Miracle-dz/UniFAR-FTUs huggingface dataset:
AutoCache: Employing Machine Learning to Automate Caching in Distributed File Systems

The use of computational platforms such as Hadoop and Spark is growing rapidly as a successful paradigm for processing large-scale data residing in distributed file systems like HDFS. Increasing memory sizes have recently led to the introduction of caching and in-memory file systems. However, these systems lack any automated caching mechanisms for storing data in memory. This paper presents AutoCache, a caching framework that automates the decisions for when and which files to store in, or remove from, the cache for increasing system performance. The decisions are based on machine learning models that track and predict file access patterns from evolving data processing workloads. Our evaluation using real-world workload traces from a Facebook production cluster compares our approach with several other policies and showcases significant benefits in terms of both workload performance and cluster efficiency.
Distribution-Driven, Embedded Synthetic Data Generation System and Tool for RDBMS

Many self-managing relational database management systems (RDBMS) need to programmatically generate synthetic data to train machine learning models. This paper proposes the concept of shadow database and a framework to derive shadow database from production database that matches distribution properties of source data. Moreover, we have designed and implemented an embedded synthetic data generation tool that takes data distribution profile as input and generates a shadow database according to histograms of source data. The distribution profile is passed into the tool either through an export-import mechanism or as a JSON string. The shadow database can scale to be larger or smaller than the original database and serve as a testbed to train learning models. Unlike most other data generation tools, our tool is implemented as SQL procedures that can be embedded in the underlying RDBMS.
Nepali SMS filtering Using Decision Trees, Neural Network and Support Vector Machine

Automated spam detection and filtering is the task of categorizing Short Massage Services (SMS) into predefined category: Spam and Non-Spam, based on their content with the models learned from the training SMS dataset. This work evaluates some of the most widely used machine learning techniques- Decision Tree, Support Vector Machine (SVM) and Neural Networks- to address the automatic SMS filtering problem. To experiment the system, a Nepali SMS Corpus of 500 SMS (with 350 Non-Spam and 150 Spam) is collected manually with some existing SMS dataset. Classification and Regression Tree (CART) is used in Decision Trees, Linear and RBF kernels are used in SVM ad Back-propagation is used in Neural Network. To train these models, TF-IDF as well as other binary features are extracted from the preprocessed SMS corpus. The average empirical analysis shows that the Neural Network with Back-Propagation is outperforming the other three algorithms with the average classification accuracy of 85.75%. It is followed by SVM Linear with accuracy of 82.50%, Decision Trees with accuracy 77.15%. The least performing model was SVM with RBF kernel having accuracy 60.03%.
Spatio-Temporal Specification for Mobile Cyber Physical Systems

Cyber physical systems (CPS) is the integration of three processes: physical process, communication process and computation process. Due to the high demand of safety and real- time feature, CPS requires massive real-time spatial and temporal messages. Therefore, representation and demonstration of spatio- temporal information is vital in every stage, such as specification, design, operation, control, and supervision of CPS. In this paper, we propose a method combining clock theory and RCC-8 together indicating spatio-temporal information of CPS, and we present spatio-temporal sequence diagram for change tracking under the specific scene. We take vehicle system as instance to see how the method work in description of spatio-temporal information between vehicles and vehicles, vehicles and people, vehicle and surroundings in different situations.
Robust Dynamic Output Feedback Controller Design for Systems with Uncertain Time-delays

Dynamic output feedback controller design to robustly stabilize linear time-invariant retarded time-delay systems is considered. The considered systems may have multiple uncertain time-delays. In order to achieve best guaranteed exponential decay rate, minimization of the spectral abscissa is also aimed. A minimax optimization problem is formulated for this purpose and algorithms to design such a controller are proposed. These algorithms also include an initialization procedure so that the controller is initialized with the minimum possible dimension and appropriate modes in order to satisfy the parity interlacing property. A number of examples are also presented to demonstrate the proposed approach.
FLS-Based Fuzzy Synchronization Control of Complex Dynamical Networks Under Network Attacks and Actuator Faults

The synchronization control problem for a class of nonlinear complex dynamical networks with network attacks and actuator faults is addressed in this paper. The main contributions are as follows: (1) The developed controller is capable of compensating for nonlinearity and actuator faults simultaneously; (2) Compared with existing network attacks results, the input matrix considered in this paper is more general. To be more specific, by approximating the nonlinear uncertainty by a fuzzy logical system (FLS), a new FLS-based distributed adaptive controller is developed. By virtue of estimating the norm of weight vector in the FLS, the developed controller can compensate for the effect of nonlinearity under actuator faults. By using matrix factorization technique and sliding mode control method, the developed method is shown to be resilient to network attacks. Moreover, it is proven that the developed controller can guarantee asymptotic convergent of synchronisation errors. Finally, an illustrative example is used to demonstrate the effectiveness and efficiency of the proposed method.
Generalized Random Surfer-Pair Models

