Neural Information Processing Systems http://nips.cc/

The complete list may be seen in Table 8. Here are a few general notes about these strings: 1. Based on their recommendations, we did the following: 1. zh, zh_Latn: This resulted in the special filters described below. URLs) the corpora were in languages different from the LangID predictions. This is mainly mis-rendered PDFs and may have practical applications for denoising, or for decoding such garbled PDFs.

Such asymmetry presents two issues.

Studying the behavior of non-human primates is essential for gaining evolutionary insights (Langer-graber et al., 2012), conducting biomedical research (Schapiro et al., 2005), and improving animal

In this paper, we propose polynomial forms to represent distributions of state variables over time for discrete-time stochastic dynamical systems. This problem arises in a variety of applications in areas ranging from biology to robotics. Our approach allows us to rigorously represent the probability distribution of state variables over time, and provide guaranteed bounds on the expectations, moments and probabilities of tail events involving the state variables. First, we recall ideas from interval arithmetic, and use them to rigorously represent the state variables at time t as a function of the initial state variables and noise symbols that model the random exogenous inputs encountered before time t. Next, we show how concentration of measure inequalities can be employed to prove rigorous bounds on the tail probabilities of these state variables. We demonstrate interesting applications that demonstrate how our approach can be useful in some situations to establish mathematically guaranteed bounds that are of a different nature from those obtained through simulations with pseudo-random numbers.

Ontheonehand, thediversedata processing steps lead tothelack oflarge-scale7 benchmarks and therefore decelerate iterative methodology improvement on a8 single problem.

During Sudan's civil war, which erupted in April 2023, both sides have increasingly relied on drones, and civilians have borne the brunt of the carnage. The conflict between the Sudanese armed forces (SAF) and the Rapid Support Forces (RSF) paramilitary group is an example of war transformed by commercially available, easily concealable unmanned aerial vehicles (UAVs), or drones. Modular, well-adapted to sanctions evasions and devastatingly effective, drones have killed scores of civilians, crippled infrastructure and plunged Sudanese cities into darkness. In this visual investigation, Al Jazeera examines the history of drone warfare in Sudan, the types of drones used by the warring sides, how they are sourced, where the attacks have occurred and the human toll. The RSF traces its origins to what at the time was a government-linked militia known as the Janjaweed.