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

On some problems related to the boundary of Markov chains

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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

Alleviating Pain: From Policy to Practice

No abstract available

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

User Interface for Input With Switches Using Machine Learned Huffman Codes

Users with special circumstances, such as limited mobility or physical strength, are often unable to utilize the normal keyboard of a device. To overcome these difficulties, these users utilize alternative mechanisms for typed input, such as a mouse, trackpad, switches, buttons, etc. These mechanisms operate by mapping the full set of possible inputs onto a limited number of buttons, which makes their use cumbersome and slow. This disclosure utilizes Huffman coding to optimize the encoding of a large set of symbols into a set of codewords based on the probability of use of each symbol, calculated via a trained machine learning model. Given a reasonably accurate machine-learned prediction model, the techniques of this disclosure ensure that generating the desired typed input can be accomplished with minimal number of switch selections