By David Suzuki with contributions from Senior Editor Ian Hanington David Suzuki Foundation In 2012, North Carolina’s Coastal Resources Commission warned that sea levels there could rise by a metre over the next century. The warning was based in part … Continue reading →

Many people have regular jobs that they love, and which enable them to use their creative skills to make money. This could be anything from coding video games to being an expert in SEO or designing...

Creative interactions call for creative measures - numerous extensions reduce, minimize or dilute the frustration of the customers and resolve issues quickly without the customer support team need....

Tags: novelty, tornado

“You can consult canada.ca/coronavirus to get the best updated information about the spread of the virus.” – Justin Trudeau, April 3rd, 2020 A daily mantra rings out from government officials around the world: The call to visit official websites to get the latest information on the COVID-19 pandemic and to access essential services. Yet to many constituents accessing […]

The post The Internet is an Essential Service appeared first on MOR10.

Center for Biological Diversity Press Release WASHINGTON – Four commonly used neonicotinoid pesticides can harm bees and other pollinators, according to a new analysis by California’s Department of Pesticide Regulation. The study found that current approved uses of the “neonics” … Continue reading →

By Jessica Corbett Common Dreams “This is a wake-up call,” says one Australian marine biologist. “Given sea temperatures usually increase as we get towards March, this is probably conservative.” Delivering yet another “wake-up call” after recent studies have shown that … Continue reading →

By Jon Queally Common Dreams “This is the stuff that worries me most. We don’t know what we’re doing, not trying to stop it, [and] with big consequences we don’t really understand.” The first global scientific review of its kind … Continue reading →

By Jessica Corbett Common Dreams Far-reaching global assessment details how humanity is undermining the very foundations of the natural world     On the heels of an Earth Day that featured calls for radical action to address the current “age … Continue reading →

Andrew Rickmann posted a photo:

Andrew Rickmann posted a photo:

By Jessica Corbett Common Dreams “This is a wake-up call,” says one Australian marine biologist. “Given sea temperatures usually increase as we get towards March, this is probably conservative.” Delivering yet another “wake-up call” after recent studies have shown that … Continue reading →

By Jon Queally Common Dreams “This is the stuff that worries me most. We don’t know what we’re doing, not trying to stop it, [and] with big consequences we don’t really understand.” The first global scientific review of its kind … Continue reading →

By Jessica Corbett Common Dreams Far-reaching global assessment details how humanity is undermining the very foundations of the natural world     On the heels of an Earth Day that featured calls for radical action to address the current “age … Continue reading →

In early 2014 I had just gotten married and recently moved into a new home. With two major life events out of the way, I decided I was ready to lead a WordCamp. I originally planned to organize WordCamp Detroit. I was an organizer twice before and the event had missed a year and I […]

The post 5 Critical Lessons Learned Organizing WordCamp Ann Arbor for the Third Time appeared first on Psychology of Web Design | 3.7 Blog.

You’re hiring a web designer or providing web design services, what’s included in a normal project? In other words, what are the deliverables, and the use of a membership website builder could be essential for this. Let’s start by defining what a deliverable is. Wikipedia defines a deliverable as: …a tangible or intangible good or […]

The post Modern Website Deliverables appeared first on Psychology of Web Design | 3.7 Blog.

Designer Spotlight: Burnt Toast

Abelardo Morell
Camera Obscura: Early Morning View of the East Side of Midtown Manhattan, , 2014
Website - AbelardoMorell.net

Abelardo Morell was born in Havana, Cuba in 1948. He immigrated to the United States with his parents in 1962. Morell received his undergraduate degree in 1977 from Bowdoin College and an MFA from The Yale University School of Art in 1981. In 1997 he received an honorary degree from Bowdoin College.

His publications include a photographic illustration of Alice’s Adventures in Wonderland (1998) by Dutton Children’s Books, A Camera in a Room (1995) by Smithsonian Press, A Book of Books (2002) and Camera Obscura (2004) by Bulfinch Press and Abelardo Morell (2005), published by Phaidon Press. Recent publications include a limited edition book by The Museum of Modern Art in New York of his Cliché Verre images with a text by Oliver Sacks.

His work has been collected and shown in many galleries, institutions and museums, including the Museum of Modern Art, The Whitney Museum of American Art, the Metropolitan Art Museum in New York, The Chicago Art Institute, The San Francisco Museum of Modern Art, The Houston Museum of Art, The Boston Museum of Fine Art, The Victoria & Albert Museum and over seventy other museums in the United States and abroad. A retrospective of his work organized jointly by the Art Institute of Chicago, The Getty in Los Angeles and The High Museum in Atlanta closed in May 2014 after a year of travel. Abelardo will be having his first show at the Edwynn Houk Gallery in New York opening October 23, 2014 and will run until December 20, 2014 featuring a selection of new pictures.

