A non-linear model of rubber shear springs validated by experiments

Vibrating flip-flow screens provide an effective solution for screening highly viscous or fine materials. However, yet, only linear theory has been applied to their design. Yet, to understand deficiencies and to improve performance an accurate model especially of the rubber shear springs equipped in screen frames is critical for its dynamics to predict e.g. frequency- and amplitude-dependent behaviour. In this paper, the amplitude dependency of the rubber shear spring is represented by employing a friction model in which parameters are fitted to an affine function rather constant values used for the classic Berg’s model; the fractional derivative model is used to describe its frequency dependency and compared to conventional dashpot and Maxwell models with its elasticity being represented by a nonlinear spring. The experimentally validated results indicate that the proposed model with a nonlinear spring, friction and fractional derivative model is able to more accurately describe the dynamic characteristics of a rubber shear spring compared with other models

Explanatory debugging: Supporting end-user debugging of machine-learned programs

Many machine-learning algorithms learn rules of behavior from individual end users, such as task-oriented desktop organizers and handwriting recognizers. These rules form a “program” that tells the computer what to do when future inputs arrive. Little research has explored how an end user can debug these programs when they make mistakes. We present our progress toward enabling end users to debug these learned programs via a Natural Programming methodology. We began with a formative study exploring how users reason about and correct a text-classification program. From the results, we derived and prototyped a concept based on “explanatory debugging”, then empirically evaluated it. Our results contribute methods for exposing a learned program's logic to end users and for eliciting user corrections to improve the program's predictions

Ubiquitination and proteasomal degradation of ATG12 regulates its proapoptotic activity

During macroautophagy, conjugation of ATG12 to ATG5 is essential for LC3 lipidation and autophagosome formation. Additionally, ATG12 has ATG5-independent functions in diverse processes including mitochondrial fusion and mitochondrial-dependent apoptosis. In this study, we investigated the regulation of free ATG12. In stark contrast to the stable ATG12–ATG5 conjugate, we find that free ATG12 is highly unstable and rapidly degraded in a proteasome-dependent manner. Surprisingly, ATG12, itself a ubiquitin-like protein, is directly ubiquitinated and this promotes its proteasomal degradation. As a functional consequence of its turnover, accumulation of free ATG12 contributes to proteasome inhibitor-mediated apoptosis, a finding that may be clinically important given the use of proteasome inhibitors as anticancer agents. Collectively, our results reveal a novel interconnection between autophagy, proteasome activity, and cell death mediated by the ubiquitin-like properties of ATG12

Integrating rich user feedback into intelligent user interfaces

The potential for machine learning systems to improve via a mutually beneficial exchange of information with users has yet to be explored in much detail. Previously, we found that users were willing to provide a generous amount of rich feedback to machine learning systems, and that the types of some of this rich feedback seem promising for assimilation by machine learning algorithms. Following up on those findings, we ran an experiment to assess the viability of incorporating real-time keyword-based feedback in initial training phases when data is limited. We found that rich feedback improved accuracy but an initial unstable period often caused large fluctuations in classifier behavior. Participants were able to give feedback by relying heavily on system communication in order to respond to changes. The results show that in order to benefit from the user’s knowledge, machine learning systems must be able to absorb keyword-based rich feedback in a graceful manner and provide clear explanations of their predictions

A Study of Total and Reduced Glutathione with Oxygen Content and Capacity in the Blood of Pregnant and Non-Pregnant Women

Much speculation has been offered as to the exact nature and function of glutathione in circulating blood. It has often been considered as playing some part in cell respiration. A study has been made on total and reduced glutathione content of blood in the non-pregnant, in the pregnant during the labor and the parturition including cord blood, during the postpartum four-ten days, and in the toxemias of pregnancy. This offered an opportunity to study the relation between total and reduced glutathione in blood under conditions of varying oxygen content and capacity of systemic blood. The study also included a group of experiments in which blood (with and without fluorides) kept at -3°, 23°, and 38°C. for periods as long as seventy-two hours was analyzed at given intervals for total and reduced glutathione. Oxygen content of the blood was also determined simultaneously with glutathione on the specimen kept at 38°C

