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"Han, Yu"
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The impossible fairy tale : a novel
\"The Impossible Fairy Tale is the story of two unexceptional grade-school girls. Mia is \"lucky\"--she is spoiled by her mother and, as she explains, her two fathers. She gloats over her exotic imported color pencils and won't be denied a coveted sweater. Then there is the Child who, by contrast, is neither lucky nor unlucky. She makes so little impression that she seems not even to merit a name. At school, their fellow students, whether lucky or luckless or unlucky, seem consumed by an almost murderous rage. Adults are nearly invisible, and the society the children create on their own is marked by cruelty and soul-crushing hierarchies. Then, one day, the Child sneaks into the classroom after hours and adds ominous sentences to her classmates' notebooks. This sinister but initially inconsequential act unlocks a series of events that end in horrible violence. But that is not the end of this eerie, unpredictable novel. A teacher, who is also this book's author, wakes from an intense dream. When she arrives at her next class, she recognizes a student: the Child, who knows about the events of the novel's first half, which took place years before. The Impossible Fairy Tale is a fresh and terrifying exploration of the ethics of art making and of the stinging consequences of neglect.\" -- Publisher's website
Book
Feature Extraction and Image Recognition with Convolutional Neural Networks
The human has a very complex perception system, including vision, auditory, olfactory, touch, and gustation. This paper will introduce the recent studies about providing a technical solution for image recognition, by applying a algorithm called Convolutional Neural Network (CNN) which is inspired by animal visual system. Convolution serves as a perfect realization of an optic nerve cell which merely responds to its receptive field and it performs well in image feature extraction. Being highly-hierarchical networks, CNN is structured with a series of different functional layers. The function blocks are separated and described clearly by each layer in this paper. Additionally, the recognition process and result of a pioneering CNN on MNIST database are presented.
Journal Article
Unsupervised Generative Modeling Using Matrix Product States
Generative modeling, which learns joint probability distribution from data and generates samples according to it, is an important task in machine learning and artificial intelligence. Inspired by probabilistic interpretation of quantum physics, we propose a generative model using matrix product states, which is a tensor network originally proposed for describing (particularly one-dimensional) entangled quantum states. Our model enjoys efficient learning analogous to the density matrix renormalization group method, which allows dynamically adjusting dimensions of the tensors and offers an efficient direct sampling approach for generative tasks. We apply our method to generative modeling of several standard data sets including the Bars and Stripes random binary patterns and the MNIST handwritten digits to illustrate the abilities, features, and drawbacks of our model over popular generative models such as the Hopfield model, Boltzmann machines, and generative adversarial networks. Our work sheds light on many interesting directions of future exploration in the development of quantum-inspired algorithms for unsupervised machine learning, which are promisingly possible to realize on quantum devices.
Journal Article
Galectin-9 interacts with PD-1 and TIM-3 to regulate T cell death and is a target for cancer immunotherapy
The two T cell inhibitory receptors PD-1 and TIM-3 are co-expressed during exhausted T cell differentiation, and recent evidence suggests that their crosstalk regulates T cell exhaustion and immunotherapy efficacy; however, the molecular mechanism is unclear. Here we show that PD-1 contributes to the persistence of PD-1
+
TIM-3
+
T cells by binding to the TIM-3 ligand galectin-9 (Gal-9) and attenuates Gal-9/TIM-3-induced cell death. Anti-Gal-9 therapy selectively expands intratumoral TIM-3
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cytotoxic CD8 T cells and immunosuppressive regulatory T cells (T
reg
cells). The combination of anti-Gal-9 and an agonistic antibody to the co-stimulatory receptor GITR (glucocorticoid-induced tumor necrosis factor receptor-related protein) that depletes T
reg
cells induces synergistic antitumor activity. Gal-9 expression and secretion are promoted by interferon β and γ, and high Gal-9 expression correlates with poor prognosis in multiple human cancers. Our work uncovers a function for PD-1 in exhausted T cell survival and suggests Gal-9 as a promising target for immunotherapy.
Galectin-9 regulates several cellular processes including TIM-3-mediated T cell death. Here the authors show that co-expressed PD-1 protects TIM-3
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T cells from galectin-9-induced cell death and that anti-galectin-9 in combination with GITR agonism promotes an anti-tumor immune response.
Journal Article
Engineering β-ketoamine covalent organic frameworks for photocatalytic overall water splitting
Covalent organic frameworks (COFs) are an emerging type of crystalline and porous photocatalysts for hydrogen evolution, however, the overall water splitting activity of COFs is rarely known. In this work, we firstly realized overall water splitting activity of
β
-ketoamine COFs by systematically engineering N-sites, architecture, and morphology. By in situ incorporating sub-nanometer platinum (Pt) nanoparticles co-catalyst into the pores of COFs nanosheets, both Pt@TpBpy-NS and Pt@TpBpy-2-NS show visible-light-driven overall water splitting activity, with the optimal H
2
and O
2
evolution activities of 9.9 and 4.8 μmol in 5 h for Pt@TpBpy-NS, respectively, and a maximum solar-to-hydrogen efficiency of 0.23%. The crucial factors affecting the activity including N-sites position, nano morphology, and co-catalyst distribution were systematically explored. Further mechanism investigation reveals the tiny diversity of N sites in COFs that induces great differences in electron transfer as well as reaction potential barriers.
Covalent organic frameworks (COFs) are an emerging type of crystalline and porous photocatalysts for hydrogen evolution. Here, the authors report a β-ketoamine COF by systematically engineering N-sites, architecture, and morphology for improved water splitting activity.
Journal Article