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These beautiful residences are brand new and featuring many designer features; select from single- and two-story open floorplans with gourmet kitchens, lofts, upstairs laundry rooms and a GenSmart Suite® at Kadence.

The COVID-19 pandemic has affected everything, and building predictions during this time is difficult. Data science teams need to update their models to prepare for the recovery, and know how to properly train 2020 data models to learn from the coronavirus anomaly.

Optimus, FQ-GAN and Prevalent bring new ideas to apply generative models at large scale.

Train a "blackbox" GBM model on a real dataset and make it explainable with SHAP.

Check out this comprehensive guide to model optimization techniques.

FiveThirtyEight compared six Covid-19 models for a sense of where we might be…

Tags: coronavirus, FiveThirtyEight, modeling

Mirage JS is helping simplify modern front-end development by providing the ability for front-end engineers to craft applications without relying on an actual back-end service. In this article, I’ll be taking a framework-agnostic approach to show you Mirage JS models and associations. If you haven’t heard of Mirage JS, you can read my previous article in which I introduce it and also integrate it with the progressive framework Vue.js.

In this paper, we generalize the finiteness of models theorem in [BCHM06] to Kawamata log terminal pairs with fixed Kodaira dimension. As a consequence, we prove that a Kawamata log terminal pair with $mathbb{R}-$boundary has a canonical model, and can be approximated by log pairs with $mathbb{Q}-$boundary and the same canonical model.

Starting from the Hamiltonian formulation of ${cal N}{=},2$ supersymmetric Calogero models associated with the classical $A_n, B_n, C_n$ and $D_n$ series and their hyperbolic/trigonometric cousins, we provide their superspace description. The key ingredients include $n$ bosonic and $2n(n{-}1)$ fermionic ${cal N}{=},2$ superfields, the latter being subject to a nonlinear chirality constraint. This constraint has a universal form valid for all Calogero models. With its help we find more general supercharges (and a superspace Lagrangian), which provide the ${cal N}{=},2$ supersymmetrization for bosonic potentials with arbitrary repulsive two-body interactions.

We consider Landau-Ginzburg models stemming from groups comprised of non-diagonal symmetries, and we describe a rule for the mirror LG model. In particular, we present the non-abelian dual group, which serves as the appropriate choice of group for the mirror LG model. We also describe an explicit mirror map between the A-model and the B-model state spaces for two examples. Further, we prove that this mirror map is an isomorphism between the untwisted broad sectors and the narrow diagonal sectors for Fermat type polynomials.

Large-scale pretrained language models are the major driving force behind recent improvements in performance on the Winograd Schema Challenge, a widely employed test of common sense reasoning ability. We show, however, with a new diagnostic dataset, that these models are sensitive to linguistic perturbations of the Winograd examples that minimally affect human understanding. Our results highlight interesting differences between humans and language models: language models are more sensitive to number or gender alternations and synonym replacements than humans, and humans are more stable and consistent in their predictions, maintain a much higher absolute performance, and perform better on non-associative instances than associative ones. Overall, humans are correct more often than out-of-the-box models, and the models are sometimes right for the wrong reasons. Finally, we show that fine-tuning on a large, task-specific dataset can offer a solution to these issues.

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.

Human visual attention is a complex phenomenon. A computational modeling of this phenomenon must take into account where people look in order to evaluate which are the salient locations (spatial distribution of the fixations), when they look in those locations to understand the temporal development of the exploration (temporal order of the fixations), and how they move from one location to another with respect to the dynamics of the scene and the mechanics of the eyes (dynamics). State-of-the-art models focus on learning saliency maps from human data, a process that only takes into account the spatial component of the phenomenon and ignore its temporal and dynamical counterparts. In this work we focus on the evaluation methodology of models of human visual attention. We underline the limits of the current metrics for saliency prediction and scanpath similarity, and we introduce a statistical measure for the evaluation of the dynamics of the simulated eye movements. While deep learning models achieve astonishing performance in saliency prediction, our analysis shows their limitations in capturing the dynamics of the process. We find that unsupervised gravitational models, despite of their simplicity, outperform all competitors. Finally, exploiting a crowd-sourcing platform, we present a study aimed at evaluating how strongly the scanpaths generated with the unsupervised gravitational models appear plausible to naive and expert human observers.

