Zuckerberg decided to go visit the startup's headquarters after loving his first demo with Oculus' virtual reality headset in Facebook's office.

Our jugaad of giving missed calls when mobile telephony was expensive has created a habit that’s still prevalent. Then came OTP, which saw nationwide acceptance when regulators mandated it for banking transactions.

Margaret Parker, MD, MCCM, speaks with Katherine J. Steineck, PharmD, pediatric clinical pharmacist at the University of Minnesota Amplatz

Indian-American NASA astronaut Sunita Williams has advised Indian students stranded in the US due to the coronavirus-linked global travel restrictions to use the occasion to think how they could be a productive and positive addition to the society.

While over 20 technical and scientific institutions are working on developing a vaccine for coronavirus, IITs have already set up "COVID-19 specific research centres", encouraging developments in the innovation ecosystem.

Grand Canyon National Park implemented Level 2 water conservation measures on Wednesday, October 3, due to a series of breaks in the Transcanyon Waterline. This morning Water Utility staff at the park responded to another pipeline break. Additionally, attempts to pump water beyond Indian Garden yesterday failed. Park staff are currently assessing the situation and attempting to resolve the problem as quickly as possible. https://www.nps.gov/grca/learn/news/grand-canyon-nat-pk-prepares-for-potential-increase-in-water-conservation-methods.htm

Rather than opting for the costly and complex process of using stem cells to cure age-related macular degeneration, scientists used skin cells.

The post Irreversible No Longer: Blind Mice See Again Thanks To New Method of Synthesizing Lost Cells appeared first on Good News Network.

Using my latest techniques to produce and improved tabbed pages with persist

Increasing concern over the management and harvest of special forest products (SFP) from national forest lands has led to the development of new Forest Service policy directives.

The Fall River research site in coastal Washington is an affiliate installation of the North American Long-Term Soil Productivity (LTSP) network, which constitutes one of the world's largest coordinated research programs addressing forest management impacts on sustained productivity. Overall goals of the Fall River study are to assess effects of biomass removals, soil compaction, tillage, and vegetation control on site properties and growth of planted Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco). Biomass-removal treatments included removal of commercial bole (BO), bole to 5-cm top diameter (BO5), total tree (TT), and total tree plus all legacy woody debris (TT+). Vegetation control (VC) effects were tested in BO, while soil compaction and compaction plus tillage were imposed in BO+VC treatment. All treatments were imposed in 1999. The preharvest stand contained similar amounts of carbon (C) above the mineral soil (292 Mg/ha) as within the mineral soil to 80- cm depth including roots (298 Mg/ha). Carbon stores above the mineral soil ordered by size were live trees (193 Mg/ha), old-growth logs (37 Mg/ha), forest floor (27 Mg/ha), old-growth stumps and snags (17 Mg/ha), coarse woody debris (11 Mg/ha), dead trees/snags (7 Mg/ha), and understory vegetation (0.1 Mg/ha). The mineral soil to 80-cm depth contained 248 Mg C/ha, and roots added 41 Mg/ha. Total nitrogen (N) in mineral soil and roots (13 349 kg/ha) was more than 10 times the N store above the mineral soil (1323 kg/ha). Postharvest C above mineral soil decreased to 129, 120, 63, and 50 Mg/ha in BO, BO5, TT, and TT+, respectively. Total N above the mineral soil decreased to 722, 747, 414, and 353 Mg/ha in BO, BO5, TT, and TT+, respectively. The ratio of total C above the mineral soil to total C within the mineral soil was markedly altered by biomass removal, but proportions of total N stores were reduced only 3 to 6 percent owing to the large soil N reservoir on site.

The marbled murrelet (Brachyramphus marmoratus) ranges from Alaska to California and is listed under the Endangered Species Act as a threatened species in Washington, Oregon, and California. Marbled murrelet recovery depends, in large part, on conservation and restoration of breeding habitat on federally managed lands. A major objective of the Northwest Forest Plan (the Plan) is to conserve and restore nesting habitat that will sustain a viable marbled murrelet population. Under the Plan, monitoring is an essential component and is designed to help managers understand the degree to which the Plan is meeting this objective. This report describes methods used to assess the status and trend of marbled murrelet populations under the Plan.

