Market Drivers: Rising complexity and diversity of mobile networks is driving the market of deep learning. These increasing complexity has made the managing of the network difficult.

Ripple is looking for a Staff Data Engineer to help build scalable Data infrastructure. You will be part of the effort to enable Ripples to democratize data by developing self-serving and curated data models through financial Hub. Data is core to everything that Ripple does today and you will design and mentor the data engineers and data Ops pods that will be at the center of data. Our data science research direction, machine learning strategy, and analytics will rely on the infrastructure you build. As a hands-on leader for this team,You will incorporate best practices, learn about and leverage new technologies, work with an experienced team of engineers, and be part of a team that has a lot of fun along the way!

For example, there are limited means available to separate repositories containing engineered software projects from other repositories, such as personal projects or those that use GitHub for free cloud storage (Kalliamvakou et al., 2014; Munaiah et al., 2017). To make it easier for users to identify relevant repositories for their wide variety of use cases, GitHub has been adding features to its service, such as README files, topics tags, and showcases (where contributors describe, add keywords, and label their repository). However, these features are insufficient for many use cases. For example, while achieving generalizability of the results is the primary objective of many empirical papers, modern computing research is largely application domain independent (Capiluppi et al., 2020). Application domains are the sections of reality for which a software system is designed. Their importance relies on their serving as the starting point for actual state analysis and usually includes domain-specific language, meaning that developers in this domain think about their project in a specific way, with particular terms and concepts (Züllighoven, 2004). Application domains are not a feature currently implemented by GitHub to catalogue repositories. Previous work has found that repository quality indicators, such as object-oriented metrics, can be "extremely sensitive to application domains" (Capiluppi and Ajienka, 2019), and that the application domain is an important factor in predicting repository popularity (Borges et al., 2016). Furthermore, since documentation of GitHub repositories is often incomplete (Prana et al., 2019), information about the application domain of a repository can be crucial to gain a high-level understanding of its content and purpose.

Recent work shows issues of consistency with explanations, with methods generating local explanations that seem reasonable instance-wise, but that are inconsistent across instances. This suggests not only that instance-wise explanations can be unreliable, but mainly that, when interacting with a system via multiple inputs, a user may actually lose confidence in the system. To better analyse this issue, in this work we treat explanations as objects that can be subject to reasoning and present a formal model of the interactive scenario between user and system, via sequences of inputs, outputs, and explanations. We argue that explanations can be thought of as committing to some model behaviour (even if only prima facie), suggesting a form of entailment, which, we argue, should be thought of as non-monotonic. This allows: 1) to solve some considered inconsistencies in explanation, such as via a specificity relation; 2) to consider properties from the non-monotonic reasoning literature and discuss their desirability, gaining more insight on the interactive explanation scenario.

Since MIP linear problems include both continuous and integer variables, they are proved to belong to the NP-hard class (see [38] for a more detailed analysis), meaning that they are not solvable in polynomial time. The complete exploration of the integer feasible set, whose cardinality grows exponentially with the number of variables, is yet possible to achieve the optimal solution, but for most of the practically significant instances, it would require unacceptable computational effort. In fact, the only way to solve to optimality any mixed-integer problem is to apply some of the well-known Branch and Bound techniques. However, despite combinatorial optimization community provided a great deal of these algorithms, for which the reader should refer to [31, 34, 16], MIP problems complexity is inherent with their belonging to NP-hard class. Therefore, when tackling MIP problems, one either seeks particular structures allowing to bring down the complexity, such as the availability, for a given class of problems, of the optimal formulation or exploits cutting plane generation to dramatically reduce the feasible region dimension. However, we often encounter MIP problems without having any prior knowledge of possible structures and, thus, pursuing the globally optimal solution could be in practice impossible or inefficient, since for our purpose a sub-optimal approximation is considered to be good enough. This makes heuristics one of the most widespread and feasible ways to achieve sub-optimal solutions of MIP problems within an affordable computational time. For the purpose of highlighting the perspective of our research, we can define two classes of MIP heuristics: improvement heuristics and start heuristics.