Showing items that do not match search query intent degrades customer experience in e-commerce. These mismatches result from counterfactual biases of the ranking algorithms toward noisy behavioral signals such as clicks and purchases in the search logs. Mitigating the problem requires a large labeled dataset, which is expensive and time-consuming to obtain. In this paper, we develop a deep, end-to-end model that learns to effectively classify mismatches and to generate hard mismatched examples to improve the classifier. We train the model end-to-end by introducing a latent variable into the cross-entropy loss that alternates between using the real and generated samples. This not only makes the classifier more robust but also boosts the overall ranking performance. Our model achieves a relative gain compared to baselines by over 26% in F-score, and over 17% in Area Under PR curve. On live search traffic, our model gains significant improvement in multiple countries.

In recent years there has been a burgeoning interest in the use of computational methods to distinguish between elicited speech samples produced by patients with dementia, and those from healthy controls. The difference between perplexity estimates from two neural language models (LMs) - one trained on transcripts of speech produced by healthy participants and the other trained on transcripts from patients with dementia - as a single feature for diagnostic classification of unseen transcripts has been shown to produce state-of-the-art performance. However, little is known about why this approach is effective, and on account of the lack of case/control matching in the most widely-used evaluation set of transcripts (DementiaBank), it is unclear if these approaches are truly diagnostic, or are sensitive to other variables. In this paper, we interrogate neural LMs trained on participants with and without dementia using synthetic narratives previously developed to simulate progressive semantic dementia by manipulating lexical frequency. We find that perplexity of neural LMs is strongly and differentially associated with lexical frequency, and that a mixture model resulting from interpolating control and dementia LMs improves upon the current state-of-the-art for models trained on transcript text exclusively.

We present the Perceptimatic English Benchmark, an open experimental benchmark for evaluating quantitative models of speech perception in English. The benchmark consists of ABX stimuli along with the responses of 91 American English-speaking listeners. The stimuli test discrimination of a large number of English and French phonemic contrasts. They are extracted directly from corpora of read speech, making them appropriate for evaluating statistical acoustic models (such as those used in automatic speech recognition) trained on typical speech data sets. We show that phone discrimination is correlated with several types of models, and give recommendations for researchers seeking easily calculated norms of acoustic distance on experimental stimuli. We show that DeepSpeech, a standard English speech recognizer, is more specialized on English phoneme discrimination than English listeners, and is poorly correlated with their behaviour, even though it yields a low error on the decision task given to humans.

While the exact definition and implementation of accountability depend on the specific context, at its core accountability describes a mechanism that will make decisions transparent and often provides means to sanction "bad" decisions. As such, accountability is specifically relevant for Cyber-Physical Systems, such as robots or drones, that embed themselves into a human society, take decisions and might cause lasting harm. Without a notion of accountability, such systems could behave with impunity and would not fit into society. Despite its relevance, there is currently no agreement on its meaning and, more importantly, no way to express accountability properties for these systems. As a solution we propose to express the accountability properties of systems using Structural Causal Models. They can be represented as human-readable graphical models while also offering mathematical tools to analyze and reason over them. Our central contribution is to show how Structural Causal Models can be used to express and analyze the accountability properties of systems and that this approach allows us to identify accountability patterns. These accountability patterns can be catalogued and used to improve systems and their architectures.

In recent years, all-neural end-to-end approaches have obtained state-of-the-art results on several challenging automatic speech recognition (ASR) tasks. However, most existing works focus on building ASR models where train and test data are drawn from the same domain. This results in poor generalization characteristics on mismatched-domains: e.g., end-to-end models trained on short segments perform poorly when evaluated on longer utterances. In this work, we analyze the generalization properties of streaming and non-streaming recurrent neural network transducer (RNN-T) based end-to-end models in order to identify model components that negatively affect generalization performance. We propose two solutions: combining multiple regularization techniques during training, and using dynamic overlapping inference. On a long-form YouTube test set, when the non-streaming RNN-T model is trained with shorter segments of data, the proposed combination improves word error rate (WER) from 22.3% to 14.8%; when the streaming RNN-T model trained on short Search queries, the proposed techniques improve WER on the YouTube set from 67.0% to 25.3%. Finally, when trained on Librispeech, we find that dynamic overlapping inference improves WER on YouTube from 99.8% to 33.0%.

