DropMix: Reducing Class Dependency in Mixed Sample Data Augmentation

Mixed sample data augmentation (MSDA) is a widely used technique that has been found to improve performance in a variety of tasks. However, in this paper, we show that the effects of MSDA are class-dependent, with some classes seeing an improvement in performance while others experience a decline. To reduce class dependency, we propose the DropMix method, which excludes a specific percentage of data from the MSDA computation. By training on a combination of MSDA and non-MSDA data, the proposed method not only improves the performance of classes that were previously degraded by MSDA, but also increases overall average accuracy, as shown in experiments on two datasets (CIFAR-100 and ImageNet) using three MSDA methods (Mixup, CutMix and PuzzleMix).Comment: 17 pages, 10 figure

Test-Time Mixup Augmentation for Data and Class-Dependent Uncertainty Estimation in Deep Learning Image Classification

Uncertainty estimation of the trained deep learning networks is valuable for optimizing learning efficiency and evaluating the reliability of network predictions. In this paper, we propose a method for estimating uncertainty in deep learning image classification using test-time mixup augmentation (TTMA). To improve the ability to distinguish correct and incorrect predictions in existing aleatoric uncertainty, we introduce the TTMA data uncertainty (TTMA-DU) by applying mixup augmentation to test data and measuring the entropy of the predicted label histogram. In addition to TTMA-DU, we propose the TTMA class-dependent uncertainty (TTMA-CDU), which captures aleatoric uncertainty specific to individual classes and provides insight into class confusion and class similarity within the trained network. We validate our proposed methods on the ISIC-18 skin lesion diagnosis dataset and the CIFAR-100 real-world image classification dataset. Our experiments show that (1) TTMA-DU more effectively differentiates correct and incorrect predictions compared to existing uncertainty measures due to mixup perturbation, and (2) TTMA-CDU provides information on class confusion and class similarity for both datasets

Elucidating the Interactive Roles of Glia in Alzheimer's Disease Using Established and Newly Developed Experimental Models

Alzheimer's disease (AD) is an irreversible neurodegenerative illness and the exact etiology of the disease remains unknown. It is characterized by long preclinical and prodromal phases with pathological features including an accumulation of amyloid-beta (Aβ) peptides into extracellular Aβ plaques in the brain parenchyma and the formation of intracellular neurofibrillary tangles (NFTs) within neurons as a result of abnormal phosphorylation of microtubule-associated tau proteins. In addition, prominent activation of innate immune cells is also observed and/or followed by marked neuroinflammation. While such neuroinflammatory responses may function in a neuroprotective manner by clearing neurotoxic factors, they can also be neurotoxic by contributing to neurodegeneration via elevated levels of proinflammatory mediators and oxidative stress, and altered levels of neurotransmitters, that underlie pathological symptoms including synaptic and cognitive impairment, neuronal death, reduced memory, and neocortex and hippocampus malfunctions. Glial cells, particularly activated microglia and reactive astrocytes, appear to play critical and interactive roles in such dichotomous responses. Accumulating evidences clearly point to their critical involvement in the prevention, initiation, and progression, of neurodegenerative diseases, including AD. Here, we review recent findings on the roles of astrocyte-microglial interactions in neurodegeneration in the context of AD and discuss newly developed in vitro and in vivo experimental models that will enable more detailed analysis of glial interplay. An increased understanding of the roles of glia and the development of new exploratory tools are likely to be crucial for the development of new interventions for early stage AD prevention and cures

Trinitarian Theology and Piety: The Attributes of God in the Thought of Stephen Charnock (1628-1680) and William Perkins (1558-1602)