Are you looking for the best GoDaddy alternative for domain registration and web hosting? Without a doubt, Godaddy is one of the most popular names when it comes to registering domain names and hosting your business online. Over the last 22 years, GoDaddy has managed to establish a stronghold in the market. In this article, […]

The post 12 Best GoDaddy Alternatives for Domain & Web Hosting (2020) appeared first on IsItWP - Free WordPress Theme Detector.

Adam says that like drill and ceremony and calling cadence, which start with a first step, so does recovery from a brain injury and/or PTSD.

Adam shares an inspiring story about a friend with TBI and PTSD who almost ended his life but instead found the courage to ask for help — even though at the time he may not have known what that help looked like.

Language sets humans apart in the animal world. Language allows us to communicate complex ideas and emotions.  But too often after brain injury be it stroke or trauma, language is lost. 

During an interview on "Fox and Friends," Trump explained why he chose not to go on a firing spree amid Special Counsel Robert Mueller's Russia investigation a la Nixon's Saturday Night Massacre during the Watergate scandal. "I learned a lot from Richard Nixon: Don't fire people," the President said. "I learned a lot. I study history, and the firing of everybody ... .I should've, in one way," he continued. "But I'm glad I didn't because look at the way it turned out."

Roy Horn, famed tiger handler and co-star of the magic duo known as Siegfried and Roy, died of complications from the coronavirus in a hospital in Las Vegas on Friday. He was 75 years old. "Today, the world has lost one of the greats of magic, but I have lost my best friend," Siegfried Fischbacher said in a statement. "From the moment we met, I knew Roy and I, together, would change the world." "There could be no Siegfried without Roy, and no Roy without Siegfried." This is a developing story. Please check back for updates.

In small, daily actions you’re creating outcomes for yourself and by extension, creating your life. My man Tim Ferriss is a master at deconstructing the work of others and de-stilling it into a working practice. In fact, he wrote his book Tools of Titans as a reference of some of the tactics, routines, and habits of billionaires. In this quick episode, he shares the 3 key themes he’s seen in over 200 hundred people he’s interviewed. Enjoy! FOLLOW TIM: instagram | twitter | website Listen to the Podcast Subscribe   Watch the Episode  This podcast is brought to you by CreativeLive. CreativeLive is the world’s largest hub for online creative education in photo/video, art/design, music/audio, craft/maker, money/life and the ability to make a living in any of those disciplines. They are high quality, highly curated classes taught by the world’s top experts — Pulitzer, Oscar, Grammy Award winners, New York Times best selling authors and the best entrepreneurs of our times.

The post Win the Morning. Win the Day with Tim Ferriss appeared first on Chase Jarvis Photography.

Since the introduction of Node.js by Ryan Dahl at the European JSConf on 8 November 2009, it has seen wide usage across the tech industry. Companies such as Netflix, Uber, and LinkedIn give credibility to the claim that Node.js can withstand a high amount of traffic and concurrency. Armed with basic knowledge, beginner and intermediate developers of Node.js struggle with many things: “It’s just a runtime!” “It has event loops!

Prolific is a crowd-sourcing platform for running studies. In contrast to the widely-used Mechanical Turk, it’s specific to studies, has a much better interface, pricing that’s fair to participants, and useful filters to find the right people for your study. Amazon's Mechanical Turk is used for many empirical studies published in the visualization literature, but […]

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We review some recent results on the phenomenon of surface superconductivity in the framework of Ginzburg-Landau theory for extreme type-II materials. In particular, we focus on the response of the superconductor to a strong longitudinal magnetic field in the regime where superconductivity survives only along the boundary of the wire. We derive the energy and density asymptotics for samples with smooth cross section, up to curvature-dependent terms. Furthermore, we discuss the corrections in presence of corners at the boundary of the sample.

We examine Chern-Simons theory as a deformation of a 3-dimensional BF theory that is partially holomorphic and partially topological. In particular, we introduce a novel gauge that leads naturally to a one-loop exact quantization of this BF theory and Chern-Simons theory. This approach illuminates several important features of Chern-Simons theory, notably the bulk-boundary correspondence of Chern-Simons theory with chiral WZW theory. In addition to rigorously constructing the theory, we also explain how it applies to a large class of closely related 3-dimensional theories and some of the consequences for factorization algebras of observables.

