ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
ARTS: Automotive Repository of Traffic Signs for the United States

Traffic signs recognition (TSR) is a crucial sub-domain of computer vision, particularly relevant to autonomous vehicles and autonomous driver-assistance systems (ADAS). TSR systems can further augment efforts in many other applications, such as highway asset maintenance and management. Despite the relative success of detectors with hand-crafted features in Europe, most, if not all, non-deep-learning based systems are not scalable to accurately recognize a large subset of traffic signs in the United States. The recent works in the domain of object detection using machine learning have shown the necessity of deep neural networks (DNNs) in TSR, whereby a DNN can learn features automatically without hand-crafting them. Due to the lack of datasets in the U.S. and the inefficient use of traditional methods for traffic sign recognition (TSR) in the U.S., we created the Automotive Repository of Traffic Signs (ARTS), a new dataset for traffic signs recognition in the U.S. ARTS covers a wide range of sign-types, including Regulatory, Guide, Warning, and Temporary signs as defined in the Manual on Uniform Traffic Control Devices (MUTCD). It also features geospatial data to localize signs using their GPS coordinates. Benchmarks are presented to assess the performance of state-of-the-art deep learning based detectors.
Vision models fine-tuned by cinema professionals for High Dynamic Range imaging in movies

Many challenges that deal with processing of HDR material remain very much open for the film industry, whose extremely demanding quality standards are not met by existing automatic methods. Therefore, when dealing with HDR content, substantial work by very skilled technicians has to be carried out at every step of the movie production chain. Based on recent findings and models from vision science, we propose in this work effective tone mapping and inverse tone mapping algorithms for production, post-production and exhibition. These methods are automatic and real-time, and they have been both fine-tuned and validated by cinema professionals, with psychophysical tests demonstrating that the proposed algorithms outperform both the academic and industrial state-of-the-art. We believe these methods bring the field closer to having fully automated solutions for important challenges for the cinema industry that are currently solved manually or sub-optimally. Another contribution of our research is to highlight the limitations of existing image quality metrics when applied to the tone mapping problem, as none of them, including two state-of-the-art deep learning metrics for image perception, are able to predict the preferences of the observers.
Bayesian Deep-Learning-Based Health Prognostics Toward Prognostics Uncertainty

Deep-learning-based health prognostics is receiving ever-increasing attention. Most existing methods leverage advanced neural networks for prognostics performance improvement, providing mainly point estimates as prognostics results without addressing prognostics uncertainty. However, uncertainty is critical for both health prognostics and subsequent decision making, especially for safety-critical applications. Inspired by the idea of Bayesian machine learning, a Bayesian deep-learning-based (BDL-based) method is proposed in this paper for health prognostics with uncertainty quantification. State-of-the-art deep learning models are extended into Bayesian neural networks (BNNs), and a variational-inference-based method is presented for the BNNs learning and inference. The proposed method is validated through a ball bearing dataset and a turbofan engine dataset. Other than point estimates, health prognostics using the BDL-based method is enhanced with uncertainty quantification. Scalability and generalization ability of state-of-the-art deep learning models can be well inherited. Stochastic regularization techniques, widely available in mainstream software libraries, can be leveraged to efficiently implement the BDL-based method for practical applications.
Carbon-negative synthetic biology: challenges and emerging trends of cyanobacterial technology

Global warming and climate instability have spurred interest in using renewable carbon resources for the sustainable production of chemicals. Cyanobacteria are ideal cellular factories for carbon-negative production of chemicals owing to their great potentials for directly utilizing light and CO2 as sole energy and carbon sources, respectively. However, several challenges in adapting cyanobacterial technology to industry, such as low productivity, poor tolerance, and product harvesting difficulty, remain. Synthetic biology may finally address these challenges. Here, we summarize recent advances in the production of value-added chemicals using cyanobacterial cell factories, particularly in carbon-negative synthetic biology and emerging trends in cyanobacterial applications. We also propose several perspectives on the future development of cyanobacterial technology for commercialization.
Self-Taught Feature Learning for Hyperspectral Image Classification

