Xiang Zhang

Bio: Xiang Zhang is an academic researcher from Baylor College of Medicine. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 154, co-authored 1733 publications receiving 117576 citations. Previous affiliations of Xiang Zhang include University of California, Berkeley & University of Texas MD Anderson Cancer Center.

Micro-stereolithography of polymeric and ceramic microstructures

Abstract: Micro-stereolithography (μSL) is a novel micro-manufacturing process which builds the truly 3D microstructures by solidifying the liquid monomer in a layer by layer fashion. In this work, an advanced μSL apparatus is designed and developed which includes an Ar + laser, the beam delivery system, computer-controlled precision x–y–z stages and CAD design tool, and in situ process monitoring systems. The 1.2 μm resolution of μSL fabrication has been achieved with this apparatus. The microtubes with high aspect ratio of 16 and real 3D microchannels and microcones are fabricated on silicon substrate. For the first time, μSL of ceramic microgears has been successfully demonstrated.

Enhanced ferroelectricity in ultrathin films grown directly on silicon.

Abstract: Ultrathin ferroelectric materials could potentially enable low-power logic and nonvolatile memories1,2. As ferroelectric materials are made thinner, however, the ferroelectricity is usually suppressed. Size effects in ferroelectrics have been thoroughly investigated in perovskite oxides—the archetypal ferroelectric system3. Perovskites, however, have so far proved unsuitable for thickness scaling and integration with modern semiconductor processes4. Here we report ferroelectricity in ultrathin doped hafnium oxide (HfO2), a fluorite-structure oxide grown by atomic layer deposition on silicon. We demonstrate the persistence of inversion symmetry breaking and spontaneous, switchable polarization down to a thickness of one nanometre. Our results indicate not only the absence of a ferroelectric critical thickness but also enhanced polar distortions as film thickness is reduced, unlike in perovskite ferroelectrics. This approach to enhancing ferroelectricity in ultrathin layers could provide a route towards polarization-driven memories and ferroelectric-based advanced transistors. This work shifts the search for the fundamental limits of ferroelectricity to simpler transition-metal oxide systems—that is, from perovskite-derived complex oxides to fluorite-structure binary oxides—in which ‘reverse’ size effects counterintuitively stabilize polar symmetry in the ultrathin regime. Enhanced switchable ferroelectric polarization is achieved in doped hafnium oxide films grown directly onto silicon using low-temperature atomic layer deposition, even at thicknesses of just one nanometre.

Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images

Abstract: Background A novel coronavirus (COVID-19) has emerged recently as an acute respiratory syndrome. The outbreak was originally reported in Wuhan, China, but has subsequently been spread world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. Materials and Methods We collected chest CT scans of 88 patients diagnosed with the COVID-19 from hospitals of two provinces in China, 101 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the collected dataset, a deep learning-based CT diagnosis system (DeepPneumonia) was developed to identify patients with COVID-19. Results The experimental results showed that our model can accurately identify the COVID-19 patients from others with an excellent AUC of 0.99 and recall (sensitivity) of 0.93. In addition, our model was capable of discriminating the COVID-19 infected patients and bacteria pneumonia-infected patients with an AUC of 0.95, recall (sensitivity) of 0.96. Moreover, our model could localize the main lesion features, especially the ground-glass opacity (GGO) that is of great help to assist doctors in diagnosis. The diagnosis for a patient could be finished in 30 seconds, and the implementation on Tianhe-2 supercompueter enables a parallel executions of thousands of tasks simultaneously. An online server is available for online diagnoses with CT images by http://biomed.nscc-gz.cn/server/Ncov2019. Conclusions The established models can achieve a rapid and accurate identification of COVID-19 in human samples, thereby allowing identification of patients.

Ultra-compact silicon nanophotonic modulator with broadband response

Abstract: Electro-optic modulators have been identifi ed as the key drivers for optical communication and signal processing. With an ongoing miniaturization of photonic circuitries, an outstanding aim is to demonstrate an on-chip, ultra-compact, electro-optic modulator without sacrifi cing bandwidth and modulation strength. While silicon-based electro-optic modulators have been demonstrated, they require large device footprints of the order of millimeters as a result of weak non-linear electro-optical properties. The modulation strength can be increased by deploying a high-Q resonator, however with the trade-off of signifi cantly sacrifi cing bandwidth. Furthermore, design challenges and temperature tuning limit the deployment of such resonance-based modulators. Recently, novel materials like graphene have been investigated for electro-optic modulation applications with a 0.1 dB per micrometer modulation strength, while showing an improvement over pure silicon devices, this design still requires device lengths of tens of micrometers due to the ineffi cient overlap between the thin graphene layer, and the optical mode of the silicon waveguide. Here we experimentally demonstrate an ultra-compact, silicon-based, electro-optic modulator with a record-high 1 dB per micrometer extinction ratio over a wide bandwidth range of 1 μm in ambient conditions. The device is based on a plasmonic metal-oxide-semiconductor (MOS) waveguide, which effi ciently concentrates the optical modes ’ electric fi eld into a nanometer thin region comprised of an absorption coeffi cient-tuneable indium-tin-oxide (ITO) layer. The modulation mechanism originates from electrically changing the free carrier concentration of the ITO layer which dramatically increases the loss of this MOS mode. The seamless integration of such a strong optical beam modulation into an existing silicon-on-insulator platform bears signifi cant potential towards broadband, compact and effi cient communication links and circuits.

Deep Residual Learning for Image Recognition

Abstract: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

Very Deep Convolutional Networks for Large-Scale Image Recognition

Abstract: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

Deep Residual Learning for Image Recognition

Abstract: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

Going deeper with convolutions

Abstract: We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.