In this paper we study the following set[A={p(n)+2^nd mod 1: ngeq 1}subset [0.1],] where $p$ is a polynomial with at least one irrational coefficient on non constant terms, $d$ is any real number and for $ain [0,infty)$, $a mod 1$ is the fractional part of $a$. By a Bernoulli decomposition method, we show that the closure of $A$ must have full Hausdorff dimension.

In this paper, we give an alternate definition of the well-known Parry measure on an aperiodic subshift of finite type using correlation between the forbidden words. We use the concept of the local escape rate to obtain this definition. We also compute Perron eigenvectors corresponding to the Perron root of the associated adjacency matrix.

In 1978, Dwight Duffus---editor-in-chief of the journal "Order" from 2010 to 2018 and chair of the Mathematics Department at Emory University from 1991 to 2005---wrote that "it is not obvious that $P$ is connected and $P^P$ isomorphic to $Q^Q$ implies that $Q$ is connected," where $P$ and $Q$ are finite non-empty posets. We show that, indeed, under these hypotheses $Q$ is connected and $Pcong Q$.

We investigate the Boundary Harnack Principle in H"older domains of exponent $alpha>0$ by the analytical method developed in our previous work "A short proof of Boundary Harnack Principle".

Predicting the spatial configuration of gas molecules in nanopores of shale formations is crucial for fluid flow forecasting and hydrocarbon reserves estimation. The key challenge in these tight formations is that the majority of the pore sizes are less than 50 nm. At this scale, the fluid properties are affected by nanoconfinement effects due to the increased fluid-solid interactions. For instance, gas adsorption to the pore walls could account for up to 85% of the total hydrocarbon volume in a tight reservoir. Although there are analytical solutions that describe this phenomenon for simple geometries, they are not suitable for describing realistic pores, where surface roughness and geometric anisotropy play important roles. To describe these, molecular dynamics (MD) simulations are used since they consider fluid-solid and fluid-fluid interactions at the molecular level. However, MD simulations are computationally expensive, and are not able to simulate scales larger than a few connected nanopores. We present a method for building and training physics-based deep learning surrogate models to carry out fast and accurate predictions of molecular configurations of gas inside nanopores. Since training deep learning models requires extensive databases that are computationally expensive to create, we employ active learning (AL). AL reduces the overhead of creating comprehensive sets of high-fidelity data by determining where the model uncertainty is greatest, and running simulations on the fly to minimize it. The proposed workflow enables nanoconfinement effects to be rigorously considered at the mesoscale where complex connected sets of nanopores control key applications such as hydrocarbon recovery and CO2 sequestration.

Temporal event segmentation of a long video into coherent events requires a high level understanding of activities' temporal features. The event segmentation problem has been tackled by researchers in an offline training scheme, either by providing full, or weak, supervision through manually annotated labels or by self-supervised epoch based training. In this work, we present a continual learning perceptual prediction framework (influenced by cognitive psychology) capable of temporal event segmentation through understanding of the underlying representation of objects within individual frames. Our framework also outputs attention maps which effectively localize and track events-causing objects in each frame. The model is tested on a wildlife monitoring dataset in a continual training manner resulting in $80\%$ recall rate at $20\%$ false positive rate for frame level segmentation. Activity level testing has yielded $80\%$ activity recall rate for one false activity detection every 50 minutes.

Differential machine learning (ML) extends supervised learning, with models trained on examples of not only inputs and labels, but also differentials of labels to inputs.

Differential ML is applicable in all situations where high quality first order derivatives wrt training inputs are available. In the context of financial Derivatives risk management, pathwise differentials are efficiently computed with automatic adjoint differentiation (AAD). Differential ML, combined with AAD, provides extremely effective pricing and risk approximations. We can produce fast pricing analytics in models too complex for closed form solutions, extract the risk factors of complex transactions and trading books, and effectively compute risk management metrics like reports across a large number of scenarios, backtesting and simulation of hedge strategies, or capital regulations.

The article focuses on differential deep learning (DL), arguably the strongest application. Standard DL trains neural networks (NN) on punctual examples, whereas differential DL teaches them the shape of the target function, resulting in vastly improved performance, illustrated with a number of numerical examples, both idealized and real world. In the online appendices, we apply differential learning to other ML models, like classic regression or principal component analysis (PCA), with equally remarkable results.

This paper is meant to be read in conjunction with its companion GitHub repo https://github.com/differential-machine-learning, where we posted a TensorFlow implementation, tested on Google Colab, along with examples from the article and additional ones. We also posted appendices covering many practical implementation details not covered in the paper, mathematical proofs, application to ML models besides neural networks and extensions necessary for a reliable implementation in production.

