Miracle-dz/UniFAR-FTUs huggingface dataset:
Generating Optimal Grasps Under A Stress-Minimizing Metric

We present stress-minimizing (SM) metric, a new metric of grasp qualities. Unlike previous metrics that ignore the material of target objects, we assume that target objects are made of homogeneous isotopic materials. SM metric measures the maximal resistible external wrenches without causing fracture in the target objects. Therefore, SM metric is useful for robot grasping valuable and fragile objects. In this paper, we analyze the properties of this new metric, propose grasp planning algorithms to generate globally optimal grasps maximizing the SM metric, and compare the performance of the SM metric and a conventional metric. Our experiments show that SM metric is aware of the geometries of target objects while the conventional metric are not. We also show that the computational cost of the SM metric is on par with that of the conventional metric.
Scalable Grouped Gaussian Processes via Direct Cholesky Functional Representations

We consider multi-task regression models where observations are assumed to be a linear combination of several latent node and weight functions, all drawn from Gaussian process (GP) priors that allow nonzero covariance between grouped latent functions. We show that when these grouped functions are conditionally independent given a group-dependent pivot, it is possible to parameterize the prior through sparse Cholesky factors directly, hence avoiding their computation during inference. Furthermore, we establish that kernels that are multiplicatively separable over input points give rise to such sparse parameterizations naturally without any additional assumptions. Finally, we extend the use of these sparse structures to approximate posteriors within variational inference, further improving scalability on the number of functions. We test our approach on multi-task datasets concerning distributed solar forecasting and show that it outperforms several multi-task GP baselines and that our sparse specifications achieve the same or better accuracy than non-sparse counterparts.
An Introduction to Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications.

Spiking Neural Networks (SNNs) are distributed trainable systems whose computing elements, or neurons, are characterized by internal analog dynamics and by digital and sparse synaptic communications. The sparsity of the synaptic spiking inputs and the corresponding event-driven nature of neural processing can be leveraged by hardware implementations that have demonstrated significant energy reductions as compared to conventional Artificial Neural Networks (ANNs). Most existing training algorithms for SNNs have been designed either for biological plausibility or through conversion from pre-trained ANNs via rate encoding. This paper aims at providing an introduction to SNNs by focusing on a probabilistic signal processing methodology that enables the direct derivation of learning rules leveraging the unique time encoding capabilities of SNNs. To this end, the paper adopts discrete-time probabilistic models for networked spiking neurons, and it derives supervised and unsupervised learning rules from first principles by using variational inference. Examples and open research problems are also provided.
Paracoherent Answer Set Semantics meets Argumentation Frameworks

Abstract In the last years, abstract argumentation has met with great success in AI, since it has served to capture several non-monotonic logics for AI. Relations between argumentation framework (AF) semantics and logic programming ones are investigating more and more. In particular, great attention has been given to the well-known stable extensions of an AF, that are closely related to the answer sets of a logic program. However, if a framework admits a small incoherent part, no stable extension can be provided. To overcome this shortcoming, two semantics generalizing stable extensions have been studied, namely semi-stable and stage. In this paper, we show that another perspective is possible on incoherent AFs, called paracoherent extensions, as they have a counterpart in paracoherent answer set semantics. We compare this perspective with semi-stable and stage semantics, by showing that computational costs remain unchanged, and moreover an interesting symmetric behaviour is maintained.
Real-time Background-aware 3D Textureless Object Pose Estimation

In this work, we present a modified fuzzy decision forest for real-time 3D object pose estimation based on typical template representation. We employ an extra preemptive background rejector node in the decision forest framework to terminate the examination of background locations as early as possible, result in a significantly improvement on efficiency. Our approach is also scalable to large dataset since the tree structure naturally provides a logarithm time complexity to the number of objects. Finally we further reduce the validation stage with a fast breadth-first scheme. The results show that our approach outperform the state-of-the-arts on the efficiency while maintaining a comparable accuracy.
A Revisit on Blockchain-based Smart Contract Technology

