2,550 research outputs found
Analysis customer satisfaction of food service in Commons at RIT
School foodservice is the largest food-service business in the world.. Students and faculty choose to have meals in the school, because they might lack the time and it is convenient. However, The students in the college and university represent a hard-to please sort of consumers. This study selected the specific cafeteria, Commons at Rochester Institute of Technology. The students and faculty filled out the survey which focused on the food and asked students how they felt about the food, what they liked and did not like and what foods they felt were served not enough or too often. Additional question on the survey asked the students and faculty about their feelings on the service and atmosphere of this dinning hall. Last, this study also evaluated the survey and explored the overall satisfaction of the students and faculty in the cafeteria, the analysis showed areas where satisfaction was being net and also where improvement could be made
Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal
Deep Convolutional Neural Networks (CNNs) are widely employed in modern
computer vision algorithms, where the input image is convolved iteratively by
many kernels to extract the knowledge behind it. However, with the depth of
convolutional layers getting deeper and deeper in recent years, the enormous
computational complexity makes it difficult to be deployed on embedded systems
with limited hardware resources. In this paper, we propose two
computation-performance optimization methods to reduce the redundant
convolution kernels of a CNN with performance and architecture constraints, and
apply it to a network for super resolution (SR). Using PSNR drop compared to
the original network as the performance criterion, our method can get the
optimal PSNR under a certain computation budget constraint. On the other hand,
our method is also capable of minimizing the computation required under a given
PSNR drop.Comment: This paper was accepted by 2018 The International Symposium on
Circuits and Systems (ISCAS
-wave Pairing in BiS Superconductors
Recent angle resolved photoemission spectroscopy(ARPES) experiments have
suggested that BiS based superconductors are at very low electron doping.
Using random phase approximation(RPA) and functional renormalization group(FRG)
methods, we find that -wave pairing symmetry belonging to A
irreducible representation is dominant at electron doping . The pairing
symmetry is determined by inter-pocket nesting and orbital characters on the
Fermi surfaces and is robust in a two-orbital model including both Hund's
coupling , and Hubbard-like Coulomb interactions and with
relatively small (). With the increasing electron doping, the
g-wave state competes with both the s-wave and d-wave states
and no pairing symmetry emerges dominantly.Comment: published version, EPL(editor's choice
Translational drug interaction study using text mining technology
Indiana University-Purdue University Indianapolis (IUPUI)Drug-Drug Interaction (DDI) is one of the major causes of adverse drug reaction (ADR) and
has been demonstrated to threat public health. It causes an estimated 195,000
hospitalizations and 74,000 emergency room visits each year in the USA alone. Current
DDI research aims to investigate different scopes of drug interactions: molecular level of
pharmacogenetics interaction (PG), pharmacokinetics interaction (PK), and clinical
pharmacodynamics consequences (PD). All three types of experiments are important, but
they are playing different roles for DDI research. As diverse disciplines and varied studies
are involved, interaction evidence is often not available cross all three types of evidence,
which create knowledge gaps and these gaps hinder both DDI and pharmacogenetics
research.
In this dissertation, we proposed to distinguish the three types of DDI evidence (in vitro
PK, in vivo PK, and clinical PD studies) and identify all knowledge gaps in experimental
evidence for them. This is a collective intelligence effort, whereby a text mining tool will
be developed for the large-scale mining and analysis of drug-interaction information such
that it can be applied to retrieve, categorize, and extract the information of DDI from
published literature available on PubMed. To this end, three tasks will be done in this
research work: First, the needed lexica, ontology, and corpora for distinguishing three
different types of studies were prepared. Despite the lexica prepared in this work, a
comprehensive dictionary for drug metabolites or reaction, which is critical to in vitro PK study, is still lacking in pubic databases. Thus, second, a name entity recognition tool will
be proposed to identify drug metabolites and reaction in free text. Third, text mining tools
for retrieving DDI articles and extracting DDI evidence are developed. In this work, the
knowledge gaps cross all three types of DDI evidence can be identified and the gaps
between knowledge of molecular mechanisms underlying DDI and their clinical
consequences can be closed with the result of DDI prediction using the retrieved drug
gene interaction information such that we can exemplify how the tools and methods can
advance DDI pharmacogenetics research.2 year
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