Generative feature matching network (GFMN) is an approach for training implicit generative models for images by performing moment matching on features from pre-trained neural networks. In this paper, we present new GFMN formulations that are effective for sequential data. Our experimental results show the effectiveness of the proposed method, SeqGFMN, for three distinct generation tasks in English: unconditional text generation, class-conditional text generation, and unsupervised text style transfer. SeqGFMN is stable to train and outperforms various adversarial approaches for text generation and text style transfer.

This paper is the first to consider online algorithms to schedule a proportionate flexible flow shop of batching machines (PFFB). The scheduling model is motivated by manufacturing processes of individualized medicaments, which are used in modern medicine to treat some serious illnesses. We provide two different online algorithms, proving also lower bounds for the offline problem to compute their competitive ratios. The first algorithm is an easy-to-implement, general local scheduling heuristic. It is 2-competitive for PFFBs with an arbitrary number of stages and for several natural scheduling objectives. We also show that for total/average flow time, no deterministic algorithm with better competitive ratio exists. For the special case with two stages and the makespan or total completion time objective, we describe an improved algorithm that achieves the best possible competitive ratio $varphi=frac{1+sqrt{5}}{2}$, the golden ratio. All our results also hold for proportionate (non-flexible) flow shops of batching machines (PFB) for which this is also the first paper to study online algorithms.

Sentence level quality estimation (QE) for machine translation (MT) attempts to predict the translation edit rate (TER) cost of post-editing work required to correct MT output. We describe our view on sentence-level QE as dictated by several practical setups encountered in the industry. We find consumers of MT output---whether human or algorithmic ones---to be primarily interested in a binary quality metric: is the translated sentence adequate as-is or does it need post-editing? Motivated by this we propose a quality classification (QC) view on sentence-level QE whereby we focus on maximizing recall at precision above a given threshold. We demonstrate that, while classical QE regression models fare poorly on this task, they can be re-purposed by replacing the output regression layer with a binary classification one, achieving 50-60\% recall at 90\% precision. For a high-quality MT system producing 75-80\% correct translations, this promises a significant reduction in post-editing work indeed.

Sparse models require less memory for storage and enable a faster inference by reducing the necessary number of FLOPs. This is relevant both for time-critical and on-device computations using neural networks. The stabilized lottery ticket hypothesis states that networks can be pruned after none or few training iterations, using a mask computed based on the unpruned converged model. On the transformer architecture and the WMT 2014 English-to-German and English-to-French tasks, we show that stabilized lottery ticket pruning performs similar to magnitude pruning for sparsity levels of up to 85%, and propose a new combination of pruning techniques that outperforms all other techniques for even higher levels of sparsity. Furthermore, we confirm that the parameter's initial sign and not its specific value is the primary factor for successful training, and show that magnitude pruning cannot be used to find winning lottery tickets.

In encoder-decoder neural models, multiple encoders are in general used to represent the contextual information in addition to the individual sentence. In this paper, we investigate multi-encoder approaches in documentlevel neural machine translation (NMT). Surprisingly, we find that the context encoder does not only encode the surrounding sentences but also behaves as a noise generator. This makes us rethink the real benefits of multi-encoder in context-aware translation - some of the improvements come from robust training. We compare several methods that introduce noise and/or well-tuned dropout setup into the training of these encoders. Experimental results show that noisy training plays an important role in multi-encoder-based NMT, especially when the training data is small. Also, we establish a new state-of-the-art on IWSLT Fr-En task by careful use of noise generation and dropout methods.

Simplified Chinese to Traditional Chinese character conversion is a common preprocessing step in Chinese NLP. Despite this, current approaches have poor performance because they do not take into account that a simplified Chinese character can correspond to multiple traditional characters. Here, we propose a model that can disambiguate between mappings and convert between the two scripts. The model is based on subword segmentation, two language models, as well as a method for mapping between subword sequences. We further construct benchmark datasets for topic classification and script conversion. Our proposed method outperforms previous Chinese Character conversion approaches by 6 points in accuracy. These results are further confirmed in a downstream application, where 2kenize is used to convert pretraining dataset for topic classification. An error analysis reveals that our method's particular strengths are in dealing with code-mixing and named entities.

