Stardock Entertainment continually breaks new technical ground across our catalog of PC game titles spanning 4X and Real-Time Strategy and Action Adventure. We are currently seeking a Network Programmer to work on our next-generation game engine for both announced and unannounced titles to build up our team of dedicated and experienced talent! This is a salaried, full-time position at our Plymouth, Michigan studio.  Primary Responsibilities Include: Development of multiplayer connectivity and gameplay for real-time strategy-simulation games  Management of user-created content in a multiplayer environment Backend development of updating of the multiplayer game state across multiple machines Frontend development of login systems, lobbies, and in-game chat Develop fast, reliable and most importantly, fun multiplayer features in collaboration with gameplay and design teams Develop and maintain tools for testing and analysis of the multiplayer environment Investigate and resolve bugs related to multiplayer and networking  Education and/or Experience Desired: A Bachelor’s degree in Computer Science or Software Engineering 3+ years of experience in developing and optimizing network code in C++ Shipped at least one commercial game  Experience using industry-standard tools for debugging network traffic such as Fiddler Experience with network optimization, and multi-threading Solid communication skills with colleagues, management and stakeholders  

The old paradigm for supply chain networks has run its course, and the future is in multi-enterprise, or multi-party business networks

The real challenge in the world of employees working from their own homes is not the slack in productivity or the threat of transmission of the virus, but cybersecurity.

Douglas Willson, MD, discusses an article he published in the July 2006 issue of Pediatric Critical Care Medicine, "The Collaborative Pediatric Critical Care Research Network." Dr. Willson is medical director of the pediatric intensive care unit at the University of Virginia Health Sciences Center and the chairman of the Steering Committee for the Collaborative Pediatric Critical Care Research Network. (Pediatr Crit Care Med. 2006; 7:301)

Over the past 30 years, the Pacific Northwest Interagency Natural Areas Committee has promoted the establishment and management of natural areas in Oregon and Washington--protected areas devoted to research, education, and conservation of biodiversity.

Using CSS3 to produce a social network circle of icons.

Using CSS3 to produce a Rotating set of Social Network Icons - for Safari ONLY at the moment.

Hello to our lovely Divi community! We’re well into Q2 and also quarantining. ???? Remote life is not too uncommon for our community but this global pandemic is not without significant challenges. However, we’ve had some nice positives come out of this trial our world is experiencing. Virtual events are how our Divi communities are […]

The post Divi Meetup Network Community Update: April 2020 appeared first on Elegant Themes Blog.

This document introduces a computer software named SNAP for ArcGIS®, which has been developed to streamline scheduling and transportation planning for timber harvest areas. Using modern optimization techniques, it can be used to spatially schedule timber harvest with consideration of harvesting costs, multiple products, alternative destinations, and transportation systems. SNAP for ArcGIS attempts either to maximize a net present value or minimize discounted costs of harvesting and transportation over the planning horizon while meeting given harvest volume and acreage constraints. SNAP for ArcGIS works in the ArcGIS environment and provides an easy-to-use analytical tool for sophisticated spatial planning of timber harvest.

An NHL NETWORK documentary on the 50th anniversary of the BOSTON BRUINS' 1970 STANLEY CUP championship is narrated by DROPKICK MURPHYS founder KEN CASEY. "THE 1970 BRUINS: BIG, BAD … more

Network Rail owns and operates the entire railway infrastructure in the United Kingdom, managing 18 of the largest stations in England, Scotland and Wales. Network Rail delivers 4.5 million journeys a day for its customers, managing rail timetabling by working...

A little taste of Phillies baseball on the Spanish Beisbol Network

Concussion is a common childhood injury that may lead to long-term physical, behavioral, and neurocognitive effects, affecting learning and school performance. There is increasing concern about the potential for repeat concussions among professional and high school athletes, with specific attention focused on understanding how sustaining a concussion alters future concussion risk. Addressing repeat concussion risk among youth has substantial implications for clinical practice in terms of managing exposure — particularly regarding youth sports participation — and long-term health and development.

If you own an AirPort base station, you can use the Timed Access feature to control the days and times when users access the Internet. This could come in handy in a variety of situations. For example, if you own a cafe and provide free wi-fi access, you can configure the AirPort to block all access to the Internet when your business is closed. And if you have children, you can set time limits for specific devices in your home.

There are two ways to use the timed access feature. You can create a default allow policy to allow all devices to access the Internet at any time, and then specify custom schedules for specific devices. Or you can create a default deny policy to prevent all devices from accessing the Internet according the schedule you specify, and then exempt specific devices by creating custom schedules.