Blockchain-based smart contract has become a growing field in the blockchain technology. What was once a technology used to solve digital transaction issues turns out to have some wider usage, including smart contract. The development of smart contract can be traced from the numerous platforms facilitating it, however the issue on how well each platform works as oppose to each other has yet been fully explored. The usage of smart contract can be seen from the applications that are built on top of the smart contract platform, such as the tokenization of real world to virtual world assets. However smart contract contains several issues concerning security and codifying which could be solved by various tools that are proposed by existing research. This paper aims to revisit the blockchain-based smart contract technology in order to understand and discuss the research gaps gathered from existing research and to provide guidance for future research.
Spectral Graph Matching and Regularized Quadratic Relaxations I: The Gaussian Model

Graph matching aims at finding the vertex correspondence between two unlabeled graphs that maximizes the total edge weight correlation. This amounts to solving a computationally intractable quadratic assignment problem. In this paper we propose a new spectral method, GRAph Matching by Pairwise eigen-Alignments (GRAMPA). Departing from prior spectral approaches that only compare top eigenvectors, or eigenvectors of the same order, GRAMPA first constructs a similarity matrix as a weighted sum of outer products between all pairs of eigenvectors of the two graphs, with weights given by a Cauchy kernel applied to the separation of the corresponding eigenvalues, then outputs a matching by a simple rounding procedure. The similarity matrix can also be interpreted as the solution to a regularized quadratic programming relaxation of the quadratic assignment problem. For the Gaussian Wigner model in which two complete graphs on $n$ vertices have Gaussian edge weights with correlation coefficient $1-\sigma^2$, we show that GRAMPA exactly recovers the correct vertex correspondence with high probability when $\sigma = O(\frac{1}{\log n})$. This matches the state of the art of polynomial-time algorithms, and significantly improves over existing spectral methods which require $\sigma$ to be polynomially small in $n$. The superiority of GRAMPA is also demonstrated on a variety of synthetic and real datasets, in terms of both statistical accuracy and computational efficiency. Universality results, including similar guarantees for dense and sparse Erd\H{o}s-R\'{e}nyi graphs, are deferred to the companion paper.
Rydberg atom quantum technologies

This topical review addresses how Rydberg atoms can serve as building blocks for emerging quantum technologies. Whereas the fabrication of large numbers of artificial quantum systems with the uniformity required for the most attractive applications is difficult if not impossible, atoms provide stable quantum systems which, for the same species and isotope, are all identical. Whilst atomic ground states provide scalable quantum objects, their applications are limited by the range over which their properties can be varied. In contrast, Rydberg atoms offer strong and controllable atomic interactions that can be tuned by selecting states with different principal quantum number or orbital angular momentum. In addition Rydberg atoms are comparatively long-lived, and the large number of available energy levels and their separations allow coupling to electromagnetic fields spanning over 6 orders of magnitude in frequency. These features make Rydberg atoms highly desirable for developing new quantum technologies. After giving a brief introduction to how the properties of Rydberg atoms can be tuned, we give several examples of current areas where the unique advantages of Rydberg atom systems are being exploited to enable new applications in quantum computing, electromagnetic field sensing, and quantum optics.
Bayesian estimation of probabilistic sensitivity measures

Computer experiments are becoming increasingly important in scientific investigations. In the presence of uncertainty, analysts employ probabilistic sensitivity methods to identify the key-drivers of change in the quantities of interest. Simulation complexity, large dimensionality and long running times may force analysts to make statistical inference at small sample sizes. Methods designed to estimate probabilistic sensitivity measures at relatively low computational costs are attracting increasing interest. We propose a fully Bayesian approach to the estimation of probabilistic sensitivity measures based on a one-sample design. We discuss, first, new estimators based on placing piecewise constant priors on the conditional distributions of the output given each input, by partitioning the input space. We then present two alternatives, based on Bayesian non-parametric density estimation, which bypass the need for predefined partitions. In all cases, the Bayesian paradigm guarantees the quantification of uncertainty in the estimation process through the posterior distribution over the sensitivity measures, without requiring additional simulator evaluations. The performance of the proposed methods is compared to that of traditional point estimators in a series of numerical experiments comprising synthetic but challenging simulators, as well as a realistic application. $\textit{An Updated Version of the Manuscript is Forthcoming in Statistics and Computing. }$
Online Constraint Satisfaction via Tolls in MDP Congestion Games

