Miracle-dz/UniFAR-FTUs huggingface dataset:
Finite-Time Consensus and Finite-Time $H_{\infty }$ Consensus of Multi-Agent Systems Under Directed Topology

In this paper, we respectively discuss the finite-time consensus (FTC) and finite-time $H_{\infty }$ consensus (FTHC) problems for a kind of nonlinear multi-agent systems under directed topology. Firstly, with the help of some inequality techniques and the designed state feedback controller, a FTC criterion is presented for multi-agent system. In addition, two adaptive control strategies for ensuring the FTC of multi-agent system are also developed. Moreover, we further discuss the FTHC for multi-agent system with external disturbances, and some FTHC criteria are given by exploiting the designed state feedback controller and adaptive state feedback controllers. Finally, the effectiveness of these obtained results are verified by two numerical examples.
Hybrid, Job-Aware, and Preemptive Datacenter Scheduling

Scheduling in datacenters is an important, yet challenging problem. Datacenters are composed of a large number, typically tens of thousands, of commodity computers running a variety of data-parallel jobs. The role of the scheduler is to assign cluster resources to jobs, which is not trivial due to the large scale of the cluster, as well as the high scheduling load (tens of thousands of scheduling decisions per second). Additionally to scalability, modern datacenters face increasingly heterogeneous workloads composed of long batch jobs, e.g., data analytics, and latency-sensitive short jobs, e.g., operations of user-facing services. In such workloads, and especially if the cluster is highly utilized, it is challenging to avoid short running jobs getting stuck behind long running jobs, i.e. headof-line blocking. Schedulers have evolved from being centralized (one single scheduler for the entire cluster) to distributed (many schedulers that take scheduling decisions in parallel). Although distributed schedulers can handle the large-scale nature of datacenters, they trade scheduling latency for accuracy. The complexity of scheduling in datacenters is exacerbated by the data-parallel nature of the jobs. That is, a job is composed of multiple tasks and the job completes only when all of its tasks complete. A scheduler that takes into account this fact, i.e. job-aware, could use this information to provide better scheduling decisions. Furthermore, to improve the quality of their scheduling decisions, most of datacenter schedulers use job runtime estimates. Obtaining accurate runtime estimates is, however, far from trivial, and erroneous estimates may lead to sub-optimal scheduling decisions. Considering these challenges, in this dissertation we argue the following: (i) that a hybrid centralized/distributed design can get the best of both worlds by scheduling long jobs in a centralized way and short jobs in a distributed way; (ii) such a hybrid scheduler can avoid head-of-line blocking and provide job-awareness by dynamically partitioning the cluster for short and long jobs and by executing a job to completion once it started; (iii) a scheduler can dispense with runtime estimates by sharing the resources of a node with preemption, and load balancing jobs among the nodes.
Stability of lipid membranes with orthotropic symmetry

Lipid membranes routinely undergo protein-mediated morphological remodeling during vital processes such as cellular transport and division. These membrane remodeling proteins can be broadly classified into two categories: one that generates a spherical shape and another that generates a cylindrical shape. To gain physical insights into membrane shape transitions, it is important to investigate the stability of membranes in the presence of these two types of proteins. However, the existing membrane theory is mostly restricted to the class of membranes that interact with the sphere shape-generating proteins and possess isotropic symmetry. In this work, we use curvature elasticity of the lipid membranes to derive the stability criterion for membranes that interact with the cylindrical-shape-generating proteins that possess orthotropic symmetry. We derive the convexity condition followed by the stability criterion for a generalized form of strain energy that can entertain material heterogeneity. The proposed framework would allow for a rigorous analysis of a broader set of membrane–protein interactions during key cellular processes.
Properties of the Lunar Detection Mode for ZeV-Scale Particles with LOFAR

The steep decrease of the flux of ultra-high energy cosmic rays (UHECR) provides a challenge to answer the long standing question about their origin and nature. A significant increase in detector volume may be achieved byemploying Earth’s moon as a detector that is read out using existing Earth-bound radio telescopes by searching for the radio pulses emitted by the particle shower in the lunar rock. In this contribution we will report on the properties of a corresponding detection mode currently under development for the LOFAR Radio telescope.
Formation Control for Multiple Quadrotor Aircraft via Fixed-time Consensus Algorithm

This paper mainly studies the formation control problem of multiple quadrotor aircraft via fixed-time control theory. First, based on the bilimit homogeneous theory and the framework of multiagent theory, for multiaircraft, a fixed-time formation control strategy is proposed. Considering the external disturbance existing on the attitude loop of the aircraft, the corresponding fixed-time disturbance observer is designed with the observer technology. Then, a fixed-time attitude controller is designed based on the accurate observation and fast compensation from the disturbance observer. Finally, some simulations are performed to verify the effectiveness of the proposed theoretical method.
Static and free vibration analysis of graphene platelets reinforced composite truncated conical shell, cylindrical shell, and annular plate using theory of elasticity and DQM

