In this paper we design suboptimal control laws for an unknown linear system on the basis of measured data. We focus on the suboptimal linear quadratic regulator problem and the suboptimal H2 control problem. For both problems, we establish conditions under which a given data set contains sufficient information for controller design. We follow up by providing a data-driven parameterization of all suboptimal controllers. We will illustrate our results by numerical simulations, which will reveal an interesting trade-off between the number of collected data samples and the achieved controller performance.

We introduce primal and dual stochastic gradient oracle methods for decentralized convex optimization problems. Both for primal and dual oracles the proposed methods are optimal in terms of the number of communication steps. However, for all classes of the objective, the optimality in terms of the number of oracle calls per node in the class of methods with optimal number of communication steps takes place only up to a logarithmic factor and the notion of smoothness. By using mini-batching technique we show that all proposed methods with stochastic oracle can be additionally parallelized at each node.

This work presents a novel data-driven framework for constructing eigenfunctions of the Koopman operator geared toward prediction and control. The method leverages the richness of the spectrum of the Koopman operator away from attractors to construct a rich set of eigenfunctions such that the state (or any other observable quantity of interest) is in the span of these eigenfunctions and hence predictable in a linear fashion. The eigenfunction construction is optimization-based with no dictionary selection required. Once a predictor for the uncontrolled part of the system is obtained in this way, the incorporation of control is done through a multi-step prediction error minimization, carried out by a simple linear least-squares regression. The predictor so obtained is in the form of a linear controlled dynamical system and can be readily applied within the Koopman model predictive control framework of [12] to control nonlinear dynamical systems using linear model predictive control tools. The method is entirely data-driven and based purely on convex optimization, with no reliance on neural networks or other non-convex machine learning tools. The novel eigenfunction construction method is also analyzed theoretically, proving rigorously that the family of eigenfunctions obtained is rich enough to span the space of all continuous functions. In addition, the method is extended to construct generalized eigenfunctions that also give rise Koopman invariant subspaces and hence can be used for linear prediction. Detailed numerical examples with code available online demonstrate the approach, both for prediction and feedback control.

Among all dissipative solutions of the multi-dimensional isentropic Euler equations there exists at least one that minimizes the acceleration, which implies that the solution is as close to being a weak solution as possible. The argument is based on a suitable selection procedure.

We apply the paracontrolled calculus to study the asymptotic behavior of a certain quasilinear PDE with smeared mild noise, which originally appears as the space-time scaling limit of a particle system in random environment on one dimensional discrete lattice. We establish the convergence result and show a local in time well-posedness of the limit stochastic PDE with spatial white noise. It turns out that our limit stochastic PDE does not require any renormalization. We also show a comparison theorem for the limit equation.

Recent studies of optimization methods and GNC of spacecraft near small bodies focusing on descent, landing, rendezvous, etc., with key safety constraints such as line-of-sight conic zones and soft landings have shown promising results; this paper considers descent missions to an asteroid surface with a constraint that consists of an onboard camera and asteroid surface markers while using a stochastic convex MPC law. An undermodeled asteroid gravity and spacecraft technology inspired measurement model is established to develop the constraint. Then a computationally light stochastic Linear Quadratic MPC strategy is presented to keep the spacecraft in satisfactory field of view of the surface markers while trajectory tracking, employing chance based constraints and up-to-date estimation uncertainty from navigation. The estimation uncertainty giving rise to the tightened constraints is particularly addressed. Results suggest robust tracking performance across a variety of trajectories.

In this work we study the dynamical behavior Tonelli Lagrangian systems defined on the tangent bundle of the torus $mathbb{T}^2=mathbb{R}^2 / mathbb{Z}^2$. We prove that the Lagrangian flow restricted to a high energy level $ E_L^{-1}(c)$ (i.e $ c> c_0(L)$) has positive topological entropy if the flow satisfies the Kupka-Smale propriety in $ E_L^{-1}(c)$ (i.e, all closed orbit with energy $c$ are hyperbolic or elliptic and all heteroclinic intersections are transverse on $E_L^{-1}(c)$). The proof requires the use of well-known results in Aubry-Mather's Theory.

