FRAGILE STATES: DEFINING DIFFICULT ENVIRONMENTS FOR POVERTY REDUCTION

Food Security and Poverty,

Heat production and energy balance in nanoengines driven by time-dependent fields

We present a formalism to study the heat transport and the power developed by the local driving fields on a quantum system coupled to macroscopic reservoirs. We show that, quite generally, two important mechanisms can take place: (i) directed heat transport between reservoirs induced by the ac potentials and (ii) at slow driving, two oscillating out of phase forces perform work against each other, while the energy dissipated into the reservoirs is negligibleComment: 5 pages, 4 figure

Online learning and detection of faces with low human supervision

The final publication is available at link.springer.comWe present an efficient,online,and interactive approach for computing a classifier, called Wild Lady Ferns (WiLFs), for face learning and detection using small human supervision. More precisely, on the one hand, WiLFs combine online boosting and extremely randomized trees (Random Ferns) to compute progressively an efficient and discriminative classifier. On the other hand, WiLFs use an interactive human-machine approach that combines two complementary learning strategies to reduce considerably the degree of human supervision during learning. While the first strategy corresponds to query-by-boosting active learning, that requests human assistance over difficult samples in function of the classifier confidence, the second strategy refers to a memory-based learning which uses ¿ Exemplar-based Nearest Neighbors (¿ENN) to assist automatically the classifier. A pre-trained Convolutional Neural Network (CNN) is used to perform ¿ENN with high-level feature descriptors. The proposed approach is therefore fast (WilFs run in 1 FPS using a code not fully optimized), accurate (we obtain detection rates over 82% in complex datasets), and labor-saving (human assistance percentages of less than 20%). As a byproduct, we demonstrate that WiLFs also perform semi-automatic annotation during learning, as while the classifier is being computed, WiLFs are discovering faces instances in input images which are used subsequently for training online the classifier. The advantages of our approach are demonstrated in synthetic and publicly available databases, showing comparable detection rates as offline approaches that require larger amounts of handmade training data.Peer ReviewedPostprint (author's final draft

Interplay of space radiation and microgravity in DNA damage and DNA damage response

In space, multiple unique environmental factors, particularly microgravity and space radiation, pose constant threat to the DNA integrity of living organisms. Specifically, space radiation can cause damage to DNA directly, through the interaction of charged particles with the DNA molecules themselves, or indirectly through the production of free radicals. Although organisms have evolved strategies on Earth to confront such damage, space environmental conditions, especially microgravity, can impact DNA repair resulting in accumulation of severe DNA lesions. Ultimately these lesions, namely double strand breaks, chromosome aberrations, micronucleus formation, or mutations, can increase the risk for adverse health effects, such as cancer. How spaceflight factors affect DNA damage and the DNA damage response has been investigated since the early days of the human space program. Over the years, these experiments have been conducted either in space or using ground-based analogs. This review summarizes the evidence for DNA damage induction by space radiation and/or microgravity as well as spaceflight-related impacts on the DNA damage response. The review also discusses the conflicting results from studies aimed at addressing the question of potential synergies between microgravity and radiation with regard to DNA damage and cellular repair processes. We conclude that further experiments need to be performed in the true space environment in order to address this critical question.publishe

Development of a Minimally Invasive Device Based Therapy Incorporating Simultaneous Adjustable Passive Support and Synchronous Active Assist Designed to Treat Congestive Heart Failure

The technology described herein is a device based therapy targeting recovery of cardiac function in patients with congestive heart failure. This represents a shift in the present paradigm wherein available treatment options conservatively target inhibiting disease progression, e.g. non-adjustable cardiac support devices and/or alleviating symptoms, e.g. blood pumps for circulatory assist. Specifically, the innovation is a minimally invasive device incorporating adjustable passive cardiac support and synchronous active cardiac assist - device based technology designed to provide rehabilitative physical therapy for the heart muscle, mediating restorative remodeling processes to facilitate recovery of cardiac function. CHF affects more than 5.3 million people in the U.S. with 550,000 new cases diagnosed each year. For 300,000 Americans in end-stage failure, transplant is the preferred treatment; however, with less than 3,000 hearts available this treatment plan is epidemiologically trivial. The development of a therapeutic option targeting recovery of cardiac function would be a substantial advancement in the treatment of heart failure, and consequently a great benefit to the healthcare economy, biomedical science, and society as whole. Device performance was assessed in an acute implantation in an ovine model of acute heart failure (esmolol overdose). In the study it was confirmed that the device which was designed to be collapsible into a 1 1/2" diameter deployment tube and could be deployed using minimally invasive procedures. In examining pressure-volume loops, it was confirmed that the passive component of the device enabled a leftward shift in the enddiastolic pressure-volume relationship; important as disease typically shifts this relationship to the right. Further, it was verified that the active component of the device was capable of restoring stroke work lost in the esmolol induced failure model. Finally, the device did not invert the curvature of the heart, did not interfere with normal cardiac function, and remained in place through an intrinsic pneumatic attachment and thus did not require tethering to the myocardium. The versatile combination of support and assist provide the cardiologist with powerful therapeutic options to treat a wide variety of patient specific anomalies - with the primary target, rehabilitation of the heart and recovery of cardiac function and performance

MEASURING CAPACITY AND WILLINGNESS FOR POVERTY REDUCTION IN FRAGILE STATES

Food Security and Poverty,

Boosted Random ferns for object detection

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper we introduce the Boosted Random Ferns (BRFs) to rapidly build discriminative classifiers for learning and detecting object categories. At the core of our approach we use standard random ferns, but we introduce four main innovations that let us bring ferns from an instance to a category level, and still retain efficiency. First, we define binary features on the histogram of oriented gradients-domain (as opposed to intensity-), allowing for a better representation of intra-class variability. Second, both the positions where ferns are evaluated within the sliding window, and the location of the binary features for each fern are not chosen completely at random, but instead we use a boosting strategy to pick the most discriminative combination of them. This is further enhanced by our third contribution, that is to adapt the boosting strategy to enable sharing of binary features among different ferns, yielding high recognition rates at a low computational cost. And finally, we show that training can be performed online, for sequentially arriving images. Overall, the resulting classifier can be very efficiently trained, densely evaluated for all image locations in about 0.1 seconds, and provides detection rates similar to competing approaches that require expensive and significantly slower processing times. We demonstrate the effectiveness of our approach by thorough experimentation in publicly available datasets in which we compare against state-of-the-art, and for tasks of both 2D detection and 3D multi-view estimation.Peer ReviewedPostprint (author's final draft