Set Of Different States Research Articles

On the Comparability and Optimal Aggressiveness of the Adversarial Scenario-Based Safety Testing of Robots

This article studies the class of scenario-based safety testing algorithms in the black-box safety testing configuration. For algorithms sharing the same state–action set coverage with different sampling distributions, it is commonly believed that prioritizing the exploration of high-risk states and actions leads to a better sampling efficiency. Our proposal disputes the above intuition by introducing an impossibility theorem that provably shows that all the safety testing algorithms of the aforementioned difference perform equally well with the same expected sampling efficiency. Moreover, for testing algorithms covering different sets of states and actions, the sampling efficiency criterion is no longer applicable as different algorithms do not necessarily converge to the same termination condition. We then propose a testing aggressiveness definition based on the almost safe set concept along with an unbiased and efficient algorithm that compares the aggressiveness between testing algorithms. Empirical observations from the safety testing of bipedal locomotion controllers and vehicle decision-making modules are also presented to support the proposed theoretical implications and methodologies.

Nondestructive verification of entangled states via fidelity witnessing

Assessing the quality of an ensemble of noisy entangled states is a central task in quantum information processing. Usually this is done by measuring and hence destroying multiple copies, from which state tomography or fidelity estimation can be employed to characterize states. Here we propose several methods to directly distinguish between two different sets of states, e.g. if their fidelity is above or below a certain threshold value. This turns out to be significantly more efficient, and importantly keeps the verified states intact. We make use of auxiliary entanglement or an ensemble of larger size, where we operate on the whole ensemble, but measure only a small fraction where information has been concentrated. For certain state families, we demonstrate that such an approach can even outperform optimal methods that collectively measure directly a fixed fraction of the ensemble.

A study on spot welding quality judgment of stainless steel plate based on semi-supervised conditional generation adversarial network

During RSW, the number of qualified samples is much more than the unqualified ones, forming an unbalanced set, thus affecting the training effect of the model, meanwhile, most samples are unlabeled, and if all the joints are marked, it is more expensive. Based on this, a method of spot welding quality judgment of stainless steel plate based on semi-supervised conditional generation adversarial network is proposed. Firstly, labels are added to the noise to generate labeled data and unlabeled data, which are mixed in a certain proportion to ensure the diversity of generated data. Then, the real data is divided into two parts, in which the unlabeled part plays a game with the generated data to generate samples as close to the real as possible, meanwhile, the data with true discriminant results and labeled data are input into the autoencoder to obtain the feature vectors set of different states. Finally, the training parameters and test samples are input into the classifier to obtain the judgment results. The proposed method was applied to application case, and the results showed that it not only had a good fitting effect, but also had a high classification accuracy. Consequently, the method proposed was effective.

Flexible learning of quantum states with generative query neural networks

Deep neural networks are a powerful tool for characterizing quantum states. Existing networks are typically trained with experimental data gathered from the quantum state that needs to be characterized. But is it possible to train a neural network offline, on a different set of states? Here we introduce a network that can be trained with classically simulated data from a fiducial set of states and measurements, and can later be used to characterize quantum states that share structural similarities with the fiducial states. With little guidance of quantum physics, the network builds its own data-driven representation of a quantum state, and then uses it to predict the outcome statistics of quantum measurements that have not been performed yet. The state representations produced by the network can also be used for tasks beyond the prediction of outcome statistics, including clustering of quantum states and identification of different phases of matter.

Hierarchy of correlation quantifiers comparable to negativity

Quantum systems generally exhibit different kinds of correlations. In order to compare them on equal footing, one uses the so-called distance-based approach where different types of correlations are captured by the distance to different sets of states. However, these quantifiers are usually hard to compute as their definition involves optimization aiming to find the closest states within the set. On the other hand, negativity is one of the few computable entanglement monotones, but its comparison with other correlations required further justification. Here we place negativity as part of a family of correlation measures that has a distance-based construction. We introduce a suitable distance, discuss the emerging measures and their applications, and compare them to relative entropy-based correlation quantifiers. This work is a step towards correlation measures that are simultaneously comparable and computable.

Detecting a randomly moving COVID-19 using weight Tchebycheff optimization technique

This paper completely characterizes the search for randomly moving COVID-19 among a finite set of different states (cells). A search effort at each fixed number of time intervals is a random variable with a given distribution. Both of probability of detecting the COVID-19 in a certain state j at a certain time i and the detection functions are supposed to be known to the searcher or robot. We seek for the optimal distribution of the effort to deduce an explicit formula for the optimality distribution of the random variable effort using weighted Tchebycheff optimization technique. We find the necessary conditions for the optimal search technique. An algorithm is constructed for generated weighted Tchebycheff optimization technique. The effectiveness of these techniques is illustrated by numerical results which indicate that the proposed techniques are promising.