A graph $G=(V,E)$ is $delta$-hyperbolic if for any four vertices $u,v,w,x$, the two larger of the three distance sums $d(u,v)+d(w,x)$, $d(u,w)+d(v,x)$, and $d(u,x)+d(v,w)$ differ by at most $2delta geq 0$. Recent work shows that many real-world graphs have small hyperbolicity $delta$. This paper describes the eccentricity terrain of a $delta$-hyperbolic graph. The eccentricity function $e_G(v)=max{d(v,u) : u in V}$ partitions the vertex set of $G$ into eccentricity layers $C_{k}(G) = {v in V : e(v)=rad(G)+k}$, $k in mathbb{N}$, where $rad(G)=min{e_G(v): vin V}$ is the radius of $G$. The paper studies the eccentricity layers of vertices along shortest paths, identifying such terrain features as hills, plains, valleys, terraces, and plateaus. It introduces the notion of $eta$-pseudoconvexity, which implies Gromov's $epsilon$-quasiconvexity, and illustrates the abundance of pseudoconvex sets in $delta$-hyperbolic graphs. In particular, it shows that all sets $C_{leq k}(G)={vin V : e_G(v) leq rad(G) + k}$, $kin mathbb{N}$, are $(2delta-1)$-pseudoconvex. Additionally, several bounds on the eccentricity of a vertex are obtained which yield a few approaches to efficiently approximating all eccentricities. An $O(delta |E|)$ time eccentricity approximation $hat{e}(v)$, for all $vin V$, is presented that uses distances to two mutually distant vertices and satisfies $e_G(v)-2delta leq hat{e}(v) leq {e_G}(v)$. It also shows existence of two eccentricity approximating spanning trees $T$, one constructible in $O(delta |E|)$ time and the other in $O(|E|)$ time, which satisfy ${e}_G(v) leq e_T(v) leq {e}_G(v)+4delta+1$ and ${e}_G(v) leq e_T(v) leq {e}_G(v)+6delta$, respectively. Thus, the eccentricity terrain of a tree gives a good approximation (up-to an additive error $O(delta))$ of the eccentricity terrain of a $delta$-hyperbolic graph.

This paper addresses the problem of efficiently achieving visual predictive control tasks. To this end, a memory of motion, containing a set of trajectories built off-line, is used for leveraging precomputation and dealing with difficult visual tasks. Standard regression techniques, such as k-nearest neighbors and Gaussian process regression, are used to query the memory and provide on-line a warm-start and a way point to the control optimization process. The proposed technique allows the control scheme to achieve high performance and, at the same time, keep the computational time limited. Simulation and experimental results, carried out with a 7-axis manipulator, show the effectiveness of the approach.

In this paper, we work on intra-variable handwriting, where the writing samples of an individual can vary significantly. Such within-writer variation throws a challenge for automatic writer inspection, where the state-of-the-art methods do not perform well. To deal with intra-variability, we analyze the idiosyncrasy in individual handwriting. We identify/verify the writer from highly idiosyncratic text-patches. Such patches are detected using a deep recurrent reinforcement learning-based architecture. An idiosyncratic score is assigned to every patch, which is predicted by employing deep regression analysis. For writer identification, we propose a deep neural architecture, which makes the final decision by the idiosyncratic score-induced weighted average of patch-based decisions. For writer verification, we propose two algorithms for patch-fed deep feature aggregation, which assist in authentication using a triplet network. The experiments were performed on two databases, where we obtained encouraging results.

Stabilizing multi-steered articulated vehicles in backward motion is a complex task for any human driver. Unless the vehicle is accurately steered, its structurally unstable joint-angle kinematics during reverse maneuvers can cause the vehicle segments to fold and enter a jack-knife state. In this work, a model predictive path-following controller is proposed enabling automatic low-speed steering control of multi-steered articulated vehicles, comprising a car-like tractor and an arbitrary number of trailers with passive or active steering. The proposed path-following controller is tailored to follow nominal paths that contains full state and control-input information, and is designed to satisfy various physical constraints on the vehicle states as well as saturations and rate limitations on the tractor's curvature and the trailer steering angles. The performance of the proposed model predictive path-following controller is evaluated in a set of simulations for a multi-steered 2-trailer with a car-like tractor where the last trailer has steerable wheels.

