ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Subcategory Classifiers for Multiple-Instance Learning and Its Application to Retinal Nerve Fiber Layer Visibility Classification

We propose a novel multiple-instance learning (MIL) method to assess the visibility (visible/not visible) of the retinal nerve fiber layer (RNFL) in fundus camera images. Using only image-level labels, our approach learns to classify the images as well as to localize the RNFL visible regions. We transform the original feature space into a discriminative subspace, and learn a region-level classifier in that subspace. We propose a margin-based loss function to jointly learn this subspace and the region-level classifier. Experiments with an RNFL data set containing 884 images annotated by two ophthalmologists give a system-annotator agreement (kappa values) of 0.73 and 0.72, respectively, with an interannotator agreement of 0.73. Our system agrees better with the more experienced annotator. Comparative tests with three public data sets (MESSIDOR and DR for diabetic retinopathy, and UCSB for breast cancer) show that our novel MIL approach improves performance over the state of the art. Our MATLAB code is publicly available at https://github.com/ManiShiyam/Sub-categoryclassifiers-for-Multiple-Instance-Learning/wiki.
Semantic-integrated software watermarking with tamper-proofing

The existing works of software watermarking have the intrinsic defects: watermarking is independent of program semantics and have weak strength and resilience to state-of-the-art reverse engineering such as symbolic execution, dynamic taint analysis and theorem proving. In this paper, we propose a semantic-integrated watermarking with tamper-proofing to mitigate such problems. This work chooses neural network as the "integrator" and skillfully integrates the watermarking and tamper-proofing module into program semantics. The difficult of reverse engineering or tampering with watermarked program is equal to extracting the rules from neural networks, which had be proven as a NP-hard problem. We have deployed our work in SPECint-2006 benchmarks to evaluate the overhead, strength and resilience. Experiment results show that our watermarking could effectively resist the state-of-the-art reverse engineering, and the introduced overhead is acceptable.
In vitro particle-associated uridyltransferase activity of the rotavirus VP1 polymerase

Rotaviruses are 11-segmented, double-stranded RNA (dsRNA) viruses with a unique intra-particle RNA synthesis mechanism. During genome replication, the RNA-dependent RNA polymerase (VP1) performs minus-strand RNA (-ssRNA) synthesis on positive-strand RNA (+ssRNA) templates to create dsRNA segments. Recombinant VP1 catalyzes -ssRNA synthesis using substrate NTPs in vitro, but only when the VP2 core shell protein or virus-like particles made of VP2 and VP6 (2/6-VLPs) are included in the reaction. The dsRNA product can be labeled using [α32P]-UTP and separated from the input +ssRNA template by polyacrylamide gel electrophoresis. Here, we report the generation of [α32P]-labeled rotavirus +ssRNA templates in reactions that lacked non-radiolabeled NTPs but contained catalytically-active VP1, 2/6-VLPs, and [α32P]-UTP. Non-radiolabeled UTP competed with [α32P]-UTP to decrease product levels, whereas CTP and GTP had little effect. Interesting, ATP stimulated [α32P]-labeled product production. These results suggest that rotavirus VP1 transferred [α32P]-UMP onto viral + ssRNA in vitro via a particle-associated uridyltransferase activity.
Pulmonary drug delivery: an effective and convenient delivery route to combat COVID-19

The recent outbreak of coronavirus disease 2019 (COVID-19) in Wuhan, China has spread rapidly around the world, leading to a widespread and urgent effort to develop and use comprehensive approaches in the treatment of COVID-19. While oral therapy is accepted as an effective and simple method, since the primary site of infection and disease progression of COVID-19 is mainly through the lungs, inhaled drug delivery directly to the lungs may be the most appropriate route of administration. To prevent or treat primary SARS-CoV-2 infections, it is essential to target the virus port of entry in the respiratory tract and airway epithelium, which requires rapid and high-intensity inhibition or control of viral entry or replication. To achieve success in this field, inhalation therapy is the most attractive treatment approach due to efficacy/safety profiles. In this review article, pulmonary drug delivery as a unique treatment option in lung diseases will be briefly reviewed. Then, possible inhalation therapies for the treatment of symptoms of COVID-19 will be discussed and the results of clinical trials will be presented. By pulmonary delivery of the currently approved drugs for COVID-19, efficacy of the treatment would be improved along with reducing systemic side effects.
Cell population dynamics in the course of adult hippocampal neurogenesis: Remaining unknowns

