo rotation. Asymmetries in the CMB towards M31 had been seen in the Planck data and ascribed to the rotational Doppler shift of the M31 halo. Subsequently, the same methods were used in the direction of five other galaxies belonging to the Local Group, namely M33, M81, M82, NGC 5128, and NGC 4594. It had been proved that there could be stable clouds of gas and dust in thermal equilibrium with the CMB at 2.7 K, which had been called “virial clouds”. In this paper, adopting this scenerio, an attempt is made to constrain the fraction of dust grains and gas molecules in the clouds.
Distributed Multirobot Control for Non-Cooperative Herding

In this paper, we consider the problem of protecting a high-value area from being breached by sheep agents by crafting motions for dog robots. We use control barrier functions to pose constraints on the dogs' velocities that induce repulsion in the sheep relative to the high-value area. This paper extends the results developed in our prior work on the same topic in three ways. Firstly, we implement and validate our previously developed centralized herding algorithm on many robots. We show herding of up to five sheep agents using three dog robots. Secondly, as an extension to the centralized approach, we develop two distributed herding algorithms, one favoring feasibility while the other favoring optimality. In the first algorithm, we allocate a unique sheep to a unique dog, making that dog responsible for herding its allocated sheep away from the protected zone. We provide feasibility proof for this approach, along with numerical simulations. In the second algorithm, we develop an iterative distributed reformulation of the centralized algorithm, which inherits the optimality (i.e. budget efficiency) from the centralized approach. Lastly, we conduct real-world experiments of these distributed algorithms and demonstrate herding of up to five sheep agents using five dog robots.
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration

We consider the problem of cooperative exploration where multiple robots need to cooperatively explore an unknown region as fast as possible. Multi-agent reinforcement learning (MARL) has recently become a trending paradigm for solving this challenge. However, existing MARL-based methods adopt action-making steps as the metric for exploration efficiency by assuming all the agents are acting in a fully synchronous manner: i.e., every single agent produces an action simultaneously and every single action is executed instantaneously at each time step. Despite its mathematical simplicity, such a synchronous MARL formulation can be problematic for real-world robotic applications. It can be typical that different robots may take slightly different wall-clock times to accomplish an atomic action or even periodically get lost due to hardware issues. Simply waiting for every robot being ready for the next action can be particularly time-inefficient. Therefore, we propose an asynchronous MARL solution, Asynchronous Coordination Explorer (ACE), to tackle this real-world challenge. We first extend a classical MARL algorithm, multi-agent PPO (MAPPO), to the asynchronous setting and additionally apply action-delay randomization to enforce the learned policy to generalize better to varying action delays in the real world. Moreover, each navigation agent is represented as a team-size-invariant CNN-based policy, which greatly benefits real-robot deployment by handling possible robot lost and allows bandwidth-efficient intra-agent communication through low-dimensional CNN features. We first validate our approach in a grid-based scenario. Both simulation and real-robot results show that ACE reduces over 10% actual exploration time compared with classical approaches. We also apply our framework to a high-fidelity visual-based environment, Habitat, achieving 28% improvement in exploration efficiency.
Equivariant and Steerable Neural Networks: A review with special emphasis on the symmetric group

Convolutional neural networks revolutionized computer vision and na-trual language processing. Their eﬃciency, as compared to fully connected neural networks, has its origin in the architecture, where convolutions reﬂect the translation invariance in space and time in pattern or speech recognition tasks. Recently, Cohen and Welling have put this in the broader perspective of invariance under symmetry groups, which leads to the concept of group equivaiant neural networks and more generally steerable neural networks. In this article, we review the architecture of such networks including equivariant layers and ﬁlter banks, activation with capsules and group pooling. We apply this formalism to the symmetric group, for which we work out a number of details on representations and capsules that are not found in the literature.
Automatic Generation of German Drama Texts Using Fine Tuned GPT-2 Models

This study is devoted to the automatic generation of German drama texts. We suggest an approach consisting of two key steps: fine-tuning a GPT-2 model (the outline model) to generate outlines of scenes based on keywords and fine-tuning a second model (the generation model) to generate scenes from the scene outline. The input for the neural model comprises two datasets: the German Drama Corpus (GerDraCor) and German Text Archive (Deutsches Textarchiv or DTA). In order to estimate the effectiveness of the proposed method, our models are compared with baseline GPT-2 models. Our models perform well according to automatic quantitative evaluation, but, conversely, manual qualitative analysis reveals a poor quality of generated texts. This may be due to the quality of the dataset or training inputs.