WAN Optimization Available as Optional Add-on in Q2, 2020

Driving Transformation in the Marketplace with Relentless Innovation

Trefoil Networks Ltd on Monday, 10 February 2020, introduced OurTV to the Nigerian market.

This partnership with Veracity Networks and Telarus marks the first time Cisco's Webex suite of products can be sold through the agent channel providing a complete solution to customers.

The artist of Colombian origin, Gael Morantes, recently released his new single titled "Mojaita", a song with a color and a sound different from what is usually heard in the urban movement.

The Hope Channel, RT, and their Spanish language counterparts join several other ETG channels within FreeCast's SmartGuide.

FreeCast adds fast-growing networks One America News Encore and AWE Encore to its SmartGuide products.

Cover Girl has launched its newest clothing line in activewear on Amazon exclusively with e-commerce experts Quantum Networks.

The Czech Republic and the United States have signed a joint declaration Wednesday for cooperating on security of 5G technology.

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By default, your iPhone automatically connects to known wi-fi networks. (To stop an iPhone from automatically connecting, you can tell your iPhone to forget a wi-fi network.) But what happens if you take your iPhone to a new location? You'll need to manually connect your iPhone to a wi-fi network.

That's a hassle. But if you have the foresight and inclination, you can save yourself time in the future by making your iPhone ask to join wi-fi networks when no known networks are available. Instead of having to open settings to join a network, you'll be able to easily select a network from an on-screen prompt.

Here's how to make your iPhone ask to join wi-fi networks:

1. From the home screen, tap Settings.
   
   
2. Tap Wi-Fi. The window shown below appears.
   
   
3. Move the Ask to Join Networks slider to the On position.
   
   
4. The next time you're in a location with no known networks, your iPhone will prompt you to connect to an available wi-fi network, as shown below.
   
   

In the future, this prompt will be displayed when no known networks are available. (To actually see the prompt, you'll need to do something that requires network access, like try to check your email or open a webpage.) To connect to a wi-fi network, select a network and enter a password, if one is required.

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• How to Connect an iPhone to a Wi-Fi Network
• Tell Your iPhone to Forget a Wireless Network

Meet Your Macinstructor

Matt Cone, the author of Master Your Mac, has been a Mac user for over 20 years. A former ghost writer for some of Apple's most notable instructors, Cone founded Macinstruct in 1999, a site with OS X tutorials that boasts hundreds of thousands of unique visitors per month. You can email him at: matt@macinstruct.com.

This paper addresses the problem of identifying the graph structure of a dynamical network using measured input/output data. This problem is known as topology identification and has received considerable attention in recent literature. Most existing literature focuses on topology identification for networks with node dynamics modeled by single integrators or single-input single-output (SISO) systems. The goal of the current paper is to identify the topology of a more general class of heterogeneous networks, in which the dynamics of the nodes are modeled by general (possibly distinct) linear systems. Our two main contributions are the following. First, we establish conditions for topological identifiability, i.e., conditions under which the network topology can be uniquely reconstructed from measured data. We also specialize our results to homogeneous networks of SISO systems and we will see that such networks have quite particular identifiability properties. Secondly, we develop a topology identification method that reconstructs the network topology from input/output data. The solution of a generalized Sylvester equation will play an important role in our identification scheme.

In this work, we investigate multi-task learning as a way of pre-training models for classification tasks in digital pathology. It is motivated by the fact that many small and medium-size datasets have been released by the community over the years whereas there is no large scale dataset similar to ImageNet in the domain. We first assemble and transform many digital pathology datasets into a pool of 22 classification tasks and almost 900k images. Then, we propose a simple architecture and training scheme for creating a transferable model and a robust evaluation and selection protocol in order to evaluate our method. Depending on the target task, we show that our models used as feature extractors either improve significantly over ImageNet pre-trained models or provide comparable performance. Fine-tuning improves performance over feature extraction and is able to recover the lack of specificity of ImageNet features, as both pre-training sources yield comparable performance.

Image analysis in the field of digital pathology has recently gained increased popularity. The use of high-quality whole slide scanners enables the fast acquisition of large amounts of image data, showing extensive context and microscopic detail at the same time. Simultaneously, novel machine learning algorithms have boosted the performance of image analysis approaches. In this paper, we focus on a particularly powerful class of architectures, called Generative Adversarial Networks (GANs), applied to histological image data. Besides improving performance, GANs also enable application scenarios in this field, which were previously intractable. However, GANs could exhibit a potential for introducing bias. Hereby, we summarize the recent state-of-the-art developments in a generalizing notation, present the main applications of GANs and give an outlook of some chosen promising approaches and their possible future applications. In addition, we identify currently unavailable methods with potential for future applications.

