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.

An optimal base station (BS) location depends on the traffic (user) distribution, propagation pathloss and many system parameters, which renders its analytical study difficult so that numerical algorithms are widely used instead. In this paper, the problem is studied analytically. First, it is formulated as a convex optimization problem to minimize the total BS transmit power subject to quality-of-service (QoS) constraints, which also account for fairness among users. Due to its convex nature, Karush-Kuhn-Tucker (KKT) conditions are used to characterize a globally-optimum location as a convex combination of user locations, where convex weights depend on user parameters, pathloss exponent and overall geometry of the problem. Based on this characterization, a number of closed-form solutions are obtained. In particular, the optimum BS location is the mean of user locations in the case of free-space propagation and identical user parameters. If the user set is symmetric (as defined in the paper), the optimal BS location is independent of pathloss exponent, which is not the case in general. The analytical results show the impact of propagation conditions as well as system and user parameters on optimal BS location and can be used to develop design guidelines.

With the growing reliance on the ubiquitous availability of IT systems and services, these systems become more global, scaled, and complex to operate. To maintain business viability, IT service providers must put in place reliable and cost efficient operations support. Artificial Intelligence for IT Operations (AIOps) is a promising technology for alleviating operational complexity of IT systems and services. AIOps platforms utilize big data, machine learning and other advanced analytics technologies to enhance IT operations with proactive actionable dynamic insight.

In this paper we share our experience applying the AIOps approach to a production cloud object storage service to get actionable insights into system's behavior and health. We describe a real-life production cloud scale service and its operational data, present the AIOps platform we have created, and show how it has helped us resolving operational pain points.

Vision-and-Language Navigation (VLN) requires an agent to follow natural-language instructions, explore the given environments, and reach the desired target locations. These step-by-step navigational instructions are crucial when the agent is navigating new environments about which it has no prior knowledge. Most recent works that study VLN observe a significant performance drop when tested on unseen environments (i.e., environments not used in training), indicating that the neural agent models are highly biased towards training environments. Although this issue is considered as one of the major challenges in VLN research, it is still under-studied and needs a clearer explanation. In this work, we design novel diagnosis experiments via environment re-splitting and feature replacement, looking into possible reasons for this environment bias. We observe that neither the language nor the underlying navigational graph, but the low-level visual appearance conveyed by ResNet features directly affects the agent model and contributes to this environment bias in results. According to this observation, we explore several kinds of semantic representations that contain less low-level visual information, hence the agent learned with these features could be better generalized to unseen testing environments. Without modifying the baseline agent model and its training method, our explored semantic features significantly decrease the performance gaps between seen and unseen on multiple datasets (i.e. R2R, R4R, and CVDN) and achieve competitive unseen results to previous state-of-the-art models. Our code and features are available at: https://github.com/zhangybzbo/EnvBiasVLN

This paper illustrates five different techniques to assess the distinctiveness of topics, key terms and features, speed of information dissemination, and network behaviors for Covid19 tweets. First, we use pattern matching and second, topic modeling through Latent Dirichlet Allocation (LDA) to generate twenty different topics that discuss case spread, healthcare workers, and personal protective equipment (PPE). One topic specific to U.S. cases would start to uptick immediately after live White House Coronavirus Task Force briefings, implying that many Twitter users are paying attention to government announcements. We contribute machine learning methods not previously reported in the Covid19 Twitter literature. This includes our third method, Uniform Manifold Approximation and Projection (UMAP), that identifies unique clustering-behavior of distinct topics to improve our understanding of important themes in the corpus and help assess the quality of generated topics. Fourth, we calculated retweeting times to understand how fast information about Covid19 propagates on Twitter. Our analysis indicates that the median retweeting time of Covid19 for a sample corpus in March 2020 was 2.87 hours, approximately 50 minutes faster than repostings from Chinese social media about H7N9 in March 2013. Lastly, we sought to understand retweet cascades, by visualizing the connections of users over time from fast to slow retweeting. As the time to retweet increases, the density of connections also increase where in our sample, we found distinct users dominating the attention of Covid19 retweeters. One of the simplest highlights of this analysis is that early-stage descriptive methods like regular expressions can successfully identify high-level themes which were consistently verified as important through every subsequent analysis.

