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A Gap Analysis on Generation Y Customer Expectations and Perceptions towards Lifestyle Hotels
This study conducts a gap analysis on Generation Y (Gen Y) customer expectation and perception towards lifestyle hotels. In the result, 11 gaps between Gen Y customer expectation and perceptions on lifestyle hotels are identified. An Importance-Performance (I-P) map further demonstrates how lifestyle hotels satisfy Gen Y clients, and suggests future improvements for lifestyle hotels to better cater Gen Y guests. The findings indicate that Gen Yers are overall satisfied with lifestyle hotels, and lifestyle hotels have relatively high performance in general. But there are also a few aspects lifestyle hotels could enhance for higher customer satisfactions. This research not only enriches limited existing understandings on Gen Y customer expectations, perceptions, and lifestyle hotel performance as well for academies; but also gives industry professionals useful recommendations for future improvements
A Batch Rival Penalized Expectation-Maximization Algorithm for Gaussian Mixture Clustering with Automatic Model Selection
Within the learning framework of maximum weighted likelihood (MWL) proposed by Cheung, 2004 and 2005, this paper will develop a batch Rival Penalized Expectation-Maximization (RPEM) algorithm for density mixture clustering provided that all observations are available before the learning process. Compared to the adaptive RPEM algorithm in Cheung, 2004 and 2005, this batch RPEM need not assign the learning rate analogous to the Expectation-Maximization (EM) algorithm (Dempster et al., 1977), but still preserves the capability of automatic model selection. Further, the convergence speed of this batch RPEM is faster than the EM and the adaptive RPEM in general. The experiments show the superior performance of the proposed algorithm on the synthetic data and color image segmentation
ScratchDet: Training Single-Shot Object Detectors from Scratch
Current state-of-the-art object objectors are fine-tuned from the
off-the-shelf networks pretrained on large-scale classification dataset
ImageNet, which incurs some additional problems: 1) The classification and
detection have different degrees of sensitivity to translation, resulting in
the learning objective bias; 2) The architecture is limited by the
classification network, leading to the inconvenience of modification. To cope
with these problems, training detectors from scratch is a feasible solution.
However, the detectors trained from scratch generally perform worse than the
pretrained ones, even suffer from the convergence issue in training. In this
paper, we explore to train object detectors from scratch robustly. By analysing
the previous work on optimization landscape, we find that one of the overlooked
points in current trained-from-scratch detector is the BatchNorm. Resorting to
the stable and predictable gradient brought by BatchNorm, detectors can be
trained from scratch stably while keeping the favourable performance
independent to the network architecture. Taking this advantage, we are able to
explore various types of networks for object detection, without suffering from
the poor convergence. By extensive experiments and analyses on downsampling
factor, we propose the Root-ResNet backbone network, which makes full use of
the information from original images. Our ScratchDet achieves the
state-of-the-art accuracy on PASCAL VOC 2007, 2012 and MS COCO among all the
train-from-scratch detectors and even performs better than several one-stage
pretrained methods. Codes will be made publicly available at
https://github.com/KimSoybean/ScratchDet.Comment: CVPR2019 Oral Presentation. Camera Ready Versio
Motor Vehicle Emission Modeling and Software Simulation Computing for Roundabout in Urban City
In urban road traffic systems, roundabout is considered as one of the core traffic bottlenecks, which are also a core impact of vehicle emission and city environment. In this paper, we proposed a transport control and management method for solving traffic jam and reducing emission in roundabout. The platform of motor vehicle testing system and VSP-based emission model was established firstly. By using the topology chart of the roundabout and microsimulation software, we calculated the instantaneous emission rates of different vehicle and total vehicle emissions. We argued that Integration-Model, combing traffic simulation and vehicle emission, can be performed to calculate the instantaneous emission rates of different vehicle and total vehicle emissions at the roundabout. By contrasting the exhaust emissions result between no signal control and signal control in this area at the rush hour, it draws a conclusion that setting the optimizing signal control can effectively reduce the regional vehicle emission. The proposed approach has been submitted to a simulation and experiment that involved an environmental assessment in Satellite Square, a roundabout in medium city located in China. It has been verified that setting signal control with knowledge engineering and Integration-Model is a practical way for solving the traffic jams and environmental pollution
Hydrogen isotope separation using graphene-based membranes in liquid water
Hydrogen isotope separation has been effectively achieved using gaseous H2/D2
filtered through graphene/Nafion composite membranes. Nevertheless, deuteron
nearly does not exist in the form of gaseous D2 in nature but in liquid water.
Thus, it is a more feasible way to separate and enrich deuterium from water.
Herein we have successfully transferred monolayer graphene to a rigid and
porous polymer substrate PITEM (polyimide tracked film), which could avoid the
swelling problem of the Nafion substrate, as well as keep the integrity of
graphene. Meanwhile, defects in large area of CVD graphene could be
successfully repaired by interfacial polymerization resulting in high
separation factor. Moreover, a new model was proposed for the proton transport
mechanism through monolayer graphene based on the kinetic isotope effect (KIE).