We consider the toll design problem that arise for a game designer of a congested stochastic network when the decision makers are myopically updating their strategies based on current system state. If both the designer and the decision makers are adaptively searching for an optimal strategy, it is not clear how and if both parties can attain their optimal strategies. We formulate the toll synthesis problem of inducing an equilibrium distribution that satisfies a given set of design specifications as a bilevel optimization problem, in which the lower level consists of decision makers playing a Markov decision process (MDP) congestion game. In addition to showing the existence of an optimal tolling threshold, we formulate learning algorithms that can be employed by both the game designer and the players to jointly determine the optimal toll and induced equilibrium, respectively, and analyze its convergence properties.
Next Generation Resilient Cyber-Physical Systems

Cyber-Physical Systems (CPS) consist of distributed engineered environments where the monitoring and surveillance tasks are governed by tightly integrated computing, communication and control technologies. CPS are omnipresent in our everyday life. Hacking and failures of such systems have impact on critical services with potentially significant and lasting consequences. In this paper, we review which requirements a CPS must meet to address the challenges of tomorrow. Two key challenges are understanding and reinforcing the resilience of CPS.
cyTRON and cyTRON/JS: two Cytoscape-based applications for the inference of cancer evolution models

The increasing availability of sequencing data of cancer samples is fueling the development of algorithmic strategies to investigate tumor heterogeneity and infer reliable models of cancer evolution. We here build up on previous works on cancer progression inference from genomic alteration data, to deliver two distinct Cytoscape-based applications, which allow to produce, visualize and manipulate cancer evolution models, also by interacting with public genomic and proteomics databases. In particular, we here introduce cyTRON, a stand-alone Cytoscape app, and cyTRON/JS, a web application which employs the functionalities of Cytoscape/JS. cyTRON was developed in Java; the code is available at https://github.com/BIMIB-DISCo/cyTRON and on the Cytoscape App Store http://apps.cytoscape. org/apps/cytron. cyTRON/JS was developed in JavaScript and R; the source code of the tool is available at https://github.com/BIMIB-DISCo/cyTRON-js and the tool is accessible from https://bimib.disco. unimib.it/cytronjs/welcome.
Fault Covering Problems In Reconfigurable Vlsi Systems

Thank you very much for downloading fault covering problems in reconfigurable vlsi systems. Maybe you have knowledge that, people have look hundreds times for their chosen novels like this fault covering problems in reconfigurable vlsi systems, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some infectious virus inside their computer.
Review on large-scale unmanned system swarm intelligence control method

Concerning the problem of the large-scale unmanned system swarm intelligence control(SIC) method,this paper sketched the significance of swarm intelligence in large-scale unmanned system control and gave an overview of all kinds of SIC methods and related research works.The local rules control method was the basis of SIC,its key lay in the design and description of the internal local rules.The soft control method was based on without-destroying the local rules of system,added controllable individual into swarm to realize external control or interventions.The pilot control method realized SIC by the guiding action of informed information or higher degree of intelligence individuals.The artificial potential control method described the interaction among individuals and between individuals and environment by a potential function.Finally,this paper pointed out the application trend of various control methods and the problems needed to be studied further.
Bioactivity of Three Plants' Essential Oils Against Aphis glycines

