We show that standard machine-learning algorithms may be trained to predict certain invariants of low genus arithmetic curves. Using datasets of size around one hundred thousand, we demonstrate the utility of machine-learning in classification problems pertaining to the BSD invariants of an elliptic curve (including its rank and torsion subgroup), and the analogous invariants of a genus 2 curve. Our results show that a trained machine can efficiently classify curves according to these invariants with high accuracies (>0.97). For problems such as distinguishing between torsion orders, and the recognition of integral points, the accuracies can reach 0.998.

Kidney transplantation is the best treatment for end-stage renal failure patients. The predominant method used for kidney quality assessment is the Cox regression-based, kidney donor risk index. A machine learning method may provide improved prediction of transplant outcomes and help decision-making. A popular tree-based machine learning method, random forest, was trained and evaluated with the same data originally used to develop the risk index (70,242 observations from 1995-2005). The random forest successfully predicted an additional 2,148 transplants than the risk index with equal type II error rates of 10%. Predicted results were analyzed with follow-up survival outcomes up to 240 months after transplant using Kaplan-Meier analysis and confirmed that the random forest performed significantly better than the risk index (p<0.05). The random forest predicted significantly more successful and longer-surviving transplants than the risk index. Random forests and other machine learning models may improve transplant decisions.

Machine Learning (ML) algorithms leverage large datasets to determine patterns and construct meaningful recommendations. Likewise, credit risk modelling is a field with access to a large amount of diverse data where ML can be deployed to add analytical value. In the following analysis, we explore how various ML techniques can be used for assessing probability of default (PD) and compare their performance in a real-world setting. A recent publication by the Bank of England (BoE) and the Financial Conduct Authority (FCA) reports the results of a survey on the use of ML in United Kingdom (UK) financial services.[1] Results show that two-thirds of respondents use ML in some form.

Brain-computer interfaces (BCIs) use decoding algorithms to control prosthetic devices based on brain signals for restoration of lost function. Computer-brain interfaces (CBIs), on the other hand, use encoding algorithms to transform external sensory signals into neural stimulation patterns for restoring sensation or providing sensory feedback for closed-loop prosthetic control. In this article, we introduce brain co-processors, devices that combine decoding and encoding in a unified framework using artificial intelligence (AI) to supplement or augment brain function. Brain co-processors can be used for a range of applications, from inducing Hebbian plasticity for rehabilitation after brain injury to reanimating paralyzed limbs and enhancing memory. A key challenge is simultaneous multi-channel neural decoding and encoding for optimization of external behavioral or task-related goals. We describe a new framework for developing brain co-processors based on artificial neural networks, deep learning and reinforcement learning. These "neural co-processors" allow joint optimization of cost functions with the nervous system to achieve desired behaviors. By coupling artificial neural networks with their biological counterparts, neural co-processors offer a new way of restoring and augmenting the brain, as well as a new scientific tool for brain research. We conclude by discussing the potential applications and ethical implications of brain co-processors.

Social media popularity and importance is on the increase, due to people using it for various types of social interaction across multiple channels. This social interaction by online users includes submission of feedback, opinions and recommendations about various individuals, entities, topics, and events. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Therefore, through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence, which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, natural language processing tasks and other aspects derived from the published studies. Such multi-source information fusion plays a fundamental role in mining of people's social opinions from social media platforms. These can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. Future research directions are presented, whereas further research and development has the potential of leaving a wider academic and societal impact.

Deep AUC Maximization (DAM) is a paradigm for learning a deep neural network by maximizing the AUC score of the model on a dataset. Most previous works of AUC maximization focus on the perspective of optimization by designing efficient stochastic algorithms, and studies on generalization performance of DAM on difficult tasks are missing. In this work, we aim to make DAM more practical for interesting real-world applications (e.g., medical image classification). First, we propose a new margin-based surrogate loss function for the AUC score (named as the AUC margin loss). It is more robust than the commonly used AUC square loss, while enjoying the same advantage in terms of large-scale stochastic optimization. Second, we conduct empirical studies of our DAM method on difficult medical image classification tasks, namely classification of chest x-ray images for identifying many threatening diseases and classification of images of skin lesions for identifying melanoma. Our DAM method has achieved great success on these difficult tasks, i.e., the 1st place on Stanford CheXpert competition (by the paper submission date) and Top 1% rank (rank 33 out of 3314 teams) on Kaggle 2020 Melanoma classification competition. We also conduct extensive ablation studies to demonstrate the advantages of the new AUC margin loss over the AUC square loss on benchmark datasets. To the best of our knowledge, this is the first work that makes DAM succeed on large-scale medical image datasets.

