Steven Quinn was fined a total of £360 and ordered to pay £62 compensation after walking out without settling-up at Lau's restaurant, in Newcastle City Centre

Owner-chef Bob Arora will be launching a takeaway service this May as well as hosting online cookery classes

Newcastle United goalkeeper Rob Elliot has opened up about team-mate Isaac Hayden and what makes him such an important player

Here's a video segment on the fog machine Kit that we use. A transcription of the video is available after the jump.

We join The Nature Conservancy as they search for eastern indigo snakes released at the Apalachicola Bluffs and Ravines Preserve.

CEDAR FALLS — Like many high school seniors across the country, Norwalk’s Bowen Born is unsure when he’ll be able to get on campus at the University of Northern Iowa and begin...

At the turn of the 20th century, a vaccine had been developed for smallpox, a virus that killed millions in the 1800s. Those who survived the disease were often left badly scarred or blind. The...

DES MOINES — More than 20 days after Iowa signed a $26 million contract with a Utah company to expand testing in the state, the machines the firm supplied to run the samples still have not...

IRENE ELIZABETH SCHINDLER
Marengo


Irene Elizabeth Schindler, 90, of Marengo, passed away Wednesday, May 6, 2020, at the Rose Haven Nursing Home in Marengo. Funeral services will be held at 2:30 p.m. Saturday, May 9, at St. John's Lutheran Church in Marengo, with Pastor Andrew Gray officiating. Burial will be in the Marengo Cemetery in Marengo. A memorial fund has been established. Kloster Funeral Home, Marengo, is assisting the family with arrangements.
Irene is survived by three nephews, Kevin (Pam) Dye of Cedar Rapids, John (Andi) Schindler of Marion and Bill Schindler of Cedar Rapids; two nieces, Deanne (Todd) Mumm of Parnell and Vera Dye of Williamsburg; a cousin, Will Klotz of Gibson; many great, and great, great-nieces and -nephews; and a sister-in-law, W. Irene Schindler of Marengo.
She was preceded in death by her parents; three brothers, Delmar, Calvin and Milver Schindler; and a sister, Shirley Dye.
Irene was born Jan. 10, 1930, in Marengo, the daughter of Charles and Katie (Klotz) Schindler. She graduated from Marengo High School in 1952. Irene worked at Collins Radio in Cedar Rapids from September 1952, until she retired in 1989 on the circuit board assembly line. She was a lifelong member of St. John's Lutheran Church and was a part of the quilting group. Irene enjoyed watching pro wrestling, going bowling, gardening baking, and was an Iowa Hawkeyes fan. She loved spending time with family and friends.
Online condolences may be left at www.klosterfuneralhome.com.

Note: This post is adapted from my original viral Twitter thread. Ffrom November 2003 through July 2005, I worked in the prepaid cell phone and phone card industry. Most of my work was in BFE meth towns and urban ghettoes. I learned things about the poor in America you won’t want to believe… But this […]

I talked about Disabling search engine on search pages in a previous post using the meta tag. Today lets go over the web bots Cached copy of your site. If you are working on builder your blog, or have a temporary site up, use the following code: <meta name="robots" content="noarchive"> This will tell any bot […]

The post Disable caching of your site or post appeared first on WPCult.

At the turn of the 20th century, a vaccine had been developed for smallpox, a virus that killed millions in the 1800s. Those who survived the disease were often left badly scarred or blind.

The vaccine’s protection, though, lasted only five years and had to be renewed. And people forgot to do that, leading to occasional epidemics, including a serious outbreak on the Meskwaki Settlement near Tama.

In February 1901, three dozen Iowa communities reported smallpox outbreaks. When Des Moines reported 200 smallpox cases in late February, the mayor closed the schools and prohibited public gatherings. Still, no one died. That would not be the case at the Meskwaki Settlement.

On Oct. 22, 1901, an area resident told Dr. Benjamin Thompson of Tama, “I believe the Indians have the smallpox.”

Thompson went to the nearby Meskwaki Settlement, home to 309 people, to investigate.

He learned that an Indian from the Winnebago tribe had visited the settlement Sept. 23. He had become ill, but no doctor was called and he continued meeting with tribe members.

Two weeks later, the Meskwaki were becoming ill.

Thompson went back to the settlement with Dr. George Carpenter of Toledo. The first wickiup they visited had four active smallpox cases and three people recovering from the virus. Two Meskwaki who’d lived there had died.

The doctors found another five cases and were told of two other deaths.