Neural machine translation (NMT) needs large parallel corpora for state-of-the-art translation quality. Low-resource NMT is typically addressed by transfer learning which leverages large monolingual or parallel corpora for pre-training. Monolingual pre-training approaches such as MASS (MAsked Sequence to Sequence) are extremely effective in boosting NMT quality for languages with small parallel corpora. However, they do not account for linguistic information obtained using syntactic analyzers which is known to be invaluable for several Natural Language Processing (NLP) tasks. To this end, we propose JASS, Japanese-specific Sequence to Sequence, as a novel pre-training alternative to MASS for NMT involving Japanese as the source or target language. JASS is joint BMASS (Bunsetsu MASS) and BRSS (Bunsetsu Reordering Sequence to Sequence) pre-training which focuses on Japanese linguistic units called bunsetsus. In our experiments on ASPEC Japanese--English and News Commentary Japanese--Russian translation we show that JASS can give results that are competitive with if not better than those given by MASS. Furthermore, we show for the first time that joint MASS and JASS pre-training gives results that significantly surpass the individual methods indicating their complementary nature. We will release our code, pre-trained models and bunsetsu annotated data as resources for researchers to use in their own NLP tasks.

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the Blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous and a non-invasive BP measurement system is proposed using Photoplethysmogram (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo pre-processing and feature extraction steps. Time, frequency and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for Systolic BP (SBP) and Diastolic BP (DBP) estimation individually. Gaussian Process Regression (GPR) along with ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root-mean-square error (RMSE) of 6.74 and 3.59 respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.

Despite recent progress on computer vision and natural language processing, developing video understanding intelligence is still hard to achieve due to the intrinsic difficulty of story in video. Moreover, there is not a theoretical metric for evaluating the degree of video understanding. In this paper, we propose a novel video question answering (Video QA) task, DramaQA, for a comprehensive understanding of the video story. The DramaQA focused on two perspectives: 1) hierarchical QAs as an evaluation metric based on the cognitive developmental stages of human intelligence. 2) character-centered video annotations to model local coherence of the story. Our dataset is built upon the TV drama "Another Miss Oh" and it contains 16,191 QA pairs from 23,928 various length video clips, with each QA pair belonging to one of four difficulty levels. We provide 217,308 annotated images with rich character-centered annotations, including visual bounding boxes, behaviors, and emotions of main characters, and coreference resolved scripts. Additionally, we provide analyses of the dataset as well as Dual Matching Multistream model which effectively learns character-centered representations of video to answer questions about the video. We are planning to release our dataset and model publicly for research purposes and expect that our work will provide a new perspective on video story understanding research.

Bayesian predictive coding is a putative neuromorphic method for acquiring higher-level neural representations to account for sensory input. Although originating in the neuroscience community, there are also efforts in the machine learning community to study these models. This paper reviews some of the more well known models. Our review analyzes module connectivity and patterns of information transfer, seeking to find general principles used across the models. We also survey some recent attempts to cast these models within a deep learning framework. A defining feature of Bayesian predictive coding is that it uses top-down, reconstructive mechanisms to predict incoming sensory inputs or their lower-level representations. Discrepancies between the predicted and the actual inputs, known as prediction errors, then give rise to future learning that refines and improves the predictive accuracy of learned higher-level representations. Predictive coding models intended to describe computations in the neocortex emerged prior to the development of deep learning and used a communication structure between modules that we name the Rao-Ballard protocol. This protocol was derived from a Bayesian generative model with some rather strong statistical assumptions. The RB protocol provides a rubric to assess the fidelity of deep learning models that claim to implement predictive coding.

The temporal segmentation of events is an essential task and a precursor for the automatic recognition of human actions in the video. Several attempts have been made to capture frame-level salient aspects through attention but they lack the capacity to effectively map the temporal relationships in between the frames as they only capture a limited span of temporal dependencies. To this end we propose a complete end-to-end supervised learning approach that can better learn relationships between actions over time, thus improving the overall segmentation performance. The proposed hierarchical recurrent attention framework analyses the input video at multiple temporal scales, to form embeddings at frame level and segment level, and perform fine-grained action segmentation. This generates a simple, lightweight, yet extremely effective architecture for segmenting continuous video streams and has multiple application domains. We evaluate our system on multiple challenging public benchmark datasets, including MERL Shopping, 50 salads, and Georgia Tech Egocentric datasets, and achieves state-of-the-art performance. The evaluated datasets encompass numerous video capture settings which are inclusive of static overhead camera views and dynamic, ego-centric head-mounted camera views, demonstrating the direct applicability of the proposed framework in a variety of settings.