This week at Amazon Web Services' re:Invent 2020 conference, Amazon announced Amazon Monitron, an end-to-end machine-monitoring system composed of sensors, a gateway, and a machine learning model that detects anomalies in vibration (structure-borne sound) or temperature and predicts when equipment may require maintenance. Machine condition monitoring was also the topic of a challenge at the Workshop on the Detection and Classification of Acoustic Scenes and Events (DCASE 2020), in November, in which Amazon took the top three spots, out of 117 submissions. The challenge was to determine whether the sounds emitted by a machine -- such as a fan, pump, or valve -- were normal or anomalous. Forty academic and industry teams submitted entries, an average of almost three submissions per team. In a pair of papers (paper 1 paper 2) we presented at the workshop, we describe the two different neural-network-based approaches we took in our submissions to the challenge.

A growing attention in the empirical literature has been paid to the incidence of climate shocks and change in migration decisions. Previous literature leads to different results and uses a multitude of traditional empirical approaches. This paper proposes a tree-based Machine Learning (ML) approach to analyze the role of the weather shocks towards an individual's intention to migrate in the six agriculture-dependent-economy countries such as Burkina Faso, Ivory Coast, Mali, Mauritania, Niger, and Senegal. We perform several tree-based algorithms (e.g., XGB, Random Forest) using the train-validation-test workflow to build robust and noise-resistant approaches. Then we determine the important features showing in which direction they are influencing the migration intention. This ML-based estimation accounts for features such as weather shocks captured by the Standardized Precipitation-Evapotranspiration Index (SPEI) for different timescales and various socioeconomic features/covariates. We find that (i) weather features improve the prediction performance although socioeconomic characteristics have more influence on migration intentions, (ii) country-specific model is necessary, and (iii) international move is influenced more by the longer timescales of SPEIs while general move (which includes internal move) by that of shorter timescales.

As machine learning (ML) has been applied to facilitate decision-making in various areas, such as hiring, loan grading etc., there have been and continue to be increasing concerns [3, 31] that ML models will inevitably "learn undesired bias" from the training data and make unfair predictions. From algorithms erroneously detecting suspects of crimes base on the color of their skin [7] and deciding who goes to jail [26], to algorithms used to predict test scores that provide unfairly higher scores to socioeconomically privileged students, allowing them to enter universities at a higher rate [27], the lack of understanding and control of undesired bias in ML models has tangible consequences. To deal with this challenge of biased models, many researchers have devoted their efforts, e.g., [15, 17, 35, 8, 2] to define, detect and mitigate bias in ML over the past decade. These approaches mainly measure and reduce undesired bias with respect to a sensitive attribute, such as age or race, in the training dataset. Although many of them provide various effective approaches, they all focus on centralized ML, where the training dataset is stored in a single place, executing the learning procedure, and hence assume full access to the entire dataset.

WUHAN, China – In the early days in Wuhan, the first city first struck by the virus, getting a COVID-19 test was so difficult that residents compared it to winning the lottery. Throughout the Chinese city in January, thousands of people waited in hourslong lines for hospitals, sometimes next to corpses lying in hallways. But most couldn't get the test they needed to be admitted as patients. And for the few who did, the tests were often faulty, resulting in false negatives. The widespread test shortages and problems at a time when the virus could have been slowed were caused largely by secrecy and cronyism at China's top disease control agency, an Associated Press investigation has found. The flawed testing system prevented scientists and officials from seeing how fast the virus was spreading -- another way China fumbled its early response to the virus. Earlier reporting showed how top Chinese leaders delayed warning the public and withheld information from the World Health Organization, supplying the most comprehensive picture yet of China's initial missteps. Taken together, these mistakes in January facilitated the virus's spread through Wuhan and across the world undetected, in a pandemic that has now sickened more than 64 million people and killed almost 1.5 million.