Medical Visual Question Answering (VQA) is a combination of medical artificial intelligence and popular VQA challenges. Given a medical image and a clinically relevant question in natural language, the medical VQA system is expected to predict a plausible and convincing answer. Although the general-domain VQA has been extensively studied, the medical VQA still needs specific investigation and exploration due to its task features. In the first part of this survey, we cover and discuss the publicly available medical VQA datasets up to date about the data source, data quantity, and task feature. In the second part, we review the approaches used in medical VQA tasks. In the last part, we analyze some medical-specific challenges for the field and discuss future research directions.

Minimizers and convolutional neural networks (CNNs) are two quite distinct popular techniques that have both been employed to analyze biological sequences. At face value, the methods seem entirely dissimilar. Minimizers use min-wise hashing on a rolling window to extract a single important k-mer feature per window. CNNs start with a wide array of randomly initialized convolutional filters, paired with a pooling operation, and then multiple additional neural layers to learn both the filters themselves and how those filters can be used to classify the sequence. In this manuscript, I demonstrate through a careful mathematical analysis of hash function properties that there are deep theoretical connections between minimizers and convolutional filters -- in short, for sequences over a categorical alphabet, random Gaussian initialization of convolutional filters with max-pooling is equivalent to choosing minimizers from a random hash function biased towards more distinct k-mers. This provides a partial explanation for the unreasonable effectiveness of CNNs in categorical sequence analysis.

IndyGeneUS AI (pronounced "indigenous", a South Africa Founder Institute portfolio company) opens its seed round with a $1.5M investment from South African based venture capital company IsimoVest Venture Capital Partners in addition to an undisclosed amount from a South African angel investor. Located in Cape Town, IsimoVest is a Pan-African venture capital company that specializes in early stage, high-potential technology ventures that will likely provide highly impactful corporate social investment solutions. As a cutting-edge investment firm, IsimoVest is dedicated to economically empowering communities for the benefit of Africans. To that end, the firm will support IndyGeneUS AI in establishing its sequencing facility in Cape Town and establishing local key strategic partnerships. IndyGeneUS AI is developing a Multiomic data analysis and management platform that can detect new signature sequences such as biomarkers or polygenic risk scores by integrating "omics" data, meta data and textual information such as Electronic Healthcare Record data.

Precision medicine requires big data. In order to improve the treatment of individuals with cancer, or to understand rare diseases, scientists and clinicians, as well as AI technologies require access to larger sets of health research data that covers diverse populations and wide ranges of conditions. For AI, more data means a better understanding of diseases, which will lead to more accurate diagnosis and treatment. At the same time, each hospital will only see a relatively small number of individuals with a disease, and even across the province, we have access to only a small portion of the total data available worldwide. To build the large-scale datasets needed to drive forward precision medicine, sharing of data across the country and around the world is critical.

You might be in high school reading this, or simply thinking of switching careers and delving into Artificial Intelligence - this is for you. The industry actually exists and is not just something unreachable which you see on television. First and foremost, you need in-depth knowledge of data science and statistics, as well as data processing and software engineering. Fortunately, these are subjects that you can find in South African higher education institutions. Getting a degree or certificate in any of those fields builds the foundation of your aspired job in AI.

In December 2019, a novel virus called COVID-19 had caused an enormous number of causalities to date. The battle with the novel Coronavirus is baffling and horrifying after the Spanish Flu 2019. While the front-line doctors and medical researchers have made significant progress in controlling the spread of the highly contiguous virus, technology has also proved its significance in the battle. Moreover, Artificial Intelligence has been adopted in many medical applications to diagnose many diseases, even baffling experienced doctors. Therefore, this survey paper explores the methodologies proposed that can aid doctors and researchers in early and inexpensive methods of diagnosis of the disease. Most developing countries have difficulties carrying out tests using the conventional manner, but a significant way can be adopted with Machine and Deep Learning. On the other hand, the access to different types of medical images has motivated the researchers. As a result, a mammoth number of techniques are proposed. This paper first details the background knowledge of the conventional methods in the Artificial Intelligence domain. Following that, we gather the commonly used datasets and their use cases to date. In addition, we also show the percentage of researchers adopting Machine Learning over Deep Learning. Thus we provide a thorough analysis of this scenario. Lastly, in the research challenges, we elaborate on the problems faced in COVID-19 research, and we address the issues with our understanding to build a bright and healthy environment.