The doctors that night reported to a joint meeting of the boards of health in Tama, Toledo and Montour.

William Malin, the Interior Department’s Indian agent for the settlement, insisted there was no problem. But after another medical visit to the settlement, the three cities quarantined themselves.

By Nov. 2, 70 Meskwaki had smallpox, and nine members of the tribe had died. Two weeks later, the totals had risen to 90 Meskwaki with smallpox and 35 deaths.

To complicate matters, the Meskwaki declined offers to go to the hospital, they refused smallpox vaccinations and they refused to stay on the settlement. If they became sick, they would hide from the doctors.

The Tama Herald reported, “It looks as though the disease must run its course through the tribe, carrying off the aged and the infirm and weakening the constitution of those who may survive.”

The Iowa governor appealed to Interior Secretary Ethan Hitchcock for authority to enforce the quarantine among the Meskwaki. Hitchcock complied, giving the state the authority to “take any necessary action.”

National Guard hospital tents and cots were shipped to the settlement. The Meskwaki were vaccinated, and they acquiesced to the quarantine.

The local towns began raising money for medicines and provisions not covered by the federal government.

Meanwhile, Congress appropriated $50,000 — more than $1 million in today’s dollars — to eradicate smallpox on the settlement. The bill gave the Interior Department the authority to quarantine the village and, if necessary, burn Meskwaki wickiups and clothing to halt the contagion.

The state of Iowa chipped in another $7,000 to burn the clothing, blankets, wickiups and other property belonging to the Tama tribe and to provide replacements for the Meskwaki.

The money paid for disinfectants — formaldehyde gas and corrosive sublimate fluid — to bathe tribal members and their dogs. Members of the tribe were forced to move to a camping ground while their village was decontaminated.

Malin, the Indian agent, reported to the Interior Department it took seven days to clean and renovate “the Indian camp.”

“During this process, a large number of the wickiups, where the disease had been prevalent, also large quantities of clothing, bedding and other infected property, were committed to the flames and new goods of similar character supplied,” he reported.

“Twenty-four new board houses, built of good lumber, and some 2,700 square yards of very heavy duck for tents, to those who preferred tents to houses, were given in lien of the wickiups destroyed. ... The Indians emerged from the trying ordeal through which they had passed and came out into the world again, after having been confined to the limited area of their camping grounds ... with a higher and better conception of the white man’s civilization.”

That assessment aside, the Meskwaki Settlement survived and grew. It now covers 8,000 acres and is home to 800 of the tribe’s 1,300 members.

It wasn’t until 1980 that smallpox was declared eradicated worldwide.

l Comments: d.fannonlangton@gmail.com

DES MOINES — More than 20 days after Iowa signed a $26 million contract with a Utah company to expand testing in the state, the machines the firm supplied to run the samples still have not passed muster.

A time frame for completing the validation process for the Test Iowa lab machines is unknown, as the process can vary by machine, University of Iowa officials said Friday.

The validation process is undertaken to determine if the machines are processing tests accurately. To this point, the lab has processed the Test Iowa results using machines the State Hygienic Lab already had, officials told The Gazette.

Running side-by-side testing is part of the validation process. The lab then compares whether the machines yield the same results when the sample is run, officials said Friday. The side-by-side testing means the Test Iowa samples are being run at least twice to compare results.

The state does not break out how many of the 331,186 Iowans who by Friday have completed the coronavirus assessment at TestIowa.com have actually been tested. Test Iowa was initiated last month to ramp up testing of essential workers and Iowans showing COVID-19 symptoms. The state’s fourth drive-though location where people with appointments can be tested opened Thursday at the Kirkwood Continuing Education Training Center in Cedar Rapids.

On Friday, Iowa posted a fourth straight day of double-digit deaths from coronavirus, with the latest 12 deaths reported by the state Department of Public Health bringing the statewide toll to 243 since COVID-19 was first confirmed March 8 in Iowa.

State health officials reported another 398 Iowans tested positive for the respiratory ailment, bringing that count to 11,457 of the 70,261 residents who have been tested — a positive rate of more than 16 percent.

One in 44 Iowans has been tested for COVID-19, with 58,804 posting negative results, according to state data. A total of 4,685 people have recovered from the disease.

During a Thursday media briefing, Gov. Kim Reynolds told reporters a backlog of test results that occurred due to validation of Test Iowa equipment had been “caught up,” but some Iowans who participated in drive-through sites set up around the state indicated they still were awaiting results.