Mathematical search technique for detecting moving novel coronavirus disease (COVID-19) based on minimizing the weight function

The study of search plans has found considerable interest between searchers due to its interesting applications in our real life like searching for located and moving targets. This paper develops a method for detecting moving targets. We propose a novel strategy based on weight function W(Z), W(Z)=λH(Z)+(1-λ)L(Z), where H(Z), L(Z) are the total probabilities of un-detecting, and total effort respectively, is searching for moving novel coronavirus disease (COVID-19) cells among finite set of different states. The total search effort will be presented in a more flexible way, so it will be presented as a random variable with a given distribution. The objective is searching for COVID-19 which hidden in one of n cells in each fixed number of time intervals m and the detection functions are supposed to be known to the searcher or robot. We look in depth for the optimal distribution of the total effort which minimizes the probability of undetected the target over the set of possible different states. The effectiveness of this model is illustrated by presenting a numerical example.

Detecting a randomly moving COVID-19 using weight Tchebycheff optimization technique

This paper completely characterizes the search for randomly moving COVID-19 among a finite set of different states (cells). A search effort at each fixed number of time intervals is a random variable with a given distribution. Both of probability of detecting the COVID-19 in a certain state j at a certain time i and the detection functions are supposed to be known to the searcher or robot. We seek for the optimal distribution of the effort to deduce an explicit formula for the optimality distribution of the random variable effort using weighted Tchebycheff optimization technique. We find the necessary conditions for the optimal search technique. An algorithm is constructed for generated weighted Tchebycheff optimization technique. The effectiveness of these techniques is illustrated by numerical results which indicate that the proposed techniques are promising.

Optimal Discrete Search for a Randomly Moving COVID19

In this paper, we subedit a search for a randomly moving Coronavirus (COVID-19) among a finite set of different states We use a monitoring system to search for COVID-19 which is hidden in one of the n cells of the respiratory system in the human body in each fixed number of time intervals m The expected rescue time of the patient and detecting COVID-19 has been obtained Also, we extend the results and obtain the total optimal expected search time of COVID-19 The optimal search strategy is derived suing a dynamic programming algorithm An illustrative real life example introduced to clear the applicability of this model © 2020 Natural Sciences Publishing All rights reserved

Monitoring Novel Corona Virus (COVID-19) Infections in India by Cluster Analysis.

It is a great challenge of identification as well as formation of groups of infectious disease data set. Data mining, a process of uncovering silent characteristics of big data is one of such techniques which have nowadays become more popular for treating massive volume of infectious disease data set. In the current study, we apply cluster analysis, one of the data mining techniques to classify real groups of infectious disease “novel corona virus disease (COVID-19)” data set of different states and union territories (UTs) in India according to their high similarity to each other. The results obtained permit us to have a sense of clusters of affected Indian states and UTs. The main objective of clustering in this study is to optimize monitoring techniques in affected states and UTs in India which will be very valuable to the government, doctors, the police and others involved in understanding seriousness of the spread of novel coronavirus (COVID-19) to improve government policies, decisions, medical facilities (ventilators, testing kits, masks etc.), treatment etc. to reduce number of infected and deceased persons.

The first law of general quantum resource theories

We extend the tools of quantum resource theories to scenarios in which multiple quantities (or resources) are present, and their interplay governs the evolution of physical systems. We derive conditions for the interconversion of these resources, which generalise the first law of thermodynamics. We study reversibility conditions for multi-resource theories, and find that the relative entropy distances from the invariant sets of the theory play a fundamental role in the quantification of the resources. The first law for general multi-resource theories is a single relation which links the change in the properties of the system during a state transformation and the weighted sum of the resources exchanged. In fact, this law can be seen as relating the change in the relative entropy from different sets of states. In contrast to typical single-resource theories, the notion of free states and invariant sets of states become distinct in light of multiple constraints. Additionally, generalisations of the Helmholtz free energy, and of adiabatic and isothermal transformations, emerge. We thus have a set of laws for general quantum resource theories, which generalise the laws of thermodynamics. We first test this approach on thermodynamics with multiple conservation laws, and then apply it to the theory of local operations under energetic restrictions.

M-states search problem for a lost target with multiple sensors

This paper considers the discrete search problem for a single lost target by using multiple sensors. The lost target either located in one of a finite set of different states (cells) or moved through them according to random process with discrete time and discrete state space. A search effort at each fixed number of time intervals is a random variable with a given distribution. The probability that the target in a certain state j at a certain time i and the detection function is supposed to be known to the searchers. We seek for the optimal distribution of the effort that minimises the probability of undetection the target over the set of possible different states. Our aim is to deduce an explicit formula for the random distribution of the random variable effort. An algorithm is constructed for obtaining this optimal solution. The effectiveness of this method is illustrated using some examples with numerical results.

