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DELIMIT is a framework extension for deep learning in diffusion imaging, which extends the basic framework PyTorch towards spherical signals. Based on several novel layers, deep learning can be applied to spherical diffusion imaging data in a very convenient way. First, two spherical harmonic interpolation layers are added to the extension, which allow to transform the signal from spherical surface space into the spherical harmonic space, and vice versa. In addition, a local spherical convolution layer is introduced that adds the possibility to include gradient neighborhood information within the network. Furthermore, these extensions can also be utilized for the preprocessing of diffusion signals.

Furthermore, traveling in any direction orthogonal to the gradient maintains the value of the function. In this work, we show that these orthogonal directions that are ignored by gradient descent can be critical in equilibrium problems. Equilibrium problems have drawn heightened attention in machine learning due to the emergence of the Generative Adversarial Network (GAN). We use the framework of Variational Inequalities to analyze popular training algorithms for a fundamental GAN variant: the Wasserstein Linear-Quadratic GAN. We show that the steepest descent direction causes divergence from the equilibrium, and guaranteed convergence to the equilibrium is achieved through following a particular orthogonal direction. We call this successful technique Crossing-the-Curl, named for its mathematical derivation as well as its intuition: identify the game's axis of rotation and move "across" space in the direction towards smaller "curling".

Structural causal models (SCMs), also known as (non-parametric) structural equation models (SEMs), are widely used for causal modeling purposes. A large body of theoretical results is available for the special case in which cycles are absent (i.e., acyclic SCMs, also known as recursive SEMs). However, in many application domains cycles are abundantly present, for example in the form of feedback loops. In this paper, we provide a general and rigorous theory of cyclic SCMs. The paper consists of two parts: the first part gives a rigorous treatment of structural causal models, dealing with measure-theoretic and other complications that arise in the presence of cycles. In contrast with the acyclic case, in cyclic SCMs solutions may no longer exist, or if they exist, they may no longer be unique, or even measurable in general. We give several sufficient and necessary conditions for the existence of (unique) measurable solutions. We show how causal reasoning proceeds in these models and how this differs from the acyclic case. Moreover, we give an overview of the Markov properties that hold for cyclic SCMs. In the second part, we address the question of how one can marginalize an SCM (possibly with cycles) to a subset of the endogenous variables. We show that under a certain condition, one can effectively remove a subset of the endogenous variables from the model, leading to a more parsimonious marginal SCM that preserves the causal and counterfactual semantics of the original SCM on the remaining variables. Moreover, we show how the marginalization relates to the latent projection and to latent confounders, i.e. latent common causes.

This paper describes how the "SP Theory of Intelligence" with the "SP Computer Model", outlined in an Appendix, may throw light on aspects of commonsense reasoning (CSR) and commonsense knowledge (CSK), as discussed in another paper by Ernest Davis and Gary Marcus (DM). In four main sections, the paper describes: 1) The main problems to be solved; 2) Other research on CSR and CSK; 3) Why the SP system may prove useful with CSR and CSK 4) How examples described by DM may be modelled in the SP system. With regard to successes in the automation of CSR described by DM, the SP system's strengths in simplification and integration may promote seamless integration across these areas, and seamless integration of those area with other aspects of intelligence. In considering challenges in the automation of CSR described by DM, the paper describes in detail, with examples of SP-multiple-alignments. how the SP system may model processes of interpretation and reasoning arising from the horse's head scene in "The Godfather" film. A solution is presented to the 'long tail' problem described by DM. The SP system has some potentially useful things to say about several of DM's objectives for research in CSR and CSK.

This paper describes a roadmap for the development of the "SP Machine", based on the "SP Theory of Intelligence" and its realisation in the "SP Computer Model". The SP Machine will be developed initially as a software virtual machine with high levels of parallel processing, hosted on a high-performance computer. The system should help users visualise knowledge structures and processing. Research is needed into how the system may discover low-level features in speech and in images. Strengths of the SP system in the processing of natural language may be augmented, in conjunction with the further development of the SP system's strengths in unsupervised learning. Strengths of the SP system in pattern recognition may be developed for computer vision. Work is needed on the representation of numbers and the performance of arithmetic processes. A computer model is needed of "SP-Neural", the version of the SP Theory expressed in terms of neurons and their inter-connections. The SP Machine has potential in many areas of application, several of which may be realised on short-to-medium timescales.

While it might not always be easy to pull the plug on your electronics, doing so is rarely a case of moral dilemma. But, powering down might be a lot more difficult if your devices were begging you not to do it. A new study explores the ways in which social robots can manipulate their owners by pulling on our heartstrings. When robots protested, shouting things such as'No! Please do not switch me off' and implying they were afraid of the dark, participants hesitated and sometimes even refused to turn them off.

A mere sprinkling of autonomous vehicles exist in a few dozen cities today. And none of them -- at least not yet -- have been deployed as a true commercial enterprise. While the bulk of this nascent industry fixates on the system of sensors, maps and AI necessary for vehicles to drive without a human behind the wheel, the founders of startup RideOS are directing their efforts to the day when fleets of self-driving cars hit the streets. It's there, where human-driven and automated vehicles will be forced to mingle, that RideOS co-founders Chris Blumenberg and Justin Ho see opportunity. The company, which has existed for all of 12 months, has raised $25 million in a Series B funding round led by Next47, the venture arm of Siemens. Sequoia, an existing investor, and Singapore-based ST Ventures, also participated in the round.

Hollywood producer Harvey Weinstein is seeking to get the criminal case against him thrown out of court. On Friday, his lawyers filed a defence motion citing dozens of "warm" emails they say Mr Weinstein received from one of his accusers after an alleged rape. His team argue prosecutors should have shared the evidence with the Grand Jury that indicted him. Mr Weinstein has pleaded not guilty to six charges involving three different women. The accuser in question has retained her anonymity.

President Xi Jinping's "Made in China 2025" strategy, unveiled in 2015 and now thrust back into the limelight by President Trump's bellicose stance on trade, holds three important lesson for global industry. This should be a non-controversial statement, but it is not. Economists often mock "the manufacturing fetish" and argue there is no reason to consider manufacturing a better driver of economic growth than any other sector. As economies get richer, they tend to shift from agriculture to industry, and then to services. Manufacturing accounted for nearly 30% of the U.S. economy in the 1950s; it was still 20% in the 1980s; today it accounts for just 11%.