We empirically evaluate an undervolting technique, i.e., underscaling the circuit supply voltage below the nominal level, to improve the power-efficiency of Convolutional Neural Network (CNN) accelerators mapped to Field Programmable Gate Arrays (FPGAs). Undervolting below a safe voltage level can lead to timing faults due to excessive circuit latency increase. We evaluate the reliability-power trade-off for such accelerators. Specifically, we experimentally study the reduced-voltage operation of multiple components of real FPGAs, characterize the corresponding reliability behavior of CNN accelerators, propose techniques to minimize the drawbacks of reduced-voltage operation, and combine undervolting with architectural CNN optimization techniques, i.e., quantization and pruning. We investigate the effect of environmental temperature on the reliability-power trade-off of such accelerators. We perform experiments on three identical samples of modern Xilinx ZCU102 FPGA platforms with five state-of-the-art image classification CNN benchmarks. This approach allows us to study the effects of our undervolting technique for both software and hardware variability. We achieve more than 3X power-efficiency (GOPs/W) gain via undervolting. 2.6X of this gain is the result of eliminating the voltage guardband region, i.e., the safe voltage region below the nominal level that is set by FPGA vendor to ensure correct functionality in worst-case environmental and circuit conditions. 43% of the power-efficiency gain is due to further undervolting below the guardband, which comes at the cost of accuracy loss in the CNN accelerator. We evaluate an effective frequency underscaling technique that prevents this accuracy loss, and find that it reduces the power-efficiency gain from 43% to 25%.

Neural style transfer is a well-known branch of deep learning research, with many interesting works and two major drawbacks. Most of the works in the field are hard to use by non-expert users and substantial hardware resources are required. In this work, we present a solution to both of these problems. We have applied neural style transfer to real-time video (over 25 frames per second), which is capable of running on mobile devices. We also investigate the works on achieving temporal coherence and present the idea of fine-tuning, already trained models, to achieve stable video. What is more, we also analyze the impact of the common deep neural network architecture on the performance of mobile devices with regard to number of layers and filters present. In the experiment section we present the results of our work with respect to the iOS devices and discuss the problems present in current Android devices as well as future possibilities. At the end we present the qualitative results of stylization and quantitative results of performance tested on the iPhone 11 Pro and iPhone 6s. The presented work is incorporated in Kunster - AR Art Video Maker application available in the Apple's App Store.

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.

MineRL 2019 competition challenged participants to train sample-efficient agents to play Minecraft, by using a dataset of human gameplay and a limit number of steps the environment. We approached this task with behavioural cloning by predicting what actions human players would take, and reached fifth place in the final ranking. Despite being a simple algorithm, we observed the performance of such an approach can vary significantly, based on when the training is stopped. In this paper, we detail our submission to the competition, run further experiments to study how performance varied over training and study how different engineering decisions affected these results.

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.

Recent works imply that the channel pruning can be regarded as searching optimal sub-structure from unpruned networks.

However, existing works based on this observation require training and evaluating a large number of structures, which limits their application.

In this paper, we propose a novel differentiable method for channel pruning, named Differentiable Markov Channel Pruning (DMCP), to efficiently search the optimal sub-structure.

Our method is differentiable and can be directly optimized by gradient descent with respect to standard task loss and budget regularization (e.g. FLOPs constraint).

In DMCP, we model the channel pruning as a Markov process, in which each state represents for retaining the corresponding channel during pruning, and transitions between states denote the pruning process.

In the end, our method is able to implicitly select the proper number of channels in each layer by the Markov process with optimized transitions. To validate the effectiveness of our method, we perform extensive experiments on Imagenet with ResNet and MobilenetV2.

