ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
PSIGAN: Joint Probabilistic Segmentation and Image Distribution Matching for Unpaired Cross-Modality Adaptation-Based MRI Segmentation

We developed a new joint probabilistic segmentation and image distribution matching generative adversarial network (PSIGAN) for unsupervised domain adaptation (UDA) and multi-organ segmentation from magnetic resonance (MRI) images. Our UDA approach models the co-dependency between images and their segmentation as a joint probability distribution using a new structure discriminator. The structure discriminator computes structure of interest focused adversarial loss by combining the generated pseudo MRI with probabilistic segmentations produced by a simultaneously trained segmentation sub-network. The segmentation sub-network is trained using the pseudo MRI produced by the generator sub-network. This leads to a cyclical optimization of both the generator and segmentation sub-networks that are jointly trained as part of an end-to-end network. Extensive experiments and comparisons against multiple state-of-the-art methods were done on four different MRI sequences totalling 257 scans for generating multi-organ and tumor segmentation. The experiments included, (a) 20 T1-weighted (T1w) in-phase mdixon and (b) 20 T2-weighted (T2w) abdominal MRI for segmenting liver, spleen, left and right kidneys, (c) 162 T2-weighted fat suppressed head and neck MRI (T2wFS) for parotid gland segmentation, and (d) 75 T2w MRI for lung tumor segmentation. Our method achieved an overall average DSC of 0.87 on T1w and 0.90 on T2w for the abdominal organs, 0.82 on T2wFS for the parotid glands, and 0.77 on T2w MRI for lung tumors.
Improve Model Testing by Integrating Bounded Model Checking and Coverage Guided Fuzzing

Eectromechanical systems built by Simulink or Ptolemy have been widely used in industry fields, such as autonomous systems and robotics. It is an urgent need to ensure the safety and security of those systems. Test case generation technologies are widely used to ensure the safety and security. State-of-the-art testing tools employ model-checking techniques or search-based methods to generate test cases. Traditional search-based techniques based on Simulink simulation are plagued by problems such as low speed and high overhead. Traditional model-checking techniques such as symbolic execution have limited performance when dealing with nonlinear elements and complex loops. Recently, coverage guided fuzzing technologies are known to be effective for test case generation, due to their high efficiency and impressive effects over complex branches of loops. In this paper, we apply fuzzing methods to improve model testing and demonstrate the effectiveness. The fuzzing methods aim to cover more program branches by mutating valuable seeds. Inspired by this feature, we propose a novel integration technology SPsCGF, which leverages bounded model checking for symbolic execution to generate test cases as initial seeds and then conduct fuzzing based upon these worthy seeds. Over the evaluated benchmarks which consist of industrial cases, SPsCGF could achieve 8% to 38% higher model coverage and 3x-10x time efficiency compared with the state-of-the-art works.
Cross-Domain Correspondence for Sketch-Based 3D Model Retrieval Using Convolutional Neural Network and Manifold Ranking

Due to the huge difference in the representation of sketches and 3D models, sketch-based 3D model retrieval is a challenging problem in the areas of graphics and computer vision. Some state-of-the-art approaches usually extract features from 2D sketches and produce multiple projection views of 3D models, and then select one view of 3D models to match sketch. It's hard to find "the best view" and views from different perspectives of a 3D model may be completely different. Other methods apply learning features to retrieve 3D models based on 2D sketch. However, sketches are abstract images and are usually drawn subjectively. It is difficult to be learned accurately. To address these problems, we propose cross-domain correspondence method for sketch-based 3D model retrieval based on manifold ranking. Specifically, we first extract learning features of sketches and 3D models by two-parts CNN structures. Subsequently, we generate cross-domain undirected graphs using learning features and semantic labels to create correspondence between sketches and 3D models. Finally, the retrieval results are computed by manifold ranking. Experimental results on SHREC 13 and SHREC 14 datasets show the superior performance in all 7 standard metrics, compared to the state-of-the-art approaches.
Identifying the Neuroanatomical Basis of Cognitive Impairment in Alzheimer's Disease by Correlation- and Nonlinearity-Aware Sparse Bayesian Learning

