When I wrote recently that California's recall election process was terribly flawed and in need of serious reform, the angry messages came flowing in, calling me a hypocrite. The writers didn't believe for a second that I objected to the recall on principle -- they assumed that as a loyal Democrat, I was just shilling for Gov. Gavin Newsom. "You're in his pocket," said one dismissive tweet. Would I still be vehemently opposed to the recall if, instead of being used against a Democratic governor, it was targeting a Trump-supporting right-wing governor -- someone who, say, was unleashing the fossil fuel industry, hoping to do away with the minimum wage and fighting mask and vaccine mandates? Would I still feel the recall was a troubling, badly structured, overused, undemocratic tool that should be reformed or abolished?

In recent years, medical resources in China have been in a state of short supply. Doctors from the top hospitals in modern cities have to face tedious consultation or surgical work every day. Meanwhile, some doctors also need to do scientific research related work for the development of medicine. However, in doctors' daily work, a large part of the work is cumbersome but easy to use IT systems to improve efficiency, such as the management of medical terms, the collection of medical knowledge, and so on. The organization of medical knowledge is mostly for daily diagnosis and treatment in accordance with certain gold standards or guidelines, and many of them are based on some established rules to make inference.

Autonomous vehicles need to model the behavior of surrounding human driven vehicles to be safe and efficient traffic participants. Existing approaches to modeling human driving behavior have relied on both data-driven and rule-based methods. While data-driven models are more expressive, rule-based models are interpretable, which is an important requirement for safety-critical domains like driving. However, rule-based models are not sufficiently representative of data, and data-driven models are yet unable to generate realistic traffic simulation due to unrealistic driving behavior such as collisions. In this paper, we propose a methodology that combines rule-based modeling with data-driven learning. While the rules are governed by interpretable parameters of the driver model, these parameters are learned online from driving demonstration data using particle filtering. We perform driver modeling experiments on the task of highway driving and merging using data from three real-world driving demonstration datasets. Our results show that driver models based on our hybrid rule-based and data-driven approach can accurately capture real-world driving behavior. Further, we assess the realism of the driving behavior generated by our model by having humans perform a driving Turing test, where they are asked to distinguish between videos of real driving and those generated using our driver models.

Event definitions in Complex Event Processing systems are constrained by the expressiveness of each system's language. Some systems allow the definition of instantaneous complex events, while others allow the definition of durative complex events. While there are exceptions that offer both options, they often lack of intervals relations such as those specified by the Allen's interval algebra. In this paper, we propose a new logic based temporal phenomena definition language, specifically tailored for Complex Event Processing, that allows the representation of both instantaneous and durative phenomena and the temporal relations between them. Moreover, we demonstrate the expressiveness of our proposed language by employing a maritime use case where we define maritime events of interest. Finally, we analyse the execution semantics of our proposed language for stream processing and introduce the `Phenesthe' implementation prototype.

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.