Miracle-dz/UniFAR-FTUs huggingface dataset:
Shear-sensitive adhesion enables size-independent adhesive performance in stick insects

The ability to climb with adhesive pads conveys significant advantages, and is hence widespread in the animal kingdom. The physics of adhesion predict that attachment is more challenging for large animals, whereas detachment is harder for small animals, due to the difference in surface-to-volume ratios. Here, we use stick insects to show that this problem is solved at both ends of the scale by linking adhesion to the applied shear force. Adhesive forces of individual insect pads, measured with perpendicular pull-offs, increased approximately in proportion to a linear pad dimension across instars. In sharp contrast, whole-body force measurements suggested area-scaling of adhesion. This discrepancy is explained by the presence of shear forces during whole-body measurements, as confirmed in experiments with pads sheared prior to detachment. When we applied shear forces proportional to either pad area or body weight, pad adhesion also scaled approximately with area or mass, respectively, providing a mechanism that can compensate for the size-related loss of adhesive performance predicted by isometry. We demonstrate that the adhesion-enhancing effect of shear forces is linked to pad sliding, which increased the maximum adhesive force per area sustainable by the pads. As shear forces in natural conditions are expected to scale with mass, sliding is more frequent and extensive in large animals, thus ensuring that large animals can attach safely, while small animals can still detach their pads effortlessly. Our results therefore help to explain how nature’s climbers maintain a dynamic attachment performance across seven orders of magnitude in body weight.
Modelling multivariate financial time series using vector autoregressive processes

Time series analysis is used to predict future behaviour of processes and is widely used in the finance sector. In this paper we will analyse the modelling of multivariate time series of financial data using vector autoregressive processes. The goal is that the reader will understand the presented models and could theoretically perform time series analysis by himself. Two specific models will be explained: the Vector Autoregressive model (VAR model) and the Vector Error Correction Model (VECM). We will describe various methods to analyse multivariate time series using these models, such as forecasting the process, variance decomposition of the forecast error, causality analysis and impulse response analysis. Examples of these models and analysis methods will be presented and investigated. Finally, we will perform a time series analysis with these models on Dutch indices and stock data. We conclude that real-world data often does not fit the VAR model and VECM requirements and that further improved models should be considered as well.
Parameterizing robust manipulator controllers under approximate inverse dynamics: A double‐Youla approach

We consider the goal of ensuring robust stability when a given manipulator feedback control law is modified online, for example, to safely improve the performance by a learning module. To this end, the factorization approach is applied to both the plant and controller models to characterize robustly stabilizing controllers for rigid‐body manipulators under approximate inverse dynamics control. Outer‐loop controllers to stabilize the nonlinear uncertain loop that results from approximate inverse dynamics are often derived by lumping uncertainty in a single term and subsequent analysis of the error system. Here, by contrast, the well‐known norm bounds of these uncertain dynamics are first recast into a generalized plant configuration that preserves the characteristic uncertainty structure. Then, the overall loop uncertainty is expressed with respect to the nominal outer‐loop feedback controller by means of an uncertain dual‐Youla operator. Therefore, using the dual‐Youla parameterization, we provide a novel way to rigorously quantify permissible perturbations of robot manipulator feedforward/feedback controllers. The method proposed in this paper does not constitute another robust control law for rigid‐body manipulators, but rather a characterization of a set of robustly stabilizing controllers. The resulting double‐Youla parameterization for the control of robot manipulators is amenable to numerous advanced design methods. The result is thoroughly discussed by a planar elbow manipulator and exemplified with a six‐degree‐of‐freedom robot scenario with varying payload.
Domain wall motion in stoichiometric LiTaO3 induced by low-energy electron beam

Scanning electron microscopy was used to study the mobility of domain walls in vapor-transport equilibrated stoichiometric LiTaO3. By using low-acceleration voltage, switching occurs solely for polarization pointing up domains, resulting in fast domain-wall motion. When the incoming electron beam was more energetic with larger penetration depth, the switching instead occurred for polarization pointing down domains. Our results are discussed in terms of the interaction of the scanning electron beam with the polarization-screening charges at the crystal surface.Scanning electron microscopy was used to study the mobility of domain walls in vapor-transport equilibrated stoichiometric LiTaO3. By using low-acceleration voltage, switching occurs solely for polarization pointing up domains, resulting in fast domain-wall motion. When the incoming electron beam was more energetic with larger penetration depth, the switching instead occurred for polarization pointing down domains. Our results are discussed in terms of the interaction of the scanning electron beam with the polarization-screening charges at the crystal surface.
High-Efficient Adaptive Modulation for PWM-Based Multi-Level Perpendicular Magnetic Recording on Insufficient Resolution Channel