Reynolds spokesman Pat Garrett confirmed Thursday that “a very small percentage” of coronavirus test samples collected under the Test Iowa program could not be processed because they were “potentially damaged,” resulting in incomplete results.

There were 407 Iowans who were hospitalized (with 34 admitted in the past 24 hours) for coronavirus-related illnesses and symptoms with 164 being treated in intensive care units and 109 requiring ventilators to assist their breathing.

Health officials said the 12 deaths reported Friday were: three in Woodbury County, two in Linn County and one each in Black Hawk, Dallas, Dubuque, Jasper, Louisa, Muscatine and Scott counties. No other information about the COVID-19 victims was available from state data.

According to officials, 51 percent of the Iowans who have died from coronavirus have been male — the same percentage that tested positive.

Iowans over the age of 80 represent 46 percent of the COVID-19 victims, followed by 41 percent between 61 and 80.

Of those who have tested positive, state data indicates about 42 percent are age 18 to 40; 37 percent are 41 to 60; 14 percent are 61 to 80 and 5 percent are 81 or older.

Counties with the highest number of positive test results are Polk (2,150), Woodbury (1,532), Black Hawk (1,463) and Linn (813).

Earlier this week, state officials revamped the data available to the public at coronavirus.iowa.gov, with the new format no longer listing the age range of Iowans who died from coronavirus and providing information using a different timeline than before.

The governor did not hold a daily media briefing Friday due to scheduling conflicts created by Vice President Mike Pence’s trip to Iowa. Garrett said Reynolds would resume her COVID-19 briefings next week.

John McGlothlen and Zack Kucharski of The Gazette contributed to this report.

Have you chosen Mac for its reliable system? They really have a lot of advantages and are of the best quality. Mac users don’t face serious problems with hard drives often. But the reality is such...

Facebook Software Engineer and HipHop for PHP team member Jason Evans provides details on Facebook’s move to a new high-performance PHP virtual machine. Described by Evans is ”a new PHP execution engine based on the HipHop language runtime that we call the HipHop Virtual Machine (hhvm).” He sees it as replacement for the HipHop PHP Read the rest...

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 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 →

It’s A Living Mural for X-Games China & Innersect

This work has been motivated by recent papers that quantify the density of values of generic quadratic forms and other polynomials at integer points, in particular ones that use Rogers' second moment estimates. In this paper we establish such results in a very general framework. Namely, given any subhomogeneous function (a notion to be defined) $f: mathbb{R}^n o mathbb{R}$, we derive a necessary and sufficient condition on the approximating function $psi$ for guaranteeing that a generic element $fcirc g$ in the $G$-orbit of $f$ is $psi$-approximable; that is, $|fcirc g(mathbf{v})| le psi(|mathbf{v}|)$ for infinitely many $mathbf{v} in mathbb{Z}^n$. We also deduce a sufficient condition in the case of uniform approximation. Here, $G$ can be any closed subgroup of $operatorname{ASL}_n(mathbb{R})$ satisfying certain axioms that allow for the use of Rogers-type estimates.

In a multitype branching process, it is assumed that immigrants arrive according to a nonhomogeneous Poisson or a generalized Polya process (both processes are formulated as a nonhomogeneous birth process with an appropriate choice of transition intensities). We show that the renormalized numbers of objects of the various types alive at time $t$ for supercritical, critical, and subcritical cases jointly converge in distribution under those two different arrival processes. Furthermore, some transient moment analysis when there are only two types of particles is provided. AMS 2000 subject classifications: Primary 60J80, 60J85; secondary 60K10, 60K25, 90B15.

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.

The connection between visual input and tactile sensing is critical for object manipulation tasks such as grasping and pushing. In this work, we introduce the challenging task of estimating a set of tactile physical properties from visual information. We aim to build a model that learns the complex mapping between visual information and tactile physical properties. We construct a first of its kind image-tactile dataset with over 400 multiview image sequences and the corresponding tactile properties. A total of fifteen tactile physical properties across categories including friction, compliance, adhesion, texture, and thermal conductance are measured and then estimated by our models. We develop a cross-modal framework comprised of an adversarial objective and a novel visuo-tactile joint classification loss. Additionally, we develop a neural architecture search framework capable of selecting optimal combinations of viewing angles for estimating a given physical property.