Unsupervised machine translation (MT) has recently achieved impressive results with monolingual corpora only. However, it is still challenging to associate source-target sentences in the latent space. As people speak different languages biologically share similar visual systems, the potential of achieving better alignment through visual content is promising yet under-explored in unsupervised multimodal MT (MMT). In this paper, we investigate how to utilize visual content for disambiguation and promoting latent space alignment in unsupervised MMT. Our model employs multimodal back-translation and features pseudo visual pivoting in which we learn a shared multilingual visual-semantic embedding space and incorporate visually-pivoted captioning as additional weak supervision. The experimental results on the widely used Multi30K dataset show that the proposed model significantly improves over the state-of-the-art methods and generalizes well when the images are not available at the testing time.

Narrowband internet-of-things (NB-IoT) is a competitive 5G technology for massive machine-type communication scenarios, but meanwhile introduces narrowband interference (NBI) to existing broadband transmission such as the long term evolution (LTE) systems in enhanced mobile broadband (eMBB) scenarios. In order to facilitate the harmonic and fair coexistence in wireless heterogeneous networks, it is important to eliminate NB-IoT interference to LTE systems. In this paper, a novel sparse machine learning based framework and a sparse combinatorial optimization problem is formulated for accurate NBI recovery, which can be efficiently solved using the proposed iterative sparse learning algorithm called sparse cross-entropy minimization (SCEM). To further improve the recovery accuracy and convergence rate, regularization is introduced to the loss function in the enhanced algorithm called regularized SCEM. Moreover, exploiting the spatial correlation of NBI, the framework is extended to multiple-input multiple-output systems. Simulation results demonstrate that the proposed methods are effective in eliminating NB-IoT interference to LTE systems, and significantly outperform the state-of-the-art methods.

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.

Different state-of-the-art methods of the detection of schizophrenia symptoms based on the estimation of text coherence have been analyzed. The analysis of a text at the level of phrases has been suggested. The method based on the graph of the consistency of phrases has been proposed to evaluate the semantic coherence and the cohesion of a text. The semantic coherence, cohesion, and other linguistic features (lexical diversity, lexical density) have been taken into account to form feature vectors for the training of a model-classifier. The training of the classifier has been performed on the set of English-language interviews. According to the retrieved results, the impact of each feature on the output of the model has been analyzed. The results obtained can indicate that the proposed method based on the graph of the consistency of phrases may be used in the different tasks of the detection of mental illness.

The Cannabis Issue It's been almost a month since the COVID-19 pandemic forced folks inside and made "social distancing" part of our daily lexicons.…

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With all due respect to Greta Thunberg and Billie Eilish, nobody had a better 2019 than Baby Yoda. The real star of the Disney+ flagship Star Wars series The Mandalorian, the little green puppeteering/CGI marvel (aka "the Child") might be the most adorable creature ever created.…

Uncut Gems is one of those "his own worst enemy" capers. You know, the kind of movie where you sit there for two hours watching some doofus constantly trip over his own laces — usually figuratively, sometimes literally — on the way to a personal epiphany about how all his bad choices and lack of useful self-awareness have led him to whatever unpleasant place they lead him to.…

THE SHOW WILL GO ON…

By Johnny Diaz The New York Times Company…

Warm summer nights are on the way.…

A little more than a year ago, local French-inspired bakery miFlavour moved into a building on East Sprague Avenue after two years in a shared commercial kitchen.…

Processing metadata includes storing, in a data storage system, a specification for each of multiple sources, each specification including information identifying one or more data elements of the corresponding source; and processing, in a data processing system coupled to the data storage system, data elements from the sources, including generating a set of rules for each source based on a corresponding one of the stored specifications, and matching data elements of different sources and determining a quality metric characterizing a given match between a first data element of a first source and a second data element of a second source according to the set of rules generated for the first source and the set of rules generated for the second source.

A system and method that facilitates urban traffic state detection based on support vector machine (SVM) and multilayer perceptron (MLP) classifiers is provided. Moreover, the SVM and MLP classifiers are fused into a cascaded two-tier classifier that improves the accuracy of the traffic state classification. To further improve the accuracy, the cascaded two-tier classifier (e.g., MLP-SVM), a single SVM classifier and a single MLP classifier are fused to determine a final decision for a traffic state. In addition, fusion strategies are employed during training and implementation phases to compensate for data acquisition and classification errors caused by noise and/or outliers.

