Showing papers by "University of Central Florida published in 2020"

Mini-LED, Micro-LED and OLED displays: present status and future perspectives

Abstract: Presently, liquid crystal displays (LCDs) and organic light-emitting diode (OLED) displays are two dominant flat panel display technologies Recently, inorganic mini-LEDs (mLEDs) and micro-LEDs (μLEDs) have emerged by significantly enhancing the dynamic range of LCDs or as sunlight readable emissive displays "mLED, OLED, or μLED: who wins?" is a heated debatable question In this review, we conduct a comprehensive analysis on the material properties, device structures, and performance of mLED/μLED/OLED emissive displays and mLED backlit LCDs We evaluate the power consumption and ambient contrast ratio of each display in depth and systematically compare the motion picture response time, dynamic range, and adaptability to flexible/transparent displays The pros and cons of mLED, OLED, and μLED displays are analysed, and their future perspectives are discussed

Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets.

Abstract: Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.

Li metal deposition and stripping in a solid-state battery via Coble creep.

Abstract: Solid-state lithium metal batteries require accommodation of electrochemically generated mechanical stress inside the lithium: this stress can be1,2 up to 1 gigapascal for an overpotential of 135 millivolts. Maintaining the mechanical and electrochemical stability of the solid structure despite physical contact with moving corrosive lithium metal is a demanding requirement. Using in situ transmission electron microscopy, we investigated the deposition and stripping of metallic lithium or sodium held within a large number of parallel hollow tubules made of a mixed ionic-electronic conductor (MIEC). Here we show that these alkali metals—as single crystals—can grow out of and retract inside the tubules via mainly diffusional Coble creep along the MIEC/metal phase boundary. Unlike solid electrolytes, many MIECs are electrochemically stable in contact with lithium (that is, there is a direct tie-line to metallic lithium on the equilibrium phase diagram), so this Coble creep mechanism can effectively relieve stress, maintain electronic and ionic contacts, eliminate solid-electrolyte interphase debris, and allow the reversible deposition/stripping of lithium across a distance of 10 micrometres for 100 cycles. A centimetre-wide full cell—consisting of approximately 1010 MIEC cylinders/solid electrolyte/LiFePO4—shows a high capacity of about 164 milliampere hours per gram of LiFePO4, and almost no degradation for over 50 cycles, starting with a 1× excess of Li. Modelling shows that the design is insensitive to MIEC material choice with channels about 100 nanometres wide and 10–100 micrometres deep. The behaviour of lithium metal within the MIEC channels suggests that the chemical and mechanical stability issues with the metal–electrolyte interface in solid-state lithium metal batteries can be overcome using this architecture. By containing lithium metal within oriented tubes of a mixed ionic-electronic conductor, a 3D anode for lithium metal batteries is produced that overcomes chemomechanical stability issues at the electrolyte interface.

A solution to minimum sample size for regressions

Abstract: Regressions and meta-regressions are widely used to estimate patterns and effect sizes in various disciplines. However, many biological and medical analyses use relatively low sample size (N), contributing to concerns on reproducibility. What is the minimum N to identify the most plausible data pattern using regressions? Statistical power analysis is often used to answer that question, but it has its own problems and logically should follow model selection to first identify the most plausible model. Here we make null, simple linear and quadratic data with different variances and effect sizes. We then sample and use information theoretic model selection to evaluate minimum N for regression models. We also evaluate the use of coefficient of determination (R2) for this purpose; it is widely used but not recommended. With very low variance, both false positives and false negatives occurred at N < 8, but data shape was always clearly identified at N ≥ 8. With high variance, accurate inference was stable at N ≥ 25. Those outcomes were consistent at different effect sizes. Akaike Information Criterion weights (AICc wi) were essential to clearly identify patterns (e.g., simple linear vs. null); R2 or adjusted R2 values were not useful. We conclude that a minimum N = 8 is informative given very little variance, but minimum N ≥ 25 is required for more variance. Alternative models are better compared using information theory indices such as AIC but not R2 or adjusted R2. Insufficient N and R2-based model selection apparently contribute to confusion and low reproducibility in various disciplines. To avoid those problems, we recommend that research based on regressions or meta-regressions use N ≥ 25.

Opinion: In the wake of COVID-19, academia needs new solutions to ensure gender equity.