Predicting cognitive performance of subjects from their magnetic resonance imaging (MRI) measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimer's disease. Traditionally, this task is performed by formulating a linear regression problem. Recently, it is found that using a linear sparse regression model can achieve better prediction accuracy. However, most existing studies only focus on the exploitation of sparsity of regression coefficients, ignoring useful structure information in regression coefficients. Also, these linear sparse models may not capture more complicated and possibly nonlinear relationships between cognitive performance and MRI measures. Motivated by these observations, in this work we build a sparse multivariate regression model for this task and propose an empirical sparse Bayesian learning algorithm. Different from existing sparse algorithms, the proposed algorithm models the response as a nonlinear function of the predictors by extending the predictor matrix with block structures. Further, it exploits not only inter-vector correlation among regression coefficient vectors, but also intra-block correlation in each regression coefficient vector. Experiments on the Alzheimer's Disease Neuroimaging Initiative database showed that the proposed algorithm not only achieved better prediction performance than state-of-the-art competitive methods, but also effectively identified biologically meaningful patterns.
ACER: An Agglomerative Clustering Based Electrode Addressing and Routing Algorithm for Pin-Constrained EWOD Chips

The problem of pin-constrained electrowetting-ondielectric (EWOD) biochips becomes a serious issue to realize complex bio-chemical operations. Due to limited number of control pins and routing resources, additional Printed Circuit Board (PCB) routing layers may be required which potentially raises the fabrication cost. Previous state-of-the-art work has tried to develop a framework that uses a network-flow-based method for broadcast electrodeaddressing EWOD biochips. Nevertheless, greedily merging of electrical pins in previous works is at high risk of producing unroutable design. Routability should have higher priority than pin reduction. While previous works dedicated their effort on pin reduction, we have addressed our attention on routability of broadcast addressing. Experimental results demonstrate that taking routability into consideration can even have higher pin reduction. Viewed in this light, we present ACER, a routability driven clustering algorithm followed by escape routing using integer linear programming that effectively solves both pin merging and routing in broadcast addressing framework. Our proposed algorithm does not greedily focus on pin-reduction. Instead, routability is taken into consideration through agglomerative clustering. Compared to previous state-of-the-art, our proposed algorithm can further reduce required control pins by an average of 13% and route the design using 68% less wirelength.
Pollution Pods: The merging of art and psychology to engage the public in climate change

Environmental artists have risen to the challenge of communicating the urgency of public action to address environmental problems such as air pollution and climate change. Joining this challenge, the immersive artwork Pollution Pods (PPs) was created through a synthesis of knowledge from the fields of environmental psychology, empirical aesthetics, and activist art. This study summarizes the scientific process in this transdisciplinary project and reports the findings from a questionnaire study (N = 2662) evaluating the effect of the PPs on visitors. Data were collected at the first two exhibitions of the installation, one in a public park in Trondheim, Norway, and one at Somerset House, London, UK. Intentions to act were strong and slightly increased after visiting the art installation. Individual changes in intentions were positively associated with self-reported emotions of sadness, helplessness, and anger and self-reported cognitive assessment their awareness of the environmental consequences of their action, their willingness to take responsibility for their consequences, and belief in the relevance of environmental problems for daily life. Education and age were negatively associated with intentions. Despite favorable intentions, however, taking advantage of an actual behavioral opportunity to track one's climate change emissions behavior after visiting the PPs could not be detected. We conclude that environmental art can be useful for environmental communication and give recommendations for communicators on how to best make use of it. We emphasize the potential benefits of art that encourages personal responsibility and the need for valid behavior measures in environmental psychological research.
Face Verification Using the LARK Representation

We present a novel face representation based on locally adaptive regression kernel (LARK) descriptors. Our LARK descriptor measures a self-similarity based on "signal-induced distance" between a center pixel and surrounding pixels in a local neighborhood. By applying principal component analysis (PCA) and a logistic function to LARK consecutively, we develop a new binary-like face representation which achieves state-of-the-art face verification performance on the challenging benchmark "Labeled Faces in the Wild" (LFW) dataset. In the case where training data are available, we employ one-shot similarity (OSS) based on linear discriminant analysis (LDA). The proposed approach achieves state-of-the-art performance on both the unsupervised setting and the image restrictive training setting (72.23% and 78.90% verification rates), respectively, as a single descriptor representation, with no preprocessing step. As opposed to combined 30 distances which achieve 85.13%, we achieve comparable performance (85.1%) with only 14 distances while significantly reducing computational complexity.
Pooling the Convolutional Layers in Deep ConvNets for Video Action Recognition