abstract: This research represents a forward step in the control of Aphis glycines. Essential oils from Acorus tatarinowii, Mentha haplocalyx, and Stemona japonica were studied for repellency and anti-molting activities. For the laboratory choice-experiment, of two soybean plants with 4 to 6 leaves each, one was treated with either a single or a mixture of essential oils, while the other was treated with a control solution. The plants were then placed in a cage and twenty aphids were released between the two plants. For no-choice tests, only one plant, treated with essential oil, was placed in the center of a cage and 20 aphids were released around the plant. Viable aphid numbers on plants were recorded at 6, 12, 24, and 36 hours after initiating each test. After 36 hours of observation, numbers of exuviae around the plant were recorded in the no-choice test. In the greenhouse experiment, two potted faba bean plants were used. Five mL of essential oil was sprayed on one plant, and 5 mL of control solution on the other plant. One hundred adult aphids were released into each cage between the two plants. Repellency was observed at 12 and 24 hours of exposure. A mixture of A. tatarinowii plus S. japonica oils demonstrated maximum repellency in both lab (71.25 ± 3.50%) and greenhouse (85.61 ± 1.56). Anti-molting activities were observed when the essential oil of S. japonica was used. Results suggest that a mixture of A. tatarinowii and S. japonica essential oils are effective at controlling A. glycine.
Anomalies in the Sand Fly Lutzomyia longipalpis (Diptera: Psychodidae) in Brazil

Lutzomyia longipalpis is the main species involved in the epidemiological cycle of visceral leishmaniasis in Brazil. This species shows a wide geographic distribution and belongs to a group that has 2 setae in the paramere with the curved tips towards the apex of this structure among other characteristics. Morphological characters in the genitalia of phlebotomines are essential for correct identification of species. The aim of this study was to report the finding of unilateral and bilateral anomalies in the paramere of L. longipalpis males collected in Várzea da Palma municipality, Minas Gerais State, Brazil. Twelve collections were made during the period April 2009 to March 2010, using Centers for Disease Control and Prevention light traps, HP model. Of the 8,832 male L. longipalpis collected, we found 6 types of unilateral anomalies and 2 types of bilateral anomalies in 0.5% of the insects collected. The anomalies were described according to number, position, and size of the setae of paramere. This information will be useful in the validation of new species.
A Dynamic Resilience Approach for WDM Optical Networks

Abstract Optical fibres have been developed as a transmission medium to carry traffic in order to provide various services in telecommunications platform. Failure of this fibre caused loss of data which can interrupt communication services. This paper has been focused only on survivable schemes in order to guarantee both protection and restoration in WDM optical networks. In this paper, a dynamic resilience approach has been proposed whose objective is to route the flows in a way which minimizes the total amount of bandwidth used for working and protection paths. In the proposed approach, path-based protection is utilized because it yields lower overhead and is also suitable for global optimization where, in case of a single link failure, all the flows utilizing the failed link are re-routed to a pre-computed set of paths. The simulation results demonstrate that proposed approach is much more efficient as it provides better quality of services (QoS) in terms of network resource utilization, blocking probability etc. as compared to conventional protection and restoration schemes. The proposed approach seems to offer an attractive combination of features, with both ring like speed and mesh-like efficiency.
The Effect of Disturbance on Macroinvertebrate Community Structure in Northeastern Oregon

Abstract Biodiversity in aquatic ecosystems is generally viewed as a reliable indicator of ecosystem health. High biodiversity indicates a healthy ecosystem that functions properly because of the right combination of physical, chemical, and biological factors. Anthropogenic disturbance decreases biodiversity by modifying these factors. The Grande Ronde and Imnaha River basins in northeastern Oregon are occupied by land uses that could be disturbing aquatic ecosystems. Biological assessments (bioassessments) were used in this study to determine the effects of disturbance on macroinvertebrate community structure within the Grande Ronde, Wallowa, and Imnaha rivers. Macroinvertebrate distribution and abundance data collected from upstream and downstream sites on each river were used to calculate species- and community-level metrics (e.g., richness, abundance and the Family Biotic Index [FBI]) for each site. These metrics revealed differences between sites and between rivers, as well as a decrease in water quality and biodiversity for the Grande Ronde and Imnaha rivers. The Wallowa River did not conform to this trend, likely because the upstream site was directly below an oligotrophic lake. In general, metrics were not indicative of impaired macroinvertebrate communities, but management efforts should be taken to conserve biodiversity in lower reaches of rivers within the Grande Ronde and Imnaha river basins.
Spatial Analysis of the Soil Carbon Sequestration Potential of Crop-Residue Return in China Based on Model Simulation

