Abstract: A cordless indicia reader including a multifunction coil that can be configured to either transmit or receive electromagnetic energy is disclosed. In this way, the multifunction coil facilitates both the wireless charging of a battery and the wireless deactivation of electronic article surveillance (EAS) tags. The multifunction coil, and a plurality of modules to perform these functions, are integrated within the cordless indicia reader's hand-supportable housing.

Abstract: An “Interestingness Modeler” uses deep neural networks to learn deep semantic models (DSM) of “interestingness.” The DSM, consisting of two branches of deep neural networks or their convolutional versions, identifies and predicts target documents that would interest users reading source documents. The learned model observes, identifies, and detects naturally occurring signals of interestingness in click transitions between source and target documents derived from web browser logs. Interestingness is modeled with deep neural networks that map source-target document pairs to feature vectors in a latent space, trained on document transitions in view of a “context” and optional “focus” of source and target documents. Network parameters are learned to minimize distances between source documents and their corresponding “interesting” targets in that space.

Abstract: A search engine is described herein for providing search results based on a context in which a query has been submitted, as expressed by context information. The search engine operates by ranking a plurality of documents based on a consideration of the query, and based, in part, on a context concept vector and a plurality of document concept vectors, both generated using a deep learning model (such as a deep neural network). The context concept vector is formed by a projection of the context information into a semantic space using the deep learning model. Each document concept vector is formed by a projection of document information, associated with a particular document, into the same semantic space using the deep learning model. The ranking operates by favoring documents that are relevant to the context within the semantic space, and disfavoring documents that are not relevant to the context.

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: A computer-implemented method and system for Web search ranking are provided herein. The method includes generating a number of training samples from clickthrough data, wherein the training samples include positive query-document pairs and negative query-document pairs. The method also includes discriminatively training a translation model based on the training samples and ranking a number of documents for a Web search based on the translation model.

Abstract: There is provided a computer-implemented method for user query reformulation. A graph is created to represent a relationship between previous user query terms. The graph may represent the previous user searches in n-grams that correspond to nodes. A random walk analysis is performed to determine probabilities that various n-grams corresponding to nodes of the graph could be used to effectively alter a user search term. The probabilities represent a quantification of relationships between nodes of the graph. A determination may be made regarding whether to reformulate the user query based on a relationship between a user search term in the user query and a graphed search term represented by a node of the graph. The determination takes into account a relationship between the user search term and the graphed search term.

Abstract: A self-checkout shopping system improves the retail shopping experience, permitting the self-serve customer to perform checkout tasks that are typically performed at the retailer's checkout area as the customer is selecting items for purchase. The system includes a mobile computer device and an indicia reader. The customer uses the mobile computer device to scan items selected for purchase. The item numbers are then compiled onto a list of acquired merchandise. When the customer has finished shopping, the system transmits the list of acquired merchandise, and any additional information such as customer financial information, to a point-of-sale terminal to facilitate the customer's payment for the merchandise.

Abstract: There is provided a computer-implemented method and system for ranking documents. The method includes identifying a number of query-document pairs based on clickthrough data for a number of documents. The method also includes building a latent semantic model based on the query-document pairs and ranking the documents for a search based on the latent semantic model.

Abstract: A deep structured semantic module (DSSM) is described herein which uses a model that is discriminatively trained based on click-through data, e.g., such that a conditional likelihood of clicked documents, given respective queries, is maximized, and a condition likelihood of non-clicked documents, given the queries, is reduced. In operation, after training is complete, the DSSM maps an input item into an output item expressed in a semantic space, using the trained model. To facilitate training and runtime operation, a dimensionality-reduction module (DRM) can reduce the dimensionality of the input item that is fed to the DSSM. A search engine may use the above-summarized functionality to convert a query and a plurality of documents into the common semantic space, and then determine the similarity between the query and documents in the semantic space. The search engine may then rank the documents based, at least in part, on the similarity measures.

Abstract: Various technologies described herein pertain to use of path-constrained random walks for query expansion and/or query document matching. Clickthrough data from search logs is represented as a labeled and directed graph. Path-constrained random walks are executed over the graph based upon an input query. The graph includes a first set of nodes that represent queries included in the clickthrough data from search logs, a second set of nodes that represent documents included in the clickthrough data from the search logs, a third set of nodes that represent words from the queries and the documents, and edges between nodes that represent relationships between queries, documents, and words. The path-constrained random walks include traversals over edges of the graph between nodes. Further, a score for a relationship between a target node and a source node representative of the input query is computed based at least in part upon the path-constrained random walks.

Abstract: Various technologies described herein pertain to training and utilizing a general, statistical framework for modeling translation via Markov random fields (MRFs). An MRF-based translation model can be employed in a statistical machine translation (SMT) system. The MRF-based translation model allows for arbitrary features extracted from a phrase pair to be incorporated as evidence. The parameters of the model are estimated using a large-scale discriminative training approach based on stochastic gradient ascent and an N-best list based expected Bilingual Evaluation Understudy (BLEU) as an objective function.