Bayesian predictive coding is a putative neuromorphic method for acquiring higher-level neural representations to account for sensory input. Although originating in the neuroscience community, there are also efforts in the machine learning community to study these models. This paper reviews some of the more well known models. Our review analyzes module connectivity and patterns of information transfer, seeking to find general principles used across the models. We also survey some recent attempts to cast these models within a deep learning framework. A defining feature of Bayesian predictive coding is that it uses top-down, reconstructive mechanisms to predict incoming sensory inputs or their lower-level representations. Discrepancies between the predicted and the actual inputs, known as prediction errors, then give rise to future learning that refines and improves the predictive accuracy of learned higher-level representations. Predictive coding models intended to describe computations in the neocortex emerged prior to the development of deep learning and used a communication structure between modules that we name the Rao-Ballard protocol. This protocol was derived from a Bayesian generative model with some rather strong statistical assumptions. The RB protocol provides a rubric to assess the fidelity of deep learning models that claim to implement predictive coding.

By The New York Times The New York Times Company As President Donald Trump presses for states to reopen their economies, his administration is privately projecting a steady rise in the number of cases and deaths from the coronavirus over the next several weeks, reaching about 3,000 daily deaths June 1, according to an internal document obtained by The New York Times, nearly double from the current level of about 1,750.…

A method of extending advanced process control (APC) models includes constructing an APC model table including APC model parameters of a plurality of products and a plurality of work stations. The APC model table includes empty cells and cells filled with existing APC model parameters. Average APC model parameters of the existing APC model parameters are calculated, and filled into the empty cells as initial values. An iterative calculation is performed to update the empty cells with updated values.

Disclosed are methods, systems, and articles of manufactures for implementing physical designs by using multiple force models to iteratively morph a layout decomposition. In addition to attractive force model(s) or repulsive force model(s), the physical implementation also uses a containment force model for grouping multiple design blocks or for confining a node of a cell within the boundary of a container. Another aspect is directed at deriving a first force model at the first hierarchical level from a second force model at the second hierarchical level by directly modifying the second model based at least in part on characteristic(s) of the first hierarchical level and of the second hierarchical level. In a design with multiple hierarchies, a cell-based force model is also used to ensure child nodes of a parent cell stay within a close proximity of the parent node of the parent cell.

A speech recognition method including the steps of receiving a speech input from a known speaker of a sequence of observations and determining the likelihood of a sequence of words arising from the sequence of observations using an acoustic model. The acoustic model has a plurality of model parameters describing probability distributions which relate a word or part thereof to an observation and has been trained using first training data and adapted using second training data to said speaker. The speech recognition method also determines the likelihood of a sequence of observations occurring in a given language using a language model and combines the likelihoods determined by the acoustic model and the language model and outputs a sequence of words identified from said speech input signal. The acoustic model is context based for the speaker, the context based information being contained in the model using a plurality of decision trees and the structure of the decision trees is based on second training data.

Systems and methods are disclosed for evaluating cardiovascular treatment options for a patient. One method includes creating a three-dimensional model representing a portion of the patient's heart based on patient-specific data regarding a geometry of the patient's heart or vasculature; and for a plurality of treatment options for the patient's heart or vasculature, modifying at least one of the three-dimensional model and a reduced order model based on the three-dimensional model. The method also includes determining, for each of the plurality of treatment options, a value of a blood flow characteristic, by solving at least one of the modified three-dimensional model and the modified reduced order model; and identifying one of the plurality of treatment options that solves a function of at least one of: the determined blood flow characteristics of the patient's heart or vasculature, and one or more costs of each of the plurality of treatment options.