Results show our method can achieve consistent improvement than state-of-the-art pruning methods in various FLOPs settings. The code is available at https://github.com/zx55/dmcp

Fashion trend forecasting is a crucial task for both academia and industry. Although some efforts have been devoted to tackling this challenging task, they only studied limited fashion elements with highly seasonal or simple patterns, which could hardly reveal the real fashion trends. Towards insightful fashion trend forecasting, this work focuses on investigating fine-grained fashion element trends for specific user groups. We first contribute a large-scale fashion trend dataset (FIT) collected from Instagram with extracted time series fashion element records and user information. Further-more, to effectively model the time series data of fashion elements with rather complex patterns, we propose a Knowledge EnhancedRecurrent Network model (KERN) which takes advantage of the capability of deep recurrent neural networks in modeling time-series data. Moreover, it leverages internal and external knowledge in fashion domain that affects the time-series patterns of fashion element trends. Such incorporation of domain knowledge further enhances the deep learning model in capturing the patterns of specific fashion elements and predicting the future trends. Extensive experiments demonstrate that the proposed KERN model can effectively capture the complicated patterns of objective fashion elements, therefore making preferable fashion trend forecast.

While the exact definition and implementation of accountability depend on the specific context, at its core accountability describes a mechanism that will make decisions transparent and often provides means to sanction "bad" decisions. As such, accountability is specifically relevant for Cyber-Physical Systems, such as robots or drones, that embed themselves into a human society, take decisions and might cause lasting harm. Without a notion of accountability, such systems could behave with impunity and would not fit into society. Despite its relevance, there is currently no agreement on its meaning and, more importantly, no way to express accountability properties for these systems. As a solution we propose to express the accountability properties of systems using Structural Causal Models. They can be represented as human-readable graphical models while also offering mathematical tools to analyze and reason over them. Our central contribution is to show how Structural Causal Models can be used to express and analyze the accountability properties of systems and that this approach allows us to identify accountability patterns. These accountability patterns can be catalogued and used to improve systems and their architectures.

Visualization-oriented natural language interfaces (V-NLIs) have been explored and developed in recent years. One challenge faced by V-NLIs is in the formation of effective design decisions that usually requires a deep understanding of user queries. Learning-based approaches have shown potential in V-NLIs and reached state-of-the-art performance in various NLP tasks. However, because of the lack of sufficient training samples that cater to visual data analytics, cutting-edge techniques have rarely been employed to facilitate the development of V-NLIs. We present a new dataset, called Quda, to help V-NLIs understand free-form natural language. Our dataset contains 14;035 diverse user queries annotated with 10 low-level analytic tasks that assist in the deployment of state-of-the-art techniques for parsing complex human language. We achieve this goal by first gathering seed queries with data analysts who are target users of V-NLIs. Then we employ extensive crowd force for paraphrase generation and validation. We demonstrate the usefulness of Quda in building V-NLIs by creating a prototype that makes effective design decisions for free-form user queries. We also show that Quda can be beneficial for a wide range of applications in the visualization community by analyzing the design tasks described in academic publications.

Spiking neural networks (SNNs) are considered as a potential candidate to overcome current challenges such as the high-power consumption encountered by artificial neural networks (ANNs), however there is still a gap between them with respect to the recognition accuracy on practical tasks. A conversion strategy was thus introduced recently to bridge this gap by mapping a trained ANN to an SNN. However, it is still unclear that to what extent this obtained SNN can benefit both the accuracy advantage from ANN and high efficiency from the spike-based paradigm of computation. In this paper, we propose two new conversion methods, namely TerMapping and AugMapping. The TerMapping is a straightforward extension of a typical threshold-balancing method with a double-threshold scheme, while the AugMapping additionally incorporates a new scheme of augmented spike that employs a spike coefficient to carry the number of typical all-or-nothing spikes occurring at a time step. We examine the performance of our methods based on MNIST, Fashion-MNIST and CIFAR10 datasets. The results show that the proposed double-threshold scheme can effectively improve accuracies of the converted SNNs. More importantly, the proposed AugMapping is more advantageous for constructing accurate, fast and efficient deep SNNs as compared to other state-of-the-art approaches. Our study therefore provides new approaches for further integration of advanced techniques in ANNs to improve the performance of SNNs, which could be of great merit to applied developments with spike-based neuromorphic computing.

