In distributed second order optimization, a standard strategy is to average many local estimates, each of which is based on a small sketch or batch of the data. However, the local estimates on each machine are typically biased, relative to the full solution on all of the data, and this can limit the effectiveness of averaging. Here, we introduce a new technique for debiasing the local estimates, which leads to both theoretical and empirical improvements in the convergence rate of distributed second order methods. Our technique has two novel components: (1) modifying standard sketching techniques to obtain what we call a surrogate sketch; and (2) carefully scaling the global regularization parameter for local computations. Our surrogate sketches are based on determinantal point processes, a family of distributions for which the bias of an estimate of the inverse Hessian can be computed exactly. Based on this computation, we show that when the objective being minimized is $l_2$-regularized with parameter $\lambda$ and individual machines are each given a sketch of size $m$, then to eliminate the bias, local estimates should be computed using a shrunk regularization parameter given by $\lambda^{\prime}=\lambda\cdot(1-\frac{d_{\lambda}}{m})$, where $d_{\lambda}$ is the $\lambda$-effective dimension of the Hessian (or, for quadratic problems, the data matrix).

Recent few-shot learning works focus on training a model with prior meta-knowledge to fast adapt to new tasks with unseen classes and samples. However, conventional time-series classification algorithms fail to tackle the few-shot scenario. Existing few-shot learning methods are proposed to tackle image or text data, and most of them are neural-based models that lack interpretability. This paper proposes an interpretable neural-based framework, namely \textit{Dual Prototypical Shapelet Networks (DPSN)} for few-shot time-series classification, which not only trains a neural network-based model but also interprets the model from dual granularity: 1) global overview using representative time series samples, and 2) local highlights using discriminative shapelets. In particular, the generated dual prototypical shapelets consist of representative samples that can mostly demonstrate the overall shapes of all samples in the class and discriminative partial-length shapelets that can be used to distinguish different classes. We have derived 18 few-shot TSC datasets from public benchmark datasets and evaluated the proposed method by comparing with baselines. The DPSN framework outperforms state-of-the-art time-series classification methods, especially when training with limited amounts of data. Several case studies have been given to demonstrate the interpret ability of our model.

Artificial Intelligence (AI) is already ubiquitous in our day-to-day lives. From maps that find the optimal route, to Amazon, Netflix and Facebook who curate content and make recommendations tailored specifically to us. Your smartphone even understands voice commands and can perform tasks prompted by you. The technology is pervasive and is increasingly being applied in the education sector. Globally in the education sector, AI is being applied in tools that help develop learner skills, allow self-paced tailored learning, streamline assessment systems, and automate administrative activities.

Language is as important as expressions when reading emotion, a study has found -- meaning that being told someone looks'grumpy' can makes them seem grumpier. Researchers from Australia and the US asked volunteers to rate the emotions of people in either photographs or videos. The team found that when the participants were told that the subjects were feeling a specific emotion, this biased how they interpreted the expressions on show. The effect was most pronounced when dealing with angry, sad or scared faces -- as opposed to happy, disgusted, embarrassed, proud or surprised -- the team found. Language is as important as expressions when reading emotion, a study has found -- meaning that being told someone looks'grumpy' can makes them seem grumpier (stock image) 'The current studies demonstrate that language context alters the dimensional affective foundations that underlie our judgements of others' expressions,' the researchers wrote in their paper.

The mean, standard deviation, and 75% confidence interval values are maximum for the'Total Amount of Fines in Dollars' feature. The 25% and 50% confidence interval values are maximum for'Number of Certified Beds' and'Average Number of Residents Per Day' columns.

New developments and opportunities are opening up in artificial intelligence, says Paul Budde. I RECENTLY followed a "lunch box lecture", organised by the University of Sydney. The world is infatuated with artificial intelligence (AI), and understandably so, given its super-human ability to find patterns in big data as we all notice when using Google, Facebook, Amazon, eBay and so on. But the so-called "general intelligence" that humans possess remains elusive for AI. Interestingly, Professor Kuncic approached this topic from a physics perspective.

