Miracle-dz/UniFAR-FTUs huggingface dataset:
Computational optical imaging: Applications in synthetic aperture imaging, phase retrieval, and digital holography

Lee, Dennis Joseph Ph.D., Purdue University, May 2015. Computational Optical Imaging: Applications in Synthetic Aperture Imaging, Phase Retrieval, and Digital Holography. Major Professor: Andrew M. Weiner. Computational imaging has become an important field, as a merger of both algorithms and physical experiments. In the realm of microscopy and optical imaging, an important application is the problem of improving resolution, which is bounded by wavelength and numerical aperture according to the classic diffraction limit. We will investigate the resolution enhancement of phase objects such as transparent biological cells. One key challenge is how to measure phase experimentally. Standard interferometric techniques have the drawback of being sensitive to environmental vibrations and temperature fluctuations, and they use a reference arm which requires more space and cost. Non-holographic methods provide a way to overcome these disadvantages. Another challenge is how to reconstruct phase and amplitude from a digital hologram. The typical method of applying a filter in the frequency domain is limited by finite filter size. Optimization approaches offer a solution to this problem. The work presented here spans three main aspects of phase imaging microscopy including synthetic aperture imaging, phase retrieval, and digital holography. We develop a non-holographic microscope that uses off-axis illumination for resolution enhancement and demonstrate the first experimental measurements of referenceless phase retrieval at multiple angles. We implement a synthetic aperture microscope using an electrically tunable lens to defocus images, which avoids the need to mechanically move a camera on a translation stage. Finally, we improve the reconstruction of images from a digital hologram based on an iterative algorithm that alternatively updates amplitude and phase.
Performance characterization of a quad Pentium Pro SMP using OLTP workloads

Commercial applications are an important, yet often overlooked, workload with significantly different characteristics from technical workloads. The potential impact of these differences is that computers optimized for technical workloads may not provide good performance for commercial applications, and these applications may not fully exploit advances in processor design. To evaluate these issues, we use hardware counters to measure architectural features of a four-processor Pentium Pro-based server running a TPC-C-like workload on an Informix database. We examine the effectiveness of out-of-order execution, branch prediction, speculative execution, superscalar issue and retire, caching and multiprocessor scaling. We find that out-of-order execution, superscalar issue and retire, and branch prediction are not as effective for database workloads as they are for technical workloads, such as SPEC. We find that caches are effective at reducing processor traffic to memory; even larger caches would be helpful to satisfy more data requests. Multiprocessor scaling of this workload is good, but even modest bus utilization degrades application memory latency, limiting database throughput.
A Scalable Architecture for Modular Multiplication Based on Montgomery's Algorithm

This paper presents a scalable architecture for the computation of modular multiplication, based on the Montgomery multiplication (MM) algorithm. A word-based version of MM is presented and used to explain the main concepts in the hardware design. The proposed multiplier is able to work with any precision of the input operands, limited only by memory or control constraints. Its architecture gives enough freedom to select the word size and the degree of parallelism to be used, according to the available area and/or desired performance. Design trade offs are analyzed in order to identify adequate hardware configurations for a given area or bandwidth requirement.
A MOSFET only, step-up DC-DC micro power converter, for solar energy harvesting applications

In this paper, a step-up micro power converter for solar energy harvesting applications is presented. The circuit is based on a switched-capacitor voltage doubler architecture with MOSFET capacitors, which results in an area approximately eight times smaller than using MiM capacitors for the 0. 13µm CMOS technology. In order to compensate for the loss of efficiency, due to the larger parasitic capacitances, a charge reutilization scheme is employed. The circuit uses a phase controller, designed specifically to work with a solar cell, in order to regulate the output voltage to 1.2 V. Electrical simulations of the circuit, together with an equivalent electrical model of a solar cell, show that the circuit can deliver a power of 536.1 µW to the load, while drawing a power of 799.8 µW from the solar cell, corresponding to a maximum efficiency of 67%.
Foggy Person Re-Identification based on Adaptive Recognition of Fog Attributes

With the high evolution of society and the continuous change in the appearance of of people's living standards, the Person Re-Identification (Re-ID) model has begun to be commonly employed in safety deployment. However, in the process of re-identification, different weather conditions will affect the robustness of the model, for example, foggy weather will cause many person details to be lost, which brings great challenges to person matching. To learn the invariant representation of foggy query in the re-identification task, we proposed a plug-and-play Adaptive Recognition of Fog Attributes (ARFA), which inserts this module from the adaptive recognition fog attributes in the person re-identification model, so that even for the query in adverse weather, the matching target can still be retrieved in the gallery. Experimental results on multiple widely used benchmark datasets, such as market-1501, dukemtmc-reid, and person x, reveal that our ARFA module carries out advanced performance and also shows superiority in unsupervised domain adaptation.
Global Information Interactive of ViT for Rain Removal on Single Images

