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

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

Double shielded Public Key Cryptosystems

By introducing extra shields on Shpilrain and Ushakov\u27s Ko-Lee-like protocol based on the decomposition problem of group elements we propose two new key exchange schemes and then a number of public key cryptographic protocols. We show that these protocols are free of known attacks. Particularly,if the entities taking part in our protocols create their private keys composed by the generators of the Mihailova subgroups of Bn, we show that the safety of our protocols are very highly guarantied by the insolvability of subgroup membership problem of the Mihailova subgroups