SimRank is a widely studied link-based similarity measure that is known for its simple, yet powerful philosophy that two nodes are similar if they are referenced by similar nodes. While this philosophy has been the basis of several improvements, there is another useful, albeit less frequently discussed interpretation for SimRank known as the Random Surfer-Pair Model. In this work, we show that other well known measures related to SimRank can also be reinterpreted using Random Surfer-Pair Models, and establish a mathematically sound, general and unifying framework for several link-based similarity measures. This also serves to provide new insights into their functioning and allows for using these measures in a Monte Carlo framework, which provides several computational benefits. As an illustration of its utility in designing measures, we develop a new measure based on two existing measures under this framework, and empirically demonstrate its efficacy.
Resource Management and Admission Control for Tactile Internet in Next Generation of RAN

In this paper, a new queuing model for the Tactile Internet (TI) is proposed for the cloud radio access network (C-RAN) architecture of the next generation wireless networks, e.g., 5G, assisted via orthogonal frequency division multiple access (OFDMA) technology. This model includes both the radio remote head (RRH) and baseband processing unit (BBU) queuing delays and reliability for each end to end (E2E) connection between each pair of tactile users. For this setup, with the aim to minimize the transmit power of users subject to guaranteeing tolerable delay of users, and fronthaul and access limitations, we formulate a resource allocation problem. Since the proposed optimization problem is highly non-convex, to solve it in an efficient manner, we utilize diverse transformation techniques such as successive convex approximation (SCA) and difference of two convex functions (DC). In addition, we propose an admission control (AC) algorithm to make problem feasible. In our proposed system model, we dynamically adjust the fronthaul and access links to minimize the transmit power. Simulation results reveal that by dynamic adjustment of the access and fronthaul delays, transmit power can be saved compared to the case of fixed approach per each transmission session. Moreover, the number of rejected users in the network is significantly reduced and more users are accepted.
How We Do Things With Words: Analyzing Text as Social and Cultural Data

In this article we describe our experiences with computational text analysis involving rich social and cultural concepts. We hope to achieve three primary goals. First, we aim to shed light on thorny issues not always at the forefront of discussions about computational text analysis methods. Second, we hope to provide a set of key questions that can guide work in this area. Our guidance is based on our own experiences and is therefore inherently imperfect. Still, given our diversity of disciplinary backgrounds and research practices, we hope to capture a range of ideas and identify commonalities that resonate for many. This leads to our final goal: to help promote interdisciplinary collaborations. Interdisciplinary insights and partnerships are essential for realizing the full potential of any computational text analysis involving social and cultural concepts, and the more we bridge these divides, the more fruitful we believe our work will be.
Gravitational effects on geonium and free electron 
gs
-factor measurements in a Penning trap

We present a theoretical analysis of an electron confined by a Penning trap, also known as geonium, that is affected by gravity. In particular, we investigate the gravitational influence on the electron dynamics and the electromagnetic field of the trap. We consider the special case of a homogeneous gravitational field, which is represented by Rindler spacetime. In this spacetime the Hamiltonian of an electron with anomalous magnetic moment is constructed. Based on this Hamiltonian and the exact solution to Maxwell equations for the field of a Penning trap in Rindler spacetime, we derived the transition energies of geonium up to the relativistic corrections of $1/\mathrm{c}^2$. These transition energies are used to obtain an extension of the well known $\mathrm{g}_s$-factor formula introduced by L. S. Brown and G. Gabrielse [Rev. Mod. Phys. 58, 233 1986].
Taint analysis of the Bitcoin network

Determining the trust of an individual Bitcoin wallet is a difficult problem. There are no ratings, that offer vendors or exchanges meaningful information about the level of the taint of Bitcoins they are receiving. Lack of such information places exchanges liable in an event when the received Bitcoins are stolen or ill-gotten. In this paper, we try to solve this problem by introducing a Bitcoin address taint score called TaintRank. It provides insight into a specific wallet by taking the addresses it interacted with throughout history into consideration. This ranking method provides such Bitcoin exchange companies insight with whom they are trading.
Metal Object Detection Based on Load Impedance and Input Power Characteristics for High-dimensional WPT System