Convolutional neural networks (CNN) have shown promising results for end-to-end speech recognition, albeit still behind other state-of-the-art methods in performance. In this paper, we study how to bridge this gap and go beyond with a novel CNN-RNN-transducer architecture, which we call ContextNet. ContextNet features a fully convolutional encoder that incorporates global context information into convolution layers by adding squeeze-and-excitation modules. In addition, we propose a simple scaling method that scales the widths of ContextNet that achieves good trade-off between computation and accuracy. We demonstrate that on the widely used LibriSpeech benchmark, ContextNet achieves a word error rate (WER) of 2.1\%/4.6\% without external language model (LM), 1.9\%/4.1\% with LM and 2.9\%/7.0\% with only 10M parameters on the clean/noisy LibriSpeech test sets. This compares to the previous best published system of 2.0\%/4.6\% with LM and 3.9\%/11.3\% with 20M parameters. The superiority of the proposed ContextNet model is also verified on a much larger internal dataset.

Neural networks have opened up many new opportunities to utilize remotely sensed images in meteorology. Common applications include image classification, e.g., to determine whether an image contains a tropical cyclone, and image translation, e.g., to emulate radar imagery for satellites that only have passive channels. However, there are yet many open questions regarding the use of neural networks in meteorology, such as best practices for evaluation, tuning and interpretation. This article highlights several strategies and practical considerations for neural network development that have not yet received much attention in the meteorological community, such as the concept of effective receptive fields, underutilized meteorological performance measures, and methods for NN interpretation, such as synthetic experiments and layer-wise relevance propagation. We also consider the process of neural network interpretation as a whole, recognizing it as an iterative scientist-driven discovery process, and breaking it down into individual steps that researchers can take. Finally, while most work on neural network interpretation in meteorology has so far focused on networks for image classification tasks, we expand the focus to also include networks for image translation.

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.

Dialogue engines that incorporate different types of agents to converse with humans are popular.

However, conversations are dynamic in the sense that a selected response will change the conversation on-the-fly, influencing the subsequent utterances in the conversation, which makes the response selection a challenging problem.

We model the problem of selecting the best response from a set of responses generated by a heterogeneous set of dialogue agents by taking into account the conversational history, and propose a emph{Neural Response Selection} method.

The proposed method is trained to predict a coherent set of responses within a single conversation, considering its own predictions via a curriculum training mechanism.

Our experimental results show that the proposed method can accurately select the most appropriate responses, thereby significantly improving the user experience in dialogue systems.

Making changes to a program to optimize its performance is an unscalable task that relies entirely upon human intuition and experience. In addition, companies operating at large scale are at a stage where no single individual understands the code controlling its systems, and for this reason, making changes to improve performance can become intractably difficult. In this paper, a learning system is introduced that provides AI assistance for finding recommended changes to a program. Specifically, it is shown how the evaluative feedback, delayed-reward performance programming domain can be effectively formulated via the Monte Carlo tree search (MCTS) framework. It is then shown that established methods from computational games for using learning to expedite tree-search computation can be adapted to speed up computing recommended program alterations. Estimates of expected utility from MCTS trees built for previous problems are used to learn a sampling policy that remains effective across new problems, thus demonstrating transferability of optimization knowledge. This formulation is applied to the Apache Spark distributed computing environment, and a preliminary result is observed that the time required to build a search tree for finding recommendations is reduced by up to a factor of 10x.

By Sci Dev Net Advances in printed solar cell technology promise clean renewable energy, opening possibilities for 1.3 billion people still without electric power in developing countries. The technology, which only requires the use of existing industrial-size printers, can produce … Continue reading →

The Cannabis Issue During normal times, I-90 Green House is like a destination resort for marijuana lovers.…

A spiking neuron-based working memory device is provided. The spiking neuron-based working memory device includes an input interface configured to convert input spike signals into respective burst signals having predetermined forms, and output a sequence of the burst signals, the burst signals corresponding to the input spike signals in a burst structure, and two or more memory elements (MEs) configured to sequentially store features respectively corresponding to the outputted sequence of the burst signals, each of the MEs continuously outputting spike signals respectively corresponding to the stored features.