Deep ConvNets have shown their good performance in image classification tasks. However, there still remains problems in deep video representations for action recognition. On one hand, current video ConvNets are relatively shallow compared with image ConvNets, which limits their capability of capturing the complex video action information; on the other hand, temporal information of videos is not properly utilized to pool and encode the video sequences. Toward these issues, in this paper we utilize two state-of-the-art ConvNets, i.e., the very deep spatial net (VGGNet [1]) and the temporal net from Two-Stream ConvNets [2], for action representation. The convolutional layers and the proposed new layer, called frame-diff layer, are extracted and pooled with two temporal pooling strategies: Trajectory pooling and Line pooling. The pooled local descriptors are then encoded with vector of locally aggregated descriptors (VLAD) [3] to form the video representations. In order to verify the effectiveness of the proposed framework, we conduct experiments on UCF101 and HMDB51 data sets. It achieves accuracy of 92.08% on UCF101, which is the state-of-the-art, and the accuracy of 65.62% on HMDB51, which is comparable to the state-of-the-art. In addition, we propose the new Line pooling strategy, which can speed up the extraction of feature and achieve the comparable performance of the Trajectory pooling.
Small Program, Big Needs

Resources are typically scarce in any safety program, but they may be especially limited and difficult to access when the program is relatively small. California State University, Fullerton, has a broad-scope radioactive material license with around ten faculty-users, so while the amount of radioactive material used is not large, its obligation to provide support to those users is. Research is conducted primarily using undergraduate students, so emphasis on safety is paramount. Additionally, when one's duties also include oversight to the biosafety program and support of the hazardous waste program, the ability of a single person to provide what is needed becomes part art, part miracle. I have created a program that conforms to the license's regulatory requirements, while drawing on resources in a variety of places to produce a highly-visible, high-quality program from this one-person office (me). Using humor, respect, good science and good grammar, anyone can get a higher quality, and more compliant radiation safety program than he or she thought possible. This model could be expanded to fit larger programs as well. Health Phys.99(Supplement 2): S164-S167; 2010
Reconfigurable Security Architecture (RESA) Based on PUF for FPGA-Based IoT Devices

Cybersecurity is a challenge in the utilization of IoT devices. One of the main security functions that we need for IoT devices is authentication. In this work, we used physical unclonable function (PUF) technology to propose a lightweight authentication protocol for IoT devices with long lifetimes. Our focus in this project is a solution for FPGA-based IoT devices. We evaluated the resiliency of our solution against state-of-the-art machine learning attacks.
Cross-Correlated Attention Networks for Person Re-Identification

Deep neural networks need to make robust inference in the presence of occlusion, background clutter, pose and viewpoint variations -to name a few- when the task of person re-identification is considered. Attention mechanisms have recently proven to be successful in handling the aforementioned challenges to some degree. However previous designs fail to capture inherent inter-dependencies between the attended features; leading to restricted interactions between the attention blocks. In this paper, we propose a new attention module called Cross-Correlated Attention (CCA); which aims to overcome such limitations by maximizing the information gain between different attended regions. Moreover, we also propose a novel deep network that makes use of different attention mechanisms to learn robust and discriminative representations of person images. The resulting model is called the Cross-Correlated Attention Network (COIN). Extensive experiments demonstrate that the CCAN comfortably outperforms current state-of-the-art algorithms by a tangible margin. Modeling the inherentspatial relations between different attended regions within the deep architecture. joint end-to-end cross correlated attention and representational learning. State-of-the-art results in terms of mAP and Rank-1 accuracies across several challenging datasets. (C) 2020 Elsevier B.V. All rights reserved.
Compressed Sensing Based Real-Time Dynamic MRI Reconstruction

This work addresses the problem of real-time online reconstruction of dynamic magnetic resonance imaging sequences. The proposed method reconstructs the difference between the previous and the current image frames. This difference image is sparse. We recover the sparse difference image from its partial k-space scans by using a nonconvex compressed sensing algorithm. As there was no previous fast enough algorithm for real-time reconstruction, we derive a novel algorithm for this purpose. Our proposed method has been compared against state-of-the-art offline and online reconstruction methods. The accuracy of the proposed method is less than offline methods but noticeably higher than the online techniques. For real-time reconstruction we are also concerned about the reconstruction speed. Our method is capable of reconstructing 128 x 128 images at the rate of 6 frames/s, 180 x 180 images at the rate of 5 frames/s and 256 256 images at the rate of 2.5 frames/s.
HYDRHA: Hydrogels of hyaluronic acid. New biomedical approaches in cancer, neurodegenerative diseases, and tissue engineering

In the last decade, hyaluronic acid (HA) has attracted an ever-growing interest in the biomedical engineering field as a biocompatible, biodegradable, and chemically versatile molecule. In fact, HA is a major component of the extracellular matrix (ECM) and is essential for the maintenance of cellular homeostasis and crosstalk. Innovative experimental strategies in vitro and in vivo using three-dimensional (3D) HA systems have been increasingly reported in studies of diseases, replacement of tissue and organ damage, repairing wounds, and encapsulating stem cells for tissue regeneration. The present work aims to give an overview and comparison of recent work carried out on HA systems showing advantages, limitations, and their complementarity, for a comprehensive characterization of their use. A special attention is paid to the use of HA in three important areas: cancer, diseases of the central nervous system (CNS), and tissue regeneration, discussing the most innovative experimental strategies. Finally, perspectives within and beyond these research fields are discussed.
As-similar-as-possible saliency fusion