With the increase in use of Unmanned Aerial Vehicles (UAVs)/drones, it is important to detect and identify causes of failure in real time for proper recovery from a potential crash-like scenario or post incident forensics analysis. The cause of crash could be either a fault in the sensor/actuator system, a physical damage/attack, or a cyber attack on the drone's software. In this paper, we propose novel architectures based on deep Convolutional and Long Short-Term Memory Neural Networks (CNNs and LSTMs) to detect (via Autoencoder) and classify drone mis-operations based on sensor data. The proposed architectures are able to learn high-level features automatically from the raw sensor data and learn the spatial and temporal dynamics in the sensor data. We validate the proposed deep-learning architectures via simulations and experiments on a real drone. Empirical results show that our solution is able to detect with over 90% accuracy and classify various types of drone mis-operations (with about 99% accuracy (simulation data) and upto 88% accuracy (experimental data)).

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.

Representation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but these models are targeted towards token- and sentence-level training objectives and do not leverage information on inter-document relatedness, which limits their document-level representation power. For applications on scientific documents, such as classification and recommendation, the embeddings power strong performance on end tasks. We propose SPECTER, a new method to generate document-level embedding of scientific documents based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, SPECTER can be easily applied to downstream applications without task-specific fine-tuning. Additionally, to encourage further research on document-level models, we introduce SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation. We show that SPECTER outperforms a variety of competitive baselines on the benchmark.

A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. Electroencephalograms (EEGs) used in BCIs are weak, easily contaminated by interference and noise, non-stationary for the same subject, and varying across different subjects and sessions. Therefore, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during different sessions, for different devices and tasks. Usually, a calibration session is needed to collect some training data for a new subject, which is time consuming and user unfriendly. Transfer learning (TL), which utilizes data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate learning for a new subject/session/device/task, is frequently used to reduce the amount of calibration effort. This paper reviews journal publications on TL approaches in EEG-based BCIs in the last few years, i.e., since 2016. Six paradigms and applications -- motor imagery, event-related potentials, steady-state visual evoked potentials, affective BCIs, regression problems, and adversarial attacks -- are considered. For each paradigm/application, we group the TL approaches into cross-subject/session, cross-device, and cross-task settings and review them separately. Observations and conclusions are made at the end of the paper, which may point to future research directions.

Recent single image unsupervised representation learning techniques show remarkable success on a variety of tasks. The basic principle in these works is instance discrimination: learning to differentiate between two augmented versions of the same image and a large batch of unrelated images. Networks learn to ignore the augmentation noise and extract semantically meaningful representations. Prior work uses artificial data augmentation techniques such as cropping, and color jitter which can only affect the image in superficial ways and are not aligned with how objects actually change e.g. occlusion, deformation, viewpoint change. In this paper, we argue that videos offer this natural augmentation for free. Videos can provide entirely new views of objects, show deformation, and even connect semantically similar but visually distinct concepts. We propose Video Noise Contrastive Estimation, a method for using unlabeled video to learn strong, transferable single image representations. We demonstrate improvements over recent unsupervised single image techniques, as well as over fully supervised ImageNet pretraining, across a variety of temporal and non-temporal tasks. Code and the Random Related Video Views dataset are available at https://www.github.com/danielgordon10/vince

Lake ice, as part of the Essential Climate Variable (ECV) lakes, is an important indicator to monitor climate change and global warming. The spatio-temporal extent of lake ice cover, along with the timings of key phenological events such as freeze-up and break-up, provide important cues about the local and global climate. We present a lake ice monitoring system based on the automatic analysis of Sentinel-1 Synthetic Aperture Radar (SAR) data with a deep neural network. In previous studies that used optical satellite imagery for lake ice monitoring, frequent cloud cover was a main limiting factor, which we overcome thanks to the ability of microwave sensors to penetrate clouds and observe the lakes regardless of the weather and illumination conditions. We cast ice detection as a two class (frozen, non-frozen) semantic segmentation problem and solve it using a state-of-the-art deep convolutional network (CNN). We report results on two winters ( 2016 - 17 and 2017 - 18 ) and three alpine lakes in Switzerland. The proposed model reaches mean Intersection-over-Union (mIoU) scores >90% on average, and >84% even for the most difficult lake. Additionally, we perform cross-validation tests and show that our algorithm generalises well across unseen lakes and winters.

