Crossover Photonic Switching Network with CMOS/SEED Smart Pixel Device and 2D Optical Fiber Bundle Array

A 16 X 16 Crossover photonic switching network with hybrid integrated CMOS/SEED smart pixel device and 2D optical fiber bundle array I/O access device is reported in this paper. SEEd array devices ar used as light receivers and transmitters, while CMOS devices make efficient logical processing. 4 X 40 2D multilayer optical fiber bundle arrays are fabricated and are used as I/O access devices in the crossover photonic switching network. The center to center spacing between adjacent optical fibers in the same layer of the fiber array is 125micrometers , and the spacing between adjacent layers is 250micrometers . Displacing tolerance of the fiber bundle arrays is less than 4 micrometers and the angular tilt error is less than 0.03 degree. It has the feature of high density, high precision, array permutation and easy to couple with 2D CMOS/SEED smart pixel device

Optoelectronic Switching Network with 2D Optical Fiber Bundle Array I/O Access Device

An optoelectronic switching network with 2-D optical fiber bundle arrays I/O access device is presented in this paper. An optoelectronic recirculating Banyan network based on CMOS/SEED smart pixel device is used in this configuration. Thirty-two X two single-mode fiber bundle array and 32 X 2 multi- mode fiber bundle array are fabricated respectively based on the features of high density, high precision and array permutation of the CMOS/SEED optoelectronic integrated devices. The measuring results show that the center to center spacing between adjacent optical fibers in the same layer of the fiber array is 125 micrometer, and the spacing between adjacent layers is 500 micrometer. Displacing tolerance of the fiber bundle arrays is less than 2 micrometer and the angular tilt error is less than 0.02 degree

Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markers

The discovery of patient-specific imaging markers that are predictive of future disease outcomes can help us better understand individual-level heterogeneity of disease evolution. In fact, deep learning models that can provide data-driven personalized markers are much more likely to be adopted in medical practice. In this work, we demonstrate that data-driven biomarker discovery can be achieved through a counterfactual synthesis process. We show how a deep conditional generative model can be used to perturb local imaging features in baseline images that are pertinent to subject-specific future disease evolution and result in a counterfactual image that is expected to have a different future outcome. Candidate biomarkers, therefore, result from examining the set of features that are perturbed in this process. Through several experiments on a large-scale, multi-scanner, multi-center multiple sclerosis (MS) clinical trial magnetic resonance imaging (MRI) dataset of relapsing-remitting (RRMS) patients, we demonstrate that our model produces counterfactuals with changes in imaging features that reflect established clinical markers predictive of future MRI lesional activity at the population level. Additional qualitative results illustrate that our model has the potential to discover novel and subject-specific predictive markers of future activity.Comment: Accepted to the MIABID workshop at MICCAI 202

Integration of aggregation-induced emission and delayed fluorescence into electronic donor–acceptor conjugates

A series of luminogens comprised electron donors and acceptors are found to possess two types of interesting photophysical processes of aggregation-induced emission (AIE) and delayed fluorescence. According to theory calculation, restriction of intramolecular motions accounts for their AIE characteristics. Moreover, a separated distribution of the HOMOs and the LUMOs of these luminogens leads to small DEST values and therefore delayed fluorescence

A Survey on Transferability of Adversarial Examples across Deep Neural Networks

The emergence of Deep Neural Networks (DNNs) has revolutionized various domains, enabling the resolution of complex tasks spanning image recognition, natural language processing, and scientific problem-solving. However, this progress has also exposed a concerning vulnerability: adversarial examples. These crafted inputs, imperceptible to humans, can manipulate machine learning models into making erroneous predictions, raising concerns for safety-critical applications. An intriguing property of this phenomenon is the transferability of adversarial examples, where perturbations crafted for one model can deceive another, often with a different architecture. This intriguing property enables "black-box" attacks, circumventing the need for detailed knowledge of the target model. This survey explores the landscape of the adversarial transferability of adversarial examples. We categorize existing methodologies to enhance adversarial transferability and discuss the fundamental principles guiding each approach. While the predominant body of research primarily concentrates on image classification, we also extend our discussion to encompass other vision tasks and beyond. Challenges and future prospects are discussed, highlighting the importance of fortifying DNNs against adversarial vulnerabilities in an evolving landscape