We present a new system, EEV, for verifying binarized neural networks (BNNs). We formulate BNN verification as a Boolean satisfiability problem (SAT) with reified cardinality constraints of the form $y = (x_1 + cdots + x_n le b)$, where $x_i$ and $y$ are Boolean variables possibly with negation and $b$ is an integer constant. We also identify two properties, specifically balanced weight sparsity and lower cardinality bounds, that reduce the verification complexity of BNNs. EEV contains both a SAT solver enhanced to handle reified cardinality constraints natively and novel training strategies designed to reduce verification complexity by delivering networks with improved sparsity properties and cardinality bounds. We demonstrate the effectiveness of EEV by presenting the first exact verification results for $ell_{infty}$-bounded adversarial robustness of nontrivial convolutional BNNs on the MNIST and CIFAR10 datasets. Our results also show that, depending on the dataset and network architecture, our techniques verify BNNs between a factor of ten to ten thousand times faster than the best previous exact verification techniques for either binarized or real-valued networks.

Bundle recommendation aims to recommend a bundle of items for a user to consume as a whole. Existing solutions integrate user-item interaction modeling into bundle recommendation by sharing model parameters or learning in a multi-task manner, which cannot explicitly model the affiliation between items and bundles, and fail to explore the decision-making when a user chooses bundles. In this work, we propose a graph neural network model named BGCN (short for extit{ extBF{B}undle extBF{G}raph extBF{C}onvolutional extBF{N}etwork}) for bundle recommendation. BGCN unifies user-item interaction, user-bundle interaction and bundle-item affiliation into a heterogeneous graph. With item nodes as the bridge, graph convolutional propagation between user and bundle nodes makes the learned representations capture the item level semantics. Through training based on hard-negative sampler, the user's fine-grained preferences for similar bundles are further distinguished. Empirical results on two real-world datasets demonstrate the strong performance gains of BGCN, which outperforms the state-of-the-art baselines by 10.77\% to 23.18\%.

In this work we propose lifted regression/reconstruction networks (LRRNs), which combine lifted neural networks with a guaranteed Lipschitz continuity property for the output layer. Lifted neural networks explicitly optimize an energy model to infer the unit activations and therefore---in contrast to standard feed-forward neural networks---allow bidirectional feedback between layers. So far lifted neural networks have been modelled around standard feed-forward architectures. We propose to take further advantage of the feedback property by letting the layers simultaneously perform regression and reconstruction. The resulting lifted network architecture allows to control the desired amount of Lipschitz continuity, which is an important feature to obtain adversarially robust regression and classification methods. We analyse and numerically demonstrate applications for unsupervised and supervised learning.

Diagnosis of high impedance fault (HIF) is a challenge for nowadays distribution network protections. The fault current of a HIF is much lower than that of a normal load, and fault feature is significantly affected by fault scenarios. A detection and feeder identification algorithm for HIFs is proposed in this paper, based on the high-resolution and synchronous waveform data. In the algorithm, an interval slope is defined to describe the waveform distortions, which guarantees a uniform feature description under various HIF nonlinearities and noise interferences. For three typical types of network neutrals, i.e.,isolated neutral, resonant neutral, and low-resistor-earthed neutral, differences of the distorted components between the zero-sequence currents of healthy and faulty feeders are mathematically deduced, respectively. As a result, the proposed criterion, which is based on the distortion relationships between zero-sequence currents of feeders and the zero-sequence voltage at the substation, is theoretically supported. 28 HIFs grounded to various materials are tested in a 10kV distribution networkwith three neutral types, and are utilized to verify the effectiveness of the proposed algorithm.

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

In a temporal network causal paths are characterized by the fact that links from a source to a target must respect the chronological order. In this article we study the causal paths structure in temporal networks of human face to face interactions in different social contexts. In a static network paths are transitive i.e. the existence of a link from $a$ to $b$ and from $b$ to $c$ implies the existence of a path from $a$ to $c$ via $b$. In a temporal network the chronological constraint introduces time correlations that affects transitivity. A probabilistic model based on higher order Markov chains shows that correlations that can invalidate transitivity are present only when the time gap between consecutive events is larger than the average value and are negligible below such a value. The comparison between the densities of the temporal and static accessibility matrices shows that the static representation can be used with good approximation. Moreover, we quantify the extent of the causally connected region of the networks over time.

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.