In adverse weather conditions, the quality of outdoor captured images is significantly compromised, primarily due to the presence of rain streaks that severely degrade image clarity and consequently disrupt image recognition and analysis. The spatial variations of rain streaks within individual rainy images pose a considerable challenge for their removal. Despite the notable achievements of Convolutional Neural Network (CNN)-based methods in recent years, their limited receptive fields and lack of adaptability to input content make it difficult for them to cope with real-world scenarios and restore high-quality rain-free images with accurate structural details. To address this issue, we propose an image restoration model based on the Vision Transformer (ViT) architecture. Specifically, our model first employs a U - ViT to capture contextual information, which is then fused with a high-resolution branch that preserves local details. At each stage, attention mechanisms are introduced to reweight local features, leading to the design of a novel information interaction pattern. The model achieves accelerated convergence by performing global information interaction during rain removal, resulting in image restoration that closely resembles the reference images. Furthermore, to mitigate the loss of fine grained details, we introduce a detail fusion module. The resulting tightly interconnected hierarchical structure is referred to as GIVTNet. Experimental results demonstrate that the proposed algorithm yields substantial performance improvements across multiple synthetic and real-world datasets.
Simultaneous Inference in General Parametric Models

Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre‐specified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described here is quite general and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model, robust linear models, etc. Several examples using a variety of different statistical models illustrate the breadth of the results. For the analyses we use the R add‐on package multcomp, which provides a convenient interface to the general approach adopted here. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Learning from the behavior of others : conformity, fads, and informational cascades

Learning by observing the past decisions of others can help explain some otherwise puzzling phenomena about human behavior. For example, why do people tend to converge on similar behavior? Why is mass behavior prone to error and fads? The authors argue that the theory of observational learning, and particularly of informational cascades, has much to offer economics, business strategy, political science, and the study of criminal behavior.
Study on Load Prediction Based on the MPGA-BP Neural Network Model

In order to improve the accuracy of power load prediction with randomness and complexity, a new power load prediction method is constructed by using multiple group genetic algorithms (MPGA). By improving the genetic algorithm, using the traditional genetic algorithm to optimize the weights and threshold of neurons in BP neural network, improve the prediction accuracy of BP neural network, thus greatly improve the prediction performance of BP neural network, but at the same time, because the traditional genetic algorithm is easy to precocious, into the local optimal, so the choice on this method for improvement. By comparing the prediction error of genetic algorithm (Geneticalgorithm, GA) - BP neural network model and MPGA-BP neural network model, it is found that the prediction results of MPGA-BP neural network model are better than GA-BP and BP neural network model, which proves that the proposed method can better improve the accuracy of load prediction. The results show that the prediction performance of multiple genetic GA is better than GABP and BP methods.
Nitric Oxide Synthase Inhibitor Attenuates the Effects of Repeated Restraint Stress on Synaptic Transmission in the Paraventricular Nucleus of the Rat Hypothalamus

Corticotropin-releasing hormone (CRH)-synthesizing parvocellular neuroendocrine cells (PNCs) of the hypothalamic paraventricular nucleus (PVN) play a key role in the activation of the hypothalamic-pituitary-adrenocortical (HPA) axis. Several studies have demonstrated that synaptic inputs to these cells may undergo stress-related enhancement but, on the other hand, it has been reported that exposition to the same stressor for prolonged time periods may induce a progressive reduction in the response of the HPA axis to homotypic stressors. In the present study rats were subjected to 10 min restraint sessions, repeated twice daily for 3 or 7 days. Miniature excitatory and inhibitory postsynaptic currents (mEPSCs and mIPSCs) were then recorded from PNCs in ex vivo hypothalamic slice preparations obtained 24 h after the last restraint. Restraint stress repeated over 3 days resulted in increased mean frequency and decreased rise time and decay time constant of mEPSCs, accompanied by a decrease in the excitability of PNCs, however, no such changes were evident in slices obtained from rats subjected to restraint over 7 days. There were no changes in mIPSCs after repeated restraint. Administration of the unspecific nitric oxide synthase (NOS) blocker Nω-Nitro-L-arginine (L-NNA) before each restraint, repeated over 3 days, prevented the occurrence of an increase in mEPSC frequency. However, animals receiving L-NNA and subjected to repeated restraint had similar changes in PNCs membrane excitability and mEPSC kinetics as stressed rats not receiving L-NNA. Comparison of the effects of a single 10 min restraint session followed by either an immediate or delayed (24 h) decapitation revealed an increase in the mean mEPSC frequency and a decrease in the mean mIPSC frequency in slices prepared immediately after restraint, with no apparent effects when slice preparation was delayed by 24 h. These results demonstrate that restraint, lasting 10 min and repeated twice daily for 3 days, induces a selective and long-lasting enhancement of excitatory synaptic input onto PNCs, partially by a NOS-dependent mechanism, and reduces PNC excitability, whereas prolongation of repeated stress for up to 7 days results in an adaptation.
Multi-Trip Routing of Delivery Drones with Load-Dependent Flight Speed