Neural stem cells (NSCs) generate new neurons throughout life in the mammalian hippocampus. The distinct developmental steps in the course of adult neurogenesis, including NSC activation, expansion, and neuronal integration, are increasingly well characterized down to the molecular level. However, substantial gaps remain in our knowledge about regulators and mechanisms involved in this biological process. This review highlights three long-standing unknowns. First, we discuss potency and identity of NSCs and the quest for a unifying model of short- and long-term self-renewal dynamics. Next, we examine cell death, specifically focusing on the early demise of newborn cells. Then, we outline the current knowledge on cell integration dynamics, discussing which (if any) neurons are replaced by newly added neurons in the hippocampal circuits. For each of these unknowns, we summarize the trajectory of studies leading to the current state of knowledge. Finally, we offer suggestions on how to fill the remaining gaps by taking advantage of novel technology to reveal currently hidden secrets in the course of adult hippocampal neurogenesis.
Adult acne versus adolescent acne: a narrative review with a focus on epidemiology to treatment

Acne vulgaris is one of the most common chronic inflammatory diseases and is characterized by papules, pustules, comedones, and nodules. Although adolescence is the preferential age group, acne may affect various age groups. Acne shares different properties in adults and adolescents. These differences extend from epidemiology to treatments. Increased awareness of these two subtypes will allow for better management of the disease. In this review, the authors examined all aspects of acne in adults and adolescents under the light of current literature.
Sustainability of Artists in Precarious Times; How Arts Producers and Individual Artists Have Adapted during a Pandemic

Making a living as an artist, whatever the discipline, is challenging. In addition to skills and talents, artists need resilience, adaptability, creativity, and the ability to withstand endless setbacks and rejections. Most critically, they need an on-going, stable income. Several studies have demonstrated that the income of most artists is usually very low. To survive, artists often find other sources of income aside from their creative work. Ideally, they also need a place to work, the capacity to do their work and a sense of validation from others of their work. When your livelihood disappears over night because of a pandemic, how do you then sustain that creative work? Using multiple sources of data and a qualitative methodology, including case studies and interviews, this paper addresses the ways that artists and producers from different art forms have addressed these challenges in Australia. It is concluded that while the impact of the pandemic on artists' lives has been considerable, some artists have been able to survive, adapt, and move forward.
Creating morphological diversity in reptilian temporal skull region: A review of potential developmental mechanisms

Reptilian skull morphology is highly diverse and broadly categorized into three categories based on the number and position of the temporal fenestrations: anapsid, synapsid, and diapsid. According to recent phylogenetic analysis, temporal fenestrations evolved twice independently in amniotes, once in Synapsida and once in Diapsida. Although functional aspects underlying the evolution of tetrapod temporal fenestrations have been well investigated, few studies have investigated the developmental mechanisms responsible for differences in the pattern of temporal skull region. To determine what these mechanisms might be, we first examined how the five temporal bones develop by comparing embryonic cranial osteogenesis between representative extant reptilian species. The pattern of temporal skull region may depend on differences in temporal bone growth rate and growth direction during ontogeny. Next, we compared the histogenesis patterns and the expression of two key osteogenic genes, Runx2 and Msx2, in the temporal region of the representative reptilian embryos. Our comparative analyses suggest that the embryonic histological condition of the domain where temporal fenestrations would form predicts temporal skull morphology in adults and regulatory modifications of Runx2 and Msx2 expression in osteogenic mesenchymal precursor cells are likely involved in generating morphological diversity in the temporal skull region of reptiles.
Anchoring and Sleep Inertia

Many occupational settings require individuals to make important decisions immediately after awakening. Although a plethora of psychological research has separately examined both sleep and anchoring effects on decision-making, little is known about their interaction. In the present study, we seek to shed light on the link between sleep inertia, the performance impairment immediately after awakening, and individuals' susceptibility to the anchoring bias. We proposed that sleep inertia would moderate participants' adjustment from anchors because sleep inertia leads to less cognitive effort invested, resulting in a stronger anchoring effect. One hundred four subjects were randomly assigned to an experimental group that answered anchoring tasks immediately after being awakened at nighttime or a control group that answered anchoring tasks at daytime. Our findings replicated the well-established anchoring effect in that higher anchors led participants to higher estimates than lower anchors. We did not find significant effects of sleep inertia. While the sleep inertia group reported greater sleepiness and having invested less cognitive effort compared to the control group, no systematic anchoring differences emerged, and cognitive effort did not qualify as a mediator of the anchoring effect. Bayesian analyses provide empirical evidence for these null findings. Implications for the anchoring literature and future research are discussed.
Image Synthesis in Multi-Contrast MRI With Conditional Generative Adversarial Networks