The total influence of a function is a central notion in analysis of Boolean functions, and characterizing functions that have small total influence is one of the most fundamental questions associated with it. The KKL theorem and the Friedgut junta theorem give a strong characterization of such functions whenever the bound on the total influence is $o(log n)$. However, both results become useless when the total influence of the function is $omega(log n)$. The only case in which this logarithmic barrier has been broken for an interesting class of functions was proved by Bourgain and Kalai, who focused on functions that are symmetric under large enough subgroups of $S_n$.

In this paper, we build and improve on the techniques of the Bourgain-Kalai paper and establish new concentration results on the Fourier spectrum of Boolean functions with small total influence. Our results include:

1. A quantitative improvement of the Bourgain--Kalai result regarding the total influence of functions that are transitively symmetric.

2. A slightly weaker version of the Fourier--Entropy Conjecture of Friedgut and Kalai. This weaker version implies in particular that the Fourier spectrum of a constant variance, Boolean function $f$ is concentrated on $2^{O(I[f]log I[f])}$ characters, improving an earlier result of Friedgut. Removing the $log I[f]$ factor would essentially resolve the Fourier--Entropy Conjecture, as well as settle a conjecture of Mansour regarding the Fourier spectrum of polynomial size DNF formulas.

Our concentration result has new implications in learning theory: it implies that the class of functions whose total influence is at most $K$ is agnostically learnable in time $2^{O(Klog K)}$, using membership queries.

Robots are required to operate autonomously in response to changing situations. Previously, imitation learning using 4ch-bilateral control was demonstrated to be suitable for imitation of object manipulation. However, cooperative work between humans and robots has not yet been verified in these studies. In this study, the task was expanded by cooperative work between a human and a robot. 4ch-bilateral control was used to collect training data for training robot motion. We focused on serving salad as a task in the home. The task was executed with a spoon and a fork fixed to robots. Adjustment of force was indispensable in manipulating indefinitely shaped objects such as salad. Results confirmed the effectiveness of the proposed method as demonstrated by the success of the task.

We design and implement the first private and anonymous decentralized crowdsourcing system ZebraLancer, and overcome two fundamental challenges of decentralizing crowdsourcing, i.e., data leakage and identity breach.

First, our outsource-then-prove methodology resolves the tension between the blockchain transparency and the data confidentiality to guarantee the basic utilities/fairness requirements of data crowdsourcing, thus ensuring: (i) a requester will not pay more than what data deserve, according to a policy announced when her task is published via the blockchain; (ii) each worker indeed gets a payment based on the policy, if he submits data to the blockchain; (iii) the above properties are realized not only without a central arbiter, but also without leaking the data to the open blockchain. Second, the transparency of blockchain allows one to infer private information about workers and requesters through their participation history. Simply enabling anonymity is seemingly attempting but will allow malicious workers to submit multiple times to reap rewards. ZebraLancer also overcomes this problem by allowing anonymous requests/submissions without sacrificing accountability. The idea behind is a subtle linkability: if a worker submits twice to a task, anyone can link the submissions, or else he stays anonymous and unlinkable across tasks. To realize this delicate linkability, we put forward a novel cryptographic concept, i.e., the common-prefix-linkable anonymous authentication. We remark the new anonymous authentication scheme might be of independent interest. Finally, we implement our protocol for a common image annotation task and deploy it in a test net of Ethereum. The experiment results show the applicability of our protocol atop the existing real-world blockchain.

Assistive shared-control robots have the potential to transform the lives of millions of people afflicted with severe motor impairments. The usefulness of shared-control robots typically relies on the underlying autonomy's ability to infer the user's needs and intentions, and the ability to do so unambiguously is often a limiting factor for providing appropriate assistance confidently and accurately. The contributions of this paper are four-fold. First, we introduce the idea of intent disambiguation via control mode selection, and present a mathematical formalism for the same. Second, we develop a control mode selection algorithm which selects the control mode in which the user-initiated motion helps the autonomy to maximally disambiguate user intent. Third, we present a pilot study with eight subjects to evaluate the efficacy of the disambiguation algorithm. Our results suggest that the disambiguation system (a) helps to significantly reduce task effort, as measured by number of button presses, and (b) is of greater utility for more limited control interfaces and more complex tasks. We also observe that (c) subjects demonstrated a wide range of disambiguation request behaviors, with the common thread of concentrating requests early in the execution. As our last contribution, we introduce a novel field-theoretic approach to intent inference inspired by dynamic field theory that works in tandem with the disambiguation scheme.