The co-owner of Sunderland has given advice to the potential new owners of Newcastle following his Black Cats' experience

Method Products (branded as method) is a San Francisco-based corporation which produces nontoxic, biodegradable natural cleaning supplies with a focus onminimalist product design. (source: Wikipedia) Why it’s in the Showcase: Beautiful graphics, an easy to use navigation, and an integrated...

https://uxdesign.cc/saying-goodbye-to-yesterday-and-hello-to-something-awesome-636362789e84

An inverse spectral problem for the Sturm-Liouville operator with a singular potential from the class $W_2^{-1}$ is solved by the method of spectral mappings. We prove the uniqueness theorem, develop a constructive algorithm for solution, and obtain necessary and sufficient conditions of solvability for the inverse problem in the self-adjoint and the non-self-adjoint cases

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.

In this work, we propose a simple modification of the forward-backward splitting method for finding a zero in the sum of two monotone operators. Our method converges under the same assumptions as Tseng's forward-backward-forward method, namely, it does not require cocoercivity of the single-valued operator. Moreover, each iteration only requires one forward evaluation rather than two as is the case for Tseng's method. Variants of the method incorporating a linesearch, relaxation and inertia, or a structured three operator inclusion are also discussed.

Autonomous robot manipulation involves estimating the translation and orientation of the object to be manipulated as a 6-degree-of-freedom (6D) pose. Methods using RGB-D data have shown great success in solving this problem. However, there are situations where cost constraints or the working environment may limit the use of RGB-D sensors. When limited to monocular camera data only, the problem of object pose estimation is very challenging. In this work, we introduce a novel method called SilhoNet that predicts 6D object pose from monocular images. We use a Convolutional Neural Network (CNN) pipeline that takes in Region of Interest (ROI) proposals to simultaneously predict an intermediate silhouette representation for objects with an associated occlusion mask and a 3D translation vector. The 3D orientation is then regressed from the predicted silhouettes. We show that our method achieves better overall performance on the YCB-Video dataset than two state-of-the art networks for 6D pose estimation from monocular image input.

We present a novel numerical scheme to approximate the solution map $smapsto u(s) := mathcal{L}^{-s}f$ to partial differential equations involving fractional elliptic operators. Reinterpreting $mathcal{L}^{-s}$ as interpolation operator allows us to derive an integral representation of $u(s)$ which includes solutions to parametrized reaction-diffusion problems. We propose a reduced basis strategy on top of a finite element method to approximate its integrand. Unlike prior works, we deduce the choice of snapshots for the reduced basis procedure analytically. Avoiding further discretization, the integral is interpreted in a spectral setting to evaluate the surrogate directly. Its computation boils down to a matrix approximation $L$ of the operator whose inverse is projected to a low-dimensional space, where explicit diagonalization is feasible. The universal character of the underlying $s$-independent reduced space allows the approximation of $(u(s))_{sin(0,1)}$ in its entirety. We prove exponential convergence rates and confirm the analysis with a variety of numerical examples.

Further improvements are proposed in the second part of this investigation to avoid inversion of $L$. Instead, we directly project the matrix to the reduced space, where its negative fractional power is evaluated. A numerical comparison with the predecessor highlights its competitive performance.

Graph convolution network (GCN) have achieved state-of-the-art performance in the task of node prediction in the graph structure. However, with the gradual various of graph attack methods, there are lack of research on the robustness of GCN. At this paper, we will design a robust GCN method for node prediction tasks. Considering the graph structure contains two types of information: node information and connection information, and attackers usually modify the connection information to complete the interference with the prediction results of the node, we first proposed a method to hide the connection information in the generator, named Anonymized GCN (AN-GCN). By hiding the connection information in the graph structure in the generator through adversarial training, the accurate node prediction can be completed only by the node number rather than its specific position in the graph. Specifically, we first demonstrated the key to determine the embedding of a specific node: the row corresponding to the node of the eigenmatrix of the Laplace matrix, by target it as the output of the generator, we designed a method to hide the node number in the noise. Take the corresponding noise as input, we will obtain the connection structure of the node instead of directly obtaining. Then the encoder and decoder are spliced both in discriminator, so that after adversarial training, the generator and discriminator can cooperate to complete the encoding and decoding of the graph, then complete the node prediction. Finally, All node positions can generated by noise at the same time, that is to say, the generator will hides all the connection information of the graph structure. The evaluation shows that we only need to obtain the initial features and node numbers of the nodes to complete the node prediction, and the accuracy did not decrease, but increased by 0.0293.