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

We develop a supervised machine learning algorithm that is able to learn topological phases for finite condensed matter systems from bulk data in real lattice space. The algorithm employs diagonalization in real space together with any supervised learning algorithm to learn topological phases through an eigenvector ensembling procedure. We combine our algorithm with decision trees and random forests to successfully recover topological phase diagrams of Su-Schrieffer-Heeger (SSH) models from bulk lattice data in real space and show how the Shannon information entropy of ensembles of lattice eigenvectors can be used to retrieve a signal detailing how topological information is distributed in the bulk. The discovery of Shannon information entropy signals associated with topological phase transitions from the analysis of data from several thousand SSH systems illustrates how model explainability in machine learning can advance the research of exotic quantum materials with properties that may power future technological applications such as qubit engineering for quantum computing.

The standard training algorithm in neural machine translation (NMT) suffers from exposure bias, and alternative algorithms have been proposed to mitigate this. However, the practical impact of exposure bias is under debate. In this paper, we link exposure bias to another well-known problem in NMT, namely the tendency to generate hallucinations under domain shift. In experiments on three datasets with multiple test domains, we show that exposure bias is partially to blame for hallucinations, and that training with Minimum Risk Training, which avoids exposure bias, can mitigate this. Our analysis explains why exposure bias is more problematic under domain shift, and also links exposure bias to the beam search problem, i.e. performance deterioration with increasing beam size. Our results provide a new justification for methods that reduce exposure bias: even if they do not increase performance on in-domain test sets, they can increase model robustness to domain shift.

Generative feature matching network (GFMN) is an approach for training implicit generative models for images by performing moment matching on features from pre-trained neural networks. In this paper, we present new GFMN formulations that are effective for sequential data. Our experimental results show the effectiveness of the proposed method, SeqGFMN, for three distinct generation tasks in English: unconditional text generation, class-conditional text generation, and unsupervised text style transfer. SeqGFMN is stable to train and outperforms various adversarial approaches for text generation and text style transfer.

This paper is the first to consider online algorithms to schedule a proportionate flexible flow shop of batching machines (PFFB). The scheduling model is motivated by manufacturing processes of individualized medicaments, which are used in modern medicine to treat some serious illnesses. We provide two different online algorithms, proving also lower bounds for the offline problem to compute their competitive ratios. The first algorithm is an easy-to-implement, general local scheduling heuristic. It is 2-competitive for PFFBs with an arbitrary number of stages and for several natural scheduling objectives. We also show that for total/average flow time, no deterministic algorithm with better competitive ratio exists. For the special case with two stages and the makespan or total completion time objective, we describe an improved algorithm that achieves the best possible competitive ratio $varphi=frac{1+sqrt{5}}{2}$, the golden ratio. All our results also hold for proportionate (non-flexible) flow shops of batching machines (PFB) for which this is also the first paper to study online algorithms.

Sentence level quality estimation (QE) for machine translation (MT) attempts to predict the translation edit rate (TER) cost of post-editing work required to correct MT output. We describe our view on sentence-level QE as dictated by several practical setups encountered in the industry. We find consumers of MT output---whether human or algorithmic ones---to be primarily interested in a binary quality metric: is the translated sentence adequate as-is or does it need post-editing? Motivated by this we propose a quality classification (QC) view on sentence-level QE whereby we focus on maximizing recall at precision above a given threshold. We demonstrate that, while classical QE regression models fare poorly on this task, they can be re-purposed by replacing the output regression layer with a binary classification one, achieving 50-60\% recall at 90\% precision. For a high-quality MT system producing 75-80\% correct translations, this promises a significant reduction in post-editing work indeed.

In encoder-decoder neural models, multiple encoders are in general used to represent the contextual information in addition to the individual sentence. In this paper, we investigate multi-encoder approaches in documentlevel neural machine translation (NMT). Surprisingly, we find that the context encoder does not only encode the surrounding sentences but also behaves as a noise generator. This makes us rethink the real benefits of multi-encoder in context-aware translation - some of the improvements come from robust training. We compare several methods that introduce noise and/or well-tuned dropout setup into the training of these encoders. Experimental results show that noisy training plays an important role in multi-encoder-based NMT, especially when the training data is small. Also, we establish a new state-of-the-art on IWSLT Fr-En task by careful use of noise generation and dropout methods.