Pearl, the leader in dental AI solutions, and Midway Dental, one of the leading dental technology and supply distributors in the United States, announced that Pearl is now Midway's preferred artificial intelligence (AI) solutions provider. Pearl's Practice Intelligence AI solution is already available as part of Midway's cutting-edge product sales and distribution inventory, and the collaboration will now also include Second Opinion, the first and only FDA-cleared chairside AI software to help dentists detect numerous conditions in x-rays of dental patients 12 and older. "Our partnership with Midway ensures that forward-thinking dental practices across the U.S. have access to the most advanced AI to streamline their practice management and real-time chairside radiologic evaluations" With over 15,000 dental customers, Michigan-based Midway Dental is the fastest growing full-service dental supply company in the United States, aiming to transform the dental supply industry into something new and progressive. By selecting Pearl as its preferred AI service provider, Midway has reaffirmed its commitment to empowering dental care providers with the most advanced dental technologies for better patient communication, trust, and treatment outcomes. "We selected Pearl as our preferred AI service provider because it is the only dental AI company that can actually deliver on AI's promise for our customers," said Steve Kizy, president of Midway.

Various categories of fintech firms – Buy Now, Pay Later (BNPL), digital lending, payments and collections – are increasingly leveraging predictive models built using artificial intelligence and machine learning to support core business functions such as risk decisioning. According to a report by Grand View Research, Inc., the global AI in fintech market size is expected to reach US$41.16 billion by 2030, growing at a compound annual growth rate (CAGR) of 19.7% in Asia-Pacific alone from 2022 to 2030. The success of AI in fintech, or any business for that matter, hinges on an organisation's ability to make accurate predictions based on data. While internal data (first-party data) needs to be factored into AI models, this data often fails to capture critical predictive features, causing these models to underperform. In these situations, alternative data and feature enrichment can establish a powerful advantage.

In this paper, we present an approach for minimizing the computational complexity of trained Convolutional Neural Networks (ConvNet). The idea is to approximate all elements of a given ConvNet and replace the original convolutional filters and parameters (pooling and bias coefficients; and activation function) with efficient approximations capable of extreme reductions in computational complexity. Low-complexity convolution filters are obtained through a binary (zero-one) linear programming scheme based on the Frobenius norm over sets of dyadic rationals. The resulting matrices allow for multiplication-free computations requiring only addition and bit-shifting operations. Such low-complexity structures pave the way for low-power, efficient hardware designs. We applied our approach on three use cases of different complexity: (i) a "light" but efficient ConvNet for face detection (with around 1000 parameters); (ii) another one for hand-written digit classification (with more than 180000 parameters); and (iii) a significantly larger ConvNet: AlexNet with $\approx$1.2 million matrices. We evaluated the overall performance on the respective tasks for different levels of approximations. In all considered applications, very low-complexity approximations have been derived maintaining an almost equal classification performance.

The inference of politically-charged information from text data is a popular research topic in Natural Language Processing (NLP) at both text- and author-level. In recent years, studies of this kind have been implemented with the aid of representations from transformers such as BERT. Despite considerable success, however, we may ask whether results may be improved even further by combining transformed-based models with additional knowledge representations. To shed light on this issue, the present work describes a series of experiments to compare alternative model configurations for political inference from text in both English and Portuguese languages. Results suggest that certain text representations - in particular, the combined use of BERT pre-trained language models with a syntactic dependency model - may outperform the alternatives across multiple experimental settings, making a potentially strong case for further research in the use of heterogeneous text representations in these and possibly other NLP tasks.

The presence of outliers (anomalous values) in synthetic aperture radar (SAR) data and the misspecification in statistical image models may result in inaccurate inferences. To avoid such issues, the Rayleigh regression model based on a robust estimation process is proposed as a more realistic approach to model this type of data. This paper aims at obtaining Rayleigh regression model parameter estimators robust to the presence of outliers. The proposed approach considered the weighted maximum likelihood method and was submitted to numerical experiments using simulated and measured SAR images. Monte Carlo simulations were employed for the numerical assessment of the proposed robust estimator performance in finite signal lengths, their sensitivity to outliers, and the breakdown point. For instance, the non-robust estimators show a relative bias value $65$-fold larger than the results provided by the robust approach in corrupted signals. In terms of sensitivity analysis and break down point, the robust scheme resulted in a reduction of about $96\%$ and $10\%$, respectively, in the mean absolute value of both measures, in compassion to the non-robust estimators. Moreover, two SAR data sets were used to compare the ground type and anomaly detection results of the proposed robust scheme with competing methods in the literature.