Most optimization-based community detection approaches formulate the problem in a single or bi-objective framework. In this paper, we propose two variants of a three-objective formulation using a customized non-dominated sorting genetic algorithm III (NSGA-III) to find community structures in a network. In the first variant, named NSGA-III-KRM, we considered Kernel k means, Ratio cut, and Modularity, as the three objectives, whereas the second variant, named NSGA-III-CCM, considers Community score, Community fitness and Modularity, as three objective functions. Experiments are conducted on four benchmark network datasets. Comparison with state-of-the-art approaches along with decomposition-based multi-objective evolutionary algorithm variants (MOEA/D-KRM and MOEA/D-CCM) indicates that the proposed variants yield comparable or better results. This is particularly significant because the addition of the third objective does not worsen the results of the other two objectives. We also propose a simple method to rank the Pareto solutions so obtained by proposing a new measure, namely the ratio of the hyper-volume and inverted generational distance (IGD). The higher the ratio, the better is the Pareto set. This strategy is particularly useful in the absence of empirical attainment function in the multi-objective framework, where the number of objectives is more than two.

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.

In this paper, we analyze the symbol error rate (SER) performance of the simultaneous wireless information and power transfer (SWIPT) enabled three-node differential decode-and-forward (DDF) relay networks, which adopt the power splitting (PS) protocol at the relay. The use of non-coherent differential modulation eliminates the need for sending training symbols to estimate the instantaneous channel state informations (CSIs) at all network nodes, and therefore improves the power efficiency, as compared with the coherent modulation. However, performance analysis results are not yet available for the state-of-the-art detectors such as the approximate maximum-likelihood detector. Existing works rely on Monte-Carlo simulation to show that there exists an optimal PS ratio that minimizes the overall SER. In this work, we propose a near-optimal detector with linear complexity with respect to the modulation size. We derive an accurate approximate SER expression, based on which the optimal PS ratio can be accurately estimated without requiring any Monte-Carlo simulation.

Cognitive radio is a promising technology to improve spectral efficiency. However, the secure performance of a secondary network achieved by using physical layer security techniques is limited by its transmit power and channel fading. In order to tackle this issue, a cognitive unmanned aerial vehicle (UAV) communication network is studied by exploiting the high flexibility of a UAV and the possibility of establishing line-of-sight links. The average secrecy rate of the secondary network is maximized by robustly optimizing the UAV's trajectory and transmit power. Our problem formulation takes into account two practical inaccurate location estimation cases, namely, the worst case and the outage-constrained case. In order to solve those challenging non-convex problems, an iterative algorithm based on $mathcal{S}$-Procedure is proposed for the worst case while an iterative algorithm based on Bernstein-type inequalities is proposed for the outage-constrained case. The proposed algorithms can obtain effective suboptimal solutions of the corresponding problems. Our simulation results demonstrate that the algorithm under the outage-constrained case can achieve a higher average secrecy rate with a low computational complexity compared to that of the algorithm under the worst case. Moreover, the proposed schemes can improve the secure communication performance significantly compared to other benchmark schemes.

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.

Vehicle event recorder systems are arranged to be in constant communication with remote servers and administrators via mobile wireless cellular networks. Vehicle event recorders equipped with video cameras capture video and other data records of important events relating to vehicle use. These data are then transmitted over special communications networks having very high coverage space but limited bandwidth. A vehicle may be operated over very large region while maintaining continuous communications connections with a remote fixed server. As such, systems of these inventions may be characterized as including a mobile unit having: a video camera; a microprocessor; memory; an event trigger; and mobile wireless transceivers, and a fixed network portion including: mobile wireless cellular network, a protocol translation gateway, the Internet and an application-specific server.

A composition of matter comprising an interpenetrating polymer network of a combination of a silanol-containing polysiloxane phase and a silylated triglyceride oil phase. The two phases are mixed and covalently bound to each other via siloxane crosslinks. A method for producing interpenetrating polymer networks. The method comprises providing triglycerides from oils or fats and reacting the triglycerides with a reactive silane to form a silylated triglyceride oil. The silylated triglyceride oil and a silanol terminated polysiloxane are emulsified with water in a predetermined ratio. Thereafter, crosslinking agents are added and the water is removed from the emulsions providing siloxane crosslinks between the two intimately mixed immiscible phases.

A system for intelligent monitoring and management of an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a real-time energy pricing engine, virtual system modeling engine, an analytics engine, a machine learning engine and a schematic user interface creator engine. The real-time energy pricing engine generates real-time utility power pricing data. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The machine learning engine stores acid processes patterns observed from the real-time data output and the predicted data output to forecast an aspect of the electrical system.