Observation-based user profiling and profile matching are provided. The network behavior of users of a computer-implemented social network are observed and used for user profiling. By observing network behavior instead of necessarily relying on user self-reported data, accurate and objective user profiles can be formed; user profiling is accomplished based on the observed network behaviors with or without the knowledge of the user being profiled. The observed network behaviors can include the customization of a visual graphic, a media preference, a communication preference, or a selection of words from a word list. The user profiles can be with respect to a domain and two or more users can be matched based on their profiles with respect to the same domain. User ratings and profile updating based on the ratings are also provided.

A process for the preparation of a chiral compound, in particular posaconazole, wherein the process comprises mixing and reacting the compounds of formula (I) Y3—NH2; of formula (IIa) 0=C═N—Y0 and/or of formula (IIb) and of formula (III) in a solvent in any order to obtain a reaction mixture containing a chiral compound of formula (IV) and/or formula (V).

A process is presented for quenching a process stream in a paraffin dehydrogenation process. The process comprises cooling a propane dehydrogenation stream during the hot residence time after the process stream leaves the catalytic bed reactor section. The process includes cooling and compressing the product stream, taking a portion of the product stream and passing the portion of the product stream to the mix with the process stream as it leaves the catalytic bed reactor section.

Efficient computation of complex multiplication results and very efficient fast Fourier transforms (FFTs) are provided. A parallel array VLIW digital signal processor is employed along with specialized complex multiplication instructions and communication operations between the processing elements which are overlapped with computation to provide very high performance operation. Successive iterations of a loop of tightly packed VLIWs are used allowing the complex multiplication pipeline hardware to be efficiently used. In addition, efficient techniques for supporting combined multiply accumulate operations are described.

A method, apparatus, and system for switching from an existing target end device to a next target end device in a multi-expander storage topology by using Fast Context Switching. The method enhances Fast Context Switching by allowing Fast Context Switching to reuse or extend part of an existing connection path to an end device directly attached to a remote expander. The method can include reusing or extending at least a partial path of an established connection between an initiator and the existing target end device for a connection between the initiator and the next target end device, whereby the existing target end device and the next target end device are locally attached to different expanders.

A cloud system may create physical resource tags to store relationships between cloud computing offerings, such as computing service offerings, storage offerings, and network offerings, and the specific physical resources in the cloud computing environment. Cloud computing offerings may be presented to cloud customers, the offerings corresponding to various combinations of computing services, storage, networking, and other hardware or software resources. After a customer selects one or more cloud computing offerings, a cloud resource manager or other component within the cloud infrastructure may retrieve a set of tags and determine a set of physical hardware resources associated with the selected offerings. The physical hardware resources associated with the selected offerings may be subsequently used to provision and create the new virtual machine and its operating environment.

A system, method and computer program product for allocating shared resources. Upon receiving requests for resources, the cost of bundling software in a virtual machine (VM) image is automatically generated. Software is selected by the cost for each bundle according to the time required to install it where required, offset by the time to uninstall it where not required. A number of VM images having the highest software bundle value (i.e., highest cost bundled) is selected and stored, e.g., in a machine image store. With subsequent requests for resources, VMs may be instantiated from one or more stored VM images and, further, stored images may be updated selectively updated with new images.

A server computer system determines that configuring a first virtual machine in a cloud depends on a configuration result of configuring a second virtual machine. The server computer system configures the second virtual machine in the cloud and configures the first virtual machine in the cloud using the configuration result of the second virtual machine.

A system and method for reconfiguring a computing environment comprising a consumption analysis server, a placement server, an infrastructure management client and a data warehouse in communication with a set of data collection agents and a database. The consumption analysis server operates on measured resource utilization data to yield a set of resource consumptions in regularized time blocks, collects host and virtual machine configurations from the computing environment and determines available capacity for a set of target hosts. The placement server assigns a set of target virtual machines to the target set of hosts in a new placement. In one mode of operation the new placement is nearly optimal. In another mode of operation, the new placement is “good enough” to achieve a threshold score based on an objective function of resource capacity headroom. The new placement is implemented in the computing environment.