Stephen Charnock (1628-1680) is arguably remembered for his importance, at the zenith of Puritan or English Reformed scholastic divinity, in terms of the doctrine of God’s existence and attributes. He also contributed to Reformed orthodox or Puritan theology through his writings on the knowledge of God, the doctrine of regeneration, Christology, and the atonement. He wrote all these work in the midst of the theological turbulence of the later seventeenth century, with the underlying purpose of defending the inseparability of theological system and piety. His work, with its eclectic acceptance of medieval scholastic intellectual tradition as a tool, plays a significant role in the development of an historical phase of trinitarian and federal theology. However, The Existence and Attributes of God as Charnock’s magnum opus has been unexplored in terms of its view of the full doctrine of God in its trinitarian and covenantal dimensions. This is despite the fact that the Puritan concept of the divine attributes is the very doctrinal area in which the theological loci are concentrated into “a system” associated with the pursuit of piety in the period of high orthodoxy. This lack of a comprehensive overview concerning the Reformed orthodox system has brought about a misunderstanding of his theology. Charnock’s work has been regarded, even in recent scholarship, as the product of a mere scholastic rationalism. William Perkins (1558-1602) is undoubtedly the “father” of the doctrine of God in the early Puritan or Reformed orthodox period. Although misunderstandings concerning his scholastic Puritan theology and its trinitarian system and piety have been successfully rectified by other previous researchers, a confirmation of it through an investigation of his idea of God’s attributes is necessary in our study. This is in order to prove the identity of Charnock’s doctrine of God with the Puritan Reformed orthodox theological system allowing, of course, for the development of the historical and theological context between these two periods. In particular, Charnock’s understanding of the theological prolegomena, Scriptural foundations, and God’s existence and attributes is dealt with in this current study in comparison with Perkins’ work. Charnock’s work has been viewed in terms of a continuity between the early and high orthodox doctrine of God within the flow of English Puritan thought. During this examination, giving particular attention to Charnock’s treatise The Existence and Attributes of God, we have attempted to resolve the question of whether the past interpretation of Charnock’s theology or doctrine of God as a rigid speculative doctrinal formulation of Protestant scholasticism beyond Scripture is reasonable or not

Advancing nanoelectronic device modeling through peta-scale computing and deployment on nanoHUB

Recent improvements to existing HPC codes NEMO 3-D and OMEN, combined with access to peta-scale computing resources, have enabled realistic device engineering simulations that were previously infeasible. NEMO 3-D can now simulate 1 billion atom systems, and, using 3D spatial decomposition, scale to 32768 cores. Simulation time for the band structure of an experimental P doped Si quantum computing device fell from 40 minutes to I minute. OMEN can perform fully quantum mechanical transport calculations for real-word UTB FETs on 147,456 cores in roughly 5 minutes. Both of these tools power simulation engines on the nanoHUB, giving the community access to previously unavailable research capabilities

Large Scale Simulations of Nanoelectronic devices with NEMO3-D on the Teragrid

This paper describes recent progress in large scale numerical simulations for computational nano-electronics using the NEMO3-D package. NEMO3-D is a parallel analysis tool for nano-electronic devices such as quantum dots. The atomistic model used in NEMO3-D leads to large scale computations in two main phases: strain and electronic structure. This paper focuses primarily on the electronic structure phase of the computations. The eigenvalue problem associated with the Hamiltonian matrix is challenging for a number of reasons: (i) the need for very large scale, 100 million to one billion unknowns (ii) the desired eigenvalues (along with the associated eigenvectors) lie in the interior of the spectrum and (iii) the eigenvalues are often degenerate. New results on the performance and scalability of NEMO3-D are presented, on advanced parallel architectures, including Teragrid resources. Results presented here were obtained with runs on up to 192 processors, for systems with 40 million atoms. We also report on on-going work to incorporate new advanced algorithms into NEMO3-D. We describe how the NEMO3-D code has been linked to the Teragrid through the NanoHub

Human mini-blood–brain barrier models for biomedical neuroscience research: a review

© 2022, The Author(s).The human blood–brain barrier (BBB) is a unique multicellular structure that is in critical demand for fundamental neuroscience studies and therapeutic evaluation. Despite substantial achievements in creating in vitro human BBB platforms, challenges in generating specifics of physiopathological relevance are viewed as impediments to the establishment of in vitro models. In this review, we provide insight into the development and deployment of in vitro BBB models that allow investigation of the physiology and pathology of neurological therapeutic avenues. First, we highlight the critical components, including cell sources, biomaterial glue collections, and engineering techniques to reconstruct a miniaturized human BBB. Second, we describe recent breakthroughs in human mini-BBBs for investigating biological mechanisms in neurology. Finally, we discuss the application of human mini-BBBs to medical approaches. This review provides strategies for understanding neurological diseases, a validation model for drug discovery, and a potential approach for generating personalized medicine.11Nsciescopuskc