Miracle-dz/UniFAR-FTUs huggingface dataset:
Game theory and Evolutionary-optimization methods applied to resource allocation problems in emerging computing environments: A survey

Today's intelligent computing environments, including Internet of Things, cloud computing and fog computing, allow many organizations around the world to optimize their resource allocation regarding time and energy consumption. Due to the sensitive conditions of utilizing resources by users and the real-time nature of the data, a comprehensive and integrated computing environment has not yet been able to provide a robust and reliable capability for proper resource allocation. Although, traditional methods of resource allocation in a low-capacity hardware resource system are efficient for small-scale resource providers, for a complex system in the conditions of dynamic computing resources and fierce competition in obtaining resources, they do not have the ability to develop and adaptively manage the conditions optimally. To solve this problem, computing intelligence techniques try to optimize resource allocation with minimal time delay and energy consumption. Therefore, the objective of this research is a comprehensive and systematic survey on resource allocation problems using computational intelligence methods under Game Theory and Evolutionary-optimization in emerging computing environments, including cloud, fog and Internet of Things according to the latest scientific-research achievements.
A Generally Covariant Theory of Quantized Dirac Field in de Sitter Spacetime

As a sequel to our previous work\cite{Feng2020}, we propose in this paper a quantization scheme for Dirac field in de Sitter spacetime. Our scheme is covariant under both general transformations and Lorentz transformations. We first present a Hamiltonian structure, then quantize the field following the standard approach of constrained systems. For the free field, the time-dependent quantized Hamiltonian is diagonalized by Bogliubov transformation and the eigen-states at each instant are interpreted as the observed particle states at that instant. The measurable energy-momentum of observed particle/antiparticles are the same as obtained for Klein-Gordon field. Moreover, the energy-momentum also satisfies geodesic equation, a feature justifying its measurability. As in \cite{Feng2020}, though the mathematics is carried out in terms of conformal coordinates for the sake of convenience, the whole theory can be transformed into any other coordinates based on general covariance. It is concluded that particle/antiparticle states, such as vacuum states in particular are time-dependent and vacuum states at one time evolves into non-vacuum states at later times. Formalism of perturbational calculation is provided with an extended Dirac picture.
Digital Reconstruction of Elmina Castle for Mobile Virtual Reality via Point-based Detail Transfer

Reconstructing 3D models from large, dense point clouds is critical to enable Virtual Reality (VR) as a platform for entertainment, education, and heritage preservation. Existing 3D reconstruction systems inevitably make trade-offs between three conflicting goals: the efficiency of reconstruction (e.g., time and memory requirements), the visual quality of the constructed scene, and the rendering speed on the VR device. This paper proposes a reconstruction system that simultaneously meets all three goals. The key idea is to avoid the resource-demanding process of reconstructing a high-polygon mesh altogether. Instead, we propose to directly transfer details from the original point cloud to a low polygon mesh, which significantly reduces the reconstruction time and cost, preserves the scene details, and enables real-time rendering on mobile VR devices. While our technique is general, we demonstrate it in reconstructing cultural heritage sites. We for the first time digitally reconstruct the Elmina Castle, a UNESCO world heritage site at Ghana, from billions of laser-scanned points. The reconstruction process executes on low-end desktop systems without requiring high processing power, making it accessible to the broad community. The reconstructed scenes render on Oculus Go in 60 FPS, providing a real-time VR experience with high visual quality. Our project is part of the Digital Elmina effort (http://digitalelmina.org/) between University of Rochester and University of Ghana.
Social Media COVID-19 Misinformation Interventions Viewed Positively, But Have Limited Impact