Here's how to control AirPort network access with time limits:

1. Open the AirPort Utility application. (It's in Applications → Utilities.) The window shown below appears.
   
   
2. Click the AirPort Extreme's icon. The status pop-up window appears.
   
   
3. Click Edit. The settings window appears.
   
   
4. Select the Network tab. The window shown below appears.
   
   
5. Select the Enable Access Control checkbox.
   
   
6. Click Timed Access Control. The window shown below appears.
   
   
7. Select the Unlimited (default) option. By default, this allows all of the devices connected to your AirPort to access the Internet all day, every day, but you can change this to block Internet access for all devices (except the ones you specify later) during the times you set.
   
   
8. If you'd like to limit the days and times that a specific device can access the Internet, click the + button under the Wireless Clients field. The window shown below appears.
   
   
9. Enter a name for the device in the Description field.
   
   
10. Enter the device's MAC address in the MAC Address field. You can use the following tutorials to find the device's MAC address.
    
    • How to Find Your Mac's MAC Address
    • How to Find Your iPad's MAC Address
    • How to Find Your iPhone's MAC Address
    
    
11. Use the + button under the Wireless Access Times field to create a schedule for this device's Internet access.
    
    
12. Once you've added all of your devices and customized the schedules, click Save.
    
    
13. Click Update. The AirPort will restart to apply the changes.

Congratulations! You have successfully set time limits for the devices connecting to your AirPort network. The schedule you created is effective immediately.

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.

When you connect an iPhone to a wi-fi network, the iPhone remembers that network and will automatically attempt to connect to it in the future. This is a great feature for wi-fi networks you trust and use frequently. But mistakes happen. If you connect to the wrong network at a coffee shop, your iPhone will automatically attempt to join that network every time you visit the coffee shop in the future. And if the password for a known network changes, your iPhone might have trouble connecting to it.

What's the solution? Telling your iPhone to forget the wi-fi network. Forgetting a network will remove the network's password and prevent your iPhone from joining it automatically in the future.

Here's how to tell your iPhone to forget a wireless network:

1. From the home screen, tap Settings.
   
   
2. Tap Wi-Fi. The window shown below appears.
   
   
3. Locate the wireless network you want the iPhone to forget, and then tap the blue arrow next to the network name. The window shown below appears.
   
   
4. Tap Forget this Network. The iPhone will forget the wireless network.

You have successfully told your iPhone to forget the wi-fi network. The iPhone will not attempt to connect to the network in the future. And if the network required a password, that password has been forgotten.

Related Articles

• How to Connect an iPhone to a Wi-Fi Network
• Make Your iPhone Ask to Join Wi-Fi Networks

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.

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.

Related Articles

• 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.

If you're a new iPhone owner, one of the first things you'll want to learn how to do is connect your iPhone to a wireless network. That's because there are certain times when your cellular data connection just won't cut it, even if you're lucky enough to have an unlimited data plan. Using Facetime, downloading content from iTunes, and even surfing the web can be painfully slow without a wi-fi connection.

Fortunately, it's a relatively simple process to connect an iPhone to a wi-fi network. Just be sure to commit this process to memory, because it's something you'll need to do over and over again, unless you set your iPhone to automatically detect and prompt you to connect to wi-fi networks.

Here's how to connect an iPhone to a wi-fi network:

1. From the home screen, tap Settings.
   
   
2. Tap Wi-Fi. The window shown below appears.
   
   
3. Verify that the Wi-Fi slider is in the On position. This allows your iPhone to detect and connect to wireless networks.
   
   
4. Tap the wireless network you want to join. If the network is not password protected, the iPhone will connect immediately.
   
   
5. If the wireless network you selected is protected with a password, you will be prompted to enter a password, as shown below. Enter the password and then click Join to connect to the network.
   
   
6. If the wireless network you selected is protected with a captive portal, you will be prompted to enter a password, or a username and password combination. These are increasingly common in hotels, airports, and on college campuses.

Congratulations! Your iPhone is now connected to the wi-fi network. From now on, the iPhone will automatically connect to this network when it is in range. If you accidentally selected the wrong wi-fi network, you can tell your iPhone to forget it.

How to Tell if Your iPhone is Connected to a Wi-Fi Network

There are several indicators you can use to verify that your iPhone is connected to a wi-fi network. The easiest way to visually check to the status bar in the upper-left corner of the iPhone's screen. The wi-fi symbol is displayed when you are connected to a network, as shown below.

If you're curious about which wi-fi network the iPhone is connected to, open the Wi-Fi settings. The network name is displayed in the sidebar, and a checkmark is also displayed next to the connected network, as shown above.

Related Articles

• Tell Your iPhone to Forget a Wireless Network
• Make Your iPhone Ask to Join Wi-Fi Networks

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.

