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"Dagan, David"
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Prison break : why conservatives turned against mass incarceration
\"American conservatism rose hand-in-hand with the growth of mass incarceration. For decades, conservatives deployed \"tough on crime\" rhetoric to attack liberals as out-of-touch elitists who coddled criminals while the nation spiraled toward disorder. As a result, conservatives have been the motive force in building our vast prison system. Indeed, expanding the number of Americans under lock and key was long a point of pride for politicians on the right - even as the U.S. prison population eclipsed international records. Over the last few years, conservatives in Washington, D.C. and in bright-red states like Georgia and Texas, have reversed course, and are now leading the charge to curb prison growth. In Prison Break, David Dagan and Steve Teles explain how this striking turn of events occurred, how it will affect mass incarceration, and what it teaches us about achieving policy breakthroughs in our polarized age. Combining insights from law, sociology, and political science, Teles and Dagan will offer the first comprehensive account of this major political shift. In a challenge to the conventional wisdom, they argue that the fiscal pressures brought on by recession are only a small part of the explanation for the conservatives' shift, over-shadowed by Republicans' increasing anti-statism, the waning efficacy of \"tough on crime\" politics and the increasing engagement of evangelicals. These forces set the stage for a small cadre of conservative leaders to reframe criminal justice in terms of redeeming wayward souls and rolling back government. These developments have created the potential to significantly reduce mass incarceration, but only if reformers on both the right and the left play their cards right. As Dagan and Teles stress, there is also a broader lesson in this story about the conditions for cross-party cooperation in our polarized age. Partisan identity, they argue, generally precedes position-taking, and policy breakthroughs are unlikely to come by \"reaching across the aisle,\" promoting \"compromise,\" or appealing to \"expert opinion.\" Instead, change happens when political movements redefine their own orthodoxies for their own reasons. As Dagan and Teles show, outsiders can assist in this process - and they played a crucial role in the case of criminal justice - but they cannot manufacture it. This book will not only reshape our understanding of conservatism and American penal policy, but also force us to reconsider the drivers of policy innovation in the context of American politics\"-- Provided by publisher.
Book
Enhancing medical image registration via appearance adjustment networks
Deformable image registration is fundamental for many medical image analyses. A key obstacle for accurate image registration lies in image appearance variations such as the variations in texture, intensities, and noise. These variations are readily apparent in medical images, especially in brain images where registration is frequently used. Recently, deep learning-based registration methods (DLRs), using deep neural networks, have shown computational efficiency that is several orders of magnitude faster than traditional optimization-based registration methods (ORs). DLRs rely on a globally optimized network that is trained with a set of training samples to achieve faster registration. DLRs tend, however, to disregard the target-pair-specific optimization inherent in ORs and thus have degraded adaptability to variations in testing samples. This limitation is severe for registering medical images with large appearance variations, especially since few existing DLRs explicitly take into account appearance variations. In this study, we propose an Appearance Adjustment Network (AAN) to enhance the adaptability of DLRs to appearance variations. Our AAN, when integrated into a DLR, provides appearance transformations to reduce the appearance variations during registration. In addition, we propose an anatomy-constrained loss function through which our AAN generates anatomy-preserving transformations. Our AAN has been purposely designed to be readily inserted into a wide range of DLRs and can be trained cooperatively in an unsupervised and end-to-end manner. We evaluated our AAN with three state-of-the-art DLRs - Voxelmorph (VM), Diffeomorphic Voxelmorph (DifVM), and Laplacian Pyramid Image Registration Network (LapIRN) – on three well-established public datasets of 3D brain magnetic resonance imaging (MRI) - IBSR18, Mindboggle101, and LPBA40. The results show that our AAN consistently improved existing DLRs and outperformed state-of-the-art ORs on registration accuracy, while adding a fractional computational load to existing DLRs.
Journal Article
تقنيات المعلوماتية الطبية الحيوية
لقد شهدت السنوات الأخيرة تطورا كبيرا في مجال تكنولوجيا المعلومات واستخداماتها في المجالات الصحية وخارج حدود الخدمات الصحية مما أدى إلى اكتشاف معارف جديدة في علوم الحياة والطب. لذلك يقدم هذا المرجع صورة شاملة ومحدثة عن استخدام تكنولوجيا المعلومات في المجال الطبي وينقسم هذا الكتاب إلى قسمين : أساسيات تكنولوجية والتطبيقات السريرية المتكاملة. يغطي الجزء الأول : أنظمة التصوير الطبي الرئيسية، السجلات الطبية الإلكترونية، ضغط بيانات الصورة، استرجاع الصور الطبية، النمذجة والمحاكاة، تقنيات التصوير الحدودي، معالجة البيانات وتحليلها، بيانات الاتصالات والنقل، الأمن والحماية للبيانات والصور الطبية والحوسبة البيولوجية. أما الجزء الثاني فيشتمل أرشفة الصورة ونظم الاتصالات، التصوير الطبي للمستشفيات الوثائق والنصوص الطبية، الوسائط المتعددة المتكاملة نظم سجل المرضى، التشخيص بمساعدة الكمبيوتر، نظم دعم القرار، الروبوتات الطبية وتطور الصحة الإلكترونية وMRI.
