Sala de prensa de la AHA sobre el COVID-19 DALLAS, 6 de abril del 2020— La American Heart Association cree que el hecho de permitir prematuramente que estudiantes del área de la salud proporcionen cuidado de pacientes durante la pandemia del COVID-19...

DALLAS, 8 de abril del 2020— El impacto mundial del COVID-19 continúa aumentando y, cada día, la comunidad científica aprende más sobre el efecto y la interacción de las enfermedades cardiovasculares con el COVID-19. Juntos, la American Heart Association...

Sala de prensa de la AHA sobre el COVID-19 Atención con el contenido actualizado a continuación. DALLAS, 9 de abril del 2020– Debido al aumento exponencial actual de la incidencia del COVID-19 en todo el mundo, el porcentaje de paros cardíacos con el...

Research Highlights: Black immigrants from Africa and from the Caribbean differ from U.S.-born blacks in rates of high blood pressure, diabetes, smoking and overweight/obesity. The research supports a more detailed look at black populations and the...

Puntos destacados de la investigación: En un estudio de más de 37 000 estadounidenses, aquellos que comieron la mayor cantidad de proteína vegetal tenían un 29% menos de probabilidades de morir de una enfermedad coronaria. Sustituir una porción por...

Embargoed until 8 a.m. CT / 9 a.m. ET Thursday, April 9, 2020   AHA COVID-19 newsroom   DALLAS, April 9, 2020 — With COVID-19 incidence currently increasing exponentially worldwide, the percentage of cardiac arrests with COVID-19 are also likely to...

  DALLAS, 23 de abril del 2020 — A medida que el distanciamiento social mantiene a las familias separadas, es posible que muchos de los que cuidan de un padre o un ser querido que padece insuficiencia cardíaca se pregunten cómo...

Botones para compartir de AddThis Compartir en Facebook Compartir en Twitter Compartir por correo electrónico Compartir para imprimir DALLAS y ARLINGTON, 23 de abril del 2020 — Debido a que la ciencia que emerge en torno al COVID-19...

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.

The impending release of WordPress 3.6 has been pushed back one more week to April 29. At this time, WordPress 3.6 is not yet feature complete (meaning that all intended new features have not been entirely finished), so the decision was made to push the first beta release back two weeks to March 27 and the final release back one week to April 29. This will allow the team time to focus on finalizing the in-progress new features so that they (and the brave folks who enjoy running beta software) can simply focus on testing and bug fixing rather than polishing up partial features.

For many marketers, emails are the lifeline for most marketing efforts. Every SPAM complaint, unsubscribe, or bounce has an impact on the current ROI as well as on the sender’s reputation which affects the ROI of the future campaigns. Yet the sender reputation, that you accumulate over the period of multiple email campaigns, is only...

Summer is going to look a bit different in the Corridor this year as many, long-cherished events are being canceled or postponed. And the organizations that run those events want you to know they...

Summer is going to look a bit different in the Corridor this year as many, long-cherished events are being canceled or postponed. And the organizations that run those events want you to know they aren’t any more happy about it than you are.

The organizers of these events are having to make unprecedented, tough decisions.

“Cancellation is not a good word in our business,” said Chuck Swanson, Building a Legacy executive director of Hancher. “It is something that we really don’t want to do and it takes a lot for us to come to that.

“We live for the live performance and bringing the artists and audiences together. That’s the happiest time for me, so none of these decisions have been easy.”

Hancher has had to cancel numerous upcoming events in the past few months that would have brought to Iowa City in artists from all over the country and the world. It also is holding off announcing its upcoming season — which it typically would be doing at this time of year.

this isn’t something the staff has faced since the floods of 2008 and because they book events so far in advance they are confronting additional challenges.

“You know there’s so much that goes into a show before it happens,” Swanson said. “I just think of all the anticipation, booking the artists, advancing the show, setting ticket prices, advertising and then ticket sales.

“It’s like a farmer who does all this work to get his crops ready and then at the end of the season ends up with nothing to harvest.”

He noted Hancher has been reaching out to its booked performers and, in some cases, have had performers reach out to them to cancel upcoming shows.

The significant time and resources that go into planning large-scale events is the main factor in necessitating cancellation discussions and decisions at many organizations.

“Many logistical items have to be coordinated, from renting shuttles to scheduling volunteers and staff. Initial planning for some events begins as early as 12 to 18 months in advance and proceeds all the way up to the day of the event,” said John Myers, Indian Creek Nature Center executive director.

Citing the center’s annual Maple Syrup Festival, he noted food represents a significant cost and often cannot be saved or reused.