Simplified Chinese to Traditional Chinese character conversion is a common preprocessing step in Chinese NLP. Despite this, current approaches have poor performance because they do not take into account that a simplified Chinese character can correspond to multiple traditional characters. Here, we propose a model that can disambiguate between mappings and convert between the two scripts. The model is based on subword segmentation, two language models, as well as a method for mapping between subword sequences. We further construct benchmark datasets for topic classification and script conversion. Our proposed method outperforms previous Chinese Character conversion approaches by 6 points in accuracy. These results are further confirmed in a downstream application, where 2kenize is used to convert pretraining dataset for topic classification. An error analysis reveals that our method's particular strengths are in dealing with code-mixing and named entities.

Neural machine translation (NMT) needs large parallel corpora for state-of-the-art translation quality. Low-resource NMT is typically addressed by transfer learning which leverages large monolingual or parallel corpora for pre-training. Monolingual pre-training approaches such as MASS (MAsked Sequence to Sequence) are extremely effective in boosting NMT quality for languages with small parallel corpora. However, they do not account for linguistic information obtained using syntactic analyzers which is known to be invaluable for several Natural Language Processing (NLP) tasks. To this end, we propose JASS, Japanese-specific Sequence to Sequence, as a novel pre-training alternative to MASS for NMT involving Japanese as the source or target language. JASS is joint BMASS (Bunsetsu MASS) and BRSS (Bunsetsu Reordering Sequence to Sequence) pre-training which focuses on Japanese linguistic units called bunsetsus. In our experiments on ASPEC Japanese--English and News Commentary Japanese--Russian translation we show that JASS can give results that are competitive with if not better than those given by MASS. Furthermore, we show for the first time that joint MASS and JASS pre-training gives results that significantly surpass the individual methods indicating their complementary nature. We will release our code, pre-trained models and bunsetsu annotated data as resources for researchers to use in their own NLP tasks.

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the Blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous and a non-invasive BP measurement system is proposed using Photoplethysmogram (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo pre-processing and feature extraction steps. Time, frequency and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for Systolic BP (SBP) and Diastolic BP (DBP) estimation individually. Gaussian Process Regression (GPR) along with ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root-mean-square error (RMSE) of 6.74 and 3.59 respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.

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.

Narrowband internet-of-things (NB-IoT) is a competitive 5G technology for massive machine-type communication scenarios, but meanwhile introduces narrowband interference (NBI) to existing broadband transmission such as the long term evolution (LTE) systems in enhanced mobile broadband (eMBB) scenarios. In order to facilitate the harmonic and fair coexistence in wireless heterogeneous networks, it is important to eliminate NB-IoT interference to LTE systems. In this paper, a novel sparse machine learning based framework and a sparse combinatorial optimization problem is formulated for accurate NBI recovery, which can be efficiently solved using the proposed iterative sparse learning algorithm called sparse cross-entropy minimization (SCEM). To further improve the recovery accuracy and convergence rate, regularization is introduced to the loss function in the enhanced algorithm called regularized SCEM. Moreover, exploiting the spatial correlation of NBI, the framework is extended to multiple-input multiple-output systems. Simulation results demonstrate that the proposed methods are effective in eliminating NB-IoT interference to LTE systems, and significantly outperform the state-of-the-art methods.

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.

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.…

Uncut Gems is one of those "his own worst enemy" capers. You know, the kind of movie where you sit there for two hours watching some doofus constantly trip over his own laces — usually figuratively, sometimes literally — on the way to a personal epiphany about how all his bad choices and lack of useful self-awareness have led him to whatever unpleasant place they lead him to.…

Warm summer nights are on the way.…

Processing metadata includes storing, in a data storage system, a specification for each of multiple sources, each specification including information identifying one or more data elements of the corresponding source; and processing, in a data processing system coupled to the data storage system, data elements from the sources, including generating a set of rules for each source based on a corresponding one of the stored specifications, and matching data elements of different sources and determining a quality metric characterizing a given match between a first data element of a first source and a second data element of a second source according to the set of rules generated for the first source and the set of rules generated for the second source.

A system and method that facilitates urban traffic state detection based on support vector machine (SVM) and multilayer perceptron (MLP) classifiers is provided. Moreover, the SVM and MLP classifiers are fused into a cascaded two-tier classifier that improves the accuracy of the traffic state classification. To further improve the accuracy, the cascaded two-tier classifier (e.g., MLP-SVM), a single SVM classifier and a single MLP classifier are fused to determine a final decision for a traffic state. In addition, fusion strategies are employed during training and implementation phases to compensate for data acquisition and classification errors caused by noise and/or outliers.