Abstract: The coronavirus disease 2019 (COVID-19) pandemic has upended almost every facet of academia (1) Almost overnight the system faced a sudden transition to remote teaching and learning, changes in grading systems, and the loss of access to research resources Additionally, shifts in household labor, childcare, eldercare, and physical confinement have increased students’ and faculty’s mental health needs and reduced the time available to perform academic work A pandemic naturally highlights privileges, such as financial security and access to mental health care It also amplifies the mental, physical, social, and economic impacts attributable to preexisting inequities in academia Making matters worse, in times of stress, such as pandemics, biased decision-making processes are favored (2), which threaten to deprioritize equity initiatives Many women academics will likely bear a greater burden during the coronavirus disease 2019 (COVID-19) pandemic Academia needs to enact solutions to retain and promote women faculty who already face disparities regarding merit, tenure, and promotion Image credit: Dave Cutler (artist) All this means that even among those with privileged positions, including many academics, women will likely bear a greater burden of this pandemic The burden will be even heavier for women who face intersecting systems of oppression, such as ethnicity, race, sexual orientation, gender, age, economic class, dependent status, and/or ability Thus, academia will need to enact solutions to retain and promote women faculty who already face disparities regarding merit, tenure, and promotion (3) Here, we examine ways in which COVID-19 is amplifying known barriers to women’s career advancement We propose actionable solutions, which include the formation of a Pandemic Response Faculty Fellow or Pandemic Faculty Merit Committee (PFMC), new/revised tenure and promotion metrics created by the aforementioned committee, and a framework to ensure that the new metrics and policies are adopted college-wide We also caution against the popular … [↵][1]1To whom correspondence may be addressed Email: jlmalisch{at}smcmedu [1]: #xref-corresp-1-1

Understanding and managing connected extreme events

Abstract: Extreme weather and climate events and their impacts can occur in complex combinations, an interaction shaped by physical drivers and societal forces. In these situations, governance, markets and other decision-making structures—together with population exposure and vulnerability—create nonphysical interconnections among events by linking their impacts, to positive or negative effect. Various anthropogenic actions can also directly affect the severity of events, further complicating these feedback loops. Such relationships are rarely characterized or considered in physical-sciences-based research contexts. Here, we present a multidisciplinary argument for the concept of connected extreme events, and we suggest vantage points and approaches for producing climate information useful in guiding decisions about them. The impacts of extreme weather and climate can be amplified by physical interactions among events and across a complex set of societal factors. This Perspective discusses the concept and challenge of connected extreme events, exploring research approaches and decision-making strategies.

PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search

Abstract: Differentiable architecture search (DARTS) provided a fast solution in finding effective network architectures, but suffered from large memory and computing overheads in jointly training a super-net and searching for an optimal architecture. In this paper, we present a novel approach, namely Partially-Connected DARTS, by sampling a small part of super-net to reduce the redundancy in exploring the network space, thereby performing a more efficient search without comprising the performance. In particular, we perform operation search in a subset of channels while bypassing the held out part in a shortcut. This strategy may suffer from an undesired inconsistency on selecting the edges of super-net caused by sampling different channels. We solve it by introducing edge normalization, which adds a new set of edge-level hyper-parameters to reduce uncertainty in search. Thanks to the reduced memory cost, PC-DARTS can be trained with a larger batch size and, consequently, enjoy both faster speed and higher training stability. Experiment results demonstrate the effectiveness of the proposed method. Specifically, we achieve an error rate of 2.57% on CIFAR10 within merely 0.1 GPU-days for architecture search, and a state-of-the-art top-1 error rate of 24.2% on ImageNet (under the mobile setting) within 3.8 GPU-days for search. Our code has been made available at https://www.dropbox.com/sh/on9lg3rpx1r6dkf/AABG5mt0sMHjnEJyoRnLEYW4a?dl=0.

Designing CO2 reduction electrode materials by morphology and interface engineering

Abstract: Electrochemical reduction of CO2 into value-added fuels and chemicals driven by renewable energy presents a potentially sustainable route to mitigate CO2 emissions and alleviate the dependence on fossil fuels. While tailoring the electronic structure of active components to modulate their intrinsic reactivity could tune the CO2 reduction reaction (CO2RR), their use is limited by the linear scaling relation of intermediates. Due to the high susceptibility of the CO2RR to the local CO2 concentration/pH and mass transportation of CO2/intermediates/products near the gas–solid–liquid three-phase interface, engineering catalysts’ morphological and interfacial properties holds great promise to regulate the CO2RR, which are irrelevant with linear scaling relation and possess high resistance to harsh reaction conditions. Herein, we provide a comprehensive overview of recent advances in tuning CO2 reduction electrocatalysis via morphology and interface engineering. The fundamentals of the CO2RR and design principles for electrode materials are presented firstly. Then, approaches to build an efficient three-phase interface, tune the surface wettability, and design a favorable morphology are summarized; the relationship between the properties of engineered catalysts and their CO2RR performance is highlighted to reveal the activity-determining parameters and underlying catalytic mechanisms. Finally, challenges and opportunities are proposed to suggest the future design of advanced CO2RR electrode materials.

PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation

Abstract: The need for fine-grained perception in autonomous driving systems has resulted in recently increased research on online semantic segmentation of single-scan LiDAR. Despite the emerging datasets and technological advancements, it remains challenging due to three reasons: (1) the need for near-real-time latency with limited hardware; (2) uneven or even long-tailed distribution of LiDAR points across space; and (3) an increasing number of extremely fine-grained semantic classes. In an attempt to jointly tackle all the aforementioned challenges, we propose a new LiDAR-specific, nearest-neighbor-free segmentation algorithm - PolarNet. Instead of using common spherical or bird's-eye-view projection, our polar bird's-eye-view representation balances the points across grid cells in a polar coordinate system, indirectly aligning a segmentation network's attention with the long-tailed distribution of the points along the radial axis. We find that our encoding scheme greatly increases the mIoU in three drastically different segmentation datasets of real urban LiDAR single scans while retaining near real-time throughput.

Exploring the Attack Surface of Blockchain: A Comprehensive Survey

Abstract: In this paper, we systematically explore the attack surface of the Blockchain technology, with an emphasis on public Blockchains. Towards this goal, we attribute attack viability in the attack surface to 1) the Blockchain cryptographic constructs, 2) the distributed architecture of the systems using Blockchain, and 3) the Blockchain application context. To each of those contributing factors, we outline several attacks, including selfish mining, the 51% attack, DNS attacks, distributed denial-of-service (DDoS) attacks, consensus delay (due to selfish behavior or distributed denial-of-service attacks), Blockchain forks, orphaned and stale blocks, block ingestion, wallet thefts, smart contract attacks, and privacy attacks. We also explore the causal relationships between these attacks to demonstrate how various attack vectors are connected to one another. A secondary contribution of this work is outlining effective defense measures taken by the Blockchain technology or proposed by researchers to mitigate the effects of these attacks and patch associated vulnerabilities.

COVID-19 and urology: a comprehensive review of the literature.

Abstract: OBJECTIVE: To discuss the impact of COVID-19 on global health, particularly on urological practice and to review some of the available recommendations reported in the literature. MATERIAL AND METHODS: In the current narrative review the PubMed database was searched to identify all the related reports discussing the impact of COVID-19 on the urological field. RESULTS: The COVID-19 pandemic is the latest and biggest global health threat. Medical and surgical priorities have changed dramatically to cope with the current challenge. These changes include postponements of all elective outpatient visits and surgical procedures to save facilities and resources for urgent cases and patients with COVID-19 patients. This review discuss some of the related changes in urology. CONCLUSIONS: Over the coming weeks, healthcare workers including urologists will be facing increasingly difficult challenges, and consequently, they should adopt triage strategy to avoid wasting of medical resources and they should endorse sufficient protection policies to guard against infection when dealing with COVID-19 patients.

PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation

Abstract: The requirement of fine-grained perception by autonomous driving systems has resulted in recently increased research in the online semantic segmentation of single-scan LiDAR. Emerging datasets and technological advancements have enabled researchers to benchmark this problem and improve the applicable semantic segmentation algorithms. Still, online semantic segmentation of LiDAR scans in autonomous driving applications remains challenging due to three reasons: (1) the need for near-real-time latency with limited hardware, (2) points are distributed unevenly across space, and (3) an increasing number of more fine-grained semantic classes. The combination of the aforementioned challenges motivates us to propose a new LiDAR-specific, KNN-free segmentation algorithm - PolarNet. Instead of using common spherical or bird's-eye-view projection, our polar bird's-eye-view representation balances the points per grid and thus indirectly redistributes the network's attention over the long-tailed points distribution over the radial axis in polar coordination. We find that our encoding scheme greatly increases the mIoU in three drastically different real urban LiDAR single-scan segmentation datasets while retaining ultra low latency and near real-time throughput.