A technique dynamically configures and verifies routing information of broadcast networks using link state protocols in a computer network. According to the novel technique, a router within the broadcast network receives a link state protocol routing information advertisement from an advertising router, e.g., a designated router or other adjacent neighbor. The router learns of a next-hop router (“next-hop”) to reach a particular destination from the advertisement, and determines whether the next-hop is located within the same broadcast network (e.g., subnet) as the designated router. If so, the router further determines whether the next-hop is directly addressable (i.e., reachable), such as, e.g., by checking for link adjacencies to the next-hop or by sending request/reply messages (e.g., echo messages or “ping” messages) to the next-hop. In the event the next-hop for the destination is not directly addressable by the router (e.g., no adjacency or reply), the router installs a route to the destination via the designated router. Otherwise, the router installs a route to the destination via the next-hop.

A multi-participant conference system and method is described. The multi-participant system includes a PSTN client, at least one remote client and a first participant client. The PSTN client communicates audio data and the remote clients communicate audio-video data. The first participant client includes a voice over IP (VoIP) encoder, a VoIP decoder, a first audio mixer, and a second audio mixer. The VoIP encoder compresses audio data transported to the PSTN client. The VoIP decoder then decodes audio data from the PSTN client. The first audio mixer mixes the decoded audio data from the PSTN client with the audio-video data from the first participant into a first mixed audio-video data stream transmitted to the remote client. The second audio mixer mixes the audio-video data stream from the first participant with the audio-video data stream from each remote client into a second mixed audio transmitted to the PSTN client.

A method for communication between computing devices includes identifying the parameters of a data transfer between a source computing device and a target computing device and identifying communication paths between a source computing device and target computing device, in which at least one of the communications paths is a physical network. A communication path is selected for the data transfer. When a data transfer over the physical network is selected as a communication path, a nonvolatile memory (NVM) unit is removed from the source computing device and placed in a cartridge and the cartridge is programmed with transfer information. The NVM unit and cartridge are transported through the physical network to the target computing device according to the transfer information and the NVM unit is electrically connected to the target computing device.

The invention concerns a method for optimizing antenna pattern assignment for a first wireless communication device forming a wireless network with at least one second wireless communication device, each of said communication device being equipped with a multi-sector antenna, an antenna pattern being a combination of said antenna sectors, said communication devices being adapted for sending a request and for receiving a response using a current antenna pattern assignment, said method comprising a step of: evaluating by said first communication device a first value;sending by said first communication device to said second communication device a broadcast request comprising said valuereceiving by said first communication device, a response to said broadcast request, said response being sent by said second communication device, said response depending on a second value evaluated by said second device;Switching or not, by said first communication device, to a new antenna sector assignment depending on said response; According to the invention the first value is evaluated by minimizing a first local function according to the received signal strength received by said first communication device having said current antenna pattern assignment and the second value is evaluated by minimizing a second local function according to the received signal strength received by said second communication device for its own current antenna pattern assignment.

Techniques for positioning access points and terminals in WLANs and other wireless networks are described. For access point positioning, measurements are obtained for at least one access point in a WLAN. The measurements may be based on transmission sequences (e.g., beacon frames) transmitted periodically by each access point. The measurements may be made by multiple terminals at different locations or a single mobile terminal at different locations. The location of each access point is determined based on the measurements and known locations of the terminal(s). For terminal positioning, measurements for at least one access point in a WLAN are obtained. The location of the terminal is determined based on the measurements and known location of each access point. The measurements may be round trip time (RTT) measurements, observed time difference (OTD) measurements, time of arrival (TOA) measurements, signal strength measurements, signal quality measurements, etc.

Network on Chips (NoC)s with a bufferless and nonblocking architecture are described. Core processors are communicatively coupled together on a substrate with a set of routing nodes based on nonblocking process. A network component routes data packets through the routing nodes and the core processors via communication links. A bufferless cross bar switch facilitates the communication of the data packets and/or path setup packets through the communication links among source processors and destination processors. The communication links include one or more channels, in which a channel comprises a data sub-channel, an acknowledgement sub-channel and a release sub-channel.

A resonant clock distribution network architecture is proposed that enables a resonant clock network to track the impact of parameter variations on the insertion delay of a conventional clock distribution network, thus limiting clock skew between the two networks and yielding increased performance. Such a network is generally applicable to semiconductor devices with various clock frequencies, and high-performance and low-power clocking requirements such as microprocessors, ASICs, and SOCs.