In recent years, there has been growing interest in companies using drones to deliver packages as a means of solving logistics challenges, but it is important that routes are optimized in advance. Delivery drone routing, considering variations in flight speed due to payload, has been proposed. This is referred to as Flight Speed-aware Vehicle Routing Problem (FSVRP), and approximate solution methods for the FSVRP have already been suggested. In this paper, we aim to increase the quantity of deliverable packages and reduce delivery time by proposing a delivery problem that considers multiple deliveries while accounting for variations in flight speed due to payload. Experiments demonstrate the effectiveness of the proposed method compared to the state-of-the-art methods.
Review of Risk Identification within Engineering, Procurement and Construction (EPC) Projects

Throughout their lifespan, proj ects are constantly exposed to risks and opportunities; risk management is one of the factors that contributes to project success, particularly in Engineering, Procurement and Construction (EPC) proj ects. Risks can come from several different places, including a lack of funding, unforeseen circumstances, strategic mistakes, etc. The first crucial step in effective risk management is the identification and evaluation of risks. However, EPC projects are well-known for their complexity, which makes managing and controlling them with absolute certainty challenging. Consequently, these projects are often characterized by uncertainty, especially in their early stages. To increase the likelihood of successful project completion and mitigate the effects of uncertainty, it is essential to identify and manage risks properly. This study's purpose is to present a systematic review of literature on risk assessment in EPC proj ects, identify common risks and establish criteria for prioritizing risks. The methodology includes analysing databases like Taylor & Francis, Science Direct, and Emerald, along with interviews with project managers to define relevant criteria.
Characteristics of Intravascular Large B-Cell Lymphoma on Cerebral MR Imaging

SUMMARY: IVL is characterized by a propensity for intravascular tumor cell proliferation. Premortem diagnosis of IVL is difficult because of its nonspecific clinical, laboratory, and imaging manifestations. This study examined cerebral MR imaging patterns of IVL and their changes with and without chemotherapy. Nine of 11 patients studied presented with abnormal findings. We define 5 patterns of abnormal MR imaging findings: 1) infarctlike lesions, 2) nonspecific white matter lesions, 3) meningeal enhancement, 4) masslike lesions, and 5) hyperintense lesions in the pons on T2WI. Seven patients presented with only 1 pattern, while 2 patients presented with multiple patterns. Lesions in 7 treated patients responded to chemotherapy. Pathologic specimens revealed intravascular tumor cell infiltration with associated infarctions, necrosis, congestion, demyelination, vasculitis, and tumor cell extravasation. We conclude that MR imaging patterns can be possible manifestations of intravascular-dominant infiltration by tumor cells with associated occlusion or inflammation, depending on the level of affected vessels.
DETECTION OF COMPLEX CARBOHYDRATES IN THE GOLGI APPARATUS OF RAT CELLS

Two methods used for the electron microscopic detection of glycoproteins were applied to a variety of cell types in the rat; one involved successive treatment of sections with periodic acid, chromic acid, and silver methenamine; and the other, a brief treatment with a chromic acid-phosphotungstic acid mixture. The results obtained with the two methods were identical and, whenever the comparison was possible, similar to those obtained with the periodic acid-Schiff technique of light microscopy. In secretory as well as in nonsecretory cells, parts of the Golgi apparatus are stained. The last saccule on one side of each Golgi stack is strongly reactive (mature face), and the last saccule on the other side shows little or no reactivity (immature face); a gradient of reactivity occurs in between these saccules. The more likely explanation of the increase in staining intensity is that carbohydrate is synthesized and accumulates in saccules as they migrate toward the mature face. In many secretory cells, the mature face is associated with strongly stained secretory granules. Other structures stained are: (1) small vesicles, dense and multivesicular bodies, at least some of which are presumed to be lysosomal in nature; (2) cell coat; and (3) basement membrane. The evidence suggests that the Golgi saccules provide glycoproteins not only for secretion, but also for the needs of the lysosomal system as well as for incorporation into the cell coat and perhaps basement membrane.
Performance Evaluation of Deep Learning-Based Facial Recognition Models on Mobile Computing Environments