“We have had to be mindful of the financial resources available to us and ensure that we wisely manage those to ensure (the center) can emerge from this pandemic as a functioning and healthy organization,” he said.

“None of the decisions to cancel events or how to handle subsequent financial losses are easy and they challenge everyone,” Myers added. “As our whole lives have been upended, it makes even the simplest of decisions harder and that takes an impact on morale.”

He acknowledged staff members aren’t the only ones feeling the strain.

“We have a significant core of volunteers who are no longer able to give their time, which also creates a strain on morale and increases the amount of work that needs to be done when we return,” he pointed out.

Another primary factor is what is allowed and considered safe by the city, state and Iowa Department of Public Health.

“At this point, only allowing groups of 10 or less is a far cry from the thousands or people we usually see at the Iowa Arts Festival,” said Lisa Barnes, executive director of Summer of the Arts in Iowa City, which produces the Iowa Arts Festival.

“The governor has announced that reopening the state will be done in stages, and based on what we’ve found from other events around the country, concerts and large festivals will be the last to open,” he noted.

Summer of the Arts announced just last week that the Iowa Arts Festival would not take place this year, a month in advance of the event.

“We needed to make a decision so that we can move forward with alternative plans,” Barnes said, noting the organization has had questions about the Iowa City Jazz Festival, scheduled for July 3 through 5 and added a decision regarding that festival and July programming will be made by mid-late May.

“We also needed to make the decision far enough out to be able to work with our performers and cancel the agreements,” she said.

On Wednesday, Gov. Kim Reynolds loosened some but not all of the social-distancing restrictions for the remaining 22 counties she had put in place.

Discussions about the future of these events have been happening for weeks for many organizations, highlighting they are not taken lightly.

Carissa Johnson, executive director of the Cedar Rapids Freedom Festival, said conversations about the future of this year’s event started in mid-March, right around the time the Cedar Rapids SaPaDaPaSo Parade announced its cancellation for 2020.

“We plan year ’round for the two- to three-week festival,” Johnson explained.

“Our planning really ramps up in April and May, and we have many more costs associated with producing the festival the closer we get to the start. In order to protect our time and resources, we elected to cancel before we had more costs and variables to consider.”

As for who is making the final decision, organizations said many stakeholders are involved. Barnes said the decision on the Iowa City Arts Festival, for example, included staff, the board of directors, festival planning committees, the city of Iowa City and Johnson County Public Health, along with input from some of the vendors, artists and performers.

Tapping into experts in those public health field has been key as well.

“We have these assets, people, at the University (of Iowa), that have been really helpful as we make these decisions about canceling and as we prepare to think about reopening,” Hancher’s Swanson said.

The Freedom Festival include staff and board members in discussions, with recommendations from Linn County Public Health and the city of Cedar Rapids, factoring in the health, safety and well-being of the community.

“We are just as heartbroken as the rest of the community, but this decision was to protect our community as much as possible,” Swanson said.

“This community is a family and we will all get through this together and come back stronger next year.”

Myers noted organizations such as the Indian Creek Nature Center are also rely on advice from national associations, such as the American Alliance of Museums, and discussions among the leadership of many local cultural groups.

“For many events, we have also reached out to participants to gather their input and comfort level of attending once we are able to reopen,” Myers said.

The financial effects of having to cancel is stressful for organizations, too.

“Financially, this has been a hard time for the Nature Center to endure,” Myers pointed out. “We’ve had over 100 different programs, events and facility rentals canceled between March 15 and April 30, and our losses are currently over $250,000. As we approach the summer, there are a number of other events we continue to review, including our popular summer camps.”

The Nature Center has postponed a national conference to be held there in September — due to indications of low participation — for peers from around the nation who run not-for-profit and government nature centers.

“We are losing thousands of dollars in vendor fees and sales receipts because we had to cancel,” said Barnes, of Summer of the Arts.

“We have sponsors tied to certain events, like the Iowa Arts Festival, that in some cases want to carry over their support to next year, which impacts our fundraising for this year and next.”

She noted her group already has been made aware of funding that won’t be coming in from some sponsors next year due to the financial impact those organizations are facing as well.

And that can be tough.

“When we cancel, our whole staff is involved — from the box audience and public engagement folks to the technical production team and our front-of-house staff,” Swanson said.

“Our communication is key in talking through it all and then sharing clear messages with our audiences, especially in terms of refunds. But we’ve been encouraged by so many generous friends of Hancher donating their ticket purchase price back to us.”