M-states search problem for a lost target with multiple sensors

This paper considers the discrete search problem for a single lost target by using multiple sensors. The lost target either located in one of a finite set of different states (cells) or moved through them according to random process with discrete time and discrete state space. A search effort at each fixed number of time intervals is a random variable with a given distribution. The probability that the target in a certain state j at a certain time i and the detection function is supposed to be known to the searchers. We seek for the optimal distribution of the effort that minimises the probability of undetection the target over the set of possible different states. Our aim is to deduce an explicit formula for the random distribution of the random variable effort. An algorithm is constructed for obtaining this optimal solution. The effectiveness of this method is illustrated using some examples with numerical results.

Observability analysis and model formulation for nonlinear state estimation

A suitable design of state estimators for advanced control requires a detailed and representative mathematical model for capturing the nonlinear process behavior. The system observability, i.e. when the set of measurements provides enough information to estimate all the system states, is not a premise of the derivation of the Kalman filter. However, this propriety can improve the state estimator performance. On the basis of these design tasks, we outline a state estimation tuning strategy for different model formulations and present an algorithm to select the smallest number of measured variables to guarantee the system observability. The Williams–Otto semi-batch reactor was selected as case study, since its model formulation can be represented by two different set of states: (a) a mass basis states set and (b) a mass fraction basis states set. While the process-noise covariance matrix Q in the state estimator can be a diagonal and constant for the first model formulation, the matrix Q is not diagonal and time-varying for the second one due to their highly correlated states. Our results have shown how to convert the tuning matrices between different state definitions so that similar estimation results can be achieved.

Towards a Task-Aware Proactive Sociable Robot Based on Multi-state Perspective-Taking

Robots are expected to cooperate with humans in day-to-day interaction. One aspect of such cooperation is behaving proactively. In this paper we will enable our robots, equipped with visuo-spatial perspective-taking capabilities, to behave proactively based on reasoning ‘where’ its human partner might perform a particular task with different effort levels. For this, the robot analyzes the agents’ abilities not only from the current state but also from a set of different states the agent might attain.

Is angiogenesis a hallmark of prostate cancer?

In vertebrate organogenesis, the blood vessels constitute the first organ system that arises and reaches a functional state (Persson and Buschmann, 2011). Angiogenesis, the development of new branching vessels from existing vasculature, is a complex process observed in fetal growth, wound healing and endometrial hyperplasia associated with the menstrual cycle (Carmeliet, 2003). Under these conditions, it is highly regulated: i.e., “turned on” for brief periods of time and then completely inhibited. However, many human diseases, including tumors, are driven by persistently upregulated angiogenesis (Carmeliet, 2003, 2005; Hanahan and Weinberg, 2011). In some non-malignant diseases, such as lobular capillary hemangioma or keloid formation, angiogenesis is self-limited; however, this is not true of tumor angiogenesis, which, once begun, continues indefinitely until the entire tumor is eradicated or the host dies. Without blood vessels, tumors cannot grow beyond a critical size. Angiogenesis is regulated by a balance of proand anti-angiogenic molecules (Hanahan and Weinberg, 2011), secreted from cancer cells, endothelial cells and stromal cells (Fukumura et al., 1998), the relative contributions of which are likely to change with tumor type and site, as well as with tumor growth, regression and relapse. It is also ascertained that angiogenic vessels have a disorganized and irregular structure, and that the blood flow is abnormal. This is in contrast to the organized, regular structure and normal blood flow seen in mature vessels. Angiogenesis can be depicted as a non-linear dynamic process that is discontinuous in space and time, but advances through qualitatively different states. The term state defines the configuration pattern of the process at any given moment, and a dynamic process can be represented as a set of different states and a number of transitions from one state to another over a certain time interval. The continuum of these states generates a complex ramified structure that irregularly fills the surrounding environment. The main feature of the newly generated vasculature is the structural diversity of the vessel sizes, shapes, and connecting patterns. This is mainly due to the heterogeneous distribution of angiogenic regulators, such as vascular-endothelial growth factor, basic fibroblastic growth factor and angiopoietin, leading to hypoxic and acidic tumoral regions (Karlou et al., 2010). Moreover, although it is commonly believed that the endothelial cells making-up tumor vessels are genetically stable, tumor vasculature seems to be much more unpredictable (Streubel et al., 2004). These conditions all reduce the effectiveness of treatments, modulate the production of proand antiangiogenic molecules, and select a subset of more aggressive cancer cells with higher metastatic potential. The significance of angiogenesis in prostate cancer (PC) still remains controversial (Russo et al., 2012). While there are currently no markers of the net angiogenic activity of PC that can help investigators to design specific anti-angiogenic treatment strategies, it is reasonable to assume that the quantification of various aspects of tumor vasculature may provide an indication of angiogenic activity. One oftenquantified parameter of PC vasculature is microvessel density (MVD), which is used to allow a histological assessment of tumor angiogenesis. The results of studies carried out over the last decade have suggested the value of using MVD as a prognostic index in PC, and it has also assumed that MVD may reveal the degree of angiogenic activity in PC. MVD has, however, a number of limitations. The conflicting MVD results in PC are likely due to the differences in study designs: variability in patient population size, tumor topography, approach to selection of representative tumor areas, choice of endothelial marker, and actual counting method. The selection of the tumor area for MVD assessment has been based on two different approaches: (1) analysis of a few microscopic “hot spots” containing the maximal vascular density, and (2) selection of random representative areas of the tumor. The first approach is the most applied due to its simplicity, although there is no agreement among investigators regarding optimal microscope magnification, the number of vascular hot spots, and cutoff values for low vs. high MVD. The second approach of MVD assessment within larger representative areas or whole tissue may be more objective but involves more tedious examination. Despite its importance as a prognostic indicator in untreated tumors, MVD has not been shown to be a valid measure to guide or evaluate anti-angiogenic treatment (Hlatky et al., 2002). MVD does not appear to be predictive of tumor response under anti-angiogenic treatment and therefore may not be useful for stratifying patients for clinical trials (Rubin et al., 1999; Eberhard et al., 2000; Hlatky et al., 2002; Preusser et al., 2006; Erbersdobler et al., 2010). Low MVD does not portend a poor response to anti-angiogenic therapy (Vartanian and