PLAN is a referral network program of 250 volunteer physicians, community clinics, hospitals and other affiliated health care providers who...continue reading

Tags: Jukebox, music, neural network, OpenAI

With the debut of Vert.x, the asynchronous framework is reaching an inflection point, suggests Andrew Cholakian. With Vert.x, the software is packaged together in such a way as to be extremely practical, he states. For some JVM zealots, Vert.x may meet needs recently and apparently addressed by node.js. Vert.x is an asynchronous application server – Read the rest...

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.

A standard approach to evaluating language models analyzes how models assign probabilities to valid versus invalid syntactic constructions (i.e. is a grammatical sentence more probable than an ungrammatical sentence). Our work uses ambiguous relative clause attachment to extend such evaluations to cases of multiple simultaneous valid interpretations, where stark grammaticality differences are absent. We compare model performance in English and Spanish to show that non-linguistic biases in RNN LMs advantageously overlap with syntactic structure in English but not Spanish. Thus, English models may appear to acquire human-like syntactic preferences, while models trained on Spanish fail to acquire comparable human-like preferences. We conclude by relating these results to broader concerns about the relationship between comprehension (i.e. typical language model use cases) and production (which generates the training data for language models), suggesting that necessary linguistic biases are not present in the training signal at all.

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.

High-resolution remote sensing images (HRRSIs) contain substantial ground object information, such as texture, shape, and spatial location. Semantic segmentation, which is an important task for element extraction, has been widely used in processing mass HRRSIs. However, HRRSIs often exhibit large intraclass variance and small interclass variance due to the diversity and complexity of ground objects, thereby bringing great challenges to a semantic segmentation task. In this paper, we propose a new end-to-end semantic segmentation network, which integrates lightweight spatial and channel attention modules that can refine features adaptively. We compare our method with several classic methods on the ISPRS Vaihingen and Potsdam datasets. Experimental results show that our method can achieve better semantic segmentation results. The source codes are available at https://github.com/lehaifeng/SCAttNet.

For Convolutional Neural Network-based object detection, there is a typical dilemma: the spatial information is well kept in the shallow layers which unfortunately do not have enough semantic information, while the deep layers have a high semantic concept but lost a lot of spatial information, resulting in serious information imbalance. To acquire enough semantic information for shallow layers, Feature Pyramid Networks (FPN) is used to build a top-down propagated path. In this paper, except for top-down combining of information for shallow layers, we propose a novel network called Image Pyramid Guidance Network (IPG-Net) to make sure both the spatial information and semantic information are abundant for each layer. Our IPG-Net has two main parts: the image pyramid guidance transformation module and the image pyramid guidance fusion module. Our main idea is to introduce the image pyramid guidance into the backbone stream to solve the information imbalance problem, which alleviates the vanishment of the small object features. This IPG transformation module promises even in the deepest stage of the backbone, there is enough spatial information for bounding box regression and classification. Furthermore, we designed an effective fusion module to fuse the features from the image pyramid and features from the backbone stream. We have tried to apply this novel network to both one-stage and two-stage detection models, state of the art results are obtained on the most popular benchmark data sets, i.e. MS COCO and Pascal VOC.

Deep learning-based models, such as convolutional neural networks, have advanced various segments of computer vision. However, this technology is rarely applied to seismic shot gather noise localization problem. This letter presents an investigation on the effectiveness of a multi-scale feature-fusion-based network for seismic shot-gather noise localization. Herein, we describe the following: (1) the construction of a real-world dataset of seismic noise localization based on 6,500 seismograms; (2) a multi-scale feature-fusion-based detector that uses the MobileNet combined with the Feature Pyramid Net as the backbone; and (3) the Single Shot multi-box detector for box classification/regression. Additionally, we propose the use of the Focal Loss function that improves the detector's prediction accuracy. The proposed detector achieves an AP@0.5 of 78.67\% in our empirical evaluation.

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 recent years, deep neural networks have been applied to obtain high performance of prediction, classification, and pattern recognition. However, the weights in these deep neural networks are difficult to be explained. Although a linear regression method can provide explainable results, the method is not suitable in the case of input interaction. Therefore, an explainable deep neural network (ExpDNN) with explainable layers is proposed to obtain explainable results in the case of input interaction. Three cases were given to evaluate the proposed ExpDNN, and the results showed that the absolute value of weight in an explainable layer can be used to explain the weight of corresponding input for feature extraction.

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.

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%.

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.