In this paper, we analyze and implement the Dirichlet spectral-Galerkin method for approximating simply supported vibrating plate eigenvalues with variable coefficients. This is a Galerkin approximation that uses the approximation space that is the span of finitely many Dirichlet eigenfunctions for the Laplacian. Convergence and error analysis for this method is presented for two and three dimensions. Here we will assume that the domain has either a smooth or Lipschitz boundary with no reentrant corners. An important component of the error analysis is Weyl's law for the Dirichlet eigenvalues. Numerical examples for computing the simply supported vibrating plate eigenvalues for the unit disk and square are presented. In order to test the accuracy of the approximation, we compare the spectral-Galerkin method to the separation of variables for the unit disk. Whereas for the unit square we will numerically test the convergence rate for a variable coefficient problem.

Network security has become an area of significant importance more than ever as highlighted by the eye-opening numbers of data breaches, attacks on critical infrastructure, and malware/ransomware/cryptojacker attacks that are reported almost every day. Increasingly, we are relying on networked infrastructure and with the advent of IoT, billions of devices will be connected to the internet, providing attackers with more opportunities to exploit. Traditional machine learning methods have been frequently used in the context of network security. However, such methods are more based on statistical features extracted from sources such as binaries, emails, and packet flows.

On the other hand, recent years witnessed a phenomenal growth in computer vision mainly driven by the advances in the area of convolutional neural networks. At a glance, it is not trivial to see how computer vision methods are related to network security. Nonetheless, there is a significant amount of work that highlighted how methods from computer vision can be applied in network security for detecting attacks or building security solutions. In this paper, we provide a comprehensive survey of such work under three topics; i) phishing attempt detection, ii) malware detection, and iii) traffic anomaly detection. Next, we review a set of such commercial products for which public information is available and explore how computer vision methods are effectively used in those products. Finally, we discuss existing research gaps and future research directions, especially focusing on how network security research community and the industry can leverage the exponential growth of computer vision methods to build much secure networked systems.

This paper investigates the convergence of the randomized Kaczmarz algorithm for the problem of phase retrieval of complex-valued objects. While this algorithm has been studied for the real-valued case}, its generalization to the complex-valued case is nontrivial and has been left as a conjecture. This paper establishes the connection between the convergence of the algorithm and the convexity of an objective function. Based on the connection, it demonstrates that when the sensing vectors are sampled uniformly from a unit sphere and the number of sensing vectors $m$ satisfies $m>O(nlog n)$ as $n, m ightarrowinfty$, then this algorithm with a good initialization achieves linear convergence to the solution with high probability.

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.

Several nonlinear model reduction techniques are compared for the three cases of the non-parallel version of the Kuramoto-Sivashinsky equation, the transient regime of flow past a cylinder at $Re=100$ and fully developed flow past a cylinder at the same Reynolds number. The linear terms of the governing equations are reduced by Galerkin projection onto a POD basis of the flow state, while the reduced nonlinear convection terms are obtained either by a Galerkin projection onto the same state basis, by a Galerkin projection onto a POD basis representing the nonlinearities or by applying the Discrete Empirical Interpolation Method (DEIM) to a POD basis of the nonlinearities. The quality of the reduced order models is assessed as to their stability, accuracy and robustness, and appropriate quantitative measures are introduced and compared. In particular, the properties of the reduced linear terms are compared to those of the full-scale terms, and the structure of the nonlinear quadratic terms is analyzed as to the conservation of kinetic energy. It is shown that all three reduction techniques provide excellent and similar results for the cases of the Kuramoto-Sivashinsky equation and the limit-cycle cylinder flow. For the case of the transient regime of flow past a cylinder, only the pure Galerkin techniques are successful, while the DEIM technique produces reduced-order models that diverge in finite time.

This paper reviews the Challenge on Image Demoireing that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2020. Demoireing is a difficult task of removing moire patterns from an image to reveal an underlying clean image. The challenge was divided into two tracks. Track 1 targeted the single image demoireing problem, which seeks to remove moire patterns from a single image. Track 2 focused on the burst demoireing problem, where a set of degraded moire images of the same scene were provided as input, with the goal of producing a single demoired image as output. The methods were ranked in terms of their fidelity, measured using the peak signal-to-noise ratio (PSNR) between the ground truth clean images and the restored images produced by the participants' methods. The tracks had 142 and 99 registered participants, respectively, with a total of 14 and 6 submissions in the final testing stage. The entries span the current state-of-the-art in image and burst image demoireing problems.