In this paper, we propose a high-efficient constrained adaptive 5-binary to 4-ternary (5B4T)-modulation in pulsewidth modulation (PWM)-based ternary perpendicular magnetic recording (PMR). The high-efficient modulation is required to obtain higher user-bit density (UBD) gain in low-resolution channel, in which transition interval and magnetic-grain size are equivalent on media. In our proposed constrained adaptive 5B4T-modulation, one mapping-set is adaptively selected from the prepared two different mapping-sets to satisfy a specific constraint of ternary signal in modulation block-by-block. For the signal detection in the proposed modulation, we provide a practical solution which is a blind-detection method on likelihood vector. The likelihood vector is re-calculated as joint-metric using the likelihood values of adjacent modulation blocks. By using the vector of joint-metric, a likelihood vector is further generated to decode non-binary low-density parity-check (NB-LDPC) codes. Our simulation results demonstrate that the proposed adaptive 5B4T-modulated PWM-based ternary-PMR, combined with NB-LDPC codes over GF(32), shows a significant robustness in ultra-low-resolution channel, which occurs magnetization-missing frequently.
Connections to zero forcing: Tree covers and power domination

For a simple graph G = (V,E), let S+(G) denote the set of real positive semidefinite matrices A = (aij) such that aij 6= 0 if {i, j} ∈ E, aij = 0 if {i, j} / ∈ E, and aii is any real number. The maximum positive semidefinite nullity of G, denoted M+(G), is max{null(A)|A ∈ S+(G)}. A tree cover of G is a collection of vertex-disjoint simple trees occurring as induced subgraphs of G that cover all the vertices of G. The tree cover number of G, denoted T (G), is the minimum cardinality of a tree cover. It is known that the tree cover number of a graph and the maximum positive semidefinite nullity of a graph are equal for outerplanar graphs, and it was conjectured in 2011 that T (G) ≤ M+(G) for all graphs [Barioli et al., Minimum semidefinite rank of outerplanar graphs and the tree cover number, Elec. J. Lin. Alg. , 2011]. We prove bounds on T (G) to show that if G is a connected outerplanar graph on n ≥ 2 vertices, then M+(G) = T (G) ≤ ⌈ n 2 ⌉ , and if G is a connected outerplanar graph on n ≥ 6 vertices with no three or four cycle, then M+(G) = T (G) ≤ n3 . We characterize connected outerplanar graphs with M+(G) = T (G) = ⌈ n 2 ⌉ , and for each cactus graph G, we give a formula for computing T (G) (and therefore M+(G)). Furthermore, we show that if G is a connected graph on n vertices and at least 2n edges, then T (L(G)) ≤
A chance-constrained approach for electric vehicle aggregator participation in the reserve market

As recorrentes preocupacoes ambientais tem levado a que se verifiquem alteracoes nos sistemas de energia, sendo que a aposta em sistemas distribuidos e ja uma realidade, contudo a aposta em energia renovavel esta, normalmente, associada a intermitencia no que diz respeito ao aproveitamento de energia proveniente do sol, vento, ondas, etc. Assim, neste paradigma, surgem os veiculos eletricos que fazem parte do sistema e tem boas perspetivas de verem a sua penetracao a aumentar exponencialmente num futuro proximo, pelo que devem ser estudadas novas formas de relacao entre este recurso e os agentes de mercado. Neste momento, ja e possivel carregar e descarregar os veiculos eletricos, o que, auxiliado pelas decisoes e algoritmos corretos, pode contribuir com um apoio fundamental para a rede de energia tratar de forma simplificada congestionamentos e variacoes nos valores nominais de tensao e frequencia. Tudo isto, esta dependente da disponibilidade do proprietario do veiculo para fornecer este tipo de servico e que estados minimos de carga sejam assegurados para as viagens diarias. Geralmente, os operadores da rede eletrica gerem elevados niveis de geracao e carga, pelo que surge a necessidade do conceito de agregador de veiculos eletricos, que tera a funcao de juntar varios veiculos eletricos para assim corresponder de forma mais eficaz e com capacidade de oferta conforme as necessidades da rede.Neste trabalho, pretende-se otimizar o lucro do agregador, que podera estar sujeito a penalidades em caso de falha no fornecimento de reserva a subir ou a descer, nao colocando de parte a fiabilidade no fornecimento do servico ao mercado de reserva. Deste modo, sao utilizadas tecnicas de otimizacao estocastica que pretendem modelar incertezas de uma pequena frota de veiculos eletricos como disponibilidade para fornecer o servico e perfis de consumo. A tecnica de otimizacao chance-constrained, mais precisamente as relaxacoes Big-M e McCormick, sao aplicadas a fim de analisar o risco da oferta que o agregador deve submeter em mercado.Para situacoes de mercado real, mais precisamente o FCR-N na Dinamarca, sao explorados diversos cenarios para diferentes niveis de risco submetidos em mercado, diferentes probabilidades de o veiculo estar conectado a rede
A Constructive Particle Swarm Optimizer for Virtual Network Embedding