The visualization and detection of anomalies (outliers) are of crucial importance to many fields, particularly cybersecurity. Several approaches have been proposed in these fields, yet to the best of our knowledge, none of them has fulfilled both objectives, simultaneously or cooperatively, in one coherent framework. The visualization methods of these approaches were introduced for explaining the output of a detection algorithm, not for data exploration that facilitates a standalone visual detection. This is our point of departure: UN-AVOIDS, an unsupervised and nonparametric approach for both visualization (a human process) and detection (an algorithmic process) of outliers, that assigns invariant anomalous scores (normalized to $[0,1]$), rather than hard binary-decision. The main aspect of novelty of UN-AVOIDS is that it transforms data into a new space, which is introduced in this paper as neighborhood cumulative density function (NCDF), in which both visualization and detection are carried out. In this space, outliers are remarkably visually distinguishable, and therefore the anomaly scores assigned by the detection algorithm achieved a high area under the ROC curve (AUC). We assessed UN-AVOIDS on both simulated and two recently published cybersecurity datasets, and compared it to three of the most successful anomaly detection methods: LOF, IF, and FABOD. In terms of AUC, UN-AVOIDS was almost an overall winner. The article concludes by providing a preview of new theoretical and practical avenues for UN-AVOIDS. Among them is designing a visualization aided anomaly detection (VAAD), a type of software that aids analysts by providing UN-AVOIDS' detection algorithm (running in a back engine), NCDF visualization space (rendered to plots), along with other conventional methods of visualization in the original feature space, all of which are linked in one interactive environment.

Determinantal point processes (a.k.a. DPPs) have recently become popular tools for modeling the phenomenon of negative dependence, or repulsion, in data. However, our understanding of an analogue of a classical parametric statistical theory is rather limited for this class of models. In this work, we investigate a parametric family of Gaussian DPPs with a clearly interpretable effect of parametric modulation on the observed points. We show that parameter modulation impacts the observed points by introducing directionality in their repulsion structure, and the principal directions correspond to the directions of maximal (i.e. the most long ranged) dependency. This model readily yields a novel and viable alternative to Principal Component Analysis (PCA) as a dimension reduction tool that favors directions along which the data is most spread out. This methodological contribution is complemented by a statistical analysis of a spiked model similar to that employed for covariance matrices as a framework to study PCA. These theoretical investigations unveil intriguing questions for further examination in random matrix theory, stochastic geometry and related topics.

Data-hungry deep neural networks have established themselves as the standard for many NLP tasks including the traditional sequence tagging ones. Despite their state-of-the-art performance on high-resource languages, they still fall behind of their statistical counter-parts in low-resource scenarios. One methodology to counter attack this problem is text augmentation, i.e., generating new synthetic training data points from existing data. Although NLP has recently witnessed a load of textual augmentation techniques, the field still lacks a systematic performance analysis on a diverse set of languages and sequence tagging tasks. To fill this gap, we investigate three categories of text augmentation methodologies which perform changes on the syntax (e.g., cropping sub-sentences), token (e.g., random word insertion) and character (e.g., character swapping) levels. We systematically compare them on part-of-speech tagging, dependency parsing and semantic role labeling for a diverse set of language families using various models including the architectures that rely on pretrained multilingual contextualized language models such as mBERT. Augmentation most significantly improves dependency parsing, followed by part-of-speech tagging and semantic role labeling. We find the experimented techniques to be effective on morphologically rich languages in general rather than analytic languages such as Vietnamese. Our results suggest that the augmentation techniques can further improve over strong baselines based on mBERT. We identify the character-level methods as the most consistent performers, while synonym replacement and syntactic augmenters provide inconsistent improvements. Finally, we discuss that the results most heavily depend on the task, language pair, and the model type.

In 2017, Omar Sultan Al Olama, the world's first minister for artificial intelligence (AI), published his strategy to make the UAE a global leader in AI by 2031. The plan was approved by the UAE Cabinet in 2019. Now, two years after ratification, the government has already taken significant steps. Whether or not the UAE becomes a world leader in AI by 2031, it certainly stands a good chance of taking the lead in its region. PwC estimates that while the Middle East will only capture 2% of the global benefits of AI by 2030, the UAE will enjoy the most growth, with AI accounting for 13.6% of GDP by 2030.