In learning-to-rank for information retrieval, a ranking model is automatically learned from the data and then utilized to rank the sets of retrieved documents. Therefore, an ideal ranking model would be a mapping from a document set to a permutation on the set, and should satisfy two critical requirements: (1)~it should have the ability to model cross-document interactions so as to capture local context information in a query; (2)~it should be permutation-invariant, which means that any permutation of the inputted documents would not change the output ranking. Previous studies on learning-to-rank either design uni-variate scoring functions that score each document separately, and thus failed to model the cross-document interactions; or construct multivariate scoring functions that score documents sequentially, which inevitably sacrifice the permutation invariance requirement. In this paper, we propose a neural learning-to-rank model called SetRank which directly learns a permutation-invariant ranking model defined on document sets of any size. SetRank employs a stack of (induced) multi-head self attention blocks as its key component for learning the embeddings for all of the retrieved documents jointly. The self-attention mechanism not only helps SetRank to capture the local context information from cross-document interactions, but also to learn permutation-equivariant representations for the inputted documents, which therefore achieving a permutation-invariant ranking model. Experimental results on three large scale benchmarks showed that the SetRank significantly outperformed the baselines include the traditional learning-to-rank models and state-of-the-art Neural IR models.

Introduction of spectrum-sharing in 5G and subsequent generation networks demand base-station(s) with the capability to characterize the wideband spectrum spanned over licensed, shared and unlicensed non-contiguous frequency bands. Spectrum characterization involves the identification of vacant bands along with center frequency and parameters (energy, modulation, etc.) of occupied bands. Such characterization at Nyquist sampling is area and power-hungry due to the need for high-speed digitization. Though sub-Nyquist sampling (SNS) offers an excellent alternative when the spectrum is sparse, it suffers from poor performance at low signal to noise ratio (SNR) and demands careful design and integration of digital reconstruction, tunable channelizer and characterization algorithms. In this paper, we propose a novel deep-learning framework via a single unified pipeline to accomplish two tasks: 1)~Reconstruct the signal directly from sub-Nyquist samples, and 2)~Wideband spectrum characterization. The proposed approach eliminates the need for complex signal conditioning between reconstruction and characterization and does not need complex tunable channelizers. We extensively compare the performance of our framework for a wide range of modulation schemes, SNR and channel conditions. We show that the proposed framework outperforms existing SNS based approaches and characterization performance approaches to Nyquist sampling-based framework with an increase in SNR. Easy to design and integrate along with a single unified deep learning framework make the proposed architecture a good candidate for reconfigurable platforms.

Histopathology is a reflection of the molecular changes and provides prognostic phenotypes representing the disease progression. In this study, we introduced feature scores generated from hematoxylin and eosin histology images based on deep learning (DL) models developed for prostate pathology. We demonstrated that these feature scores were significantly prognostic for time to event endpoints (biochemical recurrence and cancer-specific survival) and had simultaneously molecular biologic associations to relevant genomic alterations and molecular subtypes using already trained DL models that were not previously exposed to the datasets of the current study. Further, we discussed the potential of such feature scores to improve the current tumor grading system and the challenges that are associated with tumor heterogeneity and the development of prognostic models from histology images. Our findings uncover the potential of feature scores from histology images as digital biomarkers in precision medicine and as an expanding utility for digital pathology.

Robots are required to operate autonomously in response to changing situations. Previously, imitation learning using 4ch-bilateral control was demonstrated to be suitable for imitation of object manipulation. However, cooperative work between humans and robots has not yet been verified in these studies. In this study, the task was expanded by cooperative work between a human and a robot. 4ch-bilateral control was used to collect training data for training robot motion. We focused on serving salad as a task in the home. The task was executed with a spoon and a fork fixed to robots. Adjustment of force was indispensable in manipulating indefinitely shaped objects such as salad. Results confirmed the effectiveness of the proposed method as demonstrated by the success of the task.

For real-time semantic video segmentation, most recent works utilised a dynamic framework with a key scheduler to make online key/non-key decisions. Some works used a fixed key scheduling policy, while others proposed adaptive key scheduling methods based on heuristic strategies, both of which may lead to suboptimal global performance. To overcome this limitation, we model the online key decision process in dynamic video segmentation as a deep reinforcement learning problem and learn an efficient and effective scheduling policy from expert information about decision history and from the process of maximising global return. Moreover, we study the application of dynamic video segmentation on face videos, a field that has not been investigated before. By evaluating on the 300VW dataset, we show that the performance of our reinforcement key scheduler outperforms that of various baselines in terms of both effective key selections and running speed. Further results on the Cityscapes dataset demonstrate that our proposed method can also generalise to other scenarios. To the best of our knowledge, this is the first work to use reinforcement learning for online key-frame decision in dynamic video segmentation, and also the first work on its application on face videos.