Abstract In this paper, three-dimensional static and free vibration analysis of functionally graded graphene platelets-reinforced composite (FG-GPLRC) truncated conical shells, cylindrical shells and annular plates with various boundary conditions is carried out within the framework of elasticity theory. The main contribution of the present work is that formulation for free vibration and bending behavior of the FG-GPLRC truncated conical shell based on theory of elasticity has not yet been reported. Additionally, formulation and solution for cylindrical shell and annular plate are derived by changing the semi vertex angle in formulation and solution of FG-GPLRC truncated conical shell. A semi-analytical solution is proposed base on employing differential quadrature method (DQM) together with state-space technique. Validity of current approach is assessed by comparing its numerical results with those available in the literature. An especial attention is drawn to the role of GPLs weight fraction, patterns of GPLs distribution through the thickness direction, geometrical parameters such as semi-vertex angle, length to mid-radius ratio on natural frequencies and bending characteristics. Numerical results reveal that desirable static and free vibration response (such as lower radial deflection and higher natural frequencies) can be achieved by locating more square shaped GPLs near inner and outer surfaces.
Graph Signal Processing in a Nutshell

The framework of graph signal processing was conceived in the last decade with the ambition of generalizing the tools from classical digital signal processing to the case in which the signal is defined over an irregular structure modelled by a graph. Instead of discrete time - what one would call a regular 1-D domain, in which a signal sample is adjacent to only two neighbors and for any pair of contiguous samples the distance is the same - the signals here are defined over graphs and, therefore, the distance and relations between adjacent samples vary along the signal. For instance, one may consider the temperature signal defined from the data of a sensor mesh network. When creating the tools in such a scenario, many challenges arise even with basic concepts of the classical theory. In this paper, the core ideas of graph signal processing are presented, focusing on the two main frameworks developed along the years, and a couple of examples and applications are shown. We conclude drawing attention to a few of the many open opportunities for further studies in the field.
Logical dual concepts based on mathematical morphology in stratified institutions: applications to spatial reasoning

ABSTRACT Several logical operators are defined as dual pairs, in different types of logics. Such dual pairs of operators also occur in other algebraic theories, such as mathematical morphology. Based on this observation, this paper proposes to define, at the abstract level of institutions, a pair of abstract dual and logical operators as morphological erosion and dilation. Standard quantifiers and modalities are then derived from these two abstract logical operators. These operators are studied both on sets of states and sets of models. To cope with the lack of explicit set of states in institutions, the proposed abstract logical dual operators are defined in an extension of institutions, the stratified institutions, which take into account the notion of open sentences, whose satisfaction is parametrised by sets of states. A hint on the potential interest of the proposed framework for spatial reasoning is also provided.
Emerging rare-earth doped material platforms for quantum nanophotonics

Abstract Rare-earth dopants are arguably one of the most studied optical centers in solids, with applications spanning from laser optoelectronics, biosensing, lighting to displays. Nevertheless, harnessing rare-earth dopants’ extraordinary coherence properties for quantum information technologies is a relatively new endeavor, and has been rapidly advancing in recent years. Leveraging the state-of-the-art photonic technologies, on-chip rare-earth quantum devices functioning as quantum memories, single photon sources and transducers have emerged, often with potential performances unrivaled by other solid-state quantum technologies. These existing quantum devices, however, nearly exclusively rely on macroscopic bulk materials as substrates, which may limit future scalability and functionalities of such quantum systems. Thus, the development of new platforms beyond single crystal bulk materials has become an interesting approach. In this review article, we summarize the latest progress towards nanoscale, low-dimensional rare-earth doped materials for enabling next generation rare-earth quantum devices. Different platforms with a variety of synthesis methods are surveyed. Their key metrics measured to date are presented and compared. Special attention is placed on the connection between the topology of each platform to its target device applications. Lastly, an outlook for near term prospects of these platforms are given, with a hope to spur broader interests in rare-earth doped materials as a promising candidate for quantum information technologies.
Factors Influencing the Rheological Properties of MRSP Based on the Orthogonal Experimental Design and the Impact Energy Test

