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While he was examining algae from a nearby lake through his homemade microscope, a creature "with green and very glittering little scales," which he estimated to be a thousand times smaller than a mite, had darted across his vision. Two years later, on October 9, 1676, he followed up with another report so extraordinary that microbiologists today refer to it simply as "Letter 18": Van Leeuwenhoek (lay-u-when-hoke) had looked everywhere and found what he called animalcules (Latin for "little animals") in everything. He found them in the bellies of other animals, his food, his own mouth, and other people's mouths. When he noticed a set of remarkably rancid teeth, he asked the owner for a sample of his plaque, put it beneath his lens, and witnessed "an inconceivably great number of little animalcules" moving "so nimbly among one another, that the whole stuff seemed alive." After a particularly uncomfortable evening, which he blamed on a fatty meal of hot smoked beef, he examined his own stool beneath his lens and saw animalcules that were "somewhat longer than broad, and their belly, which was flat-like, furnished with sundry little paws"--a clear description of what we now know as the parasite giardia. With his observations of these fast, fat, and sundry-pawed creatures, Van Leeuwenhoek became the first person to ever see a microorganism--a discovery of almost incalculable significance to human health and our understanding of life on this planet.

We consider a novel formulation of the problem of Active Few-Shot Classification (AFSC) where the objective is to classify a small, initially unlabeled, dataset given a very restrained labeling budget. This problem can be seen as a rival paradigm to classical Transductive Few-Shot Classification (TFSC), as both these approaches are applicable in similar conditions. We first propose a methodology that combines statistical inference, and an original two-tier active learning strategy that fits well into this framework. We then adapt several standard vision benchmarks from the field of TFSC. Our experiments show the potential benefits of AFSC can be substantial, with gains in average weighted accuracy of up to 10% compared to state-of-the-art TFSC methods for the same labeling budget. We believe this new paradigm could lead to new developments and standards in data-scarce learning settings.

User trust is a crucial consideration in designing robust visual analytics systems that can guide users to reasonably sound conclusions despite inevitable biases and other uncertainties introduced by the human, the machine, and the data sources which paint the canvas upon which knowledge emerges. A multitude of factors emerge upon studied consideration which introduce considerable complexity and exacerbate our understanding of how trust relationships evolve in visual analytics systems, much as they do in intelligent sociotechnical systems. A visual analytics system, however, does not by its nature provoke exactly the same phenomena as its simpler cousins, nor are the phenomena necessarily of the same exact kind. Regardless, both application domains present the same root causes from which the need for trustworthiness arises: Uncertainty and the assumption of risk. In addition, visual analytics systems, even more than the intelligent systems which (traditionally) tend to be closed to direct human input and direction during processing, are influenced by a multitude of cognitive biases that further exacerbate an accounting of the uncertainties that may afflict the user's confidence, and ultimately trust in the system. In this article we argue that accounting for the propagation of uncertainty from data sources all the way through extraction of information and hypothesis testing is necessary to understand how user trust in a visual analytics system evolves over its lifecycle, and that the analyst's selection of visualization parameters affords us a simple means to capture the interactions between uncertainty and cognitive bias as a function of the attributes of the search tasks the analyst executes while evaluating explanations. We sample a broad cross-section of the literature from visual analytics, human cognitive theory, and uncertainty, and attempt to synthesize a useful perspective.

Data-driven science is an emerging paradigm where scientific discoveries depend on the execution of computational AI models against rich, discipline-specific datasets. With modern machine learning frameworks, anyone can develop and execute computational models that reveal concepts hidden in the data that could enable scientific applications. For important and widely used datasets, computing the performance of every computational model that can run against a dataset is cost prohibitive in terms of cloud resources. Benchmarking approaches used in practice use representative datasets to infer performance without actually executing models. While practicable, these approaches limit extensive dataset profiling to a few datasets and introduce bias that favors models suited for representative datasets. As a result, each dataset's unique characteristics are left unexplored and subpar models are selected based on inference from generalized datasets. This necessitates a new paradigm that introduces dataset profiling into the model selection process. To demonstrate the need for dataset-specific profiling, we answer two questions:(1) Can scientific datasets significantly permute the rank order of computational models compared to widely used representative datasets? (2) If so, could lightweight model execution improve benchmarking accuracy? Taken together, the answers to these questions lay the foundation for a new dataset-aware benchmarking paradigm.