Methods, systems, and computer readable media for monitored application of mechanical force to samples using acoustic energy and mechanical parameter value extraction using mechanical response models can be used for determining mechanical property parameters of a sample. An exemplary method includes applying acoustic energy to a sample to apply a mechanical force to the sample, measuring a response by the sample during the application of the acoustic energy, measuring a recovery response of the sample following cessation of the application of the acoustic energy, and determining a value for at least one additional mechanical property parameter of the sample based on the response measured during application of the acoustic energy and the recovery response measured following cessation of the application of acoustic energy.

A computer method of material modeling for crash test dummy finite element models includes the steps of making a material card for the material, applying the material card to validate a finite element model of a crash test dummy component, determining whether the finite element model is acceptable, ending the method if the finite element model is acceptable, and adjusting a relative volume (J) range for the material to make the material soft or stiff if the finite element model is not acceptable.

Methods for creating and using space-time surrogate models of subsurface regions, such as subsurface regions containing at least one hydrocarbon formation. The created surrogate models are explicit models that may be created from implicit models, such as computationally intensive full-physics models. The space-time surrogate models are parametric with respect to preselected variables, such as space, state, and/or design variables, while also indicating responsiveness of the preselected variables with respect to time. In some embodiments, the space-time surrogate model may be parametric with respect to preselected variables as well as to time. Methods for updating and evolving models of subsurface regions are also disclosed.

An exemplary method includes a media service provider system 1) providing an end user of an integrated media service with access to a media program by way of a plurality of different media distribution models, 2) maintaining a catalog that includes comprehensive information about the media program, the comprehensive information being an aggregate of non-redundant information about the media program obtained from a plurality of independent source catalogs corresponding to the plurality of different media distribution models, and 3) providing, based on the comprehensive information included in the catalog, a media service user interface that supports discovery of the comprehensive information about the media program regardless of a user interface context from which a user request for information about the media program is received. Corresponding systems and methods are also described.

In one embodiment, a viewer executing on an electronic device having a touch sensitive display shows a three-dimensional (3D) model of a building created using computer aided design (CAD) software. A plurality of selectable interface nodes are provided at respective locations within the 3D model. Each interface node is linked to at least one corresponding two-dimensional (2D) construction drawing that shows a section view, a plan view, an elevation view or a detail view of the building related to the location of the interface node. In response to receiving input from a user indicating selection of a particular interface node, a menu is displayed with one or more selectable menu options. In response to receiving additional input from the user indicating selection of a particular menu option, a corresponding 2D construction drawing for the particular interface node is displayed in context of the 3D model of the building.

Methods and apparatus are provided for generating computer 3D models of an object, by registering two or more scans of physical models of an object. The scans may be 3D scans registered by a curve-based registration process. A method is provided for generating a 3D model of a portion of a patient's oral anatomy for use in dental restoration design. Also provided are scanning workflows for scanning physical models of an object to obtain a 3D model.

This Mother’s Day is going to be a unique one for most Australians.

New study shows almost no improvement in diversity.

The hip hop stars landed a major fashion campaign.

Rihanna, Jourdan Dunn, Beyoncé, and more grace the top spot.

Five different business models for mobile fare payment apps are examined, as the world of apps used by transit agencies in the United States and Canada continues to steadily grow. The TRB Transit Cooperative Research Program's TCRP Synthesis 148: Business Models for Mobile Fare Apps documents current practices and experiences of transit agencies that offer mobile fare payment applications to transit riders. The report includes case examples from six cities: Santa Monica, Denver, Austin, Chicago, Dallas, ...

Upcoming Models No applications have been received since the last update. Finished Models No finished forms have been received since the last update. Moved Models No moved forms have been received since the last update. Closed Models (Open for application) No closed forms have been received since the last update. Report Forms: None. Overdue Models: […]

Amazon is taking up to $100 off the Apple Watch Series 5 this week, with prices starting at $299.99 for the 40mm GPS models. Only the Gold Aluminum Case with Pink Sport Band is available at this price. If you order today, the Apple Watch should arrive sometime next week.

Note: MacRumors is an affiliate partner with Amazon. When you click a link and make a purchase, we may receive a small payment, which helps us keep the site running.