Passive optical networks (PON) can play a vital role in data centres and access fog solutions by providing scalable, cost and energy efficient architectures. This paper proposes a Mixed Integer Linear Programming (MILP) model to optimize the placement of virtual machines (VMs) over an energy efficient WDM-TDM AWGR PON based data centre architecture. In this optimization, the use of VMs and their requirements affect the optimum number of servers utilized in the data centre when minimizing the power consumption and enabling more efficient utilization of servers is considered. Two power consumption minimization objectives were examined for up to 20 VMs with different computing and networking requirements. The results indicate that considering the minimization of the processing and networking power consumption in the allocation of VMs in the WDM-TDM AWGR PON can reduce the networking power consumption by up to 70% compared to the minimization of the processing power consumption.

Wireless sensor networks have attracted much attention because of many applications in the fields of industry, military, medicine, agriculture, and education. In addition, the vast majority of researches has been done to expand its applications and improve its efficiency. However, there are still many challenges for increasing the efficiency in different parts of this network. One of the most important parts is to improve the network lifetime in the wireless sensor network. Since the sensor nodes are generally powered by batteries, the most important issue to consider in these types of networks is to reduce the power consumption of the nodes in such a way as to increase the network lifetime to an acceptable level. The contribution of this paper is using topology control, the threshold for the remaining energy in nodes, and two of the meta-algorithms include SA (Simulated annealing) and VNS (Variable Neighbourhood Search) to increase the energy remaining in the sensors. Moreover, using a low-cost spanning tree, an appropriate connectivity control among nodes is created in the network in order to increase the network lifetime. The results of simulations show that the proposed method improves the sensor lifetime and reduces the energy consumed.

High-order entropy-stable discontinuous Galerkin (DG) methods for nonlinear conservation laws reproduce a discrete entropy inequality by combining entropy conservative finite volume fluxes with summation-by-parts (SBP) discretization matrices. In the DG context, on tensor product (quadrilateral and hexahedral) elements, SBP matrices are typically constructed by collocating at Lobatto quadrature points. Recent work has extended the construction of entropy-stable DG schemes to collocation at more accurate Gauss quadrature points.

In this work, we extend entropy-stable Gauss collocation schemes to non-conforming meshes. Entropy-stable DG schemes require computing entropy conservative numerical fluxes between volume and surface quadrature nodes. On conforming tensor product meshes where volume and surface nodes are aligned, flux evaluations are required only between "lines" of nodes. However, on non-conforming meshes, volume and surface nodes are no longer aligned, resulting in a larger number of flux evaluations. We reduce this expense by introducing an entropy-stable mortar-based treatment of non-conforming interfaces via a face-local correction term, and provide necessary conditions for high-order accuracy. Numerical experiments in both two and three dimensions confirm the stability and accuracy of this approach.

Inspired by Bitcoin, many different kinds of cryptocurrencies based on blockchain technology have turned up on the market. Due to the special structure of the blockchain, it has been deemed impossible to directly trade between traditional currencies and cryptocurrencies or between different types of cryptocurrencies. Generally, trading between different currencies is conducted through a centralized third-party platform. However, it has the problem of a single point of failure, which is vulnerable to attacks and thus affects the security of the transactions. In this paper, we propose a distributed cryptocurrency trading scheme to solve the problem of centralized exchanges, which can achieve trading between different types of cryptocurrencies. Our scheme is implemented with smart contracts on the Ethereum blockchain and deployed on the Ethereum test network. We not only implement transactions between individual users, but also allow transactions between multiple users. The experimental result proves that the cost of our scheme is acceptable.