High-dimensional wireless power transfer (WPT) systems have received increasing attention for charging mobile devices. With the receivers have higher spatial freedom, the systems are more susceptible to the metal objects in the surroundings. However, conventional methods for metal object detection (MOD) can't satisfy the requirements of safety and stability of new systems. This paper proposed a metal detection method which is more suitable for high-dimensional WPT systems. The key feature of the proposed method is combining load impedance and input power characteristics curves to determine whether the receiver is a metal object or a coil. And the influence of the metal is discussed in the circuit and magnetic model. The method is theoretically proven by the simulation calculation. And the experiments verified that the method can accurately detect the metal objects by setting the threshold curves.
Predicting Retrosynthetic Reactions Using Self-Corrected Transformer Neural Networks

Synthesis planning is the process of recursively decomposing target molecules into available precursors. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes, but at present it is cumbersome and can't provide results of satisfactory qualities. In this study, we have developed a template-free self-corrected retrosynthesis predictor (SCROP) to predict retrosynthesis by using Transformer neural networks. In the method, the retrosynthesis planning was converted to a machine translation problem from the products to molecular linear notations of reactants. By coupling with a neural network-based syntax corrector, our method achieved an accuracy of 59.0% on a standard benchmark dataset, which outperformed >21% over other deep learning methods and >6% over template-based methods. More importantly, our method was 1.7 times more accurate than other state-of-the-art methods for compounds not appearing in the training set.
On the instability of Saturn’s hypothetical retrograde co-orbitals

We find an interesting fact that fictitious retrograde co-orbitals of Saturn, or small bodies inside the retrograde 1:1 resonance with Saturn, are highly unstable in our numerical simulations. It is shown that, in the presence of Jupiter, the retrograde co-orbitals will get ejected from Saturn’s co-orbital space within a time-scale of 10 Myr. This scenario reminds us of the instability of Saturn Trojans caused by both the great inequality and the secular resonances. Therefore, we carry out in-depth inspections of both mechanisms and prove that the retrograde resonance overlap, raised by great inequality, cannot serve as an explanation for the instability of the retrograde co-orbitals, due to the weakness of the retrograde 2:5 resonance with Jupiter at low eccentricity. However, we discover that both ν5 and ν6 secular resonances contribute to the slow growth of the eccentricity and are therefore possibly the primary causes of the instability inside Saturn’s retrograde co-orbital space.
HO-3D: A Multi-User, Multi-Object Dataset for Joint 3D Hand-Object Pose Estimation

We propose a new dataset for 3D hand+object pose estimation from color images, together with a method for efficiently annotating this dataset, and a 3D pose prediction method based on this dataset. The current lack of training data makes the 3D hand+object pose estimation very challenging. This lack is due to the complexity of labeling many real images with both 3D poses and of generating synthetic images with various realistic interaction. Moreover, even if synthetic images could be used for training, annotated real images are still needed for validation. To tackle this challenge, we capture sequences with a simple setup made of a single RGB-D camera. We also use a color camera imaging the sequences from a side view, but only for validation. We introduce a novel method based on global optimization that exploits depth, color, and temporal constraints for efficiently annotating the sequences, which we use to train another novel method that predicts both the 3D poses of the hand and the object from a single color image. Our hope is to encourage other researchers to develop better annotation methods for our dataset: One can then apply such method to capture and easily annotate sequences captured with a single RGB-D camera to easily create additional training data thus solving one of the main problems of 3D hand+object pose estimation.
DeepTEGINN: Deep Learning Based Tools to Extract Graphs from Images of Neural Networks

In the brain, the structure of a network of neurons defines how these neurons implement the computations that underlie the mind and the behavior of animals and humans. Provided that we can describe the network of neurons as a graph, we can employ methods from graph theory to investigate its structure or use cellular automata to mathematically assess its function. Although, software for the analysis of graphs and cellular automata are widely available. Graph extraction from the image of networks of brain cells remains difficult. Nervous tissue is heterogeneous, and differences in anatomy may reflect relevant differences in function. Here we introduce a deep learning based toolbox to extracts graphs from images of brain tissue. This toolbox provides an easy-to-use framework allowing system neuroscientists to generate graphs based on images of brain tissue by combining methods from image processing, deep learning, and graph theory. The goals are to simplify the training and usage of deep learning methods for computer vision and facilitate its integration into graph extraction pipelines. In this way, the toolbox provides an alternative to the required laborious manual process of tracing, sorting and classifying. We expect to democratize the machine learning methods to a wider community of users beyond the computer vision experts and improve the time-efficiency of graph extraction from large brain image datasets, which may lead to further understanding of the human mind.
Coupled online learning as a way to tackle instabilities and biases in neural network parameterizations: general algorithms and Lorenz 96 case study (v1.0)

