The most common supervised task in machine learning is to learn a single-task, single-output prediction model. However, such a setting can be ill-adapted to some problems and applications. On the one hand, producing a single output can be undesirable when data is scarce and when producing reliable, possibly set-valued predictions is important (for instance in the medical domain where examples are very hard to collect for specific targets, and where predictions are used for critical decisions). Such an issue can be solved by using conformal prediction approaches [1]. It was initially proposed as a transductive online learning approach to provide set predictions (in the classification case) or interval predictions (in the case of regression) with a statistical guarantee depending on the probability of error tolerated by the user, but was then extended to handle inductive processes [2]. On the other hand, there are many situations where there are multiple, possibly correlated output variables to predict at once, and it is then natural to try to leverage such correlations to improve predictions. Such learning tasks are commonly called Multi-task in the literature [3]. Most research work on conformal prediction for multi-task learning focuses on the problem of multi-label prediction [4, 5], where each task is a binary classification one. Conformal prediction for multi-target regression has been less explored, with only a few studies dealing with it: Kuleshov et al. [6] provide a theoretical framework to use conformal predictors within manifold (e.g., to provide a mono-dimensional embedding of the multi-variate output), while Neeven and Smirnov [7] use a straightforward multi-target extension of a conformal single-output k-nearest neighbor regressor [8] to provide weather forecasts.

Persuasion is an important and yet complex aspect of human intelligence. When undertaken through dialogue, the deployment of good arguments, and therefore counterarguments, clearly has a significant effect on the ability to be successful in persuasion. Two key dimensions for determining whether an argument is good in a particular dialogue are the degree to which the intended audience believes the argument and counterarguments, and the impact that the argument has on the concerns of the intended audience. In this paper, we present a framework for modelling persuadees in terms of their beliefs and concerns, and for harnessing these models in optimizing the choice of move in persuasion dialogues. Our approach is based on the Monte Carlo Tree Search which allows optimization in real-time. We provide empirical results of a study with human participants showing that our automated persuasion system based on this technology is superior to a baseline system that does not take the beliefs and concerns into account in its strategy.

Care and Feeding is Slate's parenting advice column. Have a question for Care and Feeding? Submit it here or post it in the Slate Parenting Facebook group. My husband and I both have full-time jobs and an 18-month-old son. I am pregnant with our second child, due in February. Since our son was born, my husband seems to have regressed.

Shipments of laptop computers in Japan surged 25.1% in 2020 to hit a record high due to increased teleworking amid the novel coronavirus pandemic and the government's policy to supply computers to each elementary and junior high school student, an industry body said Tuesday. Laptop shipments reached 8.9 million units, but fell 0.2% by value to ¥679.7 billion from a year earlier, partly because demand concentrated on low-priced laptops to secure units in a limited budget to distribute them to schools, the Japan Electronics and Information Technology Industries Association said. The government has promoted the use of information technology in education since before the spread of the coronavirus, although Japan is behind in IT education efforts compared with other developed countries. The pandemic accelerated the schedule of the personal computer distribution, and most municipalities will complete handing out computers or tablets to every student in their schools by the end of March. Regarding demand related to teleworking, the purchase of PCs for working from home further increased since the government called for less commuting as the country declared its first state of emergency for urban areas such as Tokyo in April and expanded it to other parts of the country later in the month and May.

Hypergraphs are a natural modeling paradigm for a wide range of complex relational systems with multibody interactions. A standard analysis task is to identify clusters of closely related or densely interconnected nodes. While many probabilistic generative models for graph clustering have been proposed, there are relatively few such models for hypergraphs. We propose a Poisson degree-corrected hypergraph stochastic blockmodel (DCHSBM), an expressive generative model of clustered hypergraphs with heterogeneous node degrees and edge sizes. Approximate maximum-likelihood inference in the DCHSBM naturally leads to a clustering objective that generalizes the popular modularity objective for graphs. We derive a general Louvain-type algorithm for this objective, as well as a a faster, specialized "All-Or-Nothing" (AON) variant in which edges are expected to lie fully within clusters. This special case encompasses a recent proposal for modularity in hypergraphs, while also incorporating flexible resolution and edge-size parameters. We show that hypergraph Louvain is highly scalable, including as an example an experiment on a synthetic hypergraph of one million nodes. We also demonstrate through synthetic experiments that the detectability regimes for hypergraph community detection differ from methods based on dyadic graph projections. In particular, there are regimes in which hypergraph methods can recover planted partitions even though graph based methods necessarily fail due to information-theoretic limits. We use our model to analyze different patterns of higher-order structure in school contact networks, U.S. congressional bill cosponsorship, U.S. congressional committees, product categories in co-purchasing behavior, and hotel locations from web browsing sessions, that it is able to recover ground truth clusters in empirical data sets exhibiting the corresponding higher-order structure.