Thrive: Undergraduate Research Journal [Fall 2021, Volume 1, Number 1]

UNDERGRADUATE RESEARCH JOURNAL OF UNIVERSITY OF SOUTH FLORIDA\u27S STUDENT ORGANIZATION URJ VOLUME 1 | FALL 2021https://digitalcommons.usf.edu/thrive/1000/thumbnail.jp

Aroylacetylene Based Amino-Yne Click Polymerization toward Nitrogen Containing Polymers

A highly efficient, spontaneous, and atom-economic polymerization of aroylacetylenes and amines at room temperature in air was established, and poly(β-enaminone)s with high molecular weights were produced in nearly quantitative yields. Moreover, singly E-configuration polymers can be obtained efficiently with secondary amines, while absolute Z-configuration products were prepared when using primary amines. In addition, the poly(β-enaminone)s can be degraded by primary amines in aqueous system to obtain definite compounds, proving their wide application prospects as degradable nitrogen containing polymers

Improved Cyclability of Lithium−Oxygen Batteries by Synergistic Catalytic Effects of Two-Dimensional MoS2 Nanosheets Anchored on Hollow Carbon Spheres

The design and development of high-efficient electrocatalysts plays a decisive role in improving the stability of lithium-oxygen (Li-O 2 ) batteries. Here, two-dimensional (2D) MoS 2 nanosheets anchored on hollow carbon spheres (MoS 2 /HCS) composites is designed and reported as promising cathode catalysts for Li-O 2 batteries. The MoS 2 /HCS-based Li-O 2 battery shows superior electrochemical performance, in terms of high capacity (4010 mA h g -1 ) and enhanced cycling performance (104 cycles). X-ray photoelectron spectroscopy (XPS) results reveal that the formation of Li 2 CO 3 and other side products can be effectively alleviated when MoS 2 /HCS electrode is used as the cathode. On the basis of experimental studies, it is found that the synergistic effects, which originated from the superior catalytic property of MoS 2 nanosheets and the good electrical conductivity of HCS with high surface area, is the main reason for performance improvement. The synergistic effects induced by the dispersed MoS 2 nanosheets anchored on nanostructured HCS cathodes provide a promising strategy for developing catalysts of O 2 electrode for Li-O 2 batteries with excellent performance

The development of near-infrared II fluorophore for tumor drug resistance reversal based on photothermal therapy

Multidrug resistance of tumor cells has greatly inhibited the therapeutic effect of chemotherapy. The development of reliable strategies to deal with tumor multidrug resistance is highly desirable for tumor therapy. In this work, novel near-infrared II (NIR II) fluorophores were rationally developed as photothermal reagents to reverse the drug resistance of tumor cells by reducing the related proteins expression, achieving high inhibition efficiency with the synergistic effect of chemotherapeutic drugs. By enhancing the electro-accepting effect, the emission peak of fluorophore shifted from 665 nm to 973 nm with acquiring NIR II materials, which presented outstanding photo-thermal conversion ability and improved thermal-stability compared to ICG. Then, by pre-treating with the photo-thermal treatment of NIR II fluorophore, the anti-tumor efficiency of chemotherapeutic drugs, including paclitaxel, cis-platinum and doxorubicin, was significantly enhanced towards drug-resistance tumor cells. The mechanism exploration revealed that drug resistance-related proteins would be remarkably reduced and enable the cells more sensitive towards drugs. Thus, this strategy demonstrated a promising and reliable approach to reverse the drug-resistance of tumor cells for efficient tumor inhibition in clinic