We propose some new mixed finite element methods for the time dependent stochastic Stokes equations with multiplicative noise, which use the Helmholtz decomposition of the driving multiplicative noise. It is known [16] that the pressure solution has a low regularity, which manifests in sub-optimal convergence rates for well-known inf-sup stable mixed finite element methods in numerical simulations, see [10]. We show that eliminating this gradient part from the noise in the numerical scheme leads to optimally convergent mixed finite element methods, and that this conceptual idea may be used to retool numerical methods that are well-known in the deterministic setting, including pressure stabilization methods, so that their optimal convergence properties can still be maintained in the stochastic setting. Computational experiments are also provided to validate the theoretical results and to illustrate the conceptional usefulness of the proposed numerical approach.

An approach is given for solving large linear systems that combines Krylov methods with use of two different grid levels. Eigenvectors are computed on the coarse grid and used to deflate eigenvalues on the fine grid. GMRES-type methods are first used on both the coarse and fine grids. Then another approach is given that has a restarted BiCGStab (or IDR) method on the fine grid. While BiCGStab is generally considered to be a non-restarted method, it works well in this context with deflating and restarting. Tests show this new approach can be very efficient for difficult linear equations problems.

The present invention relates to a compound of formula A, wherein R is alkyl. Compound A may be used as an intermediate in the preparation of O-desmethyl venlafaxine or a salt thereof, and the present invention provides such a preparation, as well as a process for preparing the compound of formula A.

Disclosed herein is a method for producing an alcohol and an amine from an amide under an atmosphere of hydrogen with the use of, as a catalyst, a ruthenium complex that is easily prepared, easy to handle, and relatively cheaply obtained. Specifically, the method is a method for producing an alcohol and/or an amine from an amide compound under an atmosphere of hydrogen with the use of as a catalyst, a ruthenium carbonyl complex represented by the following general formula (1): RuXY(CO)(L) (1) wherein X and Y may be the same or different from each other and each represents an anionic ligand and L represents a tridentate aminodiphosphine ligand containing two phosphino groups and a —NH— group.

The invention relates to a method for producing nitrobenzene, in which crude nitrobenzene is first produced by nitrating benzene and said crude nitrobenzene is then washed in succession in at least one acid wash, in at least one alkaline wash and in at least one neutral wash, at least one additional wash with an aqueous solution of a potassium salt being interposed between the last alkaline wash and the first neutral wash.

A nonmetallic compound having an ionic bond with a halide ion is used as a catalyst for living radical polymerization. Even if a radical initiator is not used, a monomer can be subjected to a radical polymerization to obtain a polymer having narrow molecular weight distribution. The cost of the living radical polymerization can be remarkably reduced, and it is made possible to prevent adverse effects of using a radical initiator (such as side reactions). The present invention is significantly more environmentally friendly and economically excellent than conventional living radical polymerization methods, due to advantages such as low toxicity of the catalyst, low amount of the catalyst necessary, high solubility of the catalyst, mild reaction conditions, and no coloration/no odor, etc. The catalyst can be applied to various monomers and enables synthesis of high molecular weight polymers.

A method for preparation of an aryl poly(oxalkyl) quaternary ammonium compound is provided, said method comprising steps of: 1) reacting a phenol with a dihalopolyalkylene ether under the action of a phase transfer catalyst, to obtain an arylpoly(oxalkyl) halide; 2) reacting said arylpoly(oxalkyl) halide with an amination reagent under the action of a phase transfer catalyst, to obtain an arylpoly(oxalkyl) amine; 3) reacting said arylpoly(oxalkyl) amine with an alkylation reagent, to obtain an aryl poly(oxalkyl) quaternary ammonium compound; wherein R1 is H or a C1 to C16 alkyl group, located in the ortho, meta or para position; n is an integer of 2 to 6; R2 is H or a C1 to C16 alkyl group; R3 is H or a C1 to C16 alkyl group; R4 is a C1 to C16 alkyl group; X1 is Br or Cl; X is Cl, Br, or I. The preparation method according to the present invention requires low temperature and low pressure, the reaction time is short, and an overall yield can reach 75%. The operation is simple, the cost is low, and the product can be separated easily and have a purity of pharmaceutical grade, thereby facilitating the large-scale production.

