Abstract: The present disclosure is directed towards a method, system, and apparatus for distinguishing between isotopically substituted liquids, such as H2O and D2O, and sensing other isotopologues of water, such as H218O. In an embodiment, the electrical output characteristics of the apparatus or device are used as diagnostic signals to distinguish between isotopic compositions and isotopologues of a liquid. In an embodiment, water droplets with volume of several micro litre are applied to the apparatus or device generating some electrical signals which show differences between the application of H2O and D2O, for example. One advantage of the present method and system is that the configuration is simple and portable, with no need for complicated equipment and operation protocols.

Abstract: Molybdenum is alloyed into stainless steel surface by electro-spark deposition technique. Shielding gas is used during electro-spark deposition process to minimize the oxidation of materials. Control of electro-spark voltage, frequency, capacitance, time can determine the alloying depth of Molybdenum. The alloyed surface thickness varies from 5 ?m to 80 ?m depending on the electro-spark deposition parameters. The alloyed surface comprises, by weight, 15 to 40% of Molybdenum, 8 to 22% of Cr, 0-15% of other alloy elements and impurities. The molybdenum alloyed stainless steel surface exhibits improvement in micro-hardness, wear resistance, and especially corrosion resistance in sodium chloride solutions. Thus, the present invention would be utilized in marine and handling of brines application, as well as in other applications which better corrosion resistance of stainless steel is desired.

Abstract: Molybdenum is alloyed into stainless steel surface by electro-spark deposition technique. Shielding gas is used during electro-spark deposition process to minimize the oxidation of materials. Control of electro-spark voltage, frequency, capacitance, time can determine the alloying depth of Molybdenum. The alloyed surface thickness varies from 5 ?m to 80 ?m depending on the electro-spark deposition parameters. The alloyed surface comprises, by weight, 15 to 40% of Molybdenum, 8 to 22% of Cr, 0-15% of other alloy elements and impurities. The molybdenum alloyed stainless steel surface exhibits improvement in micro-hardness, wear resistance, and especially corrosion resistance in sodium chloride solutions. Thus, the present invention would be utilized in marine and handling of brines application, as well as in other applications which better corrosion resistance of stainless steel is desired.

Abstract: Aspects of the subject disclosure are directed towards managing sponsored online content based upon advertiser behavior. Defining mini-markets to represent such advertiser behavior may be accomplished by clustering queries that generate revenue from one or more campaigns. Query revenue data between queries and a set of campaigns may be used to determine such mini-markets. To illustrate, a query whose highest revenue is attributed to a campaign may be selected for that campaign's mini-market. When that query is entered as a search term, the campaign's mini-market helps allocate space for advertisements.

Abstract: Functionality is described herein for transforming first and second symbolic linguistic items into respective first and second continuous-valued concept vectors, using a deep learning model, such as a convolutional latent semantic model. The model is designed to capture both the local and global linguistic contexts of the linguistic items. The functionality then compares the first concept vector with the second concept vector to produce a similarity measure. More specifically, the similarity measure expresses the closeness between the first and second linguistic items in a high-level semantic space. In one case, the first linguistic item corresponds to a query, and the second linguistic item may correspond to a phrase, or a document, or a keyword, or an ad, etc. In one implementation, the convolutional latent semantic model is produced in a training phase based on click-through data.

Abstract: Functionality is described herein for transforming first and second symbolic linguistic items into respective first and second continuous-valued concept vectors, using a deep learning model, such as a convolutional latent semantic model. The model is designed to capture both the local and global linguistic contexts of the linguistic items. The functionality then compares the first concept vector with the second concept vector to produce a similarity measure. More specifically, the similarity measure expresses the closeness between the first and second linguistic items in a high-level semantic space. In one case, the first linguistic item corresponds to a query, and the second linguistic item may correspond to a phrase, or a document, or a keyword, or an ad, etc. In one implementation, the convolutional latent semantic model is produced in a training phase based on click-through data.

Abstract: Plural sets of items corresponding to plural tiers of a multi-tiered system are defined, where the items represent amounts of resources to allocate to the tiers. Items from respective sets are selected based on a response time constraint and a target cost. Information is provided pertaining to allocation of amounts of resources to the respective tiers based on the selected items.