Virtual network embedding (VNE) is a key technique for flexible sharing of physical resources in the modern Internet. As the VNE problem is nondeterministic polynomial hard, natural inspired population-based algorithms have been increasingly considered as promising approaches. But as the VNE problem has two tightly related tasks: node mapping and link mapping, it remains challenging for metaheuristics to deal with both tasks coordinately and effectively. This paper proposes a constructive particle swarm optimizer for virtual network embedding (CPSO-VNE). CPSO-VNE uses discrete vectors and matrices with probabilities to encode positions and velocities on VNE search space, respectively. What makes CPSO-VNE different from previous works is the step-by-step solution construction scheme introduced in CPSO-VNE. In this scheme, each node is mapped along with the mapping of its adjacent virtual links. In this way, node and link mappings are coordinated in one stage. In addition, with this step-by-step construction scheme, path information of networks can be introduced as heuristic information to guide the search, which can further improve performance. The proposed method is tested on the scenarios with a single VN request and with a set of online VN requests. The simulation results show that the proposed approach is promising.
Balancing of Parallel U-Shaped Assembly Lines with Crossover Points

This research introduces parallel U-shaped assembly lines with crossover points. Crossover points are connecting points between two parallel U-shaped lines making the lines interdependent. The assembly lines can be employed to manufacture a variety of products belonging to the same product family. This is achieved by utilizing the concepts of crossover points, multi-line stations, and regular stations. The binary programming formulation presented in this research can be employed for any scenario (e.g. task times, cycle times, and the number of tasks) in the configuration that includes a crossover point. The comparison of numerical problem solutions based on the proposed heuristic approach with the traditional approach highlights the possible reduction in the quantity of workers required. The conclusion from this research is that a wider variety of products can be manufactured at the same capital expense using parallel U-shaped assembly lines with crossover points, leading to a reduction in the total number of workers. Balancing of Parallel U-Shaped Assembly Lines with Crossover Points
Obstacle Avoidance System for Unmanned Ground Vehicles by Using Ultrasonic Sensors

Artificial intelligence is the ability of a computer to perform the functions and reasoning typical of the human mind. In its purely informatic aspect, it includes the theory and techniques for the development of algorithms that allow machines to show an intelligent ability and/or perform an intelligent activity, at least in specific areas. In particular, there are automatic learning algorithms based on the same mechanisms that are thought to be the basis of all the cognitive processes developed by the human brain. Such a powerful tool has already started to produce a new class of self-driving vehicles. With the projections of population growth that will increase until the year 2100 up to 11.2 billion, research on innovating agricultural techniques must be continued. In order to improve the efficiency regarding precision agriculture, the use of autonomous agricultural machines must become an important issue. For this reason, it was decided to test the use of the “Neural Network Toolbox” tool already present in MATLAB to design an artificial neural network with supervised learning suitable for classification and pattern recognition by using data collected by an ultrasonic sensor. The idea is to use such a protocol to retrofit kits for agricultural machines already present on the market.
Efficient multiple control variate method with applications to exotic option pricing

Abstract The Monte Carlo simulation method is still the only feasible approach to handle high dimensional problems encountered in many areas so far. The main drawback of this method is its slow convergence. A variance reduction technique is one of the main methods to speed up Monte Carlo simulations. In this paper, we reconsider the multiple control variate method and provide sufficient conditions to ensure that the variance of an m-variate control variate estimator is smaller than that of a k-variate control variate estimator for any k where . The results can be applied to a wide range of high dimensional complex problems where exact solutions do not exist. As nontrivial examples, the results are applied to problems of options pricing under the Black-Scholes-Merton’s model. For arithmetic Asian and basket options, more efficient new control variate estimators are constructed. Numerical results show that the constructed multiple control variate estimators are more efficient than estimators with fewer control variates in reducing variances.
Learning rotation-invariant binary codes for efficient object detection from remote sensing images

Abstract. Object detection is one of the most important research topics for remote sensing image (RSI) analysis. Because of the rapid development of satellite imaging technology, the resolution of RSI has increased dramatically, which brings a great challenge to the efficiency of RSI-based object detection. We present an object detection model, which greatly improves the detection speed by using learning-based hashing techniques. Specifically, first, a selective search method is used to generate many high quality object proposals that may contain objects. Then for each proposal, we learn rotation-invariant binary codes, which can handle the objects with arbitrary orientations in RSI, to quickly eliminate most nonobject proposals in Hamming space. And finally, the object detection task can be achieved by classifying the left (very limited amount of) proposals with more discriminating classification model. Experimental evaluations on a public very high-resolution remote sensing dataset show the superiority of the proposed method. Specifically, the rotation-invariant learning metric has been combined with three popular hashing methods, and performance improvements have been obtained for all the cases. Furthermore, comparisons with deep learning-based detectors show that hash-based methods fail to achieve state-of-the-art detection performance, but when the graphics processing unit is inaccessible, it achieves a good compromise between performance and runtime.
Towards and ASIP optimized for multi-standard turbo decoding