Non-Hermitian and topological photonics: optics at an exceptional point

Abstract: Abstract In the past few years, concepts from non-Hermitian (NH) physics, originally developed within the context of quantum field theories, have been successfully deployed over a wide range of physical settings where wave dynamics are known to play a key role. In optics, a special class of NH Hamiltonians – which respects parity-time symmetry – has been intensely pursued along several fronts. What makes this family of systems so intriguing is the prospect of phase transitions and NH singularities that can in turn lead to a plethora of counterintuitive phenomena. Quite recently, these ideas have permeated several other fields of science and technology in a quest to achieve new behaviors and functionalities in nonconservative environments that would have otherwise been impossible in standard Hermitian arrangements. Here, we provide an overview of recent advancements in these emerging fields, with emphasis on photonic NH platforms, exceptional point dynamics, and the very promising interplay between non-Hermiticity and topological physics.

Attention Guided Anomaly Localization in Images

Abstract: Anomaly localization is an important problem in computer vision which involves localizing anomalous regions within images with applications in industrial inspection, surveillance, and medical imaging. This task is challenging due to the small sample size and pixel coverage of the anomaly in real-world scenarios. Most prior works need to use anomalous training images to compute a class-specific threshold to localize anomalies. Without the need of anomalous training images, we propose Convolutional Adversarial Variational autoencoder with Guided Attention (CAVGA), which localizes the anomaly with a convolutional latent variable to preserve the spatial information. In the unsupervised setting, we propose an attention expansion loss where we encourage CAVGA to focus on all normal regions in the image. Furthermore, in the weakly-supervised setting we propose a complementary guided attention loss, where we encourage the attention map to focus on all normal regions while minimizing the attention map corresponding to anomalous regions in the image. CAVGA outperforms the state-of-the-art (SOTA) anomaly localization methods on MVTec Anomaly Detection (MVTAD), modified ShanghaiTech Campus (mSTC) and Large-scale Attention based Glaucoma (LAG) datasets in the unsupervised setting and when using only 2% anomalous images in the weakly-supervised setting. CAVGA also outperforms SOTA anomaly detection methods on the MNIST, CIFAR-10, Fashion-MNIST, MVTAD, mSTC and LAG datasets.

Attosecond science based on high harmonic generation from gases and solids.

Abstract: Recent progress in high power ultrafast short-wave and mid-wave infrared lasers has enabled gas-phase high harmonic generation (HHG) in the water window and beyond, as well as the demonstration of HHG in condensed matter. In this Perspective, we discuss the recent advancements and future trends in generating and characterizing soft X-ray pulses from gas-phase HHG and extreme ultraviolet (XUV) pulses from solid-state HHG. Then, we discuss their current and potential usage in time-resolved study of electron and nuclear dynamics in atomic, molecular and condensed matters.

Subterahertz spin pumping from an insulating antiferromagnet

Abstract: Spin-transfer torque and spin Hall effects combined with their reciprocal phenomena, spin pumping and inverse spin Hall effects (ISHEs), enable the reading and control of magnetic moments in spintronics. The direct observation of these effects remains elusive in antiferromagnetic-based devices. We report subterahertz spin pumping at the interface of the uniaxial insulating antiferromagnet manganese difluoride and platinum. The measured ISHE voltage arising from spin-charge conversion in the platinum layer depends on the chirality of the dynamical modes of the antiferromagnet, which is selectively excited and modulated by the handedness of the circularly polarized subterahertz irradiation. Our results open the door to the controlled generation of coherent, pure spin currents at terahertz frequencies.

Ultrasensitive and ultrathin phototransistors and photonic synapses using perovskite quantum dots grown from graphene lattice.

Abstract: Organic-inorganic halide perovskite quantum dots (PQDs) constitute an attractive class of materials for many optoelectronic applications. However, their charge transport properties are inferior to materials like graphene. On the other hand, the charge generation efficiency of graphene is too low to be used in many optoelectronic applications. Here, we demonstrate the development of ultrathin phototransistors and photonic synapses using a graphene-PQD (G-PQD) superstructure prepared by growing PQDs directly from a graphene lattice. We show that the G-PQDs superstructure synchronizes efficient charge generation and transport on a single platform. G-PQD phototransistors exhibit excellent responsivity of 1.4 × 108 AW–1 and specific detectivity of 4.72 × 1015 Jones at 430 nm. Moreover, the light-assisted memory effect of these superstructures enables photonic synaptic behavior, where neuromorphic computing is demonstrated by facial recognition with the assistance of machine learning. We anticipate that the G-PQD superstructures will bolster new directions in the development of highly efficient optoelectronic devices.

Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development

Abstract: SARS-COV-2 has roused the scientific community with a call to action to combat the growing pandemic. At the time of this writing, there are as yet no novel antiviral agents or approved vaccines available for deployment as a frontline defense. Understanding the pathobiology of COVID-19 could aid scientists in their discovery of potent antivirals by elucidating unexplored viral pathways. One method for accomplishing this is the leveraging of computational methods to discover new candidate drugs and vaccines in silico. In the last decade, machine learning-based models, trained on specific biomolecules, have offered inexpensive and rapid implementation methods for the discovery of effective viral therapies. Given a target biomolecule, these models are capable of predicting inhibitor candidates in a structural-based manner. If enough data are presented to a model, it can aid the search for a drug or vaccine candidate by identifying patterns within the data. In this review, we focus on the recent advances of COVID-19 drug and vaccine development using artificial intelligence and the potential of intelligent training for the discovery of COVID-19 therapeutics. To facilitate applications of deep learning for SARS-COV-2, we highlight multiple molecular targets of COVID-19, inhibition of which may increase patient survival. Moreover, we present CoronaDB-AI, a dataset of compounds, peptides, and epitopes discovered either in silico or in vitro that can be potentially used for training models in order to extract COVID-19 treatment. The information and datasets provided in this review can be used to train deep learning-based models and accelerate the discovery of effective viral therapies.

Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition From a Domain Adaptation Perspective

Abstract: Object frequency in the real world often follows a power law, leading to a mismatch between datasets with long-tailed class distributions seen by a machine learning model and our expectation of the model to perform well on all classes. We analyze this mismatch from a domain adaptation point of view. First of all, we connect existing class-balanced methods for long-tailed classification to target shift, a well-studied scenario in domain adaptation. The connection reveals that these methods implicitly assume that the training data and test data share the same class-conditioned distribution, which does not hold in general and especially for the tail classes. While a head class could contain abundant and diverse training examples that well represent the expected data at inference time, the tail classes are often short of representative training data. To this end, we propose to augment the classic class-balanced learning by explicitly estimating the differences between the class-conditioned distributions with a meta-learning approach. We validate our approach with six benchmark datasets and three loss functions.

iTAML: An Incremental Task-Agnostic Meta-learning Approach

Abstract: Humans can continuously learn new knowledge as their experience grows. In contrast, previous learning in deep neural networks can quickly fade out when they are trained on a new task. In this paper, we hypothesize this problem can be avoided by learning a set of generalized parameters, that are neither specific to old nor new tasks. In this pursuit, we introduce a novel meta-learning approach that seeks to maintain an equilibrium between all the encountered tasks. This is ensured by a new meta-update rule which avoids catastrophic forgetting. In comparison to previous meta-learning techniques, our approach is task-agnostic. When presented with a continuum of data, our model automatically identifies the task and quickly adapts to it with just a single update. We perform extensive experiments on five datasets in a class-incremental setting, leading to significant improvements over the state of the art methods (e.g., a 21.3% boost on CIFAR100 with 10 incremental tasks). Specifically, on large-scale datasets that generally prove difficult cases for incremental learning, our approach delivers absolute gains as high as 19.1% and 7.4% on ImageNet and MS-Celeb datasets, respectively.

A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes

Abstract: During the last half decade, convolutional neural networks (CNNs) have triumphed over semantic segmentation, which is one of the core tasks in many applications such as autonomous driving and augmented reality. However, to train CNNs requires a considerable amount of data, which is difficult to collect and laborious to annotate. Recent advances in computer graphics make it possible to train CNNs on photo-realistic synthetic imagery with computer-generated annotations. Despite this, the domain mismatch between real images and the synthetic data hinders the models’ performance. Hence, we propose a curriculum-style learning approach to minimizing the domain gap in urban scene semantic segmentation. The curriculum domain adaptation solves easy tasks first to infer necessary properties about the target domain; in particular, the first task is to learn global label distributions over images and local distributions over landmark superpixels. These are easy to estimate because images of urban scenes have strong idiosyncrasies (e.g., the size and spatial relations of buildings, streets, cars, etc.). We then train a segmentation network, while regularizing its predictions in the target domain to follow those inferred properties. In experiments, our method outperforms the baselines on two datasets and three backbone networks. We also report extensive ablation studies about our approach.