Abstract: Aspects of the subject disclosure are directed towards managing sponsored online content based upon advertiser behavior. Defining mini-markets to represent such advertiser behavior may be accomplished by clustering queries that generate revenue from one or more campaigns. Query revenue data between queries and a set of campaigns may be used to determine such mini-markets. To illustrate, a query whose highest revenue is attributed to a campaign may be selected for that campaign's mini-market. When that query is entered as a search term, the campaign's mini-market helps allocate space for advertisements.

Abstract: A log-linear model may be trained using a modified version of an original limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm. The modified version may be based on modifying the original L-BFGS algorithm using a single map-reduce implementation. In another aspect, a sparse log-linear model may be accessed. The sparse log-linear model may be trained with L1-regularization, based on data indicating past user ad selection behaviors. A probability of a user selection of an ad may be determined based on the sparse log-linear model.

Abstract: An embodiment of a method of scheduling computer processing begins with a first step of receiving job properties for a plurality of jobs to be processed in a multi-processor computing environment. At least some of the jobs each comprise a plurality of stages, one or more tasks for each stage, and precedence constraints among the stages. The method continues with a second step of determining a schedule for processing at least a subset of the plurality of jobs on processors within the multi-processor computing environment from a solution of a mathematical program that provides a near maximal completion reward. The schedule comprises a sequence of tasks for each processor. In a third step, the computer processing jobs are processed on the processors according to the sequence of tasks for each processor.

Abstract: Embodiments are directed to systems, methods, and apparatus for allocating goods to bidders in combinatorial auctions. In one embodiment, bids are received in a combinatorial auction and the winner determination problem is modeled as an interval knapsack problem (I-KP) or an interval multiple-choice knapsack problem (I-MCKP), efficient algorithms (both pseudo-polynomial-time exact algorithms and FPTAS) for I-KP (and I-MCKP) are used to compute an allocation of goods to winning bidders.

Abstract: A method of predicting user-item ratings includes providing a first matrix of hidden variables associated with individual items, a second matrix of hidden variables associated with individual users, a third matrix of predicted user-item ratings derived from an inner product of vectors in the first and second matrices, and a fourth matrix of actual user-item ratings. The first and second matrices are alternately fixed and solved with a weighted-? regularization of at least one of the first and second matrices by minimizing a sum of squared errors between actual user-item ratings in the fourth matrix and corresponding predicted user-item ratings in the third matrix repeatedly until a stopping criterion is satisfied.

Abstract: The invention is directed to systems, methods, and an apparatus for bidding in online auctions. Bids for advertising include an amount that is a function of an expected value-per-click and a fraction of a budget already spent for advertising slots.

Abstract: To perform resource allocation in a system having plural tiers, one of plural resource allocation algorithms is selected in response to determining, based on one or more conditions, which of the plural resource allocation algorithms to select. The selected resource allocation algorithm is used to allocate resources for the plural tiers of the system.

Abstract: User input regarding a target system on which a software application is to be deployed is received. A benchmark system from plural candidate benchmark systems is matched to the target system. An estimated performance of the software application on the target system or an estimated utilization of resources of the target system by the software application is computed based on information relating to the matched benchmark system.

Abstract: A representation of a network having vertices connected by arcs is provided. Different collections of vertices in the network are iteratively selected. Balancing moves are performed in corresponding collections of the network, where the collections are represented by corresponding graphs according to a predefined structure.

Abstract: A method of predicting user-item ratings includes providing a first matrix of hidden variables associated with individual items, a second matrix of hidden variables associated with individual users, a third matrix of predicted user-item ratings derived from an inner product of vectors in the first and second matrices, and a fourth matrix of actual user-item ratings. The first and second matrices are alternately fixed and solved with a weighted-? regularization of at least one of the first and second matrices by minimizing a sum of squared errors between actual user-item ratings in the fourth matrix and corresponding predicted user-item ratings in the third matrix repeatedly until a stopping criterion is satisfied.

Abstract: One embodiment is a method that generates bids at an online search auction. The method uses a threshold function to decide which slot to obtain and bids accordingly.

Abstract: A representation of a network having vertices connected by arcs is provided, where the network exhibits a pseudoflow. Arcs on which moves are to be performed are iteratively chosen for balancing excesses of vertices across the arcs until a stopping rule is satisfied. After the stopping rule is satisfied, further processing is performed to identify at least one of a minimum cut and/or maximum flow.

Abstract: Fractional, non-integer numbers of servers are calculated for respective tiers of a multi-tiered system using a server allocation algorithm. The fractional, non-integer numbers of servers are rounded up to integer numbers to compute allocated numbers of servers for respective tiers.