In this paper, we study self-taught learning for hyperspectral image (HSI) classification. Supervised deep learning methods are currently state of the art for many machine learning problems, but these methods require large quantities of labeled data to be effective. Unfortunately, existing labeled HSI benchmarks are too small to directly train a deep supervised network. Alternatively, we used self-taught learning, which is an unsupervised method to learn feature extracting frameworks from unlabeled hyperspectral imagery. These models learn how to extract generalizable features by training on sufficiently large quantities of unlabeled data that are distinct from the target data set. Once trained, these models can extract features from smaller labeled target data sets. We studied two self-taught learning frameworks for HSI classification. The first is a shallow approach that uses independent component analysis and the second is a three-layer stacked convolutional autoencoder. Our models are applied to the Indian Pines, Salinas Valley, and Pavia University data sets, which were captured by two separate sensors at different altitudes. Despite large variation in scene type, our algorithms achieve state-of-the-art results across all the three data sets.
Perfusion of microvascular free flaps in head and neck reconstruction after prior neck dissection and irradiation

Microvascular free flaps are frequently used for head and neck reconstruction after prior neck dissection (ND) and neck irradiation (RTX). The aim of this study was to investigate the influence of ND and RTX on flap perfusion as a critical factor for flap success. Overall, 392 patients reconstructed with a microvascular fasciocutaneous flap (FF) or perforator flap (PF) in the head and neck region between 2011 and 2020 were analysed retrospectively. Flap perfusion measured intraoperatively and postoperatively with the O2C tissue oxygen analysis system was compared between patients who had received neither ND nor RTX (controls), patients who had received ND but no RTX (ND group), and patients who had received both ND and RTX (ND+RTX group). Intraoperative and postoperative flap blood flow was decreased in FFs in ND group patients compared to controls (median 66.3 AU vs 86.0 AU, P = 0.023; median 73.5 AU vs 93.8 AU, P = 0.045, respectively). In the multivariable analysis, these differences showed a tendency to persist (P = 0.052 and P = 0.056). Flap success rates were similar in control patients, ND patients, and ND+RTX patients (98.7%, 94.0%, and 97.6%, respectively). Flap perfusion is not reduced in FFs and PFs in patients who have undergone ND or ND and RTX. This indicates that neck dissection and neck irradiation should not be contraindications for microvascular free flap reconstruction.
A Triple-Loop Inductive Power Transmission System for Biomedical Applications

A triple-loop wireless power transmission (WPT) system equipped with closed-loop global power control, adaptive transmitter (Tx) resonance compensation (TRC), and automatic receiver (Rx) resonance tuning (ART) is presented. This system not only opposes coupling and load variations but also compensates for changes in the environment surrounding the inductive link to enhance power transfer efficiency (PTE) in applications such as implantable medical devices (IMDs). The Tx was built around a commercial off-the-shelf (COTS) radio-frequency identification (RFID) reader, operating at 13.56 MHz. A local Tx loop finds the optimal capacitance in parallel with the Tx coil by adjusting a varactor. A global power control loop maintains the received power at a desired level in the presence of changes in coupling distance, coil misalignments, and loading. Moreover, a local Rx loop is implemented inside a power management integrated circuit (PMIC) to avoid PTE degradation due to the Rx coil surrounding environment and process variations. The PMIC was fabricated in a 0.35-pm 4M2P standard CMOS process with 2.54 mm(2) active area. Measurement results show that the proposed triple-loop system improves the overall PTE by up to 10.5% and 4.7% compared to a similar open- and single closed-loop system, respectively, at nominal coil distance of 2 cm. The added TRC and ART loops contribute 2.3% and 1.4% to the overall PTE of 13.5%, respectively. This is the first WPT system to include three loops to dynamically compensate for environment and circuit variations and improve the overall power efficiency all the way from the driver output in Tx to the load in Rx.
MulNet: A Flexible CNN Processor With Higher Resource Utilization Efficiency for Constrained Devices

Leveraging deep convolutional neural networks (DCNNs) for