Object-centric process mining is a new paradigm with more realistic assumptions about underlying data by considering several case notions, e.g., an order handling process can be analyzed based on order, item, package, and route case notions. Including many case notions can result in a very complex model. To cope with such complexity, this paper introduces a new approach to cluster similar case notions based on Markov Directly-Follow Multigraph, which is an extended version of the well-known Directly-Follow Graph supported by many industrial and academic process mining tools. This graph is used to calculate a similarity matrix for discovering clusters of similar case notions based on a threshold. A threshold tuning algorithm is also defined to identify sets of different clusters that can be discovered based on different levels of similarity. Thus, the cluster discovery will not rely on merely analysts' assumptions. The approach is implemented and released as a part of a python library, called processmining, and it is evaluated through a Purchase to Pay (P2P) object-centric event log file. Some discovered clusters are evaluated by discovering Directly Follow-Multigraph by flattening the log based on the clusters. The similarity between identified clusters is also evaluated by calculating the similarity between the behavior of the process models discovered for each case notion using inductive miner based on footprints conformance checking.

The method in which we select samples to learn more about characteristics in a given population is called hypothesis testing. Hypothesis testing is really a systematic way to test claims or ideas about a group or population. To illustrate, suppose we read an article stating that children in the United States watch an average of 3 hours of TV per week. To test whether this claim is true, we record the time (in hours) that a group of 20 American children (the sample), among all children in the United States (the population), watch TV. The mean we measure for these 20 children is a sample mean. We can then compare the sample mean we select to the population mean stated in the article. Hypothesis testing or significance testing is a method for testing a claim or hypothesis about a parameter in a population, using data measured in a sample. In this method, we test some hypothesis by determining the likelihood that a sample statistic could have been selected, if the hypothesis regarding the population parameter were true. To begin, we identify a hypothesis or claim that we feel should be tested. For example, we might want to test the claim that the mean number of hours that children in the United States watch TV is 3 hours.

We provide improved differentially private algorithms for identity testing of high-dimensional distributions. Specifically, for $d$-dimensional Gaussian distributions with known covariance $\Sigma$, we can test whether the distribution comes from $\mathcal{N}(\mu^*, \Sigma)$ for some fixed $\mu^*$ or from some $\mathcal{N}(\mu, \Sigma)$ with total variation distance at least $\alpha$ from $\mathcal{N}(\mu^*, \Sigma)$ with $(\varepsilon, 0)$-differential privacy, using only \[\tilde{O}\left(\frac{d^{1/2}}{\alpha^2} + \frac{d^{1/3}}{\alpha^{4/3} \cdot \varepsilon^{2/3}} + \frac{1}{\alpha \cdot \varepsilon}\right)\] samples if the algorithm is allowed to be computationally inefficient, and only \[\tilde{O}\left(\frac{d^{1/2}}{\alpha^2} + \frac{d^{1/4}}{\alpha \cdot \varepsilon}\right)\] samples for a computationally efficient algorithm. We also provide a matching lower bound showing that our computationally inefficient algorithm has optimal sample complexity. We also extend our algorithms to various related problems, including mean testing of Gaussians with bounded but unknown covariance, uniformity testing of product distributions over $\{-1, 1\}^d$, and tolerant testing. Our results improve over the previous best work of Canonne et al.~\cite{CanonneKMUZ20} for both computationally efficient and inefficient algorithms, and even our computationally efficient algorithm matches the optimal \emph{non-private} sample complexity of $O\left(\frac{\sqrt{d}}{\alpha^2}\right)$ in many standard parameter settings. In addition, our results show that, surprisingly, private identity testing of $d$-dimensional Gaussians can be done with fewer samples than private identity testing of discrete distributions over a domain of size $d$ \cite{AcharyaSZ18}, which refutes a conjectured lower bound of~\cite{CanonneKMUZ20}.

In The Structure of Scientific Revolutions, the philosopher of science Thomas Kuhn observed that scientists spend long periods taking small steps. They pose and solve puzzles while collectively interpreting all data within a fixed worldview or theoretical framework, which Kuhn called a paradigm. The scientists wring their hands, reexamine their assumptions and eventually make a revolutionary shift to a new paradigm, a radically different and truer understanding of nature. For several years, the particle physicists who study nature's fundamental building blocks have been in a textbook Kuhnian crisis. The crisis became undeniable in 2016, when, despite a major upgrade, the Large Hadron Collider in Geneva still hadn't conjured up any of the new elementary particles that theorists had been expecting for decades.

Introduction to equality, diversity and inclusion and development of your code of conduct – Debra Fearnshaw (University of Nottingham): This session will explore what equality, diversity and inclusion means, what EDI can look like in research and why this is important. The session will also have an interactive element to help you create a code of conduct for your event. Bio: I am an experienced Programme Manager, currently managing 2 EPSRC research projects plus additional projects within my portfolio to support my passion for improving research culture and embedding equality, diversity and inclusion into research. Recent projects include a secondment to EPSRC to complete a strategic EDI project and a Faculty of Engineering review of REF Impact and how future portfolios can become more diverse and inclusive. I am currently working on a research culture project to raise the visibility and recognition of research enabling roles.