A surfactant composition for agricultural chemicals, containing fatty acid polyoxyalkylene alkyl ether expressed by the following general formula (I), R1CO(EO)m(PO)nOR2 (I) wherein the fatty acid polyoxyalkylene alkyl ether has a narrow ratio of 55% by mass or more, where the narrow ratio is expressed by the following formula (A): Narrow ratio=Σi=nMAX−2i=nMAX+2Yi (A).

Methods are provided for refining natural oil feedstocks and producing dibasic esters and/or dibasic acids. The methods comprise reacting a terminal olefin with an internal olefin in the presence of a metathesis catalyst to form a dibasic ester and/or dibasic acid. In certain embodiments, the olefin esters are formed by reacting the feedstock in the presence of a metathesis catalyst under conditions sufficient to form a metathesized product comprising olefins and esters, separating the olefins from the esters in the metathesized product, and transesterifying the esters in the presence of an alcohol to form a transesterified product having olefin esters.

Composite material that contain epoxy resin which is toughened and strengthened with thermoplastic materials and a blend of insoluble particles. The uncured matrix resins include an epoxy resin component, a soluble thermoplastic component, a curing agent and an insoluble particulate component composed of elastic particles and rigid particles. The uncured resin matrix is combined with a fibrous reinforcement and cured/molded to form composite materials that may be used for structural applications, such as primary structures in aircraft.

Disclosed are a biomolecular sensor and a method of fabricating the same having high sensitivity and resolution by using a plurality of metal plates that change electrical properties of a plurality of nanostructures according to the attachment of biomolecules. The biomolecular sensor includes a substrate, first and second electrodes disposed to be spaced apart from each other on the substrate, a plurality of nanostructures disposed on the substrate to connect the first and second electrodes to each other, and a plurality of metal plates that change electrical properties of the plurality of nanostructures according to the attachment of biomolecules.

Various embodiments include apparatuses, stacked devices and methods of forming dice stacks on an interface die. In one such apparatus, a dice stack includes at least a first die and a second die, and conductive paths coupling the first die and the second die to the common control die. In some embodiments, the conductive paths may be arranged to connect with circuitry on alternating dice of the stack. In other embodiments, a plurality of dice stacks may be arranged on a single interface die, and some or all of the dice may have interleaving conductive paths.

A High Performance Computing (HPC) node comprises a motherboard, a switch comprising eight or more ports integrated on the motherboard, and at least two processors operable to execute an HPC job, with each processor communicably coupled to the integrated switch and integrated on the motherboard.

A data processing system arranged for receiving over a network, according to a data transfer protocol, data directed to any of a plurality of destination identities, the data processing system comprising: data storage for storing data received over the network; and a first processing arrangement for performing processing in accordance with the data transfer protocol on received data in the data storage, for making the received data available to respective destination identities; and a response former arranged for: receiving a message requesting a response indicating the availability of received data to each of a group of destination identities; and forming such a response; wherein the system is arranged to, in dependence on receiving the said message.

In an example embodiment, there is described herein an apparatus comprising an interface for communicating with a plurality of digital signal processors and logic operable to send and receive data via the interface. The logic is configured to determine a first set of digital signal processors to be maintained in a ready state, a second set of digital signal processors to be maintained in a first energy saving state, and a third set of digital signal processors to be maintained in a second energy saving state.

In one embodiment, a navigation system provides predictive natural guidance utilizing a mobile landmark based on location data. The location data may be a schedule. A controller receives data of a schedule of a mobile landmark. The location data could be collected in real time or estimated. The mobile landmark may be a vehicle or a celestial body. The controller correlates a route from an origin location to a destination location and the location of the mobile landmark. The controller generates a message based on the correlation. The message is output during presentation of the route and references the mobile landmark.