The Apple Watch Series 5 was released in September 2019 with a new OLED screen that supports an always-on feature, which represents the biggest change to the Series 5 models. The newest Apple Watch is available in 40mm and 44mm sizes, and it has the overall same design as the Series 4 models.

$100 OFF

Apple Watch S5 (40mm, GPS) for $299.99

If you're shopping for a cellular model, there are also a few solid discounts on Amazon for these devices. You can get the Gold Aluminum Case with Pink Sport Band (40mm) for $399.00, down from $499.00. Likewise, the Silver Aluminum Case with White Sport Band (40mm) is $399.00 right now.

For the 44mm cellular models, a solid deal is the Space Gray Aluminum Case with Black Sport Band at $429.00, down from $529.00. You'll find the same price on the Silver Aluminum Case with White Sport Band and the Gold Aluminum Case with Pink Sport Band.

Across the board, these sales are either new low prices on the Apple Watch Series 5, or they're matching previous low prices seen on these models on Amazon. There are a few other deals going on for different Series 5 models as well, including numerous 44mm cellular devices that are about $50 off Apple's original prices. Be sure to head to Amazon to check out the full sale before these prices expire, or the retailer runs out of stock.

Keep up with all of this week's best discounts on Apple products and related accessories in our dedicated Apple Deals roundup.
This article, "Deals: Apple Watch Series 5 Models Discounted by Up to $100 on Amazon" first appeared on MacRumors.com

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On this month’s episode of the Arsenal Women Arsecast, Tim is joined by author, broadcaster and journalist Dr. Carrie Dunn, Carrie is an expert and academic on all things women’s sport and is the author of two books about the Lionesses- Roar of the Lionesses (2016) and Pride of the Lionesses (2019)- which you can by here https://www.pitchpublishing.co.uk/shop/pride-lionesses – or from your local bookstore if they are still delivering- they would probably appreciate the business right now!

Tim talks to Carrie about role modelling in women’s football, players in the women’s game are called upon to be role models, especially for young girls and to help increase participation in the sport. Many players see this as a privilege and an important responsibility- but to what extent is it fair? Is this just invisible labour we blithely place on female athletes?

Then Tim talks to Gunners defender Louise Quinn, who is a patron for lots of initiatives geared towards increasing participation in sport for young girls. Louise talks about why she believes in the power of role modelling and about her female role models when she was growing up. Tim also talks to Louise about how her and her teammates are handling training in times of quarantine and social distancing in response to the COVID-19 pandemic.

 

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A model predicts COVID-19 deaths in the U.S. will drop to zero by June. Another suggests without a vaccine, the coronavirus will be with us for years.

Looking through Sunday's Image section, reader Katherine Wertheim of Ventura felt something was missing.

CAR TAX changes and grants for those purchasing brand new fully electric vehicles means there's "never been a better time" to switch, according to an EV expert.

RK Tangirala
Nov 1, 1995; 36:2320-2328
Articles

A new study from University of Michigan climate researchers concludes that some of the latest-generation climate models may be overly sensitive to carbon dioxide increases and therefore project future warming that is unrealistically high.

A lone developer from the company that provided aerodynamic analysis for the cars in the PlayStation game Gran Turismo has applied the tooling to demonstrate the spread of germs with and without wearing a mask

Below, Mac Hyman, Tulane University, talks about types of mathematical models--their strengths and weaknesses--the data that we currently have and what we really need, and what models can tell us about a possible second wave.

At the beginning of the video, he thanks the mathematics community for its work, and near the end says, "Our mathematical community is really playing a central role in helping to predict the spread, and help mitigate this epidemic, and prioritize our efforts. …Do not underestimate the power that mathematics can have in helping to mitigate this epidemic—-we have a role to play."

See the full set of videos on modeling COVID-19 and see media coverage of mathematics' role in modeling the pandemic.

(University of Colorado at Boulder) What can researchers do when their mathematical models of the spread of infectious diseases don't match real-world data? One research team is working on a solution.

(Penn State) A new process to harness multiple disease models for outbreak management has been developed by an international team of researchers. The team will immediately implement the process to help inform policy decisions for the COVID-19 outbreak.

Michele Heisler
Oct 1, 2007; 20:214-221
Articles