Systems-on-chips in the field of digital communications are becoming extremely diversified and complex with the continuous emerging of new digital communication systems and standards. In this field, Turbo decoding is one of the most computation, communication, and memory intensive, and thus, power-consuming component. Besides the increasing performance requirements, emerging digital communication systems imply multi-standard interoperability which introduces the new implementation flexibility requirement. In this context, recent efforts have targeted the use of Application-Specific Instruction-set Processor models (ASIP). Such an architecture model enables the designer to freely tune the flexibility/performance trade-off as required by the considered application. However, the architecture efficiency of application-specific processors is directly related to the devised instruction set and pipeline stages usage. Most of recently proposed works do not present this key issue explicitly. Hence, the main objective of this thesis work is related to unifying flexibility-oriented and optimization-oriented approaches in the design of channel decoders. Towards this objective, several contributions have been proposed: (1) designing of a multi-standard ASIP-based Turbo decoder achieving high architecture efficiency in terms of bit/cycle/iteration/mm2, (2) optimizing of the dynamic reconfiguration speed of the proposed ASIP architecture supporting all parameters of 3GPP-LTE/WiMAX/DVB-RCS standards, (3) designing of low complexity ARP and QPP interleavers for butterfly scheme with Radix4 trellis compression technique, and (4) proposing and designing of a complete FPGA prototype for the proposed multi-standard Turbo decoder. Furthermore, towards the support of LDPC decoding, a first effort has been proposed for the design of a scalable and ? exible high throughput multi-ASIP combined architecture for LDPC and Turbo decoding.
Antenas de grafeno com controle de diagrama de radiação: projeto e modelagem via método FDTD

In this work, it is developed a mathematical formulation for modelling Graphene sheets in the three-dimensional lattice used to solve Maxwell’s equations with Yee’s algorithm. The technique is based on piecewise linear recursive convolution method and on thin material sheets technique, valid in intraband range. The HFSS software is used for to designing, for the first time, graphene antennas with dynamic control of the radiation pattern main lobe by tuning the chemical potential of graphene sheets used as passive elements. The numerical method developed in this work fully agrees with the results obtained by using HFSS (this one is based on the Finite Element Method).
Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach

An increasing number of the renowned company’s investors are turning attention to stock prediction in the search for new efficient ways of hypothesizing about markets through the application of behavioral finance. Accordingly, research on stock prediction is becoming a popular direction in academia and industry. In this study, the goal is to establish a model for predicting stock price movement through knowledge graph from the financial news of the renowned companies. In contrast to traditional methods of stock prediction, our approach considers the effects of event tuple characteristics on stocks on the basis of knowledge graph and deep learning. The proposed model and other feature selection models were used to perform feature extraction on the websites of Thomson Reuters and Cable News Network. Numerous experiments were conducted to derive evidence of the effectiveness of knowledge graph embedding for classification tasks in stock prediction. A comparison of the average accuracy with which the same feature combinations were extracted over six stocks indicated that the proposed method achieves better performance than that exhibited by an approach that uses only stock data, a bag-of-words method, and convolutional neural network. Our work highlights the usefulness of knowledge graph in implementing business activities and helping practitioners and managers make business decisions.
ALZUMERIC : A decision support system for diagnosis and monitoring of cognitive impairment

Internet of things and smart cities are becoming a reality. Nowadays, more and more devices are interconnected and in order to deal with this new situation, data processing speeds are increasing to keep the pace. Smart devices like tablets and smartphones are accessible to a wide part of society in developed countries, and Internet connections for data exchange make it possible to handle large volumes of information in less time. This new reality has opened up the possibility of developing client-server architectures focused on clinical diagnosis in real time and at a very low cost. This paper illustrates the design and implementation of the ALZUMERIC system that is oriented to the diagnosis of Alzheimer’s disease (AD). It is a platform where the medical specialist can gather voice samples through non-invasive methods from patients with and without mild cognitive impairment (MCI), and the system automatically parameterizes the input signal to make a diagnose proposal. Although this type of impairment produces a cognitive loss, it is not severe enough to interfere with daily life. The present approach is based on the description of speech pathologies with regard to the following profiles: phonation, articulation, speech quality, analysis of the emotional response, language perception, and complex dynamics of the system. Privacy, confidentiality and information security have also been taken into consideration, as well as possible threats that the system could suffer, so this first prototype of services offered by ALZUMERIC has been targeted to a predetermined number of medical specialists.