10,451 research outputs found
Prolonging the past counteracts the pull of the present: protracted speciation can explain observed slowdowns in diversification.
Phylogenetic trees show a remarkable slowdown in the increase of number of lineages towards the present, a phenomenon which cannot be explained by the standard birth-death model of diversification with constant speciation and extinction rates. The birth-death model instead predicts a constant or accelerating increase in the number of lineages, which has been called the pull of the present. The observed slowdown has been attributed to nonconstancy of the speciation and extinction rates due to some form of diversity dependence (i.e., species-level density dependence), but the mechanisms underlying this are still unclear. Here, we propose an alternative explanation based on the simple concept that speciation takes time to complete. We show that this idea of protracted speciation can be incorporated in the standard birth-death model of diversification. The protracted birth-death model predicts a realistic slowdown in the rate of increase of number of lineages in the phylogeny and provides a compelling fit to four bird phylogenies with realistic parameter values. Thus, the effect of recognizing the generally accepted fact that speciation is not an instantaneous event is significant; even if it cannot account for all the observed patterns, it certainly contributes substantially and should therefore be incorporated into future studies
Event-Triggered Observers and Observer-Based Controllers for a Class of Nonlinear Systems
In this paper, we investigate the stabilization of a nonlinear plant subject
to network constraints, under the assumption of partial knowledge of the plant
state. The event triggered paradigm is used for the observation and the control
of the system. Necessary conditions, making use of the ISS property, are given
to guarantee the existence of a triggering mechanism, leading to asymptotic
convergence of the observer and system states. The proposed triggering
mechanism is illustrated in the stabilization of a robot with a flexible link
robot.Comment: Proceedings of the 2015 American Control Conference - ACC 201
Self-critical Sequence Training for Image Captioning
Recently it has been shown that policy-gradient methods for reinforcement
learning can be utilized to train deep end-to-end systems directly on
non-differentiable metrics for the task at hand. In this paper we consider the
problem of optimizing image captioning systems using reinforcement learning,
and show that by carefully optimizing our systems using the test metrics of the
MSCOCO task, significant gains in performance can be realized. Our systems are
built using a new optimization approach that we call self-critical sequence
training (SCST). SCST is a form of the popular REINFORCE algorithm that, rather
than estimating a "baseline" to normalize the rewards and reduce variance,
utilizes the output of its own test-time inference algorithm to normalize the
rewards it experiences. Using this approach, estimating the reward signal (as
actor-critic methods must do) and estimating normalization (as REINFORCE
algorithms typically do) is avoided, while at the same time harmonizing the
model with respect to its test-time inference procedure. Empirically we find
that directly optimizing the CIDEr metric with SCST and greedy decoding at
test-time is highly effective. Our results on the MSCOCO evaluation sever
establish a new state-of-the-art on the task, improving the best result in
terms of CIDEr from 104.9 to 114.7.Comment: CVPR 2017 + additional analysis + fixed baseline results, 16 page
The Feeling of Color: A Haptic Feedback Device for the Visually Disabled
Tapson J, Gurari N, Diaz J, et al. The Feeling of Color: A Haptic Feedback Device for the Visually Disabled. Presented at the Biomedical Circuits and Systems Conference (BIOCAS), Baltimore, MD.We describe a sensory augmentation system designed to provide the visually disabled with a sense of color. Our system consists of a glove with short-range optical color sensors mounted on its fingertips, and a torso-worn belt on which tactors (haptic feedback actuators) are mounted. Each fingertip sensor detects the observed objectpsilas color. This information is encoded to the tactor through vibrations in respective locations and varying modulations. Early results suggest that detection of primary colors is possible with near 100% accuracy and moderate latency, with a minimum amount of training
Prompt Electromagnetic Transients from Binary Black Hole Mergers
Binary black hole (BBH) mergers provide a prime source for current and future
interferometric GW observatories. Massive BBH mergers may often take place in
plasma-rich environments, leading to the exciting possibility of a concurrent
electromagnetic (EM) signal observable by traditional astronomical facilities.
However, many critical questions about the generation of such counterparts
remain unanswered. We explore mechanisms that may drive EM counterparts with
magnetohydrodynamic simulations treating a range of scenarios involving
equal-mass black-hole binaries immersed in an initially homogeneous fluid with
uniform, orbitally aligned magnetic fields. We find that the time development
of Poynting luminosity, which may drive jet-like emissions, is relatively
insensitive to aspects of the initial configuration. In particular, over a
significant range of initial values, the central magnetic field strength is
effectively regulated by the gas flow to yield a Poynting luminosity of
, with BBH mass
scaled to and ambient density . We also calculate the
direct plasma synchrotron emissions processed through geodesic ray-tracing.
Despite lensing effects and dynamics, we find the observed synchrotron flux
varies little leading up to merger.Comment: 22 pages, 21 figures; additional reference + clarifying text added to
match published versio
Machine-Learned Caching of Datasets
Generally, the present disclosure is directed to creating and/or modifying a pre-cache for a client device connected to a remote server containing a dataset. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict the likelihood a particular piece of data will be used (e.g. opened, edited, saved, etc.) within a time frame based on information about the data, the user’s interaction with the data, and/or the user’s schedule
Machine-Learning for Optimization of Software Parameters
Generally, the present disclosure is directed to optimizing tuning parameters in a computing system and/or software application using machine learning. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict an optimal value for tuning parameters based on metrics provided by a developer. As examples, such metrics may be related to an amount of user engagement, latency associated with the application, or efficiency of executing the software application
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