Amidst COVID-19 misinformation spreading, social media platforms like Facebook and Twitter rolled out design interventions, including banners linking to authoritative resources and more specific "false information" labels. In late March 2020, shortly after these interventions began to appear, we conducted an exploratory mixed-methods survey (N = 311) to learn: what are social media users' attitudes towards these interventions, and to what extent do they self-report effectiveness? We found that most participants indicated a positive attitude towards interventions, particularly post-specific labels for misinformation. Still, the majority of participants discovered or corrected misinformation through other means, most commonly web searches, suggesting room for platforms to do more to stem the spread of COVID-19 misinformation.
Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape

Anomaly detection aims at identifying unexpected fluctuations in the expected behavior of a given system. It is acknowledged as a reliable answer to the identification of zero-day attacks to such extent, several ML algorithms that suit for binary classification have been proposed throughout years. However, the experimental comparison of a wide pool of unsupervised algorithms for anomaly-based intrusion detection against a comprehensive set of attacks datasets was not investigated yet. To fill such gap, we exercise 17 unsupervised anomaly detection algorithms on 11 attack datasets. Results allow elaborating on a wide range of arguments, from the behavior of the individual algorithm to the suitability of the datasets to anomaly detection. We conclude that algorithms as Isolation Forests, One-Class Support Vector Machines, and Self-Organizing Maps are more effective than their counterparts for intrusion detection, while clustering algorithms represent a good alternative due to their low computational complexity. Further, we detail how attacks with unstable, distributed, or non-repeatable behavior such as Fuzzing, Worms, and Botnets are more difficult to detect. Ultimately, we digress on capabilities of algorithms in detecting anomalies generated by a wide pool of unknown attacks, showing that achieved metric scores do not vary with respect to identifying single attacks.
Surface Potential Driven Swelling of Polyelectrolyte Multilayers

The adsorption of polyelectrolytes to charged surfaces can be applied to build up multilayers of polyelectrolytes of alternating charge by layer-by-layer assembly. The hydration of such multilayers adsorbed to colloidal particles in dilute aqueous dispersions is investigated by NMR, namely, water 1H spin relaxation rates R2. For the system of alternating layers of poly(diallyl dimethylammonium chloride) (PDADMAC) and poly(sodium 4-styrenesulfate) (PSS) adsorbed to silica particles (PDADMAC/PSS), a linear increase of multilayer thickness and R2 with the number of layers is found, reflecting an increasing amount of hydration water. For the system (PAH)/PSS, involving the weak polyelectrolyte poly(allylamine hydrochloride) (PAH), it is found that while adsorption of a positive layer leads to an increase of R2, a decrease of R2 is observed after the adsorption of a negative layer. This reveals an increase of the net water mobility involving dehydration. Evidence is given that the reversible swelling behavior .
Sublinear Maximum Inner Product Search using Concomitants of Extreme Order Statistics

We propose a novel dimensionality reduction method for maximum inner product search (MIPS), named CEOs, based on the theory of concomitants of extreme order statistics. Utilizing the asymptotic behavior of these concomitants, we show that a few dimensions associated with the extreme values of the query signature are enough to estimate inner products. Since CEOs only uses the sign of a small subset of the query signature for estimation, we can precompute all inner product estimators accurately before querying. These properties yield a sublinear MIPS algorithm with an exponential indexing space complexity. We show that our exponential space is optimal for the $(1 + \epsilon)$-approximate MIPS on a unit sphere. The search recall of CEOs can be theoretically guaranteed under a mild condition. To deal with the exponential space complexity, we propose two practical variants, including sCEOs-TA and coCEOs, that use linear space for solving MIPS. sCEOs-TA exploits the threshold algorithm (TA) and provides superior search recalls to competitive MIPS solvers. coCEOs is a data and dimension co-reduction technique and outperforms sCEOs-TA on high recall requirements. Empirically, they are very simple to implement and achieve at least 100x speedup compared to the bruteforce search while returning top-10 MIPS with accuracy at least 90% on many large-scale data sets.
Supersymmetric solutions from 
N=6
 gauged supergravity