A method of patterning a plurality of layers of a work piece in a series of writing cycles in one or a plurality of write machines, the workpiece being deviced to have a number of N layers and layers of the workpiece having one or a plurality of boundary condition(s) for pattern position, the method comprising the steps of: determining the boundary conditions of layers 1 to N, calculating deviations due to the boundary conditions and calculating a compensation for the deviation of the first transformation added with the assigned part of the deviation due to the boundary conditions.

A system includes a system time counter associated with a micro controller and a secure clock module having a real time clock and an elapsed time counter. The system synchronizes operation between the secure clock module and the system time counter. The synchronized time entered into the system time counter is utilized in the operation of the system. The real time clock time can be caused to be entered into the elapsed time counter at certain point in the operation of the system. The relationship of the time provide enhanced systems security.

The disclosed embodiments relate to pulse oximetry. An exemplary pulse oximeter comprises a probe that is adapted to be attached to a body part of a patient to create a signal indicative of an oxygen saturation of blood of the patient, and a processor that is adapted to receive the signal produced by the probe, to calculate an SPO2 value based on the signal, to detect a plurality of pattern types of SPO2 indicative of pathophysiologic events, and to produce an output indicative of a detected one of the plurality of pattern types.

Apparatus and methods are described including acquiring a first set of extraluminal images of a lumen, using an extraluminal imaging device. At least one of the first set of images is designated as a roadmap image. While an endoluminal device is being moved through the lumen, a second set of extraluminal images is acquired. A plurality of features that are visible within images belonging to the second set of extraluminal images are identified. In response to the identified features in the images belonging to the second set of extraluminal images, a plurality of local calibration factors associated with respective portions of the roadmap image are determined. Other applications are also described.

Aqueous glyphosate formulations comprising a surfactant derived from metathesized natural oil feedstocks are disclosed. The formulations comprise a glyphosate salt, water, and a surfactant derived from a metathesis-derived C10-C17 monounsaturated acid, octadecene-1,18-dioic acid, or their ester derivatives. The surfactant is selected from C10 or C12 amine oxides, C10 or C12 quats, C10, C12, or C16 amidoamines, C10 or C12 amidoamine oxides, C10 imidazoline quats, C10 or C12 amidoamine quats, C10, C12, or C16 betaines, C16 amidoamine betaines, C18 diamidoamines, C18 diamidoamine oxides, C18 diamidoamine diquats, C18 diamidoamine oxide quats, C18 diamidoamine oxide betaines, Cis diamidoamine monobetaines, C18 diamidoamine monobetaine quats, C18 ester amidoamine quats, and amidoamines and their oxidized or quaternized derivatives made from self- or cross-metathesized palm or soybean oil. The surfactants noted above impart substantial stability to highly concentrated glyphosate formulations at, above, and below room temperature and perform as well or better than commercial alternatives.

Disclosed herein are mesocapsules that include agriculturally active ingredients. These mesocapsules are comprised of a polyurea shell and include hydrophilic groups on their surfaces and have a volume-average diameter of about 500 nm or less and some of them have a volume-average diameter on the order of about 300 nm or less. These mesocapsules are suited for delivering active ingredients that are not very soluble in water. Methods for making these mesocapsules include interfacial polycondensation reactions carried out in the presence of surfactants and other methods in which all or most of the surfactant is replaced by adding amino acids to the aqueous phase of the interfacial reaction mixture before forming the final emulsion.

The present invention provides an acrylic resin composition containing a polycrystal of colloidal particles of silicon oxide in an acrylic resin that is formed by curing an acrylic monomer liquid at room temperature and/or an acrylic oligomer liquid at room temperature, wherein a mean distance between the colloidal particles in the polycrystal is 140 to 330 nm. The size of the single crystal that constitutes the polycrystal can be controlled by adjusting the content of silicon oxide and/or the additive amount of impurities. An architectural material, a fashion accessory, and an optical material are provided that are formed by using the acrylic resin composition.