Abstract: Crop-residue return is a recommended practice for soil and nutrient management and is important in soil organic carbon (SOC) sequestration and CO2 mitigation. We applied a process-based Environmental Policy Integrated Climate (EPIC) model to simulate the spatial pattern of topsoil organic carbon changes from 2001 to 2010 under 4 crop-residue return scenarios in China. The carbon loss (28.89 Tg yr-1) with all crop-residue removal (CR0%) was partly reduced by 22.38 Tg C yr-1 under the status quo CR30% (30% of crop-residue return). The topsoil in cropland of China would become a net carbon sink if the crop-residue return rate was increased from 30% to 50%, or even 75%. The national SOC sequestration potential of cropland was estimated to be 25.53 Tg C yr-1 in CR50% and 52.85 Tg C yr-1 in CR75%, but with high spatial variability across regions. The highest rate of SOC sequestration potential in density occurred in Northwest and North China while the lowest was in East China. Croplands in North China tended to have stronger regional SOC sequestration potential in storage. During the decade, the reduced CO2 emissions from enhanced topsoil carbon in CR50% and CR75% were equivalent to 1.4% and 2.9% of the total CO2 emissions from fossil fuels and cement production in China, respectively. In conclusion, we recommend encouraging farmers to return crop-residue instead of burning in order to improve soil properties and alleviate atmospheric CO2 rises, especially in North China.
Process-Based Morphodynamic Modeling of a Schematized Mudflat Dominated by a Long-Shore Tidal Current at the Central Jiangsu Coast, China

Abstract Gong, Z. ; Wang, Z.B. ; Stive, M.J.F. ; Zhang, C.K. , and Chu, A. , 2012. Process-based morphodynamic modeling of a schematized mudflat dominated by a long-shore tidal current at the central Jiangsu coast, China. This paper focuses on the process-based morphodynamic modeling of a mudflat dominated by a long-shore tidal current. The model is schematized as a shore-parallel tidal basin with dimensions similar to the tidal flats near the Wanggang Mouth at the central Jiangsu coast, China. A long-shore tidal current and associated sediment transport are dominant in comparison with their cross-shore components. The mudflat evolution is reproduced for both the case with abundant sediment supply and that with insufficient sediment supply. The simulated flow patterns agree qualitatively with field observations from literature, i.e., involving tidal asymmetry, current directions, and tidal wave features. The analysis of sediment fluxes depicts that deposition occurs from spring tide to neap tide and erosion from neap tide to spring tide. The results show that siltation on intertidal flats is favored by flood dominance, as well as settling and scour lag effects, with less dependence on sediment supply. However, subtidal flats are strongly affected by sediment supply. The simulated double-convex cross-shore profile associated with an abundant sediment supply is qualitatively consistent with field observations. An insufficient sediment supply causes erosion and consequent widening of the subtidal flats. Finally, the practical case with an initial sand seabed is reproduced. The long-shore sediment transport contributes to long-shore variations of cross-shore profiles. Deposition rates increase clearly southward in the area between the lower intertidal and middle subtidal flats when sediment supply is abundant. In contrast, deposition rates increase southward only slightly when sediment supply is insufficient. In addition, stronger sediment supply generates wider intertidal flats. Most importantly, the creek patterns, i.e., the incised creeks on mudflats, are roughly reproduced.