Acquiring images of the same anatomy with multiple different contrasts increases the diversity of diagnostic information available in an MR exam. Yet, the scan time limitations may prohibit the acquisition of certain contrasts, and some contrasts may be corrupted by noise and artifacts. In such cases, the ability to synthesize unacquired or corrupted contrasts can improve diagnostic utility. For multi-contrast synthesis, the current methods learn a nonlinear intensity transformation between the source and target images, either via nonlinear regression or deterministic neural networks. These methods can, in turn, suffer from the loss of structural details in synthesized images. Here, in this paper, we propose a new approach for multi-contrast MRI synthesis based on conditional generative adversarial networks. The proposed approach preserves intermediate-to-high frequency details via an adversarial loss, and it offers enhanced synthesis performance via pixel-wise and perceptual losses for registered multi-contrast images and a cycle-consistency loss for unregistered images. Information from neighboring cross-sections are utilized to further improve synthesis quality. Demonstrations on T-1 - and T-2 - weighted images from healthy subjects and patients clearly indicate the superior performance of the proposed approach compared to the previous state-of-the-art methods. Our synthesis approach can help improve the quality and versatility of the multi-contrast MRI exams without the need for prolonged or repeated examinations.
A Flexible and Energy-Efficient Convolutional Neural Network Acceleration With Dedicated ISA and Accelerator

State-of-the-art convolutional neural networks (CNNs) usually have a large number of layers and filter weights which bring huge computation and communication overheads. A general purpose instruction set architecture (ISA) is flexible but has low code density and high power consumption. The existing CNN-specific accelerators are much more efficient but usually are inflexible or require a complex controller to handle the computation and data transfer of different CNNs. In this brief, we propose a new CNN-specific ISA which embeds the parallel computation and data reuse parameters in the instructions. An instruction generator deploys the instruction parameters according to the feature of CNNs and hardware's computation and storage resources. In addition, a reconfigurable accelerator with 225 multipliers and 24 adder trees is realized to obtain efficient parallel computation and data transfer. Compared with x86 processors, our design has 392 times better energy efficiency and 16 times higher code density. Compared with other state-of-the-art accelerators, our solution has a higher flexibility to support all popular CNNs and a higher energy efficiency.
GPU-Accelerated Adaptive PCBSO Mode-Based Hybrid RLA for Sparse LU Factorization in Circuit Simulation

LU factorization is extensively used in engineering and scientific computations for solution of large set of linear equations. Particularly, circuit simulators rely heavily on sparse version of LU factorization for solution involving circuit matrices. One of the recent advances in this field is exploiting the emerging computing platform of graphics processing units (GPUs) for parallel and sparse LU factorization. In this article, following contributions are made to advance the state of the art in hybrid right-looking algorithm (RLA): 1) a novel GPU kernel based on parallel column and block size optimization (PCBSO) is developed for adaptively allocating the block size while optimizing the number of columns for parallel execution based on the size of their associated submatrices at every level. The proposed approach helps to minimize the resource contention and to improve the computational performance and 2) an algorithm is developed to enable the execution of the new adaptive mode with dynamic parallelism. Also, a comprehensive performance comparison using a set of benchmark circuit examples is presented. The results indicate that, the proposed advancements can improve the results of state-of-the-art right looking sparse LU factorization in GPU by 1.54x (Arithmetic Mean).
WiFi Fingerprinting Indoor Localization Using Local Feature-Based Deep LSTM

Indoor localization has attracted more and more attention because of its importance in many applications. One of the most popular techniques for indoor localization is the received signal strength indicator (RSSI) based fingerprinting approach. Since RSSI values are very complicated and noisy, conventional machine learning algorithms often suffer from limited performance. Recently developed deep learning algorithms have been shown to be powerful for the analysis of complex data. In this paper, we propose a local feature-based deep long short-term memory (LF-DLSTM) approach for WiFi fingerprinting indoor localization. The local feature extractor attempts to reduce the noise effect and extract robust local features. The DLSTM network is able to encode temporal dependencies and learn high-level representations for the extracted sequential local features. Real experiments have been conducted in two different environments, i.e., a research lab and an office. We also compare the proposed approach with some state-of-the-art methods for indoor localization. The results show that the proposed approach achieves the best localization performance with mean localization errors of 1.48 and 1.75 m under the research lab and office environments, respectively. The improvements of our proposed approach over the state-of-the-art methods range from18.98% to 53.46%.
Robustly Tracking People with LIDARs in a Crowded Museum for Behavioral Analysis

This introduces a method which uses LIDAR to identify humans and track their positions, body orientation, and movement trajectories in any public space to read their various types of behavioral responses to surroundings. We use a network of LIDAR poles, installed at the shoulder level of typical adults to reduce potential occlusion between persons and/or objects even in large-scale social environments. With this arrangement, a simple but effective human tracking method is proposed that works by combining multiple sensors' data so that large-scale areas can be covered. The effectiveness of this method is evaluated in an art gallery of a real museum. The result revealed good tracking performance and provided valuable behavioral information related to the art gallery.
Real-Time GPU-Based Ultrasound Simulation Using Deformable Mesh Models