Abstract. Over the last couple of years, machine learning parameterizations have emerged as a potential way to improve the representation of subgrid processes in Earth system models (ESMs). So far, all studies were based on the same three-step approach: first a training dataset was created from a high-resolution simulation, then a machine learning algorithm was fitted to this dataset, before the trained algorithm was implemented in the ESM. The resulting online simulations were frequently plagued by instabilities and biases. Here, coupled online learning is proposed as a way to combat these issues. Coupled learning can be seen as a second training stage in which the pretrained machine learning parameterization, specifically a neural network, is run in parallel with a high-resolution simulation. The high-resolution simulation is kept in sync with the neural network-driven ESM through constant nudging. This enables the neural network to learn from the tendencies that the high-resolution simulation would produce if it experienced the states the neural network creates. The concept is illustrated using the Lorenz 96 model, where coupled learning is able to recover the “true” parameterizations. Further, detailed algorithms for the implementation of coupled learning in 3D cloud-resolving models and the super parameterization framework are presented. Finally, outstanding challenges and issues not resolved by this approach are discussed.
Road obstacle detection in stereo vision based on UV-disparity

Nowadays, road traffic safety attracts the whole world’s attention, and the application of on board obstacles detection can effectively avoid some traffic accidents. This paper focuses on detection of obstacles on all kinds of road surface in stereo vision. And the main work of this paper is to find out the relationship between the coordinates of road points and their corresponding disparity values both in the U-axis and the V-axis, and then to propose a UV-disparity based road surface estimating method which is helpful for the quick detection of road obstacles. This method has abandoned some restrictions on road conditions like flat-world assumption, the consistency of road geometric features, etc. so that it can be used in various situations, particularly in the case when the road plane is inclined. Experiments show that this method can effectively estimate positions of the obstacles under various road conditions. Furthermore, compared with other methods, this method can locate the road obstacles in front of the vehicle more accurately when the road surface is inclined or when the vehicle is traveling in the corner of the road.
Magnon decay theory of Gilbert damping in metallic antiferromagnets

Gilbert damping is a key property governing magnetization dynamics in ordered magnets. We present a theoretical study of intrinsic Gilbert damping induced by magnon decay in antiferromagnetic metals through $s$-$d$ exchange interaction. Our theory delineates the qualitative features of damping in metallic antiferromagnets owing to their bipartite nature, in addition to providing analytic expressions for the damping parameters. Magnon-induced intraband electron scattering is found to predominantly cause magnetization damping, whereas the Neel field is found to be damped via disorder. Depending on the conduction electron band structure, we predict that magnon-induced interband electron scattering around band crossings may be exploited to engineer a strong Neel field damping.
Evolutions of spinning bodies moving in rotating black hole spacetimes

The evolutions of spinning test bodies are investigated in rotating (Kerr, Bardeen-like and Hayward-like) black hole spacetimes. Spin vector precessional equations are derived in both comoving and zero 3-momentum frames from the Mathisson-Papapetrou-Dixon (MPD) equations using either the Frenkel-Mathisson-Pirani or the Tulczyjew-Dixon spin supplementary condition. The comoving and the zero 3-momentum frames are set up from either the static or the zero angular momentum observer frame by instantaneous Lorentz-boosts. However when the body passes over the ergosphere only the boosted zero angular momentum frame can be used for description of the spin dynamics during the whole evolution. Far from the black hole the difference between the boosted static and zero angular momentum frames is unsignificant. Numerical applications are presented for spinning bodies moving along spherical-like, zoom-whirl (thus their existence is confirmed based on the MDP equations) and unbound orbits. The spin evolutions are presented in the boosted static and zero angular momentum observer frames and they are compared, obtaining only differences in the near black hole region. We have found the spin magnitude influences on the orbit evolution, and the spin precessional angular velocity is highly increased near and inside the ergosphere.
A Review on Transient Stability Constrained Optimal Power Flow

Optimal power flow with transient stability constraints simultaneously considers static and dynamic constraints of the power system when operating variables will be optimize. It has become a widely adopted tool for power system preventive control against loss of synchronism. But due to the high non-linear nature of system dynamics, the solving of TSC-OPF is very difficult. In this literature transient stability of the system will be restore. The single machine equivalent (SIME) method is used to perform two main important functions of TSC-OPF approaches: first, the power system transient stability analysis will be perform by SIME; second, SIME determines a stable one machine infinite bus equivalent rotor angular trajectory that is used as the stability constraint in TSC-OPF problem. In this way, the power system dynamic behavior determines the stability constraint by using SIME, at each iteration of the TSC-OPF method. Transient stability constraints are formulated by reducing the initial multi-machine model to a one-machine infinite-bus equivalent. This equivalent allows imposing angle bounds that ensure transient stability. At first, the mathematical model of TSC-OPF is formulated. Subsequently, the difficulties of solving TSCOPF are identified. The detailed review and discussions are then given, covering their computation procedures, merits and demerits, and possible improvement directions.