Wireless sensor networks have attracted much attention because of many applications in the fields of industry, military, medicine, agriculture, and education. In addition, the vast majority of researches has been done to expand its applications and improve its efficiency. However, there are still many challenges for increasing the efficiency in different parts of this network. One of the most important parts is to improve the network lifetime in the wireless sensor network. Since the sensor nodes are generally powered by batteries, the most important issue to consider in these types of networks is to reduce the power consumption of the nodes in such a way as to increase the network lifetime to an acceptable level. The contribution of this paper is using topology control, the threshold for the remaining energy in nodes, and two of the meta-algorithms include SA (Simulated annealing) and VNS (Variable Neighbourhood Search) to increase the energy remaining in the sensors. Moreover, using a low-cost spanning tree, an appropriate connectivity control among nodes is created in the network in order to increase the network lifetime. The results of simulations show that the proposed method improves the sensor lifetime and reduces the energy consumed.

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

We study the problem of pricing under a Multinomial Logit model where we incorporate network effects over the consumer's decisions. We analyse both cases, when sellers compete or collaborate. In particular, we pay special attention to the overall expected revenue and how the behaviour of the no purchase option is affected under variations of a network effect parameter. Where for example we prove that the market share for the no purchase option, is decreasing in terms of the value of the network effect, meaning that stronger communication among costumers increases the expected amount of sales. We also analyse how the customer's utility is altered when network effects are incorporated into the market, comparing the cases where both competitive and monopolistic prices are displayed. We use tools from stochastic approximation algorithms to prove that the probability of purchasing the available products converges to a unique stationary distribution. We model that the sellers can use this stationary distribution to establish their strategies. Finding that under those settings, a pure Nash Equilibrium represents the pricing strategies in the case of competition, and an optimal (that maximises the total revenue) fixed price characterise the case of collaboration.

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.

Network security has become an area of significant importance more than ever as highlighted by the eye-opening numbers of data breaches, attacks on critical infrastructure, and malware/ransomware/cryptojacker attacks that are reported almost every day. Increasingly, we are relying on networked infrastructure and with the advent of IoT, billions of devices will be connected to the internet, providing attackers with more opportunities to exploit. Traditional machine learning methods have been frequently used in the context of network security. However, such methods are more based on statistical features extracted from sources such as binaries, emails, and packet flows.

On the other hand, recent years witnessed a phenomenal growth in computer vision mainly driven by the advances in the area of convolutional neural networks. At a glance, it is not trivial to see how computer vision methods are related to network security. Nonetheless, there is a significant amount of work that highlighted how methods from computer vision can be applied in network security for detecting attacks or building security solutions. In this paper, we provide a comprehensive survey of such work under three topics; i) phishing attempt detection, ii) malware detection, and iii) traffic anomaly detection. Next, we review a set of such commercial products for which public information is available and explore how computer vision methods are effectively used in those products. Finally, we discuss existing research gaps and future research directions, especially focusing on how network security research community and the industry can leverage the exponential growth of computer vision methods to build much secure networked systems.

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.

Person re-identification (re-ID) remains challenging in a real-world scenario, as it requires a trained network to generalise to totally unseen target data in the presence of variations across domains. Recently, generative adversarial models have been widely adopted to enhance the diversity of training data. These approaches, however, often fail to generalise to other domains, as existing generative person re-identification models have a disconnect between the generative component and the discriminative feature learning stage. To address the on-going challenges regarding model generalisation, we propose an end-to-end domain adaptive attention network to jointly translate images between domains and learn discriminative re-id features in a single framework. To address the domain gap challenge, we introduce an attention module for image translation from source to target domains without affecting the identity of a person. More specifically, attention is directed to the background instead of the entire image of the person, ensuring identifying characteristics of the subject are preserved. The proposed joint learning network results in a significant performance improvement over state-of-the-art methods on several benchmark datasets.

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.

A Non-terrestrial Network (NTN) comprising Low Earth Orbit (LEO) satellites can enable connectivity to underserved areas, thus complementing existing telecom networks. The high-speed satellite motion poses several challenges at the physical layer such as large Doppler frequency shifts. In this paper, an analytical framework is developed for statistical characterization of Doppler shift in an NTN where LEO satellites provide communication services to terrestrial users. Using tools from stochastic geometry, the users within a cell are grouped into disjoint clusters to limit the differential Doppler across users. Under some simplifying assumptions, the cumulative distribution function (CDF) and the probability density function are derived for the Doppler shift magnitude at a random user within a cluster. The CDFs are also provided for the minimum and the maximum Doppler shift magnitude within a cluster. Leveraging the analytical results, the interplay between key system parameters such as the cluster size and satellite altitude is examined. Numerical results validate the insights obtained from the analysis.

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.

It’s that time of the year again. A new TV season is upon us. With some shows beginning to air this week, I thought it best to post my “to watch” schedule for the fall. Unlike last year, I…