215 research outputs found
Wasserstein Introspective Neural Networks
We present Wasserstein introspective neural networks (WINN) that are both a
generator and a discriminator within a single model. WINN provides a
significant improvement over the recent introspective neural networks (INN)
method by enhancing INN's generative modeling capability. WINN has three
interesting properties: (1) A mathematical connection between the formulation
of the INN algorithm and that of Wasserstein generative adversarial networks
(WGAN) is made. (2) The explicit adoption of the Wasserstein distance into INN
results in a large enhancement to INN, achieving compelling results even with a
single classifier --- e.g., providing nearly a 20 times reduction in model size
over INN for unsupervised generative modeling. (3) When applied to supervised
classification, WINN also gives rise to improved robustness against adversarial
examples in terms of the error reduction. In the experiments, we report
encouraging results on unsupervised learning problems including texture, face,
and object modeling, as well as a supervised classification task against
adversarial attacks.Comment: Accepted to CVPR 2018 (Oral
Speciation analysis of 129I in atmosphere by AMS and its applications for studies of environmental processes
LEDFD: A Low Energy Consumption Distributed Fault Detection Algorithm for Wireless Sensor Networks
Detection of faulty nodes and network energy saving have become the hottest research topics. Furthermore, current fault detection algorithms always pursue high detection performance but neglect energy consumption. In order to obtain good fault detection performance and save the network power, this paper proposes a low energy consumption distributed fault detection algorithm (LEDFD), which takes full advantage of temporally correlated and spatially correlated characteristics of the sensor nodes. LEDFD utilizes the temporally correlated information to examine some faulty nodes and then utilizes the spatially correlated information to examine the nodes that have not been detected as faulty through exchanging information among neighbor nodes to determine those nodes' state. Because LEDFD takes the data produced by nodes themselves to detect certain types of faults, which means nodes need not exchange information with their neighbor nodes during the entire detection process, the energy consumption of networks is efficiently reduced. Experimental results show that the algorithm has good performance and low energy consumption compared with current algorithms. </jats:p
Positron Emission Tomography in the Neuroimaging of Autism Spectrum Disorder
Autism spectrum disorder (ASD) is a pervasive developmental disease characterized by persistent impairment, repetitive and stereotypical behaviors in social interaction, as well as restricted interests and activities. The etiology of ASD is not clear yet, which results in difficulties in clinical diagnosis and treatment, and also brings heavy burden to patients and society. Positron emission tomography (PET) is a frequently used molecular imaging technology in quantitative, dynamic and in vivo research for therapeutic efficacy evaluation, pathophysiological mechanism investigation, thereby promoting development of ASD therapeutic drugs. More and more imaging studies have been reported on ASD recently, and the physiological changes featured by PET have been disclosed. This chapter reviews the specific radioligands for PET imaging of critical biomarkers involved in ASD. Herein, we discuss cerebral blood perfusion, cerebral glucose metabolism, and neurotransmitter system (transporters, precursors and receptors), as well as some other novel targets, including arginine vasopressin receptor targets and neuroinflammation related targets. The status of application and future prospect of the PET technology in research of ASD were discussed. This chapter provides a detailed and comprehensive literature review on ASD PET probe development, thereby can help readers intuitively and conveniently understand the status quo of research on ASD PET, and develop new research directions in this field
Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks
We introduce Florence-2, a novel vision foundation model with a unified,
prompt-based representation for a variety of computer vision and
vision-language tasks. While existing large vision models excel in transfer
learning, they struggle to perform a diversity of tasks with simple
instructions, a capability that implies handling the complexity of various
spatial hierarchy and semantic granularity. Florence-2 was designed to take
text-prompt as task instructions and generate desirable results in text forms,
whether it be captioning, object detection, grounding or segmentation. This
multi-task learning setup demands large-scale, high-quality annotated data. To
this end, we co-developed FLD-5B that consists of 5.4 billion comprehensive
visual annotations on 126 million images, using an iterative strategy of
automated image annotation and model refinement. We adopted a
sequence-to-sequence structure to train Florence-2 to perform versatile and
comprehensive vision tasks. Extensive evaluations on numerous tasks
demonstrated Florence-2 to be a strong vision foundation model contender with
unprecedented zero-shot and fine-tuning capabilities
Speciation analysis of iodine isotopes (127I and 129I) in aerosol using sequential extraction and mass spectrometry techniques
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