Over the past few years, facial recognition technology has become an essential element in a range of applications, such as security systems, user verification, a nd s ocial media networks. The deployment of face recognition models on low-resource devices, such as low-end smartphones, poses considerable challenges owing to the restricted processing power and memory capacity at hand. The objective of this research is to determine the optimal deep learning-driven facial recognition algorithm that can provide prompt results in resource-constrained environments. The study conducted a comparative analysis of four widely used models, namely FaceNet, OpenFace, DeepFace, and VGGFace, with respect to their performance on the LFW (Labeled Faces in the Wild) dataset, which comprises a range of facial images. The assessment criteria comprised of accuracy, recall, F1 score, inference time, and memory utilization. The study employed an extensive codebase to preprocess and generate facial embeddings for each model, assess their efficacy, and present the findings in a visual format. The results of t he study indicate that FaceNet exhibited superior performance compared to the other models, achieving an accuracy rate of 94.04 %. Additionally, FaceNet demonstrated the shortest inference time of 0.0181 and the lowest memory usage. The exceptional proficiency exhibited by FaceNet renders it a highly suitable option for conducting facial recognition in realtime on devices with limited resources. The present investigation underscores the significance of carefully choosing suitable facial recognition models that are tailored to specific c ontexts a nd l ays t he g roundwork for forthcoming studies aimed at enhancing efficacy i n restricted settings.
Strategic Leadership: Driving Human Resource Performance in the Modern Workplace

The present research, titled “Strategic Leadership: A Driver for Enhancing Human Resource Performance in the Contemporary Workplace,” delves into the intricate interplay between strategic leadership and the performance of human resources (HR). By conducting a comprehensive evaluation of relevant scholarly works and using a comparative analysis, this study sheds light on the substantial impact of strategic leadership on employee engagement, innovation in human resources practices, and the general well-being of organizations. The key results of the study indicate that the influence of strategic leadership on HR performance is generally good. However, it is important to note that the efficacy of strategic leadership in this regard is not consistent across all organizational settings and cultures. The research further underscores the difficulties encountered by strategic leaders, namely in the task of reconciling organizational goals with the varied requirements of employees within a multinational corporate setting. The significance of adaptation and contextspecific methods in leadership is highlighted via comparative examination of leadership styles. The study adds to the current academic conversation on strategic leadership by offering novel perspectives on its growing function in improving human resources performance within contemporary work environments. The paper provides pragmatic suggestions for the enhancement of leadership skills and emphasizes the need for ongoing adjustment and scholarly inquiry in this ever-evolving domain. This research serves as a significant asset for scholars and professionals in the fields of organizational leadership and human resource management.
Canine Distemper Virus Matrix Protein Influences Particle Infectivity, Particle Composition, and Envelope Distribution in Polarized Epithelial Cells and Modulates Virulence

ABSTRACT In paramyxoviruses, the matrix (M) protein mediates the interaction between the envelope and internal proteins during particle assembly and egress. In measles virus (MeV), M mutations, such as those found in subacute sclerosing panencephalitis (SSPE) strains, and differences in vaccine and wild-type M proteins can affect the strength of interaction with the envelope glycoproteins, assembly efficiency, and spread. However, the contribution of the M protein to the replication and pathogenesis of the closely related canine distemper virus (CDV) has not been characterized. To this end this, we generated a recombinant wild-type CDV carrying a vaccine strain M protein. The recombinant virus retained the parental growth phenotype in VerodogSLAMtag cells, but displayed an increased particle-to-infectivity ratio very similar to that of the vaccine strain, likely due to inefficient H protein incorporation. Even though infectious virus was released only from the apical surface, consistent with the release polarity of the wild-type CDV strain, envelope protein distribution in polarized epithelial cells reproduced the bipolar pattern seen in vaccine strain-infected cells. Most notably, the chimeric virus was completely attenuated in ferrets and caused only a mild and transient leukopenia, indicating that the differences in particle infectivity and envelope protein sorting mediated by the vaccine M protein contribute importantly to vaccine strain attenuation.
Defected Ground Structure (DGS) Based Narrow Rectangular Patches: Detailed Investigations With Promising Improvement in Cross-Polar Radiations

While investigating defected ground structure (DGS)-integrated rectangular patches, most of the researchers concentrate on square or wide patch with aspect ratio $\gt1. $ However, the narrow/thin (aspect ratio $\lt1)$ patches are often useful for their compact configuration and inherently low cross-polar (XP) radiations. It is also found that the true potential of asymmetric DGS has not yet been explored completely. This work investigates in detail different configurations of DGS for a rectangular patch with aspect ratio 0.8 and operating near 10 GHz. It also demonstrates two new configurations of asymmetric DGSs investigated for the first time for any type of rectangular patch. These DGSs can provide upto 20 dB suppression in the peak XP radiations in the H-plane without affecting the primary radiation mode.