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This paper proposes a dynamic multi-factor copula for use in high dimensional time series applications. A novel feature of our model is that the assignment of individual variables to groups is estimated from the data, rather than being pre-assigned using SIC industry codes, market capitalization ranks, or other ad hoc methods. We adapt the k-means clustering algorithm for use in our application and show that it has excellent finite-sample properties. Applying the new model to returns on 110 US equities, we find around 20 clusters to be optimal. In out-of-sample forecasts, we find that a model with as few as five estimated clusters significantly outperforms an otherwise identical model with 21 clusters formed using two-digit SIC codes. [ABSTRACT FROM AUTHOR]

This paper discusses the econometric methodology of general-to-specific modeling, in which the modeler simplifies an initially general model that adequately characterizes the empirical evidence within his or her theoretical framework. Central aspects of this approach include the theory of reduction, dynamic specification, model selection procedures, model selection criteria, model comparison, encompassing, computer automation, and empirical implementation. This paper thus reviews the theory of reduction, summarizes the approach of general-to-specific modeling, and discusses the econometrics of model selection, noting that general-to-specific modeling is the practical embodiment of reduction. This paper then summarizes fifty-seven articles key to the development of general-to-specific modeling. [ABSTRACT FROM AUTHOR]

The article focuses on the policy of the U.S. towards lethal autonomous weapon systems (LAWS) which are a special class of weapon systems that use sensor suites and computer algorithms. It mentions autonomous or semiautonomous cyberspace systems for cyberspace operations; unarmed, unmanned platforms; unguided munitions; munitions manually guided by the operator. It also mentions types of semi-autonomous and human-supervised autonomous weapons that deliver lethal effects.

Computer Vision (CV) labeling problems play a pivotal role in low-level vision. For decades, it has been known that these problems can be elegantly formulated as discrete energy-minimization problems derived from probabilistic graphical models such as Markov Random Fields (MRFs). Despite recent advances in MRF inference algorithms (such as graph-cut and message-passing methods), the resulting energy-minimization problems are generally viewed as intractable. The emergence of quantum computations, which offer the potential for faster solutions to certain problems than classical methods, has led to an increased interest in utilizing quantum properties to overcome intractable problems. Recently, there has also been a growing interest in Quantum Computer Vision (QCV), hoping to provide a credible alternative/assistant to deep learning solutions. This study investigates a new Quantum Annealing-based inference algorithm for CV discrete energy minimization problems. Our contribution is focused on Stereo Matching as a significant CV labeling problem. As a proof of concept, we also use a hybrid quantum-classical solver provided by D-Wave System to compare our results with the best classical inference algorithms in the literature. Our results show that Quantum Annealing can yield promising results for Stereo Matching problems, with improved accuracy on certain stereo images and competitive performance on oth ers. [ABSTRACT FROM AUTHOR]

The article presents a report of the U.S. Congressional Research Service (CRS), that talks about lethal autonomous weapon systems (LAWS) that are a special class of weapon systems that use sensor suites and computer algorithms. It mentions that the U.S. Dept. of Defense Directive establishes U.S. policy on autonomy in weapons systems; and also mentions about domains of warfare, types of warfare and operational contexts.

As the most computationally intensive part of a stereo vision system, stereo matching has been the focus of intense research activities for the last four decades. We present the first attempts to compare a quantum stereo matching solution with state-of-the-art approaches on the Middlebury stereo datasets. We first looked at quantum annealing computation as a way to interact with the D-Wave quantum computer and then improved the quantum solution to the stereo matching problem found in the literature. Using a line-by-line approach, we traded accuracy off for the qubits availability in the QPU (Quantum Processor Unit). Our findings show that it is possible to obtain results from real-sized images despite the scarcity of physical qubits in the quantum hardware. While the current quantum solution solves a class P stereo matching problem, its real advantage over classical stereo matching algorithms will arise with NP-hard problems which will be the focus of our future research. [ABSTRACT FROM AUTHOR]

Algorithms increasingly shape modern life, helping Wall Street to decide stock trades, Netflix to recommend movies and judges to dispense justice. But critics say algorithms -- the seemingly inscrutable computational tools that help give artificial intelligence (AI) the ability to "think" and "learn" -- can lead to skewed results and sometimes social harm. AI might help mortgage companies decide whom to lend to, but qualified borrowers can be rejected if the underlying algorithms are faulty. Companies might use AI to screen job applicants, but skilled talent can be turned away if the algorithms reflect racial or gender bias. Moreover, the use of algorithms is raising difficult questions about who -- if anyone -- is liable when AI results in injury. The technology is even stirring fears of an AI apocalypse in which computers become so powerful and autonomous that they threaten humankind. Some experts want the federal government to strictly regulate AI to ensure it is not misused, but critics fear more rules would stifle the technology. [ABSTRACT FROM AUTHOR]