2,295 research outputs found
The 30-kW ammonia arcjet technology
The technical results are summarized of a 30 kW class ammonia propellant arcjet technology program. Evaluation of previous arcjet thruster performance, including materials analysis of used thruster components, led to the design of an arcjet with improved performance and thermal characteristics. Tests of the new engine demonstrated that engine performance is relatively insensitive to cathode tip geometry. Other data suggested a maximum sustainable arc length for a given thruster configuration, beyond which the arc may reconfigure in a destructive manner. A flow controller calibration error was identified. This error caused previously reported values of specific impulse and thrust efficiency to be 20 percent higher than the real values. Corrected arcjet performance data are given. Duration tests of 413 and 252 hours, and several tests 100 hours in duration, were performed. The cathode tip erosion rate increased with increasing arc current. Elimination of power source ripple did not affect cathode tip whisker growth. Results of arcjet modeling, diagnostic development and mission analyses are also discussed. The 30 kW ammonia arcjet may now be considered ready for development for a flight demonstration, but widespread application of 30 kW class arcjet will require improved efficiency and lifetime
Inheritance of the Sex-Determining Factor in the Absence of a Complete Y Chromosome in 46,XX Human Males
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/71879/1/j.1749-6632.1987.tb25088.x.pd
Variational Deep Semantic Hashing for Text Documents
As the amount of textual data has been rapidly increasing over the past
decade, efficient similarity search methods have become a crucial component of
large-scale information retrieval systems. A popular strategy is to represent
original data samples by compact binary codes through hashing. A spectrum of
machine learning methods have been utilized, but they often lack expressiveness
and flexibility in modeling to learn effective representations. The recent
advances of deep learning in a wide range of applications has demonstrated its
capability to learn robust and powerful feature representations for complex
data. Especially, deep generative models naturally combine the expressiveness
of probabilistic generative models with the high capacity of deep neural
networks, which is very suitable for text modeling. However, little work has
leveraged the recent progress in deep learning for text hashing.
In this paper, we propose a series of novel deep document generative models
for text hashing. The first proposed model is unsupervised while the second one
is supervised by utilizing document labels/tags for hashing. The third model
further considers document-specific factors that affect the generation of
words. The probabilistic generative formulation of the proposed models provides
a principled framework for model extension, uncertainty estimation, simulation,
and interpretability. Based on variational inference and reparameterization,
the proposed models can be interpreted as encoder-decoder deep neural networks
and thus they are capable of learning complex nonlinear distributed
representations of the original documents. We conduct a comprehensive set of
experiments on four public testbeds. The experimental results have demonstrated
the effectiveness of the proposed supervised learning models for text hashing.Comment: 11 pages, 4 figure
The Relativistic Hopfield network: rigorous results
The relativistic Hopfield model constitutes a generalization of the standard
Hopfield model that is derived by the formal analogy between the
statistical-mechanic framework embedding neural networks and the Lagrangian
mechanics describing a fictitious single-particle motion in the space of the
tuneable parameters of the network itself. In this analogy the cost-function of
the Hopfield model plays as the standard kinetic-energy term and its related
Mattis overlap (naturally bounded by one) plays as the velocity. The
Hamiltonian of the relativisitc model, once Taylor-expanded, results in a
P-spin series with alternate signs: the attractive contributions enhance the
information-storage capabilities of the network, while the repulsive
contributions allow for an easier unlearning of spurious states, conferring
overall more robustness to the system as a whole. Here we do not deepen the
information processing skills of this generalized Hopfield network, rather we
focus on its statistical mechanical foundation. In particular, relying on
Guerra's interpolation techniques, we prove the existence of the infinite
volume limit for the model free-energy and we give its explicit expression in
terms of the Mattis overlaps. By extremizing the free energy over the latter we
get the generalized self-consistent equations for these overlaps, as well as a
picture of criticality that is further corroborated by a fluctuation analysis.
These findings are in full agreement with the available previous results.Comment: 11 pages, 1 figur
Generative Invertible Networks (GIN): Pathophysiology-Interpretable Feature Mapping and Virtual Patient Generation
Machine learning methods play increasingly important roles in pre-procedural
planning for complex surgeries and interventions. Very often, however,
researchers find the historical records of emerging surgical techniques, such
as the transcatheter aortic valve replacement (TAVR), are highly scarce in
quantity. In this paper, we address this challenge by proposing novel
generative invertible networks (GIN) to select features and generate
high-quality virtual patients that may potentially serve as an additional data
source for machine learning. Combining a convolutional neural network (CNN) and
generative adversarial networks (GAN), GIN discovers the pathophysiologic
meaning of the feature space. Moreover, a test of predicting the surgical
outcome directly using the selected features results in a high accuracy of
81.55%, which suggests little pathophysiologic information has been lost while
conducting the feature selection. This demonstrates GIN can generate virtual
patients not only visually authentic but also pathophysiologically
interpretable
Neural Networks for Information Retrieval
Machine learning plays a role in many aspects of modern IR systems, and deep
learning is applied in all of them. The fast pace of modern-day research has
given rise to many different approaches for many different IR problems. The
amount of information available can be overwhelming both for junior students
and for experienced researchers looking for new research topics and directions.
Additionally, it is interesting to see what key insights into IR problems the
new technologies are able to give us. The aim of this full-day tutorial is to
give a clear overview of current tried-and-trusted neural methods in IR and how
they benefit IR research. It covers key architectures, as well as the most
promising future directions.Comment: Overview of full-day tutorial at SIGIR 201
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