We study supersymmetric solutions in four-dimensional $N=6$ gauged supergravity with $SO(6)$ gauge group. There is a unique $N=6$ supersymmetric $AdS_4$ vacuum with $SO(6)$ symmetry dual to an $N=6$ SCFT in three dimensions. We find a number of domain walls interpolating between this $AdS_4$ vacuum and singular geometries in the IR with $SO(2)\times SO(4)$, $U(3)$, $SO(3)$ and $SO(2)\times SO(2)\times SO(2)$ symmetries. The $SO(3)$ case admits $N=6$ or $N=2$ solutions depending on whether the pseudoscalars are present or not. On the other hand, all the remaining solutions preserve $N=6$ supersymmetry. These solutions describe RG flows from the $N=6$ SCFT to non-conformal field theories driven by mass deformations. In particular, the $SO(2)\times SO(4)$ solution is in agreement with the previously known mass deformations of the dual $N=6$ SCFT. We also give a supersymmetric Janus solution with $SO(2)\times SO(4)$ symmetry, describing two-dimensional conformal defects in the $N=6$ SCFT with unbroken $N=(4,2)$ supersymmetry. Finally, we find an $N=2$ supersymmetric $AdS_2\times H^2$ solution with $SO(2)\times SO(4)$ symmetry and the corresponding domain wall interpolating between this fixed point and the $AdS_4$ vacuum. The solution describes an $AdS_4$ black hole with a magnetic charge and is dual to a twisted compactification of the $N=6$ SCFT on a hyperbolic space $H^2$. We also give a domain wall interpolating between a locally supersymmetric $AdS_4$ and a curved domain wall with $SO(2)\times SO(2)\times SO(2)$ symmetry dual to an RG flow across dimensions from the $N=6$ SCFT to a supersymmetric quantum mechanics.
A Comprehensive Utility Function for Resource Allocation in Mobile Edge Computing

In mobile edge computing (MEC), one of the important challenges is how much resources of which mobile edge server (MES) should be allocated to which user equipment (UE). The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU only. This paper presents a novel comprehensive utility function for resource allocation in MEC. The utility function considers the heterogeneous nature of applications that a UE offloads to MES. The proposed utility function considers all important parameters, including CPU, RAM, hard disk space, required time, and distance, to calculate a more realistic utility value for MESs. Moreover, we improve upon some general algorithms, used for resource allocation in MEC and cloud computing, by considering our proposed utility function. We name the improved versions of these resource allocation schemes as comprehensive resource allocation schemes. The UE requests are modeled to represent the amount of resources requested by the UE as well as the time for which the UE has requested these resources. The utility function depends upon the UE requests and the distance between UEs and MES, and serves as a realistic means of comparison between different types of UE requests. Choosing (or selecting) an optimal MES with the optimal amount of resources to be allocated to each UE request is a challenging task. We show that MES resource allocation is sub-optimal if CPU is the only resource considered. By taking into account the other resources, i.e., RAM, disk space, request time, and distance in the utility function, we demonstrate improvement in the resource allocation algorithms in terms of service rate, utility, and MES energy consumption.
Optimized Random Forest Model for Botnet Detection Based on DNS Queries

The Domain Name System (DNS) protocol plays a major role in today’s Internet as it translates between website names and corresponding IP addresses. However, due to the lack of processes for data integrity and origin authentication, the DNS protocol has several security vulnerabilities. This often leads to a variety of cyber-attacks, including botnet network attacks. One promising solution to detect DNS-based botnet attacks is adopting machine learning (ML) based solutions. To that end, this paper proposes a novel optimized ML-based framework to detect botnets based on their corresponding DNS queries. More specifically, the framework consists of using information gain as a feature selection method and genetic algorithm (GA) as a hyper-parameter optimization model to tune the parameters of a random forest (RF) classifier. The proposed framework is evaluated using a state-of-the-art TI-2016 DNS dataset. Experimental results show that the proposed optimized framework reduced the feature set size by up to 60%. Moreover, it achieved a high detection accuracy, precision, recall, and F-score compared to the default classifier. This highlights the effectiveness and robustness of the proposed framework in detecting botnet attacks.
MA-Unet: An improved version of Unet based on multi-scale and attention mechanism for medical image segmentation