Provided is a method for stably and economically producing xylylenediamine with a high yield and long catalyst service life by hydrogenating dicyanobenzene that is obtained by ammoxidating xylene. By bringing an aqueous basic solution into contact with a dicyanobenzene-absorbed liquid, which is obtained by bringing an ammoxidation reaction gas into contact with an organic solvent, under specified temperature conditions, and subjecting a base and a carboxylic acid in the dicyanobenzene-absorbed liquid to a neutralization reaction so as to form an aqueous phase that contains a water-soluble salt, and then subjecting an organic phase and the aqueous phase to liquid-liquid separation so as to remove the aqueous phase, it is possible to remove the carboxylic acid contained in the dicyanobenzene-absorbed liquid with high selectivity while inhibiting loss of the dicyanobenzene. By subjecting the raw material dicyanobenzene, which is obtained by separating low boiling point compounds from the post liquid-liquid separation organic phase by distillation under reduced pressure, to hydrogenation, xylylenediamine is produced with a high yield and the service life of the hydrogenation catalyst is extended.

Provided are an anti-static and anti-microbial surface treatment agent including a quaternary ammonium salt compound as an active ingredient and a method of preventing a polymer fiber from developing static electricity by using the surface treatment agent. The quaternary ammonium salt compound has excellent anti-static and anti-microbial effects for the prevention or improvement of static electricity in a polymer fiber. Accordingly, the quaternary ammonium salt compound is suitable for use as a fabric softener, or an anti-static agent, and also, provides anti-microbial effects to a polymer fiber.

The invention relates to a method for preparing ring-halogenated N,N-dialkylbenzylamines and intermediates obtainable therefrom for preparing agrochemicals and pharmaceutically active ingredients.

A system and method assesses and manages risk of an operation of a user. A rules engine of computer executable instructions stored in the storage device determines at least one of a safety risk measurement based on key performance indicators, an operational safety risk measurement for the operation as a function of the operational safety risk measurement information stored in a storage device and/or a conditional safety risk measurement for the operation as a function of the conditional safety risk measurement information stored in the storage device. A processor connected to the storage device executes the rules engine. An output interface connected to the processor indicates the determined safety risk for the operation.

The current application is directed to monitoring subsystems, and monitoring methods incorporated within the monitoring subsystems, that monitor operation of devices and systems in order to identify normal states and to quickly determine when a device or system transitions from a normal state to an abnormal state. The methods and monitoring components to which the current application is directed employ self-organizing maps and moving-average self-organizing maps to both characterize normal system behavior and to identify transitions to abnormal system behaviors.

A system and method for providing more than two levels of classification distinction of a user state are provided. The first and second general states of a user are sensed. The first general state is classified as either a first state or a second state, and the second general state is classified as either a third state or a fourth state. The user state of the user is then classified as one of at least three different classification states.

An apparatus for selecting a motion signifying artificial feeling is provided. The apparatus includes: an feeling expression setting unit configured to set probabilities of each feeling expression behavior performed for each expression element of a robot for each predetermined feeling; a behavior combination generation unit configured to generate at least one behavior combination combined by randomly extracting the feeling expression behaviors in each expression element one by one; and a behavior combination selection unit configured to calculate an average for the probabilities of the feeling expression behaviors included in each behavior combination for each feeling of a robot and select behavior combinations in which the average of the probabilities of the feeling expression behaviors most approximates the predetermined feeling value of a robot from each behavior combination.

A method for performing relational analysis of parsed input is employed to create a visual map of knowledge information. A title, header or subject line for an input item of information is parsed into syntactical components of at least a subject component and any predicate component(s) relationally linked as topic and subtopics. A search of topics and subtopics is carried out for each parsed component. If a match is found, then the parsed component is taken as a chosen topic/subtopic label. If no match is found, then the parsed component is formatted as a new entry in the knowledge map. A translation function for translating topics and subtopics from an original language into one or more target languages is enabled by user request or indicated user preference for display on a generated visual map of knowledge information.