Disclosed is an information processing device and a task switching method that can reduce the time required for switching of tasks in a plurality of coprocessors. The information processing device (30) includes a processor core (301); coprocessors (311 to 31n) including operation units (321 to 32n) that perform operation in response to a request from the processor core (301) and operation storage units (331 to 22n) that store the contents of operation of the operation units (321 to 32n), save storage units (351 to 35n) that store the saved contents of operation, a task switching control unit (302) that outputs a save/restore request signal when switching a task on which operation is performed by the coprocessors (311 to 31n), and save/restore units (341 to 34n) that perform at least one of saving of the contents of operation in the operation storage units (331 to 33n) to the save storage units (351 to 35n) and restoration of the contents of operation in the save storage units (351 to 35 n) to the operation storage units (331 to 33n) in response to the save/restore request signal.

A system and method for controlling restarting of instruction fetching using speculative address computations in a processor are provided. The system includes a predicted target queue to hold branch prediction logic (BPL) generated target address values. The system also includes target selection logic including a recycle queue. The target selection logic selects a saved branch target value between a previously speculatively calculated branch target value from the recycle queue and an address value from the predicted target queue. The system further includes a compare block to identify a wrong target in response to a mismatch between the saved branch target value and a current calculated branch target, where instruction fetching is restarted in response to the wrong target.

A computing machine is disclosed having a memory system for storing a collection of execution nodes, a head for reading a sequence of symbols in the execution nodes in the memory system, and writing a sequence of symbols in the memory system. The machine is configured to execute a computation with a collection of pairs of execution nodes. Each pair of execution nodes represents a machine instruction. One execution node in the pair represents input of the machine instruction represented by the execution nodes. Another execution node in the pair represents output of the machine instruction represented by the execution nodes. Each execution node has a state of the machine, a sequence of symbols and a number.

An adaptable integrated circuit is disclosed having a plurality of heterogeneous computational elements coupled to an interconnection network. The interconnection network changes interconnections between the plurality of heterogeneous computational elements in response to configuration information. A first group of computational elements is allocated to form a first version of a functional unit to perform a first function by changing interconnections in the interconnection network between the first group of heterogeneous computational elements. A second group of computational elements is allocated to form a second version of a functional unit to perform the first function by changing interconnections in the interconnection network between the second group of heterogeneous computational elements. One or more of the first or second group of heterogeneous computational elements are reallocated to perform a second function by changing the interconnections between the one or more of the first or second group of heterogeneous computational elements.

A data processing device includes processing circuitry 20 for executing a first memory access instruction to a first address of a memory device 40 and a second memory access instruction to a second address of the memory device 40, the first address being different from the second address. The data processing device also includes prefetching circuitry 30 for prefetching data from the memory device 40 based on a stride length 70 and instruction analysis circuitry 50 for determining a difference between the first address and the second address. Stride refining circuitry 60 is also provided to refine the stride length based on factors of the stride length and factors of the difference calculated by the instruction analysis circuitry 50.

An array of a plurality of processing elements (PEs) are in a data packet-switched network interconnecting the PEs and memory to enable any of the PEs to access the memory. The network connects the PEs and their local memories to a common controller. The common controller may include a shared load/store (SLS) unit and an array control unit. A shared read may be addressed to an external device via the common controller. The SLS unit can continue activity as if a normal shared read operation has taken place, except that the transactions that have been sent externally may take more cycles to complete than the local shared reads. Hence, a number of transaction-enabled flags may not have been deactivated even though there is no more bus activity. The SLS unit can use this state to indicate to the array control unit that a thread switch may now take place.

A method of monitoring thread execution within a multicore processor architecture which comprises a plurality of interconnected processor elements for processing the threads, the method comprising receiving a plurality of thread parameter indicators of one or more parameters relating to the function and/or identity and/or execution location of a thread or threads, comparing at least one of the thread parameter indicators with a first plurality of predefined criteria each representative of an indicator of interest, and generating an output consequential upon thread parameter indicators which have been identified to be of interest as a result of the said comparison.

A microprocessor capable of running both x86 instruction set architecture (ISA) machine language programs and Advanced RISC Machines (ARM) ISA machine language programs. The microprocessor includes a mode indicator that indicates whether the microprocessor is currently fetching instructions of an x86 ISA or ARM ISA machine language program. The microprocessor also includes a plurality of model-specific registers (MSRs) that control aspects of the operation of the microprocessor. When the mode indicator indicates the microprocessor is currently fetching x86 ISA machine language program instructions, each of the plurality of MSRs is accessible via an x86 ISA RDMSR/WRMSR instruction that specifies an address of the MSR. When the mode indicator indicates the microprocessor is currently fetching ARM ISA machine language program instructions, each of the plurality of MSRs is accessible via an ARM ISA MRRC/MCRR instruction that specifies the address of the MSR.