By using dissipativity approach, we establish the stability condition for the feedback connection of a deterministic dynamical system $Sigma$ and a stochastic memoryless map $Psi$. After that, we extend the result to the class of large scale systems in which: $Sigma$ consists of many sub-systems; and $Psi$ consists of many "stochastic actuators" and "probability controllers" that control the actuator's output events. We will demonstrate the proposed approach by showing the design procedures to globally stabilize the manufacturing systems while locally balance the stock levels in any production process.

We show that the traveling salesman problem (TSP) and its many variants may be modeled as functional optimization problems over a graph. In this formulation, all vertices and arcs of the graph are functionals; i.e., a mapping from a space of measurable functions to the field of real numbers. Many variants of the TSP, such as those with neighborhoods, with forbidden neighborhoods, with time-windows and with profits, can all be framed under this construct. In sharp contrast to their discrete-optimization counterparts, the modeling constructs presented in this paper represent a fundamentally new domain of analysis and computation for TSPs and their variants. Beyond its apparent mathematical unification of a class of problems in graph theory, the main advantage of the new approach is that it facilitates the modeling of certain application-specific problems in their home space of measurable functions. Consequently, certain elements of economic system theory such as dynamical models and continuous-time cost/profit functionals can be directly incorporated in the new optimization problem formulation. Furthermore, subtour elimination constraints, prevalent in discrete optimization formulations, are naturally enforced through continuity requirements. The price for the new modeling framework is nonsmooth functionals. Although a number of theoretical issues remain open in the proposed mathematical framework, we demonstrate the computational viability of the new modeling constructs over a sample set of problems to illustrate the rapid production of end-to-end TSP solutions to extensively-constrained practical problems.

This paper investigates a class of optimal control problems associated with Markov processes with local state information. The decision-maker has only local access to a subset of a state vector information as often encountered in decentralized control problems in multi-agent systems. Under this information structure, part of the state vector cannot be observed. We leverage ab initio principles and find a new form of Bellman equations to characterize the optimal policies of the control problem under local information structures. The dynamic programming solutions feature a mixture of dynamics associated unobservable state components and the local state-feedback policy based on the observable local information. We further characterize the optimal local-state feedback policy using linear programming methods. To reduce the computational complexity of the optimal policy, we propose an approximate algorithm based on virtual beliefs to find a sub-optimal policy. We show the performance bounds on the sub-optimal solution and corroborate the results with numerical case studies.

In this work, we consider a group of robots working together to manipulate a rigid object to track a desired trajectory in $SE(3)$. The robots have no explicit communication network among them, and they do no know the mass or friction properties of the object, or where they are attached to the object. However, we assume they share data from a common IMU placed arbitrarily on the object. To solve this problem, we propose a decentralized adaptive control scheme wherein each agent maintains and adapts its own estimate of the object parameters in order to track a reference trajectory. We present an analysis of the controller's behavior, and show that all closed-loop signals remain bounded, and that the system trajectory will almost always (except for initial conditions on a set of measure zero) converge to the desired trajectory. We study the proposed controller's performance using numerical simulations of a manipulation task in 3D, and with hardware experiments which demonstrate our algorithm on a planar manipulation task. These studies, taken together, demonstrate the effectiveness of the proposed controller even in the presence of numerous unmodelled effects, such as discretization errors and complex frictional interactions.

In this technical report we give an elementary introduction to Quantum Computing for non-physicists. In this introduction we describe in detail some of the foundational Quantum Algorithms including: the Deutsch-Jozsa Algorithm, Shor's Algorithm, Grocer Search, and Quantum Counting Algorithm and briefly the Harrow-Lloyd Algorithm. Additionally we give an introduction to Solomonoff Induction, a theoretically optimal method for prediction. We then attempt to use Quantum computing to find better algorithms for the approximation of Solomonoff Induction. This is done by using techniques from other Quantum computing algorithms to achieve a speedup in computing the speed prior, which is an approximation of Solomonoff's prior, a key part of Solomonoff Induction. The major limiting factors are that the probabilities being computed are often so small that without a sufficient (often large) amount of trials, the error may be larger than the result. If a substantial speedup in the computation of an approximation of Solomonoff Induction can be achieved through quantum computing, then this can be applied to the field of intelligent agents as a key part of an approximation of the agent AIXI.