While disappointment still is thick in the air, organizations don’t plan to abandon their missions and is keeping an eye on serving the public.

“This is a challenging time for everyone, and our board and staff is committed to finding creative and non-traditional solutions to ensure the Freedom Festival’s return,” Johnson said. “The community and our stakeholders have been tremendous supports of the Freedom Festival and we believe they will continue to do so in the future.

“We ask for understanding and patience as we try to navigate this crisis and what we can still provide for our community.”

Freedom Festival buttons will be sold this year as they’ve already been made, and “It’s a way the community can show their support,” Johnson said.

Barnes agreed and noted the Iowa Arts Festival committee is working on ways to support the performers, artists and vendors they had scheduled by trying to develop some virtual opportunities for engagement.

While the show, or events, might not go on, organizers said they very much want to remain connected to their audiences and attendees.

“I want to make sure everybody knows we care about them and that we’re trying to find ways to stay connected because I think we’re all in this together and the arts are one of the best ways for people to get through difficult times,” Swanson said.

Myers agreed.

“Indian Creek Nature Center will be ready to welcome guests and visitors back to our events as soon as we are able to do so safely,” he said.

“In the meantime, we hope everyone finds peace in nature by taking a hike or bike ride, having a picnic or just enjoying time outside.”

A poster campaign has recently caught my attention and I’m impressed by the impact of the message it contains. The elements that work well here can be as relevant to business.

Tags: coronavirus, FiveThirtyEight, modeling

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 →

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.

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.

We present a method for optimal path planning of human walking paths in mountainous terrain, using a control theoretic formulation and a Hamilton-Jacobi-Bellman equation. Previous models for human navigation were entirely deterministic, assuming perfect knowledge of the ambient elevation data and human walking velocity as a function of local slope of the terrain. Our model includes a stochastic component which can account for uncertainty in the problem, and thus includes a Hamilton-Jacobi-Bellman equation with viscosity. We discuss the model in the presence and absence of stochastic effects, and suggest numerical methods for simulating the model. We discuss two different notions of an optimal path when there is uncertainty in the problem. Finally, we compare the optimal paths suggested by the model at different levels of uncertainty, and observe that as the size of the uncertainty tends to zero (and thus the viscosity in the equation tends to zero), the optimal path tends toward the deterministic optimal path.

A reaction-diffusion model was developed describing the spread of the COVID-19 virus considering the mean daily movement of susceptible, exposed and asymptomatic individuals. The model was calibrated using data on the confirmed infection and death from France as well as their initial spatial distribution. First, the system of partial differential equations is studied, then the basic reproduction number, R0 is derived. Second, numerical simulations, based on a combination of level-set and finite differences, shown the spatial spread of COVID-19 from March 16 to June 16. Finally, scenarios of unlockdown are compared according to variation of distancing, or partially spatial lockdown.

The main aim of this paper is to detect dynamical properties of the Gy"orgyi-Field model of the Belousov-Zhabotinsky chemical reaction. The corresponding three-variable model given as a set of nonlinear ordinary differential equations depends on one parameter, the flow rate. As certain values of this parameter can give rise to chaos, the analysis was performed in order to identify different dynamics regimes. Dynamical properties were qualified and quantified using classical and also new techniques. Namely, phase portraits, bifurcation diagrams, the Fourier spectra analysis, the 0-1 test for chaos, and approximate entropy. The correlation between approximate entropy and the 0-1 test for chaos was observed and described in detail. Moreover, the three-stage system of nested subintervals of flow rates, for which in every level the 0-1 test for chaos and approximate entropy was computed, is showing the same pattern. The study leads to an open problem whether the set of flow rate parameters has Cantor like structure.

We derive sufficient conditions for exponential decay of solutions of the delay negative feedback equation with distributed delay. The conditions are written in terms of exponential moments of the distribution. Our method only uses elementary tools of calculus and is robust towards possible extensions to more complex settings, in particular, systems of delay differential equations. We illustrate the applicability of the method to particular distributions - Dirac delta, Gamma distribution, uniform and truncated normal distributions.

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.

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.