Salient region detection has gradually become a popular topic in multimedia and computer vision research. However, existing techniques exhibit remarkable variations in methodology with inherent pros and cons. In this paper, we propose fusing the saliency hypotheses, namely the saliency maps produced by different methods, by accentuating their advantages and attenuating the disadvantages. To this end, our algorithm consists of three basic steps. First, given the test image, our method finds the similar images and their saliency hypotheses by comparing the similarity of the learned deep features. Second, the error-aware coefficients are computed from the saliency hypotheses. Third, our method produces a pixel-accurate saliency map which covers the objects of interest and exploits the advantages of the state-of-the-art methods. We then evaluate the proposed framework on three challenging datasets, namely MSRA-1000, ECSSD and iCoSeg. Extensive experimental results show that our method outperforms all state-of-the-art approaches. In addition, we have applied our method to the SquareMe application, an autonomous image resizing system. The subjective user-study experiment demonstrates that human prefers the image retargeting results obtained by using the saliency maps from our proposed algorithm.
Counting and locating high-density objects using convolutional neural network

This paper presents a Convolutional Neural Network (CNN) approach for counting and locating objects in high-density imagery. To the best of our knowledge, this is the first object counting and locating method based on a feature map enhancement combined with a multi-sigma refinement of the confidence map. The proposed method was evaluated in two counting datasets: trees and cars. For the tree dataset, our method returned a mean absolute error (MAE) of 2.05, a root-mean-squared error (RMSE) of 2.87 and a coefficient of determination (R-2) of 0.986. For the car dataset (CARPK and PUCPR+), our method was superior to state-of-the-art methods. In the these datasets, our approach achieved an MAE of 4.45 and 3.16, an RMSE of 6.18 and 4.39, and an R-2 of 0.975 and 0.999, respectively. We conclude that the proposed method is suitable for dealing with high object-density, returning a state-of-the-art performance for counting and locating objects.
Accurate statistical description of random dopant-induced threshold voltage variability

We have studied the detailed threshold voltage distribution in a state-of-the-art n-channel MOSFET in the presence of random discrete dopants. A ground-breaking sample of 100 000 transistors with statistically unique random dopant distributions were simulated using the Glasgow 3-D device simulator and advanced grid computing technologies. The results indicate that the threshold voltage distribution deviates substantially from a Gaussian distribution, which may have significant implications for the margins used in circuit design, particularly in SRAM cells.
Orthogonal Crosslinking: A Strategy to Generate Novel Protein Topology and Function

Compared to the disulfide bond, other naturally occurring intramolecular crosslinks have received little attention, presumably due to their rarity in the vast protein space. Here we presented examples of natural non-disulfide crosslinks, which we refer to as orthogonal crosslinks, emphasizing their effect on protein topology and function. We summarize recent efforts on expanding orthogonal crosslinks by using either the enzymes that catalyze protein circularization or the genetic code expansion strategy to add electrophilic amino acids site-specifically in proteins. The advantages and disadvantages of each method are discussed, along with their applications to generate novel protein topology and function. In particular, we highlight our recent work on spontaneous orthogonal crosslinking, in which a carbamate-based crosslink was generated in situ, and its applications in designing orthogonally crosslinked domain antibodies with their topology-mimicking bacterial adhesins.
No-reference stereo image quality assessment based on joint wavelet decomposition and statistical models

The widespread use of 3D acquisition and display technologies has increased the interest of stereo image dataset in various application fields. As a result, it becomes necessary to have an efficient 3D quality assessment method to measure the human perception of stereoscopic images. While most of the state-of-the-art methods belong to the class of full-reference methods which require the original stereo images to be able to assess the quality, we propose in this paper a no-reference quality metric which does not require any information of the original stereo images. The proposed method operates in the wavelet transform domain and adopts a statistical framework to predict the quality of stereo images. More precisely, a joint wavelet decomposition is first performed on the stereo images to exploit simultaneously the intra and inter-views redundancies. A wavelet transform is also applied to their associated estimated disparity maps. Then, relevant features are extracted from the resulting wavelet subbands by resorting to appropriate statistical models. Simulations, carried out on the standard Live 3D image quality database, show that our proposed design model achieves significant improvement compared to the state-of-the-art 3D quality assessment methods.