Recently, the Edge Computing paradigm has gained significant popularity both in industry and academia. Researchers now increasingly target to improve performance and reduce energy consumption of such devices. Some recent efforts focus on using emerging RRAM technologies for improving energy efficiency, thanks to their no leakage property and high integration density. As the complexity and dynamism of applications supported by such devices escalate, it has become difficult to maintain ideal performance by static RRAM controllers. Machine Learning provides a promising solution for this, and hence, this work focuses on extending such controllers to allow dynamic parameter updates. In this work we propose an Adaptive RRAM Variability-Aware Controller, AVAC, which periodically updates Wait Buffer and batch sizes using on-the-fly learning models and gradient ascent. AVAC allows Edge devices to adapt to different applications and their stages, to improve computation performance and reduce energy consumption. Simulations demonstrate that the proposed model can provide up to 29% increase in performance and 19% decrease in energy, compared to static controllers, using traces of real-life healthcare applications on a Raspberry-Pi based Edge deployment.

PRINCE Raoden of Arelon awoke early that morning, completely unaware that he had been damned for all eternity. What a way to start a novel. As a reader, I don’t think about the first words of a book when…

By Adam Liptak The New York Times Company…

A catastrophic loss in biodiversity, reckless destruction of wildland and warming temperatures have allowed disease to explode. Ignoring the connection between climate change and pandemics would be “dangerous delusion,” one scientist said. The scientists who study how diseases emerge in a changing environment knew this moment was coming.…

As this issue goes to press we are all staying home to battle the coronavirus.…

While its main goal is to introduce the humble breakfast taco to more Spokane eaters, owners of the new quick food stop Taco Vado say breakfast burritos have actually been its bestselling menu item since opening about a month ago.…

The present invention provides a pest control agent, acaricide or fungicide that contains, as the active ingredient thereof, at least one type of compound selected from the aryloxyurea compounds represented by formula (V) (wherein R1 to R5 each independently represents an alkyl group or the like, X is a halogen atom or the like, n is an integer of 0 to 5, and Z is an oxygen atom or sulfur atom) or salts thereof.

In one embodiment, a system includes a data collection system configured to collect a data from a control system by using an offline mode of operations. The system further includes a configuration management system configured to manage a hardware configuration and a software configuration for the control system based on the data. The system additionally includes a rule engine configured to use the data as input and to output a health assessment by using a rule database, and a report generator configured to provide a health assessment for the control system.

The disclosed embodiments describe a method, an apparatus, an application specific integrated circuit, and a server that provides a fast and efficient look up for data analysis. The apparatus and server may be configured to obtain data segments from a plurality of input devices. The data segments may be individual unique subsets of the entire data set obtained by a plurality input devices. A hash function may be applied to an aggregated set of the data segments. A result of the hash function may be stored in a data structure. A codebook may be generated from the hash function results.

The current invention describes processes for obtaining of concentrates of esters of eicosapentaenoic acid and docosahexaenoic acid for their use in massive and regular human consumption either as a pharmaceutical ingredient or as a food ingredient, which are characterized by having neutral and stable organoleptic properties, free of side effects, which are typical from marine oils derivatives, and with low content of Persistent Organic Pollutants (POP).

Bioassays for detecting the ability of one sample of a food substance, nutritional supplement, therapeutic agent and/or disease preventive agent relative to that of a second sample of such a substance, supplement and/or agent to inhibit, upregulate or otherwise modulate translation initiation, and thereby demonstrate a disease curative and/or preventive effect in a human and/or animal that consumes a such substance, supplement and/or agent or to whom a such substance, supplement and/or agent is administered are provided.