A process for treating offshore natural gas includes processing the natural gas on an off-shore processing facility by, (i) liquefying and fractionating the natural gas to generate a liquefied natural gas stream and a higher hydrocarbon stream, (ii) vaporizing at least a portion of the higher hydrocarbon stream, (iii) passing the vaporized higher hydrocarbon stream and steam over a steam reforming catalyst to generate a reformed gas mixture comprising methane, steam, carbon oxides and hydrogen, (iv) passing the reformed gas mixture over a methanation catalyst to generate a methane rich gas, and (v) combining the methane-rich gas with the natural gas prior to the liquefaction step.

In a process for the production of substitute natural gas, a feed gas is provided to a first and/or second and/or subsequent bulk methanator. The feed gas is subjected to methanation in the presence of a suitable catalyst. An at least partially reacted stream from the first bulk methanator is removed and supplied to the second and/or subsequent bulk methanator where it is subjected to further methanation. A product stream from the final bulk methanator is passed to a trim methanator train where it is subjected to further methanation. A recycle stream is removed downstream of the first, second or subsequent bulk methanator, and, in any order, passed through a compressor, subjected to cooling and then supplied to a trim and/or recycle methanator for further methanation before being recycled to the first and/or second and/or subsequent methanator.

The invention provides for a method of forming methanol by combining a mixture of methane, water and carbon dioxide under reaction conditions sufficient to form a mixture of hydrogen and carbon monoxide. Hydrogen and carbon monoxide are reacted under conditions sufficient to form methanol. The molar ratio of hydrogen to carbon monoxide is at least two moles of hydrogen to one mole of carbon monoxide and the overall molar ratio between methane, water and carbon dioxide is about 3:2:1. Methane, carbon dioxide and water are bi-reformed over a catalyst. The catalyst includes a single metal, a metal oxide, a mixed catalyst of a metal and a metal oxide or a mixed catalyst of at least two metal oxides.

In designing supersonic aircrafts, a method of designing a natural laminar flow wing is provided which reduces friction drag by delaying boundary layer transition under flight conditions of actual aircrafts. A target Cp distribution on wing upper surface, suited to natural laminarization in which boundary layer transition is delayed rearward in desired Reynolds number states, is defined by a functional type having as coefficients parameters depending on each spanwise station, a sensitivity analysis employing a transition analysis method is applied to the parameters, and a search is performed for the optimum combination of parameters to delay transition rearward.

A solution-processed organic electronic structural element has an improved electrode layer. Located between the active organic layer and the electrode layer there is either an interface or an interlayer containing a cesium salt.

Disclosed herein is an injection molding apparatus. That apparatus includes an axially extending bar shaped support bar and an inner mold that has a plurality of internal parts surrounding the support bar and is axially divided into a plurality of parts in which one or more corresponding parts in the inner parts have an inner circumferential surface width the same or larger than an outer circumferential surface width. The apparatus further includes an outer mold that has a plurality of axially divided external parts surrounding the inner mold and has a space between the inner circumferential surface of the outer mold and the outer circumferential surface of the inner mold.

An enhanced self-writing method for generating in-plane (horizontally-oriented) polymer lightguides that includes disposing one or more light deflecting structures in or on the upper surface of a uncured layer that deflect incident collimated light beams in a transverse direction (i.e., parallel to the uncured layer top layer surface), whereby the deflected collimated light beam polymerizes a corresponding elongated portion of the uncured material in a self-propagating manner to form in-plane polymer lightguides. When used in the fabrication of micro-truss structures, the in-plane polymer lightguides are linked to diagonal polymer lightguides to form superior truss configurations, such as that of the ideal octet-truss structure. Non-polymerized portions of the uncured layer are removed to expose the micro-truss structure for further processing.