End-user feature labeling: a locally-weighted regression approach

When intelligent interfaces, such as intelligent desktop assistants, email classifiers, and recommender systems, customize themselves to a particular end user, such customizations can decrease productivity and increase frustration due to inaccurate predictions - especially in early stages, when training data is limited. The end user can improve the learning algorithm by tediously labeling a substantial amount of additional training data, but this takes time and is too ad hoc to target a particular area of inaccuracy. To solve this problem, we propose a new learning algorithm based on locally weighted regression for feature labeling by end users, enabling them to point out which features are important for a class, rather than provide new training instances. In our user study, the first allowing ordinary end users to freely choose features to label directly from text documents, our algorithm was both more effective than others at leveraging end users' feature labels to improve the learning algorithm, and more robust to real users' noisy feature labels. These results strongly suggest that allowing users to freely choose features to label is a promising method for allowing end users to improve learning algorithms effectively

End-User Feature Labeling via Locally Weighted Logistic Regression

Applications that adapt to a particular end user often make inaccurate predictions during the early stages when training data is limited. Although an end user can improve the learning algorithm by labeling more training data, this process is time consuming and too ad hoc to target a particular area of inaccuracy. To solve this problem, we propose a new learning algorithm based on Locally Weighted Logistic Regression for feature labeling by end users, enabling them to point out which features are important for a class, rather than provide new training instances. In our user study, the first allowing ordinary end users to freely choose features to label directly from text documents, our algorithm was more effective than others at leveraging end users’ feature labels to improve the learning algorithm. Our results strongly suggest that allowing users to freely choose features to label is a promising method for allowing end users to improve learning algorithms effectively

An Assemblage of Lava Flow Features on Mercury

In contrast to other terrestrial planets, Mercury does not possess a great variety of volcanic features, its history of volcanism instead largely manifest by expansive smooth plains. However, a set of landforms at high northern latitudes on Mercury resembles surface flow features documented on Earth, the Moon, Mars, and Venus. The most striking of such landforms are broad channels that host streamlined islands and that cut through the surrounding intercrater plains. Together with narrower, more sinuous channels, coalesced depressions, evidence for local flooding of intercrater plains by lavas, and a first-order analysis of lava flow rates, the broad channels define an assemblage of flow features formed by the overland flow of, and erosion by, voluminous, high-temperature, low-viscosity lavas. This interpretation is consistent with compositional data suggesting that substantial portions of Mercury's crust are composed of magnesian, iron-poor lithologies. Moreover, the proximity of this partially flooded assemblage to extensive volcanic plains raises the possibility that the formation of these flow features may preface total inundation of an area by lavas emplaced in a flood mode and that they escaped complete burial only due to a waning magmatic supply. Finally, that these broad channels on Mercury are volcanic in nature yet resemble outflow channels on Mars, which are commonly attributed to catastrophic water floods, implies that aqueous activity is not a prerequisite for the formation of such distinctive landforms on any planetary body

Motional sidebands and direct measurement of the cooling rate in the resonance fluorescence of a single trapped ion

Resonance fluorescence of a single trapped ion is spectrally analyzed using a heterodyne technique. Motional sidebands due to the oscillation of the ion in the harmonic trap potential are observed in the fluorescence spectrum. From the width of the sidebands the cooling rate is obtained and found to be in agreement with the theoretical prediction.Comment: 4 pages, 4 figures. Final version after minor changes, 1 figure replaced; to be published in PRL, July 10, 200

Atomic matter wave scanner

We report on the experimental realization of an atom optical device, that allows scanning of an atomic beam. We used a time-modulated evanescent wave field above a glass surface to diffract a continuous beam of metastable Neon atoms at grazing incidence. The diffraction angles and efficiencies were controlled by the frequency and form of modulation, respectively. With an optimized shape, obtained from a numerical simulation, we were able to transfer more than 50% of the atoms into the first order beam, which we were able to move over a range of 8 mrad.Comment: 4 pages, 4 figure