In the preparation process of magnetorheological silly putty (MRSP), the influence factors of the relative shear thickening effect (RSE) and relative magnetorheological effect (RME) were studied by orthogonal experiments as well as range and variance analyses. The influence degree of each factor was also evaluated. The results showed that the viscosity of polydimethylsiloxane (PDMS) and the mass fraction of carbonyl iron powder (CIP) had a significant influence on the RSE and RME, respectively. With an increasing PDMS viscosity, the RSE of the silicon-boron copolymer matrix first increased and then decreased, while the RME of MRSP decreased gradually. In addition, the shear thickening mechanism of MRSP was explored by the molecular chain motion. Finally, the impact energy with different samples was tested, and it is demonstrated that with the increasing cross section, the impact energy of the sample increases gradually, but the rate of increase becomes smaller.
A Global, Continuous, and Exponentially Convergent Observer for Gyro Bias and Attitude of a Rigid Body

We propose a 12-dimensional, global, continuous, and exponentially convergent observer for gyro bias and attitude of a rigid body. Any attitude observer developed on the special orthogonal group suffers from the topological restriction that prohibits global attractivity in continuous flow. In this paper, the observer is designed in the set of 3 by 3 real matrices, thus making the topological obstruction on the special orthogonal group irrelevant. The efficacy of the proposed approach against other attitude observers is illustrated by an indoor experiment utilizing visual landmarks.
Coding-Based Large-Scale Task Assignment for Industrial Edge Intelligence

Industrial edge computing, combing various smart devices such as smart sensors, manufacturing equipment, and Internet of Things, has an ultimate goal to provide the industrial edge intelligence. Assigning large-scale tasks with multi-devices connection property for distributed edge servers is one of the main challenges to realize this goal. To address this question, a generative-coding group evolution algorithm, which involves a coding-based operator to approximate different sorts of evolutionary operators, is proposed in this paper. A simple grouping strategy is also introduced to accelerate the optimization process. Experimental results on three cases show that this algorithm is able to provide near-optimal solutions for large-scale tasks within a very short time compared with some traditional approaches.
Stars, holes, or paths across your Facebook friends: A graphlet-based characterization of many networks

Abstract Network science gathers methods coming from various disciplines which sometimes hardly cross the boundaries between these disciplines. Widely used in molecular biology in the study of protein interaction networks, the enumeration, in a network, of all possible subgraphs of a limited size (usually around four or five nodes), often called graphlets, can only be found in a few works dealing with social networks. In the present work, we apply this approach to an original corpus of about 10,000 non-overlapping Facebook ego networks gathered from voluntary participants by a survey application. To deal with so many similar networks, we adapt the relative graphlet frequency to a measure that we call graphlet representativity, which we show to be more effective to classify random networks having slight structural differences. From our data, we produce two clusterings, one of graphlets (paths, star-like, holes, light triangles, and dense), one of networks. The latter is presented with a visualization scheme using our representativity measure. We describe the distinct structural characteristics of the five clusters of Facebook ego networks so obtained and discuss the empirical differences between results obtained with 4-node and 5-node graphlets. We also provide suggestions of follow-ups of this work, both in sociology and in network science.
Higher-order Fourier Analysis and Applications

Fourier analysis has been extremely useful in many areas of mathematics. In the last several decades, it has been used extensively in theoretical computer science. Higher-order Fourier analysis is an extension of the classical Fourier analysis, where one allows to generalize the “linear phases” to higher degree polynomials. It has emerged from the seminal proof of Gowers of Szemerédi’s theorem with improved quantitative bounds, and has been developed since, chiefly by the number theory community. In parallel, it has found applications also in theoretical computer science, mostly in algebraic property testing, coding theory and complexity theory. The purpose of this book is to lay the foundations of higherorder Fourier analysis, aimed towards applications in theoretical computer science with a focus on algebraic property testing. Hamed Hatami, Pooya Hatami and Shachar Lovett (2019), “Higher-order Fourier Analysis and Applications”, Foundations and Trends© in Theoretical Computer Science: Vol. 13, No. 4, pp 247–448. DOI: 10.1561/0400000064. The version of record is available at: http://dx.doi. org/10.1561/0400000064
Evolution of magnetic vortex formation in micron-sized disks