BOOK
Flexible Multi-Layer Semi-Dry Electrode for Scalp EEG Measurements at Hairy Sites
One of the major challenges of daily wearable electroencephalogram (EEG) monitoring is that there are rarely suitable EEG electrodes for hairy sites. Wet electrodes require conductive gels, which will dry over the acquisition time, making them unstable for long-term EEG monitoring. Additionally, the electrode–scalp impedances of most dry electrodes are not adequate for high quality EEG collection at hairy sites. In view of the above problems, a flexible multi-layer semi-dry electrode was proposed for EEG monitoring in this study. The semi-dry electrode contains a flexible electrode body layer, foam layer and reservoir layer. The probe structure of the electrode body layer enables the electrode to work effectively at hairy sites. During long-term EEG monitoring, electrolytes stored in the reservoir layer are continuously released through the foam layer to the electrode–scalp interface, ensuring a lower electrode–scalp contact impedance. The experimental results showed that the average electrode–scalp impedance of the semi-dry electrode at a hairy site was only 23.89 ± 7.44 KΩ at 10 Hz, and it was lower than 40 KΩ over a long-term use of 5 h. The electrode performed well in both static and dynamic EEG monitoring, where the temporal correlation with wet electrode signals at the hairy site could reach 94.25% and 90.65%, respectively, and specific evoked EEG signals could be collected. The flexible multi-layer semi-dry electrode can be well applied to scalp EEG monitoring at hairy sites, providing a promising solution for daily long-term monitoring of wearable EEGs.
Journal Article
A Transformer-Assisted Cascade Learning Network for Choroidal Vessel Segmentation
As a highly vascular eye part, the choroid is crucial in various eye disease diagnoses. However, limited research has focused on the inner structure of the choroid due to the challenges in obtaining sufficient accurate label data, particularly for the choroidal vessels. Meanwhile, the existing direct choroidal vessel segmentation methods for the intelligent diagnosis of vascular assisted ophthalmic diseases are still unsatisfactory due to noise data, while the synergistic segmentation methods compromise vessel segmentation performance for the choroid layer segmentation tasks. Common cascaded structures grapple with error propagation during training. To address these challenges, we propose a cascade learning segmentation method for the inner vessel structures of the choroid in this paper. Specifically, we propose Transformer-Assisted Cascade Learning Network (TACLNet) for choroidal vessel segmentation, which comprises a two-stage training strategy: pre-training for choroid layer segmentation and joint training for choroid layer and choroidal vessel segmentation. We also enhance the skip connection structures by introducing a multi-scale subtraction connection module designated as MSC, capturing differential and detailed information simultaneously. Additionally, we implement an auxiliary Transformer branch named ATB to integrate global features into the segmentation process. Experimental results exhibit that our method achieves the state-of-the-art performance for choroidal vessel segmentation. Besides, we further validate the significant superiority of the proposed method for retinal fluid segmentation in optical coherence tomography (OCT) scans on a publicly available dataset. All these fully prove that our TACLNet contributes to the advancement of choroidal vessel segmentation and is of great significance for ophthalmic research and clinical application.
Journal Article
Cancer type prediction based on copy number aberration and chromatin 3D structure with convolutional neural networks
Background
With the developments of DNA sequencing technology, large amounts of sequencing data have been produced that provides unprecedented opportunities for advanced association studies between somatic mutations and cancer types/subtypes which further contributes to more accurate somatic mutation based cancer typing (SMCT). In existing SMCT methods however, the absence of high-level feature extraction is a major obstacle in improving the classification performance.
Results
We propose DeepCNA, an advanced convolutional neural network (CNN) based classifier, which utilizes copy number aberrations (CNAs) and HiC data, to address this issue. DeepCNA first pre-process the CNA data by clipping, zero padding and reshaping. Then, the processed data is fed into a CNN classifier, which extracts high-level features for accurate classification. Experimental results on the COSMIC CNA dataset indicate that 2D CNN with both cell lines of HiC data lead to the best performance. We further compare DeepCNA with three widely adopted classifiers, and demonstrate that DeepCNA has at least 78% improvement of performance.