Although convolutional neural networks (CNNs) are promoting the development of medical image semantic segmentation, the standard model still has some shortcomings. First, the feature mapping from the encoder and decoder sub-networks in the skip connection operation has a large semantic difference. Second, the remote feature dependence is not effectively modeled. Third, the global context information of different scales is ignored. In this paper, we try to eliminate semantic ambiguity in skip connection operations by adding attention gates (AGs), and use attention mechanisms to combine local features with their corresponding global dependencies, explicitly model the dependencies between channels and use multi-scale predictive fusion to utilize global information at different scales. Compared with other state-of-the-art segmentation networks, our model obtains better segmentation performance while introducing fewer parameters.
Pattern-aware Data Augmentation for Query Rewriting in Voice Assistant Systems

Query rewriting (QR) systems are widely used to reduce the friction caused by errors in a spoken language understanding pipeline. However, the underlying supervised models require a large number of labeled pairs, and these pairs are hard and costly to be collected. Therefore, We propose an augmentation framework that learns patterns from existing training pairs and generates rewrite candidates from rewrite labels inversely to compensate for insufficient QR training data. The proposed framework casts the augmentation problem as a sequence-to-sequence generation task and enforces the optimization process with a policy gradient technique for controllable rewarding. This approach goes beyond the traditional heuristics or rule-based augmentation methods and is not constrained to generate predefined patterns of swapping/replacing words. Our experimental results show its effectiveness compared with a fully trained QR baseline and demonstrate its potential application in boosting the QR performance on low-resource domains or locales.
Memory Approximate Message Passing

Approximate message passing (AMP) is a low-cost iterative parameter-estimation technique for certain high-dimensional linear systems with non-Gaussian distributions. However, AMP only applies to independent identically distributed (IID) transform matrices, but may become unreliable for other matrix ensembles, especially for ill-conditioned ones. To handle this difficulty, orthogonal/vector AMP (OAMP/VAMP) was proposed for general right-unitarily-invariant matrices. However, the Bayes-optimal OAMP/VAMP requires high-complexity linear minimum mean square error estimator. To solve the disadvantages of AMP and OAMP/VAMP, this paper proposes a memory AMP (MAMP), in which a long-memory matched filter is proposed for interference suppression. The complexity of MAMP is comparable to AMP. The asymptotic Gaussianity of estimation errors in MAMP is guaranteed by the orthogonality principle. A state evolution is derived to asymptotically characterize the performance of MAMP. Based on the state evolution, the relaxation parameters and damping vector in MAMP are optimized. For all right-unitarily-invariant matrices, the optimized MAMP converges to OAMP/VAMP, and thus is Bayes-optimal if it has a unique fixed point. Finally, simulations are provided to verify the validity and accuracy of the theoretical results.
Tracing molecular stratification within an edge-on protoplanetary disk