The field of Artificial Intelligence (AI) has undoubtedly received significant attention in recent years. AI is being adopted to provide solutions to problems in fields such as medicine, engineering, education, government and several other domains. In order to analyze the state of the art of research in the field of AI, we present a systematic literature review focusing on the Evolution of AI programming languages. We followed the systematic literature review method by searching relevant databases like SCOPUS, IEEE Xplore and Google Scholar. EndNote reference manager was used to catalog the relevant extracted papers. Our search returned a total of 6565 documents, whereof 69 studies were retained. Of the 69 retained studies, 15 documents discussed LISP programming language, another 34 discussed PROLOG programming language, the remaining 20 documents were spread between Logic and Object Oriented Programming (LOOP), ARCHLOG, Epistemic Ontology Language with Constraints (EOLC), Python, C++, ADA and JAVA programming languages. This review provides information on the year of implementation, development team, capabilities, limitations and applications of each of the AI programming languages discussed. The information in this review could guide practitioners and researchers in AI to make the right choice of languages to implement their novel AI methods.

My father, Bill Broderick, who has died aged 80 of Covid-19, was an educationist ahead of his time in the field of computing. His vision and enthusiasm led to the first computer being installed in a British secondary school, the Royal Liberty school in Romford, Essex, where he was a maths teacher, in 1965. In a broadcast by the BBC programme Tomorrow's World from the school, Bill said: "Computers are as radical and important a keystone to our standard of living and industrial wellbeing as was the steam engine." Born in Farnborough, Kent, Bill was the only son of Ralph Broderick, an engineer, and Ida (nee Massey). He was educated at Lord Wandsworth college in Long Sutton, Hampshire, then went to Hull University to study mathematics.

In Moshe Vardi's September 2020 column, "Where Have All the Domestic Graduate Students Gone?," the short but woefully incomplete answer is that the wage premium for a Ph.D. in CS is simply too small to justify foregoing five years of industry-level salary. But why is that the case? Part of the answer may be due to government policy discussed back in 1989, when an NSF document addressed the "problem" of Ph.D. salaries being too high, and suggested as a remedy increasing the pool of international students (https://bit.ly/2IuFZl7). This would swell the labor market, holding down wage growth. "A growing influx of foreign Ph.D.'s into U.S. labor markets will hold down the level of Ph.D. salaries to the extent that foreign students are attracted to U.S. doctoral programs as a way of immigrating to the U.S." But the domestic students would find that the resulting wage suppression would make Ph.D. study a bad choice: "... a key issue [for the domestic students] is pay. The relatively modest salary premium for acquiring [a] Ph.D. may be too low to attract a number of able potential graduate students ... A number of them will select alternative career paths ... by choosing to acquire a'professional' degree in business or law ... For these baccalaureates, the effective premium for acquiring a Ph.D. may actually be negative."

Sensor placement and feature selection are critical steps in engineering, modeling, and data science that share a common mathematical theme: the selected measurements should enable solution of an inverse problem. Most real-world systems of interest are nonlinear, yet the majority of available techniques for feature selection and sensor placement rely on assumptions of linearity or simple statistical models. We show that when these assumptions are violated, standard techniques can lead to costly over-sensing without guaranteeing that the desired information can be recovered from the measurements. In order to remedy these problems, we introduce a novel data-driven approach for sensor placement and feature selection for a general type of nonlinear inverse problem based on the information contained in secant vectors between data points. Using the secant-based approach, we develop three efficient greedy algorithms that each provide different types of robust, near-minimal reconstruction guarantees. We demonstrate them on two problems where linear techniques consistently fail: sensor placement to reconstruct a fluid flow formed by a complicated shock-mixing layer interaction and selecting fundamental manifold learning coordinates on a torus.

We consider the problem of estimating a function from $n$ noisy samples whose discrete Total Variation (TV) is bounded by $C_n$. We reveal a deep connection to the seemingly disparate problem of Strongly Adaptive online learning (Daniely et al, 2015) and provide an $O(n \log n)$ time algorithm that attains the near minimax optimal rate of $\tilde O (n^{1/3}C_n^{2/3})$ under squared error loss. The resulting algorithm runs online and optimally adapts to the unknown smoothness parameter $C_n$. This leads to a new and more versatile alternative to wavelets-based methods for (1) adaptively estimating TV bounded functions; (2) online forecasting of TV bounded trends in time series.