The present invention relates to pyridazine compounds of formulae I or II and the salts thereof, the N-oxides thereof and the salts of the N-oxides thereof, where the radical A is of the formula A, wherein # denotes the point of attachment to the remainder of formulae I or II, and wherein A1 is N or C—RA1, A2 is N or C—RA2, A3 is N or C—RA3, A4 is N or C—RA4 and A5 is N or C—RA5, provided that one or two of the variables A1, A2, A3, A4 or A5 is N; RA1, RA5, if present, are H, halogen, CN, NO2, C1-C6-alkyl, C1-C6-haloalkyl and the like; RA2, RA4, if present, are H, halogen, CN, NO2, C1-C10-alkyl, C1-C10-haloalkyl and the like; RA3, if present, is H, halogen, CN, NO2, C1-C10-alkyl, C1-C10-haloalkyl and the like; where W is N or C—RW and V is N or C—RV, provided that one of the variables W or V is N; Rt and Rw, if present, are H, halogen, methyl, C1-haloalkyl and the like; Ru and Rv, if present, are H, halogen, C1-C4-alkyl, C1-C3-haloalkyl and the like; X1 is S, O or NR1a, wherein R1a is H, C1-C10-alkyl and the like; X2 is OR2a, NR2bR2c, S(O)mR2d, wherein m is 0, 1 or 2, R2a is C1-C4-alkyl, C1-C4-haloalkyl and the like, R2b, R2c are H, C1-C4-alkyl, C1-C4-haloalkyl and the like, or R2b and R2c together with the nitrogen atom to which they are bound form a heterocycle, and R2d is C1-C4-alkyl, C1-C4-haloalkyl, C3-C6-cycloalkyl and the like; and R1 is H, CN, C1-C10-alkyl and the like. The present invention further relates to a method for controlling invertebrate pests, to a method for protecting plant propagation material and/or the plants which grow therefrom, to plant propagation material, comprising at least one compound according to the present invention, to a method for treating or protecting an animal from infestation or infection by parasites and to an agricultural composition containing at least one compound according to the present invention.

A method for controlling 2-isomer content in linear alkylbenzene obtained by alkylating benzene with olefins and catalyst used in the method.

The present disclosure relates to compositions and methods for producing nanoparticles to provide relatively more rapid delivery of such particles across the blood-brain barrier. The nanoparticles may be formed from bis-quaternary pyridinium-aldoxime salts that may also be of a specific polymorphic structure and which may be formed in either hydrophobic or hydrophilic type liquid media. In addition, the nanoparticle for transport across the blood-brain barrier may comprise a polymeric resin encapsulating a bis-quaternary pyridinium-2-aldoxime salt.

In a method for fabricating a graphene structure, there is formed on a fabrication substrate a pattern of a plurality of distinct graphene catalyst materials. In one graphene synthesis step, different numbers of graphene layers are formed on the catalyst materials in the formed pattern. In a method for fabricating a graphene transistor, on a fabrication substrate at least one graphene catalyst material is provided at a substrate region specified for synthesizing a graphene transistor channel and at least one graphene catalyst material is provided at a substrate region specified for synthesizing a graphene transistor source, and at a substrate region specified for synthesizing a graphene transistor drain. Then in one graphene synthesis step, at least one layer of graphene is formed at the substrate region for the graphene transistor channel, and at the regions for the transistor source and drain there are formed a plurality of layers of graphene.

An approach for introspection of a software component and generation of a conditional memory dump, a computing device executing an introspection program with respect to the software component is provided. An introspection system comprises one or more conditions for generating the conditional memory dump based on operations of the software component. In one aspect, a computing device detects, through an introspection program, whether the one or more conditions are satisfied by the software component based on information in an introspection analyzer of the introspection program. In addition, the computing device indicates, through the introspection program, if the one or more conditions are satisfied by the software component. In another aspect, responsive to the indication, the computing device generates the conditional memory dump through the introspection program.

An apparatus includes a first memory, a second memory, a processor configured to perform an initialization process including adding data that generates a first error to initialization data and storing the initialization data together with the added data in the first memory to initialize the first memory, and a controller configured to perform an exchanging process including, when a second error occurs in the second memory during reading or writing data from or to the second memory, copying the data stored in the second memory into the first memory and switching, using a selector, a memory for use in writing and reading data from the second memory to the first memory. The processor is configured to read data from the first memory and the second memory and detect a failure of the selector or a failure of the exchanging process depending on whether the first error occurs or not.