Observation-based user profiling and profile matching are provided. The network behavior of users of a computer-implemented social network are observed and used for user profiling. By observing network behavior instead of necessarily relying on user self-reported data, accurate and objective user profiles can be formed; user profiling is accomplished based on the observed network behaviors with or without the knowledge of the user being profiled. The observed network behaviors can include the customization of a visual graphic, a media preference, a communication preference, or a selection of words from a word list. The user profiles can be with respect to a domain and two or more users can be matched based on their profiles with respect to the same domain. User ratings and profile updating based on the ratings are also provided.

A process is presented for quenching a process stream in a paraffin dehydrogenation process. The process comprises cooling a propane dehydrogenation stream during the hot residence time after the process stream leaves the catalytic bed reactor section. The process includes cooling and compressing the product stream, taking a portion of the product stream and passing the portion of the product stream to the mix with the process stream as it leaves the catalytic bed reactor section.

A method, apparatus, and system for switching from an existing target end device to a next target end device in a multi-expander storage topology by using Fast Context Switching. The method enhances Fast Context Switching by allowing Fast Context Switching to reuse or extend part of an existing connection path to an end device directly attached to a remote expander. The method can include reusing or extending at least a partial path of an established connection between an initiator and the existing target end device for a connection between the initiator and the next target end device, whereby the existing target end device and the next target end device are locally attached to different expanders.

A cloud system may create physical resource tags to store relationships between cloud computing offerings, such as computing service offerings, storage offerings, and network offerings, and the specific physical resources in the cloud computing environment. Cloud computing offerings may be presented to cloud customers, the offerings corresponding to various combinations of computing services, storage, networking, and other hardware or software resources. After a customer selects one or more cloud computing offerings, a cloud resource manager or other component within the cloud infrastructure may retrieve a set of tags and determine a set of physical hardware resources associated with the selected offerings. The physical hardware resources associated with the selected offerings may be subsequently used to provision and create the new virtual machine and its operating environment.

A system, method and computer program product for allocating shared resources. Upon receiving requests for resources, the cost of bundling software in a virtual machine (VM) image is automatically generated. Software is selected by the cost for each bundle according to the time required to install it where required, offset by the time to uninstall it where not required. A number of VM images having the highest software bundle value (i.e., highest cost bundled) is selected and stored, e.g., in a machine image store. With subsequent requests for resources, VMs may be instantiated from one or more stored VM images and, further, stored images may be updated selectively updated with new images.

A server computer system determines that configuring a first virtual machine in a cloud depends on a configuration result of configuring a second virtual machine. The server computer system configures the second virtual machine in the cloud and configures the first virtual machine in the cloud using the configuration result of the second virtual machine.

A system and method for reconfiguring a computing environment comprising a consumption analysis server, a placement server, an infrastructure management client and a data warehouse in communication with a set of data collection agents and a database. The consumption analysis server operates on measured resource utilization data to yield a set of resource consumptions in regularized time blocks, collects host and virtual machine configurations from the computing environment and determines available capacity for a set of target hosts. The placement server assigns a set of target virtual machines to the target set of hosts in a new placement. In one mode of operation the new placement is nearly optimal. In another mode of operation, the new placement is “good enough” to achieve a threshold score based on an objective function of resource capacity headroom. The new placement is implemented in the computing environment.

Disclosed is an information processing device and a task switching method that can reduce the time required for switching of tasks in a plurality of coprocessors. The information processing device (30) includes a processor core (301); coprocessors (311 to 31n) including operation units (321 to 32n) that perform operation in response to a request from the processor core (301) and operation storage units (331 to 22n) that store the contents of operation of the operation units (321 to 32n), save storage units (351 to 35n) that store the saved contents of operation, a task switching control unit (302) that outputs a save/restore request signal when switching a task on which operation is performed by the coprocessors (311 to 31n), and save/restore units (341 to 34n) that perform at least one of saving of the contents of operation in the operation storage units (331 to 33n) to the save storage units (351 to 35n) and restoration of the contents of operation in the save storage units (351 to 35 n) to the operation storage units (331 to 33n) in response to the save/restore request signal.

A system and method for controlling restarting of instruction fetching using speculative address computations in a processor are provided. The system includes a predicted target queue to hold branch prediction logic (BPL) generated target address values. The system also includes target selection logic including a recycle queue. The target selection logic selects a saved branch target value between a previously speculatively calculated branch target value from the recycle queue and an address value from the predicted target queue. The system further includes a compare block to identify a wrong target in response to a mismatch between the saved branch target value and a current calculated branch target, where instruction fetching is restarted in response to the wrong target.