High-resolution observations of edge-on proto-planetary disks in emission from molecular species sampling different critical densities and formation pathways offer the opportunity to trace the vertical chemical and physical structures of protoplanetary disks. Among the problems that can be addressed is the origin and significance of the bright CN emission that is a ubiquitous feature of disks. Based on analysis of sub-arcsecond resolution Atacama Large Millimeter Array (ALMA) archival data for the edge-on Flying Saucer disk (2MASS J16281370-2431391), we establish the vertical and radial differentiation of the disk CN emitting regions with respect to those of 12CO and CS, and we model the disk physical conditions from which the CN emission arises. We demonstrate that the disk 12CO (2-1), CN (2-1), and CS J=5-4 emitting regions decrease in scale height above the midplane, such that 12CO, CN, and CS trace layers of increasing density and decreasing temperature. We find that at radii > 100 au from the central star, CN emission arises predominantly from intermediate layers, while in the inner region of the disk, CN appears to arise from layers closer to the midplane. We investigate disk physical conditions within the CN emitting regions, as well as the ranges of CN excitation temperature and column density, via RADEX non-LTE modeling of the three brightest CN hyperfine lines. Near the disk midplane, where we derive densities nH2 ∼107 cm−3 at relatively low Tkin (∼ 12 K), we find that CN is thermalized, while sub-thermal, non-LTE conditions appear to obtain for CN emission from higher (intermediate) disk layers. We consider whether and how the particular spatial location and excitation conditions of CN emission from the Flying Saucer can be related to CN production that is governed, radially and vertically, by the degree of irradiation of the flared disk by X-rays and UV photons from the central star.
The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective

Training datasets for machine learning often have some form of missingness. For example, to learn a model for deciding whom to give a loan, the available training data includes individuals who were given a loan in the past, but not those who were not. This missingness, if ignored, nullifies any fairness guarantee of the training procedure when the model is deployed. Using causal graphs, we characterize the missingness mechanisms in different real-world scenarios. We show conditions under which various distributions, used in popular fairness algorithms, can or can not be recovered from the training data. Our theoretical results imply that many of these algorithms can not guarantee fairness in practice. Modeling missingness also helps to identify correct design principles for fair algorithms. For example, in multi-stage settings where decisions are made in multiple screening rounds, we use our framework to derive the minimal distributions required to design a fair algorithm. Our proposed algorithm also decentralizes the decision-making process and still achieves similar performance to the optimal algorithm that requires centralization and non-recoverable distributions.
Simultaneous NICER and NuSTAR Observations of the Ultracompact X-Ray Binary 4U 1543–624

We present the first joint NuSTAR and NICER observations of the ultracompact X-ray binary (UCXB) 4U 1543−624 obtained in 2020 April. The source was at a luminosity of L 0.5−50 keV = 4.9(D/7 kpc)2 × 1036 erg s−1 and showed evidence of reflected emission in the form of an O viii line, Fe K line, and Compton hump within the spectrum. We used a full reflection model, known as xillverCO, that is tailored for the atypical abundances found in UCXBs, to account for the reflected emission. We tested the emission radii of the O and Fe line components and conclude that they originate from a common disk radius in the innermost region of the accretion disk (R in ≤ 1.07 R ISCO). Assuming that the compact accretor is a neutron star (NS) and the position of the inner disk is the Alfvén radius, we placed an upper limit on the magnetic field strength to be B ≤ 0.7(D/7 kpc) × 108 G at the poles. Given the lack of pulsations detected and position of R in, it was likely that a boundary layer region had formed between the NS surface and inner edge of the accretion disk with an extent of 1.2 km. This implies a maximum radius of the neutron star accretor of R NS ≤ 12.1 km when assuming a canonical NS mass of 1.4 M ⊙.
Update of the three-fluid hydrodynamics-based event simulator: Light-nuclei production in heavy-ion collisions

We present an update of the event generator based on the three-fluid dynamics (3FD), complemented by Ultra-relativistic Quantum Molecular Dynamics (UrQMD) for the late stage of the nuclear collision~-- the three-fluid Hydrodynamics-based Event Simulator Extended by UrQMD final State interactions (THESEUS). Two modifications are introduced. The THESEUS table of hadronic resonances is made consistent with that of the underlying 3FD model. The main modification is that the generator is extended to simulate the light-nuclei production in relativistic heavy-ion collisions, on the equal basis with hadrons. These modifications are illustrated by applications to the description of available experimental data. The first run of the updated generator revealed a surprisingly good reproduction of the NA49 data on the light nuclei. The reproduction is achieved without any extra parameters, while the coalescence approach in 3FD requires special tuning of the coalescence coefficients for each light nucleus separately.