In this model, graphene plays the significant role in the H/D separation
process by completely breaking the O-H/O-D bond, which can maximize the KIE
leading to prompted H/D separation performance. This work suggests a promising
application of using monolayer graphene in industry and proposes a pronounced
understanding of proton transport in grapheneComment: 10 pages, 4 figures (6pages, 6figures for SI
The Invasive MED/Q \u3cem\u3eBemisia tabaci\u3c/em\u3e Genome: A Tale of Gene Loss and Gene Gain
Background: Sweetpotato whitefly, Bemisia tabaci MED/Q and MEAM1/B, are two economically important invasive species that cause considerable damages to agriculture crops through direct feeding and indirect vectoring of plant pathogens. Recently, a draft genome of B. tabaci MED/Q has been assembled. In this study, we focus on the genomic comparison between MED/Q and MEAM1/B, with a special interest in MED/Q’s genomic signatures that may contribute to the highly invasive nature of this emerging insect pest.
Results: The genomes of both species share similarity in syntenic blocks, but have significant divergence in the gene coding sequence. Expansion of cytochrome P450 monooxygenases and UDP glycosyltransferases in MED/Q and MEAM1/B genome is functionally validated for mediating insecticide resistance in MED/Q using in vivo RNAi. The amino acid biosynthesis pathways in MED/Q genome are partitioned among the host and endosymbiont genomes in a manner distinct from other hemipterans. Evidence of horizontal gene transfer to the host genome may explain their obligate relationship. Putative loss-of-function in the immune deficiency-signaling pathway due to the gene loss is a shared ancestral trait among hemipteran insects.
Conclusions: The expansion of detoxification genes families, such as P450s, may contribute to the development of insecticide resistance traits and a broad host range in MED/Q and MEAM1/B, and facilitate species’ invasions into intensively managed cropping systems. Numerical and compositional changes in multiple gene families (gene loss and gene gain) in the MED/Q genome sets a foundation for future hypothesis testing that will advance our understanding of adaptation, viral transmission, symbiosis, and plant-insect-pathogen tritrophic interactions
Data mining tools for Salmonella characterization: application to gel-based fingerprinting analysis
BACKGROUND: Pulsed field gel electrophoresis (PFGE) is currently the most widely and routinely used method by the Centers for Disease Control and Prevention (CDC) and state health labs in the United States for Salmonella surveillance and outbreak tracking. Major drawbacks of commercially available PFGE analysis programs have been their difficulty in dealing with large datasets and the limited availability of analysis tools. There exists a need to develop new analytical tools for PFGE data mining in order to make full use of valuable data in large surveillance databases. RESULTS: In this study, a software package was developed consisting of five types of bioinformatics approaches exploring and implementing for the analysis and visualization of PFGE fingerprinting. The approaches include PFGE band standardization, Salmonella serotype prediction, hierarchical cluster analysis, distance matrix analysis and two-way hierarchical cluster analysis. PFGE band standardization makes it possible for cross-group large dataset analysis. The Salmonella serotype prediction approach allows users to predict serotypes of Salmonella isolates based on their PFGE patterns. The hierarchical cluster analysis approach could be used to clarify subtypes and phylogenetic relationships among groups of PFGE patterns. The distance matrix and two-way hierarchical cluster analysis tools allow users to directly visualize the similarities/dissimilarities of any two individual patterns and the inter- and intra-serotype relationships of two or more serotypes, and provide a summary of the overall relationships between user-selected serotypes as well as the distinguishable band markers of these serotypes. The functionalities of these tools were illustrated on PFGE fingerprinting data from PulseNet of CDC. CONCLUSIONS: The bioinformatics approaches included in the software package developed in this study were integrated with the PFGE database to enhance the data mining of PFGE fingerprints. Fast and accurate prediction makes it possible to elucidate Salmonella serotype information before conventional serological methods are pursued. The development of bioinformatics tools to distinguish the PFGE markers and serotype specific patterns will enhance PFGE data retrieval, interpretation and serotype identification and will likely accelerate source tracking to identify the Salmonella isolates implicated in foodborne diseases
MULTI: Multimodal Understanding Leaderboard with Text and Images
Rapid progress in multimodal large language models (MLLMs) highlights the
need to introduce challenging yet realistic benchmarks to the academic
community, while existing benchmarks primarily focus on understanding simple
natural images and short context. In this paper, we present MULTI as a
cutting-edge benchmark for evaluating MLLMs on understanding complex tables and
images, and reasoning with long context. MULTI provides multimodal inputs and
requires responses that are either precise or open-ended, reflecting real-life
examination styles. MULTI includes over 18,000 questions and challenges MLLMs
with a variety of tasks, ranging from formula derivation to image detail
analysis and cross-modality reasoning. We also introduce MULTI-Elite, a
500-question selected hard subset, and MULTI-Extend, with more than 4,500
external knowledge context pieces. Our evaluation indicates significant
potential for MLLM advancement, with GPT-4V achieving a 63.7% accuracy rate on
MULTI, in contrast to other MLLMs scoring between 28.5% and 55.3%. MULTI serves
not only as a robust evaluation platform but also paves the way for the
development of expert-level AI.Comment: 16 pages, 9 figures, 10 tables. Details and access are available at:
https://OpenDFM.github.io/MULTI-Benchmark
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