A computer-implemented method for classifying documents for data loss prevention may include 1) identifying training documents for a machine learning classifier configured for data loss prevention, 2) performing a semantic analysis on training documents to identify topics within the set training documents, 3) applying a similarity metric to the topics to identify at least one unrelated topic with a similarity to the other topics within the plurality of topics, as determined by the similarity metric, that falls below a similarity threshold, 4) identifying, based on the semantic analysis, at least one irrelevant training document within the set of training documents in which a predominance of the unrelated topic is above a predominance threshold, and 5) excluding the irrelevant training document from the set of training documents based on the predominance of the unrelated topic within the irrelevant training document. Various other methods, systems, and computer-readable media are also disclosed.

An apparatus for recognizing a representative user behavior includes a unit-data extracting unit configured to extract at least one unit data from sensor data, a feature-information extracting unit configured to extract feature information from each of the at least one unit data, a unit-behavior recognizing unit configured to recognize a respective unit behavior for each of the at least one unit data based on the feature information, and a representative-behavior recognizing unit configured to recognize at least one representative behavior based on the respective unit behavior recognized for each of the at least one unit data.

Systems and methods are disclosed that may be implemented for network management system (NMS) configuration management support for network devices using a learning and natural language processing application to capture the usage and behavior of the Command Line Interface (CLI) of a network device with the aid of a CLI knowledge model, which in one example may be ontology-based.

Systems and methods for recommending media content items are provided. In some implementations, a method includes, identifying a first set of media items selected by a first plurality of users; causing a second set of media items to be displayed to a second user not included in the first plurality of users in accordance with the first set of media items. The first set of media items and the second set of media items are associated with a same media item category. In some implementations, the method optionally includes, identifying the second set of media items without regard to media content item selection history associated with the second user. In some implementations, the first and second sets of media items are news items.

An approach is provided for contextual content suggestion. A recommendation platform processes and/or facilitates a processing of contextual information associated with at least one device to determine one or more locations, one or more contextual parameter values, or a combination thereof. The recommendation platform also determines popularity data associated with one or more content items with respect to the one or more locations, the one or more contextual parameter values, or a combination. The popularity data is determined from one or more other devices sharing at least substantially the one or more locations, the one or more contextual parameter values, or a combination thereof. The recommendation platform then causes, at least in part, a recommendation of the one or more content items to the at least one device based, at least in part, on the popularity information.

To provide a latent variable model estimation apparatus capable of implementing the model selection at high speed even if the number of model candidates increases exponentially as the latent state number and the kind of the observation probability increase. A variational probability calculating unit 71 calculates a variational probability by maximizing a reference value that is defined as a lower bound of an approximation amount, in which Laplace approximation of a marginalized log likelihood function is performed with respect to an estimator for a complete variable. A model estimation unit 72 estimates an optimum latent variable model by estimating the kind and a parameter of the observation probability with respect to each latent state. A convergence determination unit 73 determines whether a reference value, which is used by the variational probability calculating unit 71 to calculate the variational probability, converges.

An information providing apparatus for vehicle has a remaining capacity detecting section 110 that detects a remaining capacity of a battery; a power consumption amount detecting section 130 that detects a power consumption amount of the battery; a power consumption amount history generating section 130 that generates a power consumption amount history on the basis of the power consumption amount detected by the power consumption amount detecting section 130; a charge necessity judgment information generating section 130 that generates, on the basis of the power consumption amount history generated by the power consumption amount history generating section 130, charge necessity judgment information which is information for user's judgment about whether or not charging of the battery is necessary; and a providing section 150 that provides information of the remaining capacity of the battery and the charge necessity judgment information with these information correlated with each other to the user. The information providing apparatus can properly provide the information for user's judgment about whether or not charging of the battery to the user.

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.

A computer implemented method of constructing formal definitions in intelligent glossaries for interpreting text, comprising the steps of: providing at least one Distinction having a Boundary, an Indication, a Counter-indication and a Frame; modeling each Distinction as a diagram to provide a Distinction Model; verifying each distinction model as being an instantiation of a generic Distinction Pattern; providing at least one Arrangement made of nonintersecting Marks of Distinction containing Indications from the verified Distinction Model; writing at least one Formulation for each Indication appearing in verified Distinction model and Arrangement, providing well-founded Indications; calculating precise Formulations in Natural Language from well-founded Indications by substituting Variables symbols and/or replacing Constants symbols to transform imprecise Formulations into precise Formulations; selecting a Definition type and embedding at least one precise Formulation and Definition type as a formal Definition in an Intelligent Glossary to provide computerized Semantic Systems of Intelligent Glossaries.