This paper presents a real-time capable graphics processing unit (GPU)-based ultrasound simulator suitable for medical education. The main focus of the simulator is to synthesize realistic looking ultrasound images in real-time including artifacts, which are essential for the interpretation of this data. The simulation is based on a convolution-enhanced ray-tracing approach and uses a deformable mesh model. Deformations of the mesh model are calculated using the PhysX engine. Our method advances the state of the art for real-time capable ultrasound simulators by following the path of the ultrasound pulse, which enables better simulation of ultrasound-specific artifacts. An evaluation of our proposed method in comparison with recent generative slicing-based strategies as well as real ultrasound images is performed. Hereby, a gelatin ultrasound phantom containing syringes filled with different media is scanned with a real transducer. The obtained images are then compared to images which are simulated using a slicing-based technique and our proposed method. The particular benefit of our method is the accurate simulation of ultrasound-specific artifacts, like range distortion, refraction and acoustic shadowing. Several test scenarios are evaluated regarding simulation time, to show the performance and the bottleneck of our method. While being computationally more intensive than slicing techniques, our simulator is able to produce high-quality images in real-time, tracing over 5000 rays through mesh models with more than 2 000 000 triangles of which up to 200 000 may be deformed each frame.
Promoting a Pro-Ecological View: The Effects of Art on Engineering Students' Perceptions of the Environment

In an effort to suggest an extended role that art could play in promoting a pro-ecological worldview, this study reviews a two-week artist-led workshop, organized as part of an undergraduate art course offered by a university specializing in engineering and the natural sciences. To explore the potential impact of studio work on engineering student perceptions, we collected data from multiple sources, including field notes, participant observations, outcomes of the group projects, and participants' responses to studio work during the workshop. In particular, to provide educational implications, our review focused on the findings from post-project surveys collected through online questionnaires and in-person interviews. In order to make suggestions on art courses that are specifically designed to cultivate engineering students' perceptions of the environment, we carried out online surveys based on the New Ecological Paradigm (NEP) scale. The results of the NEP-based surveys indicated that engineering students' anti-anthropocentrism, rejection of human exceptionalism, and acknowledgement of the possibility of an eco-crisis were significantly correlated with a belief in public welfare. By comparison, respondents' stronger public welfare beliefs were not associated with beliefs in limits to growth and the fragility of nature's balance. This study responds to today's complex socio-environmental issues by contributing to the discussion about the need to integrate interdisciplinary approaches into engineering education on environmental sustainability.
Maple Leaf Inspired Conductive Fiber with Hierarchical Wrinkles for Highly Stretchable and Integratable Electronics

Stretchable and durable conductors are significant to the development of wearable devices, robots, human-machine interfaces, and other artificial intelligence products. However, the desirable strain-insensitive conductivity and low hysteresis are restricted by the failure of stretchable structures and mismatch of mechanical properties (rigid conductive layer and elastic core substrate) under large deformation. Here, based on the principles of fractal geometry, a stretchable conductive fiber with hierarchical wrinkles inspired by the unique shape of the maple leaf was fabricated by combining surface modification, interfacial polymerization, and improved prestrain finishing methods to break through this dilemma. The shape and size of wrinkles predicted by buckling analysis via the finite element method fit well with that of actual wrinkles (30-80 μm of macro wrinkles and 4-6 μm of micro wrinkles) on the fabricated fiber. Such hierarchically wrinkled conductive fiber (HWCF) exhibited not only excellent strain-insensitive conductivity denoted by the relative resistance change ΔR/R0 = 0.66 with R0 the original resistance and ΔR the change of resistance after the concrete strain reaching up to 600%, but also low hysteresis (0.04) calculated by the difference in area between stretching and releasing curve of the ΔR/R0 strain under 300% strain and long-term durability (>1000 stretching-releasing cycles). Furthermore, the elastic conductive fiber with such a bionic structure design can be applied as highly stretchable electrical circuits for illumination and monitors for the human motion under large strains through tiny and rapid resistance changes as well. Such a smart biomimetic material holds great prospects in the field of stretchable electronics.
Spatio Temporal Sparsity in Homicide Prediction Models

Homicide prediction is a challenging task due to the spatio-temporal sparsity of these crime events. In this paper we report the results of using several approaches to mitigate this sparsity condition in machine learning models specially tailored towards modeling homicides events. Since spatial resolution is a direct determinant of sparsity, we focus on the performance of these models across different resolutions of interest to police authorities. We use a simple count model as benchmark and propose some enhancements of it directed towards improving prediction performance. We then compare the results to more complex models motivated by manifold learning and graph signal processing methods. We found that the simple benchmark models are as good as state of the art models for low resolution, but, as resolution increases, the performance of machine learning models outperform the benchmark. These results provide a rationality for the use of state of the art machine learning models for homicide prediction at the high resolution of interest for the deployment of police resources.