CSRE is providing a total of $300,000 in funding for the projects, with an additional $300,000 in matching and supplemental funding from other colleges, departments, and institutes. "Today's challenges to global, national, and individual security are numerous and complex," said CSRE Director James W. Houck, "and we are delighted to support these innovative and exciting initiatives." CSRE was established in 2017 to promote interdisciplinary research and education to protect people, infrastructure and institutions from the broad range of threats and hazards confronting society today. Contributing units include the Provost and Office of the Senior Vice President for Research, as well as the colleges of Agricultural Sciences, Earth and Mineral Sciences, Engineering, Information Sciences and Technology, and the Liberal Arts; Penn State Law and the School of International Affairs; Penn State Harrisburg; Applied Research Laboratory; Institute for Computational and Data Sciences; Institutes of Energy and the Environment; Huck Institutes of the Life Sciences; and the Social Sciences Research Institute. In its first three years, CSRE has provided over $633,000 in funding, augmented by an additional $581,000 from contributing units, to a total of 39 seed projects and faculty fellowships and hosted a number of guest speakers, workshops and other events.

Complex applications such as big data analytics involve different forms of coupling relationships that reflect interactions between factors related to technical, business (domain-specific) and environmental (including socio-cultural and economic) aspects. There are diverse forms of couplings embedded in poor-structured and ill-structured data. Such couplings are ubiquitous, implicit and/or explicit, objective and/or subjective, heterogeneous and/or homogeneous, presenting complexities to existing learning systems in statistics, mathematics and computer sciences, such as typical dependency, association and correlation relationships. Modeling and learning such couplings thus is fundamental but challenging. This paper discusses the concept of coupling learning, focusing on the involvement of coupling relationships in learning systems. Coupling learning has great potential for building a deep understanding of the essence of business problems and handling challenges that have not been addressed well by existing learning theories and tools. This argument is verified by several case studies on coupling learning, including handling coupling in recommender systems, incorporating couplings into coupled clustering, coupling document clustering, coupled recommender algorithms and coupled behavior analysis for groups.

Public debate about AI is dominated by Frankenstein Syndrome, the fear that AI will become superhuman and escape human control. Although superintelligence is certainly a possibility, the interest it excites can distract the public from a more imminent concern: the rise of Artificial Stupidity (AS). This article discusses the roots of Frankenstein Syndrome in Mary Shelley's famous novel of 1818. It then provides a philosophical framework for analysing the stupidity of artificial agents, demonstrating that modern intelligent systems can be seen to suffer from 'stupidity of judgement'. Finally it identifies an alternative literary tradition that exposes the perils and benefits of AS. In the writings of Edmund Spenser, Jonathan Swift and E.T.A. Hoffmann, ASs replace, oppress or seduce their human users. More optimistically, Joseph Furphy and Laurence Sterne imagine ASs that can serve human intellect as maps or as pipes. These writers provide a strong counternarrative to the myths that currently drive the AI debate. They identify ways in which even stupid artificial agents can evade human control, for instance by appealing to stereotypes or distancing us from reality. And they underscore the continuing importance of the literary imagination in an increasingly automated society.

Answering questions that involve multi-step reasoning requires decomposing them and using the answers of intermediate steps to reach the final answer. However, state-of-the-art models in grounded question answering often do not explicitly perform decomposition, leading to difficulties in generalization to out-of-distribution examples. In this work, we propose a model that computes a representation and denotation for all question spans in a bottom-up, compositional manner using a CKY-style parser. Our model effectively induces latent trees, driven by end-to-end (the answer) supervision only. We show that this inductive bias towards tree structures dramatically improves systematic generalization to out-of-distribution examples compared to strong baselines on an arithmetic expressions benchmark as well as on CLOSURE, a dataset that focuses on systematic generalization of models for grounded question answering. On this challenging dataset, our model reaches an accuracy of 92.8%, significantly higher than prior models that almost perfectly solve the task on a random, in-distribution split.