A graph $G=(V,E)$ is $delta$-hyperbolic if for any four vertices $u,v,w,x$, the two larger of the three distance sums $d(u,v)+d(w,x)$, $d(u,w)+d(v,x)$, and $d(u,x)+d(v,w)$ differ by at most $2delta geq 0$. Recent work shows that many real-world graphs have small hyperbolicity $delta$. This paper describes the eccentricity terrain of a $delta$-hyperbolic graph. The eccentricity function $e_G(v)=max{d(v,u) : u in V}$ partitions the vertex set of $G$ into eccentricity layers $C_{k}(G) = {v in V : e(v)=rad(G)+k}$, $k in mathbb{N}$, where $rad(G)=min{e_G(v): vin V}$ is the radius of $G$. The paper studies the eccentricity layers of vertices along shortest paths, identifying such terrain features as hills, plains, valleys, terraces, and plateaus. It introduces the notion of $eta$-pseudoconvexity, which implies Gromov's $epsilon$-quasiconvexity, and illustrates the abundance of pseudoconvex sets in $delta$-hyperbolic graphs. In particular, it shows that all sets $C_{leq k}(G)={vin V : e_G(v) leq rad(G) + k}$, $kin mathbb{N}$, are $(2delta-1)$-pseudoconvex. Additionally, several bounds on the eccentricity of a vertex are obtained which yield a few approaches to efficiently approximating all eccentricities. An $O(delta |E|)$ time eccentricity approximation $hat{e}(v)$, for all $vin V$, is presented that uses distances to two mutually distant vertices and satisfies $e_G(v)-2delta leq hat{e}(v) leq {e_G}(v)$. It also shows existence of two eccentricity approximating spanning trees $T$, one constructible in $O(delta |E|)$ time and the other in $O(|E|)$ time, which satisfy ${e}_G(v) leq e_T(v) leq {e}_G(v)+4delta+1$ and ${e}_G(v) leq e_T(v) leq {e}_G(v)+6delta$, respectively. Thus, the eccentricity terrain of a tree gives a good approximation (up-to an additive error $O(delta))$ of the eccentricity terrain of a $delta$-hyperbolic graph.

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.

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.

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.

The accurate assembly of the system matrix is an important step in any code that solves partial differential equations on a mesh. We either explicitly set up a matrix, or we work in a matrix-free environment where we have to be able to quickly return matrix entries upon demand. Either way, the construction can become costly due to non-trivial material parameters entering the equations, multigrid codes requiring cascades of matrices that depend upon each other, or dynamic adaptive mesh refinement that necessitates the recomputation of matrix entries or the whole equation system throughout the solve. We propose that these constructions can be performed concurrently with the multigrid cycles. Initial geometric matrices and low accuracy integrations kickstart the multigrid, while improved assembly data is fed to the solver as and when it becomes available. The time to solution is improved as we eliminate an expensive preparation phase traditionally delaying the actual computation. We eliminate algorithmic latency. Furthermore, we desynchronise the assembly from the solution process. This anarchic increase of the concurrency level improves the scalability. Assembly routines are notoriously memory- and bandwidth-demanding. As we work with iteratively improving operator accuracies, we finally propose the use of a hierarchical, lossy compression scheme such that the memory footprint is brought down aggressively where the system matrix entries carry little information or are not yet available with high accuracy.

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.

Deep learning-based object detection and instance segmentation have achieved unprecedented progress. In this paper, we propose Complete-IoU (CIoU) loss and Cluster-NMS for enhancing geometric factors in both bounding box regression and Non-Maximum Suppression (NMS), leading to notable gains of average precision (AP) and average recall (AR), without the sacrifice of inference efficiency. In particular, we consider three geometric factors, i.e., overlap area, normalized central point distance and aspect ratio, which are crucial for measuring bounding box regression in object detection and instance segmentation. The three geometric factors are then incorporated into CIoU loss for better distinguishing difficult regression cases. The training of deep models using CIoU loss results in consistent AP and AR improvements in comparison to widely adopted $ell_n$-norm loss and IoU-based loss. Furthermore, we propose Cluster-NMS, where NMS during inference is done by implicitly clustering detected boxes and usually requires less iterations. Cluster-NMS is very efficient due to its pure GPU implementation, , and geometric factors can be incorporated to improve both AP and AR. In the experiments, CIoU loss and Cluster-NMS have been applied to state-of-the-art instance segmentation (e.g., YOLACT), and object detection (e.g., YOLO v3, SSD and Faster R-CNN) models. Taking YOLACT on MS COCO as an example, our method achieves performance gains as +1.7 AP and +6.2 AR$_{100}$ for object detection, and +0.9 AP and +3.5 AR$_{100}$ for instance segmentation, with 27.1 FPS on one NVIDIA GTX 1080Ti GPU. All the source code and trained models are available at https://github.com/Zzh-tju/CIoU

