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

gg-wave Pairing in BiS2_2 Superconductors

Recent angle resolved photoemission spectroscopy(ARPES) experiments have suggested that BiS$_2$ based superconductors are at very low electron doping. Using random phase approximation(RPA) and functional renormalization group(FRG) methods, we find that $g$-wave pairing symmetry belonging to A$_{2g}$ irreducible representation is dominant at electron doping $x<0.25$. 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 $J$, and Hubbard-like Coulomb interactions $U$ and $U'$ with relatively small $J$ ($J\leq0.2U$). With the increasing electron doping, the g-wave state competes with both the s-wave $A_{1g}$ and d-wave $B_{2g}$ 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