A framework for implementing multitenant architecture is provided. The framework comprises a framework services module which is configured to provide framework services that facilitate abstraction of Software-as-a-Service (SaaS) services and crosscutting services for a Greenfield application and a non SaaS based web application. Further the abstraction results in a SaaS based multitenant web application. The framework further comprises a runtime module configured to automatically integrate and consume the framework services and APIs to facilitate monitoring and controlling of features associated with the SaaS based multitenant web application. The framework further comprises a metadata services module configured to provide a plurality of metadata services to facilitate abstraction of storage structure of metadata associated with the framework and act as APIs for managing the metadata. The framework further comprises a role based administration module that facilitates management of the metadata through a tenant administrator and a product administrator.

The present invention relates generally to client-server architectures for allowing generic upload and download functionality between a web application at a server and a client. One exemplary method includes sending a download/upload request to a web application at the server, where the download/upload request specifies at least one file to download/upload; receiving a transmission from the server; parsing the transmission to identify a download/upload command and an associated download/upload manifest, where the download/upload manifest includes executable code that, when executed on the client, will perform the download/upload of the at least one file.

A datapath extraction tool uses machine-learning models to selectively classify clusters of cells in an integrated circuit design as either datapath logic or non-datapath logic based on cluster features. A support vector machine and a neural network can be used to build compact and run-time efficient models. A cluster is classified as datapath if both the support vector machine and the neural network indicate that it is datapath-like. The cluster features may include automorphism generators for the cell clusters, or physical information based on the cell locations from a previous (e.g., global) placement, such as a ratio of a total cell area for a given cluster to a half-perimeter of a bounding box for the given cluster.

The present disclosure relates to a superconducting rotating electrical machine and a manufacturing method for a high temperature superconducting film thereof. The superconducting rotating electrical machine includes a stator, and a rotor rotatable with respect to the stator, the rotor having a rotary shaft and a rotor winding. Here, the rotor winding includes tubes disposed on a circumference of the rotary shaft and each forming a passage for a cooling fluid therein, superconducting wires accommodated within the tubes, and a cooling fluid flowing through the inside of the tubes. This configuration may allow for direct heat exchange between the superconducting wires and a refrigerant, resulting in improvement of heat exchange efficiencies of the superconducting wires.

A system and methods for matching at least one word of an utterance against a set of template hierarchies to select the best matching template or set of templates corresponding to the utterance. Certain embodiments of the system and methods determines at least one exact, inexact, and partial match between the at least one word of the utterance and at least one term within the template hierarchy to select and populate a template or set of templates corresponding to the utterance. The populated template or set of templates may then be used to generate a narrative template or a report template.

A biometric voice command and control switching device has a microphone assembly for receiving a currently spoken challenge utterance and a reference utterance, and a voice processing circuit for creating electronic signals indicative thereof. The device further includes a memory for storing the electronic signals, and a processor for comparing the electronic signals to determine if there is a match. If there is a match, an interface circuit enables the operable control of the controlled device.

A visualization can include a set of swim lanes, each swim lane representing information about an event type. An event type can be specified, e.g., as those events having certain keywords and/or having specified value(s) for specified field(s). The swim lane can plot when (within a time range) events of the associated event type occurred. Specifically, each such event can be assigned to a bucket having a bucket time matching the event time. A swim lane can extend along a timeline axis in the visualization, and the buckets can be positioned at a point along the axis that represents the bucket time. Thus, the visualization may indicate whether events were clustered at a point in time. Because the visualization can include a plurality of swim lanes, the visualization can further indicate how timing of events of a first type compare to timing of events of a second type.

A system and method for implementing a dynamic menu characterized by a real-time menu architecture. An example method includes determining a menu structure, with reference to a data set, for use with user interface software. A menu is then displayed, wherein the menu includes menu items arranged according to the menu structure. An initial drop-down menu list of the menu includes one or more of the menu items. Each menu item is associated with a data object of the data set. A signal is generated when a change is made to a data set corresponding to a menu item. The menu is then updated based on the signal. In an illustrative embodiment, the example method further includes employing a browser to trigger implementation of the method. The browser communicates with a web server, which communicates with an application that is adapted to analyze the data set and associated data structure and provide an update in response thereto via, the signal, to the web server. The web server includes software for enabling the browser to render an updated menu or graphical representation thereof.