Automotive magnetic field sensing applications require a robust sensing concept. One way to meet the corresponding sensor requirements, such as a negligible hysteresis and a large linear working range, is to employ the vortex state. Consequently, the nucleation field H n of the vortex state becomes a highly important sensor parameter. In this study, we examine different factors that affect H n. Tunneling magnetoresistance spin-valve sensors with disk-shaped CoFeB free layers, which energetically favor the nucleation of the vortex state, are electrically characterized and compared with micromagnetic simulations. Phase transitions into intermediate magnetic states, such as various buckling states, the S-state, or the double vortex state, are extracted from hysteresis loops. The resulting phase diagrams show that the formation of the S-state only occurs below a thickness of approximately 25 nm, whereas the double vortex state nucleates frequently only above approximately 35 nm. Both the S- and double vortex states lower the nucleation field of the single vortex state compared to higher order buckling states. Understanding both the origin and the influence of the intermediate phases opens the way to designing a robust and reliable vortex sensor concept.Automotive magnetic field sensing applications require a robust sensing concept. One way to meet the corresponding sensor requirements, such as a negligible hysteresis and a large linear working range, is to employ the vortex state. Consequently, the nucleation field H n of the vortex state becomes a highly important sensor parameter. In this study, we examine different factors that affect H n. Tunneling magnetoresistance spin-valve sensors with disk-shaped CoFeB free layers, which energetically favor the nucleation of the vortex state, are electrically characterized and compared with micromagnetic simulations. Phase transitions into intermediate magnetic states, such as various buckling states, the S-state, or the double vortex state, are extracted from hysteresis loops. The resulting phase diagrams show that the formation of the S-state only occurs below a thickness of approximately 25 nm, whereas the double vortex state nucleates frequently only above approximately 35 nm. Both the S- and double.
Caching Techniques in Next Generation Cellular Networks

Content caching will be an essential feature in the next generations of cellular networks. Indeed, a network equipped with caching capabilities allows users to retrieve content with reduced access delays and consequently reduces the traffic passing through the network backhaul. However, the deployment of the caching nodes in the network is hindered by the following two challenges. First, the storage space of a cache is limited as well as expensive. So, it is not possible to store in the cache every content that can be possibly requested by the user. This calls for efficient techniques to determine the contents that must be stored in the cache. Second, efficient ways are needed to implement and control the caching node. In this thesis, we investigate caching techniques focussing to address the above-mentioned challenges, so that the overall system performance is increased. In order to tackle the challenge of the limited storage capacity, smart proactive caching strategies are needed. In the context of vehicular users served by edge nodes, we believe a caching strategy should be adapted to the mobility characteristics of the cars. In this regard, we propose a scheme called RICH (RoadsIde CacHe), which optimally caches content at the edge nodes where connected vehicles require it most. In particular, our scheme is designed to ensure in-order delivery of content chunks to end users. Unlike blind popularity decisions, the probabilistic caching used by RICH considers vehicular trajectory predictions as well as content service time by edge nodes. We evaluate our approach on realistic mobility datasets against a popularity-based edge approach called POP, and a mobility-aware caching strategy known as netPredict. In terms of content availability, our RICH edge caching scheme provides an enhancement of up to 33% and 190% when compared with netPredict and POP respectively. At the same time, the backhaul penalty bandwidth is reduced by a factor ranging between 57% and 70%.
Hybrid Differential Evolution and Particle Swarm Optimization Algorithm for the Sugarcane Cultivation Scheduling Problem

This paper focuses on optimizing scheduling solutions for the flexible flow shop problem, with tooling constraints and machine eligibility, to minimize makespan for cultivating sugarcane. Normally, preparing the soil for planting sugarcane requires six steps: 1) 7 power harrow and rototiller, 2) rotary mini combine, 3) 22/24 disc harrow, 4) rotary mini combine, 5) sugarcane plantation, and 6) sugarcane sprayer. Each of these steps requires a variety of tools. With limited availability of tools and equipment, resource allocation is important. The objective of this research was to minimize the makespan. For optimal convergence, meta-heuristics, such as a Differential Evolution algorithm, a Particle Swarm optimization algorithm, and a Hybrid DEPSO algorithm were developed to solve the problem. Experimental results showed that all three methods efficiently solved flexible flow shop problems.
THREE-DIMENSIONAL MAGNETIC AND TEMPERATURE FIELD COUPLING ANALYSIS OF DRY-TYPE TRANSFORMER CORE UNDER DIFFERENT EXCITATIONS

To study the transient magnetic field and temperature field of a dry-type transformer core and analyze the core loss and hot spot temperature rise of the core, a magnetic and temperature field coupling analysis method based on finite element method was proposed: the transient magnetic field of dry-type transformer was calculated first, and the core loss under a no-load condition was obtained. Then, the core loss density distribution was coupled to the temperature field as the heat source, and the temperature field distribution in the transformer was calculated by the fluid-thermal coupling method to obtain the hot spot temperature and the position of the core. Compared with the traditional average heat source method, the temperature field distribution calculated by the proposed method is close to the actual temperature distribution of the core. Finally, based on this method, the magnetic field and temperature field of the transformer core under different excitations were calculated, and the effect of harmonics on the core loss and temperature rise of the core was analyzed.