In a wireless network, plural downlink signals from plural base stations are transmitted to a terminal. The plural downlink signals all carry the same information to the terminal. The terminal provides feedback on the downlink channels. The feedback provides information on the taps of the channels. The amount of information fed back is constrained. Based on the feedback, transmission parameters of the downlink signals are adjusted. The process of transmitting, providing feedback, and adjusting the parameters continue so that the energy of the downlink signal is enhanced at the terminal location and suppressed elsewhere. Beam forming can be used to further suppress the energy signature at locations other than the terminal location.

A method and device that interrogates the availability of a called party before placing a communication from the calling party to the called party. A callback may be initiated so that both communications are completed simultaneously. The routing of communication may take place through any one of a number of different networks and at another time of the day, even if the caller does not otherwise have access to those networks.

A system for measuring properties of a surface under test with surface waves includes a surface wave network including a dielectric substrate, a reactive grid of a plurality of metallic patches on a first surface of the dielectric substrate, a plurality of electronic nodes on the first surface of the dielectric substrate, and a ground plane on a second surface of the dielectric substrate permeable to RF fields of the surface waves, and a controller configured for causing a respective one of the electronic nodes to transmit at least one surface wave and configured for collecting data for signals received by at least one other of the plurality of electronic nodes.

Provided is a network self-healing method in which, when a link between a parent device and a child device breaks down in a wireless communication network of a cluster-tree structure in which a main communication device (referred to an access point (AP)) manages network operation, routers that are devices capable of having their child devices, and end devices that are devices incapable of having their child devices are associated with each other in a parent-child device relationship, the link is restored. When a router becomes an orphan device, the router makes network re-association in a cluster unit while maintaining synchronized operation with its child devices, and thus time, energy and signaling burden for network self-healing is largely reduced.

Vehicle internetworks provide for communications among diverse electronic devices within a vehicle, and for communications among these devices and networks external to the vehicle. The vehicle internetwork comprises specific devices, software, and protocols, and provides for security for essential vehicle functions and data communications, ease of integration of new devices and services to the vehicle internetwork, and ease of addition of services linking the vehicle to external networks such as the Internet.

In one embodiment, a method includes receiving a search query input comprising one or more n-grams; parsing the search query input to identify keywords; generating query commands for the keywords. Each query command may specify: a particular object-type; one or more identifiers of one or more objects that match the search query input; and one or more types of relationships with respect to the objects. The method may further include searching a particular vertical that stores objects of the particular object-type having a relationship of the type of relationship with respect to one or more of the objects; generating a plurality of search-result modules corresponding to the query commands, each search-result module comprising references to objects of the particular object-type specified by the query command; and sending, to a client device, instructions for presenting an interface comprising one or more of the search-result modules.

Reconfigurable antennas in an ad-hoc network are provided where all nodes employ MIMO/SIMO/MISO communication techniques. Three types of reconfigurable antennas: Reconfigurable Printed Dipole Array (RPDA), Reconfigurable Circular Patch Antenna (RCPA) and Two-Port Reconfigurable CRLH Leaky Wave Antennas are used. The RPDA, RCPA and the CRLH Leaky Wave antennas have a different number of configurations as well as different degrees of pattern diversity between possible configurations. To effectively use these antennas in a network, the performance of centralized and decentralized antenna configuration selection schemes are quantified for reconfiguration at one or both link ends. The sum capacity of the network is used as a metric to quantify the performance of these antennas in measured and simulated network channels.

The invention includes novel devices and methods for inhibiting or preventing colonization of fluid flow networks by bacteria that have upstream surface motility. In certain aspects, the devices and methods of the invention prevent or minimize undesirable bacterial colonization of medical devices and/or treat or prevent bacterial infections.

PRAGUE (AP) — The Czech Republic and the United States have signed a joint declaration Wednesday for cooperating on security of 5G technology. The Czech government office said the document was signed remotely by Czech Prime Minister Andrej Babis and U.S. Secretary of State Mike Pompeo. The signing comes amid a global battle between the […]

Affirmed Networks helps build virtual networks for telecom customers using 5G technology.

NBC and ABC are two of the culprits.

Need help with setting up your network to allow your remote employees to access internal business services securely?

strataconf: StrataWeek: Wireless body networks bring humans into Internet of Things http://t.co/vRgkRtTTKe Pills & tattoo authenticators & NSA leaks

More than 60 Community Action Networks have been established in Gauteng to address the impact of Covid-19 in the province by providing food and social support to those in need.