Fisher information and multiparticle entanglement

The Fisher information $F$ gives a limit to the ultimate precision achievable in a phase estimation protocol. It has been shown recently that the Fisher information for a linear two-mode interferometer cannot exceed the number of particles if the input state is separable. As a direct consequence, with such input states the shot-noise limit is the ultimate limit of precision. In this work, we go a step further by deducing bounds on $F$ for several multiparticle entanglement classes. These bounds imply that genuine multiparticle entanglement is needed for reaching the highest sensitivities in quantum interferometry. We further compute similar bounds on the average Fisher information $\overline{F}$ for collective spin operators, where the average is performed over all possible spin directions. We show that these criteria detect different sets of states and illustrate their strengths by considering several examples, also using experimental data. In particular, the criterion based on $\overline{F}$ is able to detect certain bound entangled states.

A bound on the entropy of supergravity?

We determine, in two independent ways, the number of BPS quantum states arising from supergravity degrees of freedom in a system with fixed total D4D0 charge. First, we count states generated by quantizing the spacetime degrees of freedom of 'entropyless' multicentered solutions consisting of anti-D0-branes bound to a D6-anti-D6 pair. Second, we determine the number of free supergravity excitations of the corresponding AdS_3 geometry with the same total charge. We find that, although these two approaches yield a priori different sets of states, the leading degeneracies in a large charge expansion are equal to each other and that, furthermore, the number of such states is parametrically smaller than that arising from the D4D0 black hole's entropy. This strongly suggests that supergravity alone is not sufficient to capture all degrees of freedom of large supersymmetric black holes. Comparing the free supergravity calculation to that of the D6-anti-D6-D0 system we find that the bound on the free spectrum imposed by the stringy exclusion principle (a unitarity bound in the dual CFT) seems to be captured in the dynamics of the fully interacting but classcial supergravity equations of motion.

Impact excitation by hot carriers in carbon nanotubes

We investigate theoretically the efficiency of intra-molecular hot carrier induced impact ionization and excitation processes in carbon nanotubes. The electron confinement and reduced screening lead to drastically enhanced excitation efficiencies over those in bulk materials. Strong excitonic coupling favors neutral excitations over ionization, while the impact mechanism populates a different set of states than that produced by photoexcitation. The excitation rate is strongly affected by optical phonon excitation and a simple scaling of the rate with the field strength and optical phonon temperature is obtained.

A Distributed Decision-Making Structure for Dynamic Resource Allocation Using Nonlinear Functional Approximations

This paper proposes a distributed solution approach to a certain class of dynamic resource allocation problems and develops a dynamic programming-based multiagent decision-making, learning, and communication mechanism. In the class of dynamic resource allocation problems we consider, a set of reusable resources of different types has to be assigned to tasks that arrive randomly over time. The assignment of a resource to a task removes the task from the system, modifies the state of the resource, and generates a contribution. We build a decision-making scheme where the decisions regarding the resources in different sets of states are made by different agents. We explain how to coordinate the actions of different agents using nonlinear functional approximations, and show that in a distributed setting, nonlinear approximations produce sequences of min-cost network flow problems that naturally yield integer solutions. We also experimentally compare the performances of the centralized and distributed solution strategies.