Businesses generally run campaigns of 8-10 weeks duration with weekly e-mails sent to the reachable customer base. Since the customer's purchase pattern depends on the nature of products in the product catalog, the time to the next purchase is usually a month or more, depending on the category. As a result, for most of the customers, the content being sent across the weekly campaigns is usually the same because the model recommendations do not change weekly based on the historical data. Therefore, stagnant recommendations over a period of 3 to 4 weeks may lead to a bad customer experience. On the flip side, based on the frequency of purchase, sending e-mails with similar content may also serve as a reminder in case the customer misses any of the previous e-mails.

We present a universally consistent learning rule whose expected error is monotone non-increasing with the sample size under every data distribution. The question of existence of such rules was brought up in 1996 by Devroye, Gy\"orfi and Lugosi (who called them "smart"). Our rule is fully deterministic, a data-dependent partitioning rule constructed in an arbitrary domain (a standard Borel space) using a cyclic order. The central idea is to only partition at each step those cyclic intervals that exhibit a sufficient empirical diversity of labels, thus avoiding a region where the error function is convex.

The financial industry is always innovating alongside technology advancements to improve efficiency and enhance experiences for credit unions and their members. In recent years, we've seen the trend of faster payments, such as Zelle and Same Day ACH, become pervasive. Even in the last year, when the need for easy-to-use digital channels became a priority, community banks and credit unions sought to provide members with as many benefits as possible. According to a study by Aite Group, 82% of community banks and credit unions would consider adding an additional payment rail to improve customer experience. However, this rapid shift to faster payments has put pressure on teams worldwide, and small credit unions are finding that they're stretched too thin to properly accommodate the needs of their members.

With increasing linkage within value chains, the IT systems of different companies are also being connected with each other. This enables the integration of services within the movement of Industry 4.0 in order to improve the quality and performance of the processes. Enterprise architecture models form the basis for this with a better buisness IT-alignment. However, the heterogeneity of the modeling frameworks and description languages makes a concatenation considerably difficult, especially differences in syntax, semantic and relations. Therefore, this paper presents a transformation engine to convert enterprise architecture models between several languages. We developed the first generic translation approach that is free of specific meta-modeling, which is flexible adaptable to arbitrary modeling languages. The transformation process is defined by various pattern matching techniques using a rule-based description language. It uses set theory and first-order logic for an intuitive description as a basis. The concept is practical evaluated using an example in the area of a large German IT-service provider. Anyhow, the approach is applicable between a wide range of enterprise architecture frameworks.

Algorithmic decision support is rapidly becoming a staple of personalized medicine, especially for high-stakes recommendations in which access to certain information can drastically alter the course of treatment, and thus, patient outcome; a prominent example is radiomics for cancer subtyping. Because in these scenarios the stakes are high, it is desirable for decision systems to not only provide recommendations but supply transparent reasoning in support thereof. For learning-based systems, this can be achieved through an interpretable design of the inference pipeline. Herein we describe an automated yet interpretable system for uveal melanoma subtyping with digital cytology images from fine needle aspiration biopsies. Our method embeds every automatically segmented cell of a candidate cytology image as a point in a 2D manifold defined by many representative slides, which enables reasoning about the cell-level composition of the tissue sample, paving the way for interpretable subtyping of the biopsy. Finally, a rule-based slide-level classification algorithm is trained on the partitions of the circularly distorted 2D manifold. This process results in a simple rule set that is evaluated automatically but highly transparent for human verification. On our in house cytology dataset of 88 uveal melanoma patients, the proposed method achieves an accuracy of 87.5% that compares favorably to all competing approaches, including deep "black box" models. The method comes with a user interface to facilitate interaction with cell-level content, which may offer additional insights for pathological assessment.

As per some research, AI acts better than humans when it is about diagnosing diseases. You can notice that AI-based technologies are outperforming radiologists at identifying malignant tumors during clinical trials. After observing the excellent outcome, the clinicians believe that AI will replace human efforts in the medical field, but not immediately. In this article, you will get to know the potential of Artificial Intelligence and the significant obstacles to the swift execution of AI in healthcare. Machine Learning is perhaps the most common AI application that enables the system to learn the data and improve the real-time experience without any programming.

Organizations of all sizes and across all industries gather and analyze metrics or key performance indicators (KPIs) to help their businesses run effectively and efficiently. Operational metrics are used to evaluate performance, compare results, and track relevant data to improve business outcomes. For example, you can use operational metrics to determine application performance (the average time it takes to render a page for an end user) or application availability (the duration of time the application was operational). One challenge that most organizations face today is detecting anomalies in operational metrics, which are key in ensuring continuity of IT system operations. Traditional rule-based methods are manual and look for data that falls outside of numerical ranges that have been arbitrarily defined.

Here, in two fMRI experiments, we demonstrate a mechanism of abstraction built upon the valuation of sensory features. Human volunteers learned novel association rules based on simple visual features. Reinforcement-learning algorithms revealed that, with learning, high-value abstract representations increasingly guided participant behaviour, resulting in better choices and higher subjective confidence. We also found that the brain area computing value signals – the ventromedial prefrontal cortex – prioritised and selected latent task elements during abstraction, both locally and through its connection to the visual cortex. Such a coding scheme predicts a causal role for valuation. Hence, in a second experiment, we used multivoxel neural reinforcement to test for the causality of feature valuation in the sensory cortex, as a mechanism of abstraction.

A simple and effective rule of 3 to consider when leading prospects through your sales funnel to the finish line. What if I could give you a simple blueprint on how to effectively navigate a qualified lead through your sales funnel and across the finish line? How many hours and resources could you save and invest elsewhere by sticking to the rule of 3 when it comes to sales communications? One thing I know for certain, our B2B website contact form leads are our most promising leads to close and in most instances, they close in record time. I invite you to stick along for the ride as I reveal how to use the rule of 3 in the sales process.

Los Angeles County recorded more than 1,900 new coronavirus cases Friday, another major jump, as a mandatory mask restriction for inside public places takes effect Saturday night. Over the last week, L.A. County has reported an average of more than 1,000 new coronavirus cases a day -- a tally that, though merely a fraction of the sky-high counts seen during previous surges, is still six times as high as what the county was seeing in mid-June. Daily case numbers have jumped: 1,537 new cases were reported Thursday, and 1,902 more were added Friday. COVID-19 hospitalizations also doubled over that same time period, from 223 on June 15 to 462 on Thursday. More than 8,000 coronavirus-positive patients were hospitalized countywide during the darkest days of the winter wave.