The present invention relates to a wireless communication system. More particularly, the present invention relates to a method and to an apparatus for transmitting an SRS in a multi-antenna system. The method comprises the steps of: acquiring specific information for discriminating a first antenna group and a second antenna group from among a plurality of antennas, wherein said first antenna group includes one or more antennas which are set to a turned-on state to perform communication with a base station, and said second antenna group includes one or more other antennas which are set to a turned-off state; transmitting an SRS to the base station if a predetermined condition is satisfied, under the condition that the second antenna group is set to the turned-off state; and setting the second antenna group to a turned-off state after the transmission of the SRS.

A method for joining structural elements onto a shaft may include generating at least one projection at a respective joining position on the shaft, sliding at least one of the structural elements over at least one projection and maintaining the structural elements in at least on projection.

A form tap for tapping an article may include a longitudinal shank having a mounting end opposite a tip, the shank having a shank length, and a thread portion with a plurality of threads for engaging a surface of the article. The thread portion includes a first region, a second region and a third region. The form tap includes a plurality of lobes extending though the first and second regions from the tip to the second region end, the lobes being spaced circumferentially around the shank and lying on a crest circumference. Each lobe may be disposed between respective first and second convex relief portions and each relief portion may be spaced radially inward from the crest circumference. Each lobe has a lobe width, the lobe width at the second region end being greater than the lobe width at the tip. The third region is free of lobes.

The invention relates to a machine tool (1), comprising a plurality of fixed tool spindles (3) and such that can optionally be displaced from a retracted idle position to an extended working position and can be positioned at different positions in a frame-like rack (2), and a workpiece carrier (4) with at least one workpiece holder (5), with the workpiece (4) carrier being movable at least in several axes in a translational manner and preferably also in a rotational manner. In order to provide advantageous constructional conditions it is proposed that at least one of the tool spindles (3) is associated with a tool magazine (7) plus tool changer (8).

An optical module that outputs a wavelength multiplexed optical signal is disclosed. The optical module provides at least first to third optical source, a wavelength multiplexer, a polarization rotator, and a polarization multiplexer. The optical sources each outputting first to third optical signals with a wavelength different from others. The wavelength multiplexer multiplexes the first optical signal with the third optical signal. The polarization rotator rotates the polarization vector of one of the multiplexed first and third optical signals and the second signal by substantially 90°. The polarization multiplexer multiplexes the polarization rotated optical signal with the second optical signal.

An exemplary method includes a media service provider system 1) providing an end user of an integrated media service with access to a media program by way of a plurality of different media distribution models, 2) maintaining a catalog that includes comprehensive information about the media program, the comprehensive information being an aggregate of non-redundant information about the media program obtained from a plurality of independent source catalogs corresponding to the plurality of different media distribution models, and 3) providing, based on the comprehensive information included in the catalog, a media service user interface that supports discovery of the comprehensive information about the media program regardless of a user interface context from which a user request for information about the media program is received. Corresponding systems and methods are also described.

A grain cleaning system of an agricultural combine directs a flow of high velocity air in a first direction about an inlet end of a cleaning shoe. The system further monitors the cleaning shoe in order to detect a change in an operational parameter of the cleaning shoe, wherein the operational parameter is one of a transient effect of a sieve of the cleaning shoe, a flow rate about and exit end of the cleaning shoe and a flow velocity about an exit end of the cleaning shoe. Further, the flow of high velocity air about the inlet end of the cleaning shoe can be redirected in a second direction in response to a detected change in the operational parameter.

A support assembly for supporting a moveable member of an agricultural combine is provided. The support assembly includes a moveable member, such as a foldable chaff pan assembly for spreading chaff and other crop residue from the rear of an agricultural combine, a support member and a resilient member connected to the support member for supporting the moveable member. The resilient member can be configured as an arched shaped or cylindrically shaped member.

An agricultural combine having an adjustable spreader assembly is provided that includes a spreader with one or a pair of regulators. The spreader includes a discharge opening about its lateral side. The regulator is pivotably connected to the lateral side of the spreader such that the regulator is in fluid communication with the discharge opening. The regulator can be configured to move from a retracted position to an extended position or to vary the direction of discharge of crop residue. The spreader can be a vertical spreader, a horizontal spreader, or a spread board.