Abstract: An alteration candidate for a query can be scored. The scoring may include computing one or more query-dependent feature scores and/or one or more intra-candidate dependent feature scores. The computation of the query-dependent feature score(s) can be based on dependencies to multiple query terms from each of one or more alteration terms (i.e., for each of the one or more alteration terms, there can be dependencies to multiple query terms that form at least a portion of the basis for the query-dependent feature score(s)). The computation of the intra-candidate dependent feature score(s) can be based on dependencies between different terms in the alteration candidate. A candidate score can be computed using the query dependent feature score(s) and/or the intra-candidate dependent feature score(s). Additionally, the candidate score can be used in determining whether to select the candidate to expand the query. If selected, the candidate can be used to expand the query.

Abstract: A docetaxel inclusion complex having improved water-solubility (up to 5 mg/ml) and stability (stability constant Ka=2056M?1-13051M?1), comprises docetaxel and hydroxypropyl-beta-cyclodextrin and/or sulfobutyl-beta-cyclodextrin in a ratio of 1:10-150. The method includes steps as follows: docetaxel dissolved in ethanol is added into water solution of cyclodextrin via stirring, until docetaxel is completely dissolved; said solution is filtered in 0.2-04 ?m microporous membrane then ethanol is removed through reduced pressure to obtain the inclusion complex in a liquid form; or ethanol, followed by water is removed through reduced pressure, then dried to obtain the inclusion complex in a solid form.

Abstract: An architecture is discussed that provides the capability to subselect the most relevant data from an out-domain corpus to use either in isolation or in combination conjunction with in-domain data. The architecture is a domain adaptation for machine translation that selects the most relevant sentences from a larger general-domain corpus of parallel translated sentences. The methods for selecting the data include monolingual cross-entropy measure, monolingual cross-entropy difference, bilingual cross entropy, and bilingual cross-entropy difference. A translation model is trained on both the in-domain data and an out-domain subset, and the models can be interpolated together to boost performance on in-domain translation tasks.

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: A docetaxel inclusion complex having improved water-solubility (up to 15 mg/ml) and stability (stability constant Ka=2056 M?1-13051 M?1), comprises docetaxel and hydroxypropyl-beta-cyclodextrin and/or sulfobutyl-beta-cyclodextrin in a ratio of 1:10-150. The method includes steps as follows: docetaxel dissolved in ethanol is added into water solution of cyclodextrin via stirring, until docetaxel is completely dissolved; said solution is filtered in 0.2-04 ?m microporous membrane then ethanol is removed through reduced pressure to obtain the inclusion complex in a liquid form; or ethanol, followed by water is removed through reduced pressure, then dried to obtain the inclusion complex in a solid form.

Abstract: A computer-implemented method and system for Web search ranking are provided herein. The method includes generating a number of training samples from clickthrough data, wherein the training samples include positive query-document pairs and negative query-document pairs. The method also includes discriminatively training a translation model based on the training samples and ranking a number of documents for a Web search based on the translation model.

Abstract: The universal text input technique described herein addresses the difficulties of typing text in various languages and scripts, and offers a unified solution, which combines character conversion, next word prediction, spelling correction and automatic script switching to make it extremely simple to type any language from any device. The technique provides a rich and seamless input experience in any language through a universal IME (input method editor). It allows a user to type in any script for any language using a regular qwerty keyboard via phonetic input and at the same time allows for auto-completion and spelling correction of words and phrases while typing. The technique also provides a modeless input that automatically turns on and off an input mode that changes between different types of script.

Abstract: Systems, methods, and computer media for identifying query rewriting replacement terms are provided. A list of related string pairs each comprising a first string and second string is received. The first string of each related string pair is a user search query extracted from user click log data. For one or more of the related string pairs, the string pair is provided as inputs to a statistical machine translation model. The model identifies one or more pairs of corresponding terms, each pair of corresponding terms including a first term from the first string and a second term from the second string. The model also calculates a probability of relatedness for each of the one or more pairs of corresponding terms. Term pairs whose calculated probability of relatedness exceeds a threshold are characterized as query term replacements and incorporated, along with the probability of relatedness, into a query rewriting candidate database.

Abstract: A wireless scanner is described that performs a pairing operation with a wireless scanner base before commencing scanning operations in a wireless scanner network. Radio frequency identification (RFID) is used to achieve the pairing operation of the wireless scanner with the wireless scanner base by using an RFID tag associated with the wireless scanner base. The RFID tag in the wireless scanner base may contain pairing information such as a network address of the wireless scanner base for use in automatically establishing a wireless communication session with the wireless scanner base in accordance with another wireless protocol.