Image-based tracking of medical instruments is an integral part of many surgical data science applications. Previous research has addressed the tasks of detecting, segmenting and tracking medical instruments based on laparoscopic video data. However, the methods proposed still tend to fail when applied to challenging images and do not generalize well to data they have not been trained on. This paper introduces the Heidelberg Colorectal (HeiCo) data set - the first publicly available data set enabling comprehensive benchmarking of medical instrument detection and segmentation algorithms with a specific emphasis on robustness and generalization capabilities of the methods. Our data set comprises 30 laparoscopic videos and corresponding sensor data from medical devices in the operating room for three different types of laparoscopic surgery. Annotations include surgical phase labels for all frames in the videos as well as instance-wise segmentation masks for surgical instruments in more than 10,000 individual frames. The data has successfully been used to organize international competitions in the scope of the Endoscopic Vision Challenges (EndoVis) 2017 and 2019.

We show that combining a prediction model (based on neural networks), with a new method of test pooling (better than the original Dorfman method, and better than double-pooling) called 'Grid', we can reduce the number of Covid-19 tests by 73%.

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.

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

Reinforcement learning methods have been used to compute dialog policies from language-based interaction experiences. Efficiency is of particular importance in dialog policy learning, because of the considerable cost of interacting with people, and the very poor user experience from low-quality conversations. Aiming at improving the efficiency of dialog policy learning, we develop algorithm LHUA (Learning with Hindsight, User modeling, and Adaptation) that, for the first time, enables dialog agents to adaptively learn with hindsight from both simulated and real users. Simulation and hindsight provide the dialog agent with more experience and more (positive) reinforcements respectively. Experimental results suggest that, in success rate and policy quality, LHUA outperforms competitive baselines from the literature, including its no-simulation, no-adaptation, and no-hindsight counterparts.

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%.

Selecting an appropriate model to forecast product demand is critical to the manufacturing industry. However, due to the data complexity, market uncertainty and users' demanding requirements for the model, it is challenging for demand analysts to select a proper model. Although existing model selection methods can reduce the manual burden to some extent, they often fail to present model performance details on individual products and reveal the potential risk of the selected model. This paper presents DFSeer, an interactive visualization system to conduct reliable model selection for demand forecasting based on the products with similar historical demand. It supports model comparison and selection with different levels of details. Besides, it shows the difference in model performance on similar products to reveal the risk of model selection and increase users' confidence in choosing a forecasting model. Two case studies and interviews with domain experts demonstrate the effectiveness and usability of DFSeer.

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.

Public key encryption with equality test (PKEET) allows testing whether two ciphertexts are generated by the same message or not. PKEET is a potential candidate for many practical applications like efficient data management on encrypted databases. Potential applicability of PKEET leads to intensive research from its first instantiation by Yang et al. (CT-RSA 2010). Most of the followup constructions are secure in the random oracle model. Moreover, the security of all the concrete constructions is based on number-theoretic hardness assumptions which are vulnerable in the post-quantum era. Recently, Lee et al. (ePrint 2016) proposed a generic construction of PKEET schemes in the standard model and hence it is possible to yield the first instantiation of PKEET schemes based on lattices. Their method is to use a $2$-level hierarchical identity-based encryption (HIBE) scheme together with a one-time signature scheme. In this paper, we propose, for the first time, a direct construction of a PKEET scheme based on the hardness assumption of lattices in the standard model. More specifically, the security of the proposed scheme is reduces to the hardness of the Learning With Errors problem.

Several nonlinear model reduction techniques are compared for the three cases of the non-parallel version of the Kuramoto-Sivashinsky equation, the transient regime of flow past a cylinder at $Re=100$ and fully developed flow past a cylinder at the same Reynolds number. The linear terms of the governing equations are reduced by Galerkin projection onto a POD basis of the flow state, while the reduced nonlinear convection terms are obtained either by a Galerkin projection onto the same state basis, by a Galerkin projection onto a POD basis representing the nonlinearities or by applying the Discrete Empirical Interpolation Method (DEIM) to a POD basis of the nonlinearities. The quality of the reduced order models is assessed as to their stability, accuracy and robustness, and appropriate quantitative measures are introduced and compared. In particular, the properties of the reduced linear terms are compared to those of the full-scale terms, and the structure of the nonlinear quadratic terms is analyzed as to the conservation of kinetic energy. It is shown that all three reduction techniques provide excellent and similar results for the cases of the Kuramoto-Sivashinsky equation and the limit-cycle cylinder flow. For the case of the transient regime of flow past a cylinder, only the pure Galerkin techniques are successful, while the DEIM technique produces reduced-order models that diverge in finite time.

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

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