This paper proposes a novel profile likelihood method for estimating the covariance parameters in exploratory factor analysis of high-dimensional Gaussian datasets with fewer observations than number of variables. An implicitly restarted Lanczos algorithm and a limited-memory quasi-Newton method are implemented to develop a matrix-free framework for likelihood maximization. Simulation results show that our method is substantially faster than the expectation-maximization solution without sacrificing accuracy. Our method is applied to fit factor models on data from suicide attempters, suicide ideators and a control group.

OSAKA - When times are good, there is less political pressure at the local level anywhere to be economically efficient or carefully scrutinize predictions that a new public works project or expensive industrial or tourism promotion scheme will lead to prosperity in 20 or 30 years. But with their rapidly aging and declining populations and shrinking tax bases, local governments now face a daunting task in formulating political, economic, social and environmental policies that will most likely benefit the greatest number of people decades from now. In contrast to the carefree public works spending of the bubble economy of three decades ago, often based on proposals that seemed little thought out, the demand for data-driven, evidence-based projections for various policy measures among local governments has grown, lest a wrong decision lead to local economic disaster, and voter anger. Earlier this year, Nagano Prefecture announced it would rely more on computer modeling and scenarios for local policy decisions. The decision came after the prefecture cooperated with Kyoto University's Kokoro Research Center, Hitachi Ltd. and Mitsubishi UFJ Research and Consulting to create two different models using artificial intelligence. Those were put to use in research on the best policy to realize a sustainable society and how to best take advantage of the opportunities, especially related to local tourism, that might come from the planned opening of a maglev shinkansen station in the prefecture as early as 2027.

Interested in the future and want to experience even more?! eXplore More. Elon Musk's Neuralink, the secretive commercial company developing Brain Machine Interfaces (BMI) that one day they hope will connect human minds directly with AI's and machines, this week took the wrapper off the technology they've been developing. The company's goal, says Musk, is to eventually begin implanting devices in paralysed people so that they can control computers and smartphones with nothing more than their thoughts. And even though Musk gets a lot of the limelight in this area recently the US Military flexed their muscles and showed off their own version of Musk's technology that allowed paralysed volunteers to control fleets of F-35 fighter jets with just their thoughts, and elsewhere Mark Zuckerberg and his team are busy designing non-invasive BMI as part of his attempt to turn Facebook into the "world's first telepathic network." The first big advance Musk showed off was Neuralink's flexible bio-compatible "Threads," which are less likely to damage the brain than the materials used in many of today's traditional invasive BMI's.

There are those who praise the technology as the solution to some of humankind's gravest problems, and those who demonize AI as the world's greatest existential threat. Of course, these are two ends of the spectrum, and AI, surely, presents exciting opportunities for the future, as well as challenging problems to be overcome. One of the issues that's attracted much media attention in recent years has been the prospect of bias in AI. It's a topic I wrote about in TechCrunch (Tyrant in the Code) more than two years ago. The debate is raging on. At the time, Google had come under fire when research showed that when a user searched online for "hands," the image results were almost all white; but when searching for "black hands," the images were far more derogatory depictions, including a white hand reaching out to offer help to a black one, or black hands working in the earth.

Luciano Floridi has never been kind with technology. In '95, when the web as we know it today did not exist and he was a PhD Philosophy student, he wrote things such as: «No one controls the system globally, and the very structure of the internet ensures that will ever be able to control it in the future». Or: «the Internet promotes the growth of knowledge while creating forms of unprecedented ignorance». He directs the Digital Ethics Lab at the University of Oxford, he is the president of the Data Ethics Group of the Alan Turing Institute. And he serves as advisor to big tech, governments and the European Union.

Best listening experience is on Chrome, Firefox or Safari. Agencies have gotten buy-in from their employees when it comes to automating rote tasks liked data entry. But it's a tougher sell to those employees -- and the public -- to trust artificial intelligence tools for data-driven decision-making. Some agencies, including the General Services Administration, IRS and Defense Logistics Agency expect to save tens of thousands of work hours through robotic process automation-powered bots for back-office functions. There's also room for automation grow, according to research from the Partnership for Public Service and IBM's Center for the Business of Government.