Today many instances of IEEE 1149.1 Tap domains are included in integrated circuits (ICs). While all TAP domains may be serially connected on a scan path that is accessible external to the IC, it is generally preferred to have selectivity on which Tap domain or Tap domains are accessed. Therefore Tap domain selection circuitry may be included in ICs and placed in the scan path along with the Tap domains. Ideally, the Tap domain selection circuitry should only be present in the scan path when it is necessary to modify which Tap domains are selected in the scan path. The present disclosure describes a novel method and apparatus which allows the Tap domain selection circuitry to be removed from the scan path after it has been used to select Tap domains and to be replaced back into the scan path when it is necessary to select different Tap domains.

In a wireless communication system, a compact control signaling scheme is provided for signaling the selected retransmission mode and codeword identifier for a codeword retransmission when one of a plurality of codewords being transmitted over two codeword pipes to a receiver fails the transmission and when the base station/transmitter switches from a higher order channel rank to a lower order channel rank, either by including one or more additional signaling bits in the control signal to identify the retransmitted codeword, or by re-using existing control signal information in a way that can be recognized by the subscriber station/receiver to identify the retransmitted codeword. With the compact control signal, the receiver is able to determine which codeword is being retransmitted and to determine the corresponding time-frequency resource allocation for the retransmitted codeword.

According to one embodiment, a memory controller includes: a first flash encoding unit that performs flash encoding on user data according to a first scheme to generate user data flash codes; an encoding unit that performs an error correction encoding process on the user data flash codes to generate parities; a second flash encoding unit that performs flash encoding on the parities according to a second scheme to generate parity flash codes; and a memory I/F that writes the user data flash codes and the parity flash codes to the nonvolatile memory.

A method of operating a memory controller to control a memory device includes reading a read vector from the memory device and correcting one or more errors in the read vector, where a power consumed at the correcting is varied according to the number of errors in the read vector.

A wireless communication system including a sender apparatus having a plurality of transmitting antennas that performs MIMO transmission of a plurality of data blocks; and a receiver apparatus that receives the plurality of data blocks. The sender apparatus transmits a process number via a control channel different from a data channel to the receiver apparatus, and wherein when the MIMO diversity transmission is performed, the receiver apparatus performs HARQ processing in the received data blocks based on not a process number which prevents the data blocks from competing but the received process number from the sender apparatus.

In accordance with one or more aspects, an initial output string is generated by a random number generator. The initial output string is sent to a random number service, and an indication of failure is received from the random number service if the initial output string is the same as a previous initial output string received by the random number service. Operation of the device is ceased in response to the indication of failure. Additionally, entropy estimates for hash values of an entropy source can be generated by an entropy estimation service based on hash values of various entropy source values received by the entropy estimation service. The hash values can be incorporated into an entropy pool of the device, and the entropy estimate of the pool being updated based on the estimated entropy of the entropy source.

Techniques for medium access control. Some techniques include receiving, at a first computing device, a solicitation for at least a first medium access request that specifies at least one time period for transmitting the first medium access request to the second computing device; encoding the first medium access request at least in part by using a compressive sensing encoding technique to obtain a first encoded medium access request; and transmitting the first encoded medium access request to the second computing device during the at least one time period specified in the received solicitation.

The present invention relates to a process comprising the steps of reacting a reactive mixture comprising at least one silicone-containing component, at least one hydrophilic component, and at least one diluent to form an ophthalmic device having an advancing contact angle of less than about 80°; and contacting the ophthalmic device with an aqueous extraction solution at an elevated extraction temperature, wherein said at least one diluent has a boiling point at least about 10° higher than said extraction temperature.

A portable or mobile computing device, such as a smart phone or portable media player, can be used to control one or more electronic devices over an appropriate wireless channel. In one example, a user can utilize a smart phone as a mouse for a notebook computer or Internet-capable television. The user can move the portable device on a surface and press appropriate selectable elements on the portable device, as if the user is using a wireless mouse. The portable device can send the commands over the wireless channel to the electronic device, which can provide inputs and/or control signals to the electronic device. In some embodiments, the user can take advantage of the processing capability of the portable device to work directly with elements such as a wireless keyboard and wireless monitor, without the need for a notebook or other such computing element therebetween.