Conclusions
This paper demonstrates the advantages and potential of the proposed DeepCNA model for processing of somatic point mutation based gene data, and proposes that its usage may be extended to other complex genotype-phenotype association studies.
Journal Article
Study of an Oxygen Supply and Oxygen Saturation Monitoring System for Radiation Therapy Associated with the Active Breathing Coordinator
In this study, we designed an oxygen supply and oxygen saturation monitoring (OSOSM) system. This OSOSM system can provide a continuous supply of oxygen and monitor the peripheral capillary oxygen saturation (SpO2) of patients who accept radiotherapy and use an active breathing coordinator (ABC). A clinical test with 27 volunteers was conducted. The volunteers were divided into two groups based on the tendency of SpO2 decline in breath-holding without the OSOSM system: group A (12 cases) showed a decline in SpO2 of less than 2%, whereas the decline in SpO2 in group B (15 cases) was greater than 2% and reached up to 6% in some cases. The SpO2 of most volunteers declined during rest. The breath-holding time of group A without the OSOSM system was significantly longer than that of group B (p < 0.05) and was extended with the OSOSM system by 26.6% and 27.85% in groups A and B, respectively. The SpO2 recovery time was reduced by 36.1%, and the total rest time was reduced by 27.6% for all volunteers using the OSOSM system. In summary, SpO2 declines during breath-holding and rest time cannot be ignored while applying an ABC. This OSOSM system offers a simple and effective way to monitor SpO2 variation and overcome SpO2 decline, thereby lengthening breath-holding time and shortening rest time.
Journal Article
Feature covariance matrix-based dynamic hand gesture recognition
Over the past 2 decades, vision-based dynamic hand gesture recognition (HGR) has made significant progresses and been widely adopted in many practical applications. Although the advent of RGB-D cameras and deep learning-based methods provides more feasible solutions for HGR, it is still very challenging to satisfy the requirements of both high efficiency and accuracy for real-world HGR systems. In this paper, we propose a novel method using the feature covariance matrix for effective and efficient dynamic HGR. We extract a set of local feature vectors that represent local motion patterns to construct the feature covariance matrix efficiently, which also provides a compact representation of a dynamic hand gesture. By tracking hand keypoints in three successive frames and calculating their motion features, our method can be extended to both 2D dynamic HGR and 3D dynamic HGR. To evaluate the effectiveness of the proposed framework, we perform extensive experiments on three publicly available datasets (one 2D dataset and two 3D datasets). The experimental results demonstrate the effectiveness of our proposed method.
Journal Article
Robust video summarization using collaborative representation of adjacent frames
With the ever increasing volume of video content, efficient and effective video summarization (VS) techniques are urgently demanded to manage a large amount of video data. Recent developments on sparse representation based approaches have demonstrated promising results for VS. However, these existing approaches treat each frame independently, so the performance can be greatly influenced by each individual frame. In this paper, we formulate the VS problem with a collaborative representation model to take the visual similarity of adjacent frames into consideration. To be specific, during the procedure of reconstruction, both each individual frame and their adjacent frames are reconstructed collaboratively, so the impact of an individual frame can be weakened. In addition, a greedy iterative algorithm is designed for model optimization, where the sparsity and the average percentage of reconstruction (APOR) are adopted to control the iteration. Experimental results on two benchmark datasets with various types of videos demonstrate that the proposed method not only outperforms the state of the art, but also improves the robustness to transitional frames and “outlier” frames.
Journal Article
Intelligent Evaluation of Strabismus in Videos Based on an Automated Cover Test
Strabismus is a common vision disease that brings about unpleasant influence on vision, as well as life quality. A timely diagnosis is crucial for the proper treatment of strabismus. In contrast to manual evaluation, well-designed automatic evaluation can significantly improve the objectivity, reliability, and efficiency of strabismus diagnosis. In this study, we have proposed an innovative intelligent evaluation system of strabismus in digital videos, based on the cover test. In particular, the video is recorded using an infrared camera, while the subject performs automated cover tests. The video is then fed into the proposed algorithm that consists of six stages: (1) eye region extraction, (2) iris boundary detection, (3) key frame detection, (4) pupil localization, (5) deviation calculation, and (6) evaluation of strabismus. A database containing cover test data of both strabismic subjects and normal subjects was established for experiments. Experimental results demonstrate that the deviation of strabismus can be well-evaluated by our proposed method. The accuracy was over 91%, in the horizontal direction, with an error of 8 diopters; and it was over 86% in the vertical direction, with an error of 4 diopters.
Journal Article