The High-Level Expert Group on Artificial Intelligence ("AI HLEG"), an independent expert group set up by the European Commission in June 2018 as part of its AI strategy, has published its final Ethics Guidelines for Trustworthy Artificial Intelligence ("AI") (the "Guidelines"). These Guidelines form part of a wider focus by the Commission on AI, with President-elect of the European Commission, Ursula von der Leyen commenting most recently on July 16, in her proposed political guidelines, that: "In my first 100 days in office, I will put forward legislation for a coordinated European approach on the human and ethical implications of Artificial Intelligence…". The AI HLEG appreciates that AI has the potential to benefit a wide range of sectors and has a wide variety of uses. However, it also acknowledges that the use of AI also brings new challenges and raises various legal and ethical questions. It is with this in mind that the Guidelines have been developed: with a view to providing a framework to achieve and operationalize Trustworthy AI.

Next-generation cosmic microwave background (CMB) experiments will have lower noise and therefore increased sensitivity, enabling improved constraints on fundamental physics parameters such as the sum of neutrino masses and the tensor-to-scalar ratio r. Achieving competitive constraints on these parameters requires high signal-to-noise extraction of the projected gravitational potential from the CMB maps. Standard methods for reconstructing the lensing potential employ the quadratic estimator (QE). However, the QE performs suboptimally at the low noise levels expected in upcoming experiments. Other methods, like maximum likelihood estimators (MLE), are under active development. In this work, we demonstrate reconstruction of the CMB lensing potential with deep convolutional neural networks (CNN) - ie, a ResUNet. The network is trained and tested on simulated data, and otherwise has no physical parametrization related to the physical processes of the CMB and gravitational lensing. We show that, over a wide range of angular scales, ResUNets recover the input gravitational potential with a higher signal-to-noise ratio than the QE method, reaching levels comparable to analytic approximations of MLE methods. We demonstrate that the network outputs quantifiably different lensing maps when given input CMB maps generated with different cosmologies. We also show we can use the reconstructed lensing map for cosmological parameter estimation. This application of CNN provides a few innovations at the intersection of cosmology and machine learning. First, while training and regressing on images, we predict a continuous-variable field rather than discrete classes. Second, we are able to establish uncertainty measures for the network output that are analogous to standard methods. We expect this approach to excel in capturing hard-to-model non-Gaussian astrophysical foreground and noise contributions.

Due to the increased availability of online reviews, sentiment analysis had been witnessed a booming interest from the researchers. Sentiment analysis is a computational treatment of sentiment used to extract and understand the opinions of authors. While many systems were built to predict the sentiment of a document or a sentence, many others provide the necessary detail on various aspects of the entity (i.e. aspect-based sentiment analysis). Most of the available data resources were tailored to English and the other popular European languages. Although Persian is a language with more than 110 million speakers, to the best of our knowledge, there is not any public dataset on aspect-based sentiment analysis in Persian. This paper provides a manually annotated Persian dataset, Pars-ABSA, which is verified by 3 native Persian speakers. The dataset consists of 5114 positive, 3061 negative and 1827 neutral data samples from 5602 unique reviews. Moreover, as a baseline, this paper reports the performance of some state-of-the-art aspect-based sentiment analysis methods with a focus on deep learning, on Pars-ABSA. The obtained results are impressive compared to similar English state-of-the-art.

We consider the problem of learning an interpretable potential energy function from a Hamiltonian system's trajectories. We address this problem for classical, separable Hamiltonian systems. Our approach first constructs a neural network model of the potential and then applies an equation discovery technique to extract from the neural potential a closed-form algebraic expression. We demonstrate this approach for several systems, including oscillators, a central force problem, and a problem of two charged particles in a classical Coulomb potential. Through these test problems, we show close agreement between learned neural potentials, the interpreted potentials we obtain after training, and the ground truth. In particular, for the central force problem, we show that our approach learns the correct effective potential, a reduced-order model of the system.