In this paper, we present a novel method for line artefacts quantification in lung ultrasound (LUS) images of COVID-19 patients. We formulate this as a non-convex regularisation problem involving a sparsity-enforcing, Cauchy-based penalty function, and the inverse Radon transform. We employ a simple local maxima detection technique in the Radon transform domain, associated with known clinical definitions of line artefacts. Despite being non-convex, the proposed method has guaranteed convergence via a proximal splitting algorithm and accurately identifies both horizontal and vertical line artefacts in LUS images. In order to reduce the number of false and missed detection, our method includes a two-stage validation mechanism, which is performed in both Radon and image domains. We evaluate the performance of the proposed method in comparison to the current state-of-the-art B-line identification method and show a considerable performance gain with 87% correctly detected B-lines in LUS images of nine COVID-19 patients. In addition, owing to its fast convergence, which takes around 12 seconds for a given frame, our proposed method is readily applicable for processing LUS image sequences.

Recently, the Edge Computing paradigm has gained significant popularity both in industry and academia. Researchers now increasingly target to improve performance and reduce energy consumption of such devices. Some recent efforts focus on using emerging RRAM technologies for improving energy efficiency, thanks to their no leakage property and high integration density. As the complexity and dynamism of applications supported by such devices escalate, it has become difficult to maintain ideal performance by static RRAM controllers. Machine Learning provides a promising solution for this, and hence, this work focuses on extending such controllers to allow dynamic parameter updates. In this work we propose an Adaptive RRAM Variability-Aware Controller, AVAC, which periodically updates Wait Buffer and batch sizes using on-the-fly learning models and gradient ascent. AVAC allows Edge devices to adapt to different applications and their stages, to improve computation performance and reduce energy consumption. Simulations demonstrate that the proposed model can provide up to 29% increase in performance and 19% decrease in energy, compared to static controllers, using traces of real-life healthcare applications on a Raspberry-Pi based Edge deployment.

In this paper, we continue our prior work on using imitation learning (IL) and model free reinforcement learning (RL) to learn driving policies for autonomous driving in urban scenarios, by introducing a model based RL method to drive the autonomous vehicle in the Carla urban driving simulator. Although IL and model free RL methods have been proved to be capable of solving lots of challenging tasks, including playing video games, robots, and, in our prior work, urban driving, the low sample efficiency of such methods greatly limits their applications on actual autonomous driving. In this work, we developed a model based RL algorithm of guided policy search (GPS) for urban driving tasks. The algorithm iteratively learns a parameterized dynamic model to approximate the complex and interactive driving task, and optimizes the driving policy under the nonlinear approximate dynamic model. As a model based RL approach, when applied in urban autonomous driving, the GPS has the advantages of higher sample efficiency, better interpretability, and greater stability. We provide extensive experiments validating the effectiveness of the proposed method to learn robust driving policy for urban driving in Carla. We also compare the proposed method with other policy search and model free RL baselines, showing 100x better sample efficiency of the GPS based RL method, and also that the GPS based method can learn policies for harder tasks that the baseline methods can hardly learn.

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.

Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method, however, its accuracy in detection is only ~70-75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80-98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source set of algorithms called CovidCTNet that successfully differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 90% compared to radiologists (70%). The model is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. In order to facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and parametric details in an open-source format. Open-source sharing of our CovidCTNet enables developers to rapidly improve and optimize services, while preserving user privacy and data ownership.

Motivated by the current demand of clinical governance, surgical simulation is now a well-established modality for basic skills training and assessment. The practical deployment of the technique is a multi-disciplinary venture encompassing areas in engineering, medicine and psychology. This paper provides an overview of the key topics involved in surgical simulation and associated technical challenges. The paper discusses the clinical motivation for surgical simulation, the use of virtual environments for surgical training, model acquisition and simplification, deformable models, collision detection, tissue property measurement, haptic rendering and image synthesis. Additional topics include surgical skill training and assessment metrics as well as challenges facing the incorporation of surgical simulation into medical education curricula.

Breadcrumbs are a subtle element of a website that helps improve usability and navigation. They’re a utility that often receives little acknowledgment; however, breadcrumbs can have a large impact and provide a plethora of benefits, such as lowering bounce rate, increasing conversions, and improving user satisfaction.   Imagine you’re in a regular grocery store, except […]

The post 5 Best Practices for Breadcrumb Navigation  appeared first on WebFX Blog.

When you bookmark pages on the web, it’s challenging to remember the name of the page. As you dive back into your bookmarks to find it, you see a small icon next to the page. You recognize the icon and realize it’s the website you viewed prior. This icon, known as a favicon, is small, […]

The post What is a Favicon? [+4 Tips for Creating an Impactful Favicon] appeared first on WebFX Blog.

Did you know that 75% of opinions on website credibility comes from design? If you want people to look at your services and find you credible, you must invest in web design for services pages to provide your audience with a positive experience. By looking at some web design examples for service pages, you’ll get […]

The post 6 Service Page Web Design Examples to Inspire You appeared first on WebFX Blog.

Need some inspiration to build a high converting website? Websites that convert persuade visitors to become customers. These websites drive more revenue, so if you want to increase your site’s revenue, use these examples of websites that convert as inspiration! We’ll go over what makes for the best converting websites and five examples of websites […]

The post 5 Magnificent Examples of Websites That Convert Visitors into Customers appeared first on WebFX Blog.

Summary

One feature that Go does not offer that is really useful in visitor patterns is overriding methods. The basic idea is to write a concrete class that contains all the VisitX methods with empty implementations, and a subclass can choose to only override the methods it cares about, ignoring the rest.

We'll see an example of how to implement this pattern in idiomatic Go code.

Node.js 14 is available now. We wanted to get more context and details about the state of Node, and why developers should care about Node.js 14. We talked with Bethany Griggs, Node.js Technical Steering Committee member and Open-source Engineer at IBM, to find out more. 

Bethany has been a Node Core Collaborator for over two years. She contributes to the open-source Node.js runtime and is a member of the Node.js Release Working Group where she is involved with auditing commits for the long-term support (LTS) release lines and the creation of releases. 

It has been a whirlwind of a year. Looking back at where I ended 2018 and started 2019, I was not fully prepared for everything that happened. The big items from 2019 were: Yours truly turned 35. Shut down the business side of Theme…

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

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

The Cannabis Issue So many of us are stuck inside right now, and that lack of socializing means we're all probably going a little bit stir crazy.…

[IMAGE-1]After having their school year totally disrupted by the coronavirus pandemic, a return to some semblance of normalcy come summer is all many school-age kids and their families are looking forward to. For many, this anticipation includes annual summer camp traditions, from sleep-away adventures on the lake to fun-filled day camps for arts, learning or team sports.…

The CDC designed a flawed test for COVID-19, then took weeks to figure out a fix so state and local labs could use it. New York still doesn’t trust the test’s accuracy By Caroline Chen, Marshall Allen, Lexi Churchill and Isaac Arnsdorf Propublica…

Coronavirus Family Tree The University of Washington Virology lab, which is testing samples for coronavirus, tweeted last night.…

Since late last year, a new coronavirus, now dubbed COVID-19, has been sweeping the globe, sickening more than 114,000 with flu- and cold-like symptoms and killing more than 4,000 so far.…

Agencies, local authorities and national governments do not agree on who should be quarantined or what that should actually look like. Here’s what we do know. By Maya Miller, Caroline Chen and Joshua Kaplan ProPublica People who have been exposed to the coronavirus are being given incomplete or misleading information about whether they should quarantine themselves, exposing major gaps in the public health response to the pandemic and illuminating disagreement among officials about how useful the tactic even is at this point in the disease’s spread.…

After Governor Jay Inslee announced a prohibition on gatherings of 250 people or more in three Washington counties (Snohomish, King, Pierce) on Wednesday, and with public health concerns growing over the COVID-19 pandemic, many organizations in Spokane are following suit. The Inlander will be frequently updating its online calendar of events to reflect local cancelations as we hear of them.…

By learning from a MERS outbreak in 2015, South Korea was prepared and acted swiftly to ramp up testing when the new coronavirus appeared there. Meanwhile, the U.S., plagued by delay and dysfunction, wasted its advantage. By Stephen Engelberg, Lisa Song and Lydia DePillis ProPublica…

There's one thing the Innovia Foundation can never say: That it hadn't been told.…

We at the Inlander are committed to keeping people informed and connected throughout the coronavirus outbreak. We'll continue to update this page with the latest headlines.…

There’s a cliche that always follows a big tragedy — something we say after natural disasters, economic collapses, school shootings, acts of terrorisms.…

You shouldn't find yourself rewatching some sitcom for the thousandth time or sitting through a vacuous Hollywood blockbuster just because you're stoned and stuck inside during the age of social distancing.…

Low on essential supplies and fearing they’ll get sick, doctors and nurses told ProPublica in-person care for coronavirus patients has been scaled back. In some cases, it’s causing serious harm. By Joshua Kaplan, Lizzie Presser and Maya Miller, ProPublica Every morning, between 7 and 8, at Long Island Jewish Medical Center in Queens, several coronavirus patients are pronounced dead.…

The apps and devices you use are conducting surveillance with your every move Sure, you lock your home, and you probably don't share your deepest secrets with random strangers.…

[IMAGE-1] I started sobbing from the opening moments of A Beautiful Day in the Neighborhood, and I didn't stop crying for two hours. And then after I left the cinema and ran into a fellow film critic who had also just seen it, I literally could not manage a word of discussion without bursting into tears again.…

We could blame the enormous — and justifiable — success of the Lego flicks for the existence of Playmobil: The Movie, but that would be unfair to all the shameless knockoffs and cinematic coattail riders.…

This is hardly the first time the Catholic Church has to deal with a plague. Spokane Bishop Thomas Daly knows that well.…

On any other week, you can rely on the Inlander for information on live shows happening in Spokane and North Idaho, and to read about the artists and music festivals that we think are worth your time.…

You'll be forgiven if, during the daily deluge of COVID-19-related updates, you didn't notice that some seriously great musicians recently booked shows in and around Spokane for later in the year.…

The future of live music might still be a big question mark, but local musicians aren't merely sitting on their hands during quarantine: A lot of them are finding a way to connect with their fans and putting out new material, even without the benefit of physical congregation. Here's a batch of some of our favorite new releases from the past month, and there will no doubt be more in the coming weeks.…

The Buzz Bin HERE'S THE BEEF…

You might not know it from the fancy attire they wear on stage at the Fox Theater, but for the musicians in the Spokane Symphony, it's a part-time gig. It's a prestigious gig, to be sure, but like most artists, for the musicians, it's just one piece of a puzzle full of hustle they have to solve to make a living.…

Perhaps you've heard people banging on pans to support health care workers, or howling into the abyss just to let other humans know they were alive. We've gone to some extreme measures to keep ourselves entertained since much of the country went on lockdown to combat COVID-19, and here's another one that can get you out of the house while remaining safely social-distanced and supporting local artists at the same time.…

Another First Friday is here, and you can correctly assume that it will be all online once again. The May 1 Virtual First Friday is from 10 am to midnight.…

ON INLANDER.COM WORLD: Roughly two weeks after Canada's deadliest mass shooting, Prime Minister Justin Trudeau introduced an immediate ban on what he called “military-style assault weapons.”…

ON INLANDER.COM NATION: Even as U.S. President Donald Trump urges states to reopen their economies, his own administration projects that the death toll from COVID-19 will spike to 3,000 people per day.…

The latest issue of the Inlander is hitting newsstands today. Find it at your local grocery store and hundreds of other locations; use this map to find a pickup point near you.…

By Adam Liptak The New York Times Company…

The Buzz Bin VAMPY VIBES…

At every level of America's food system, mandated closures and outbreaks of COVID-19 have interrupted the finely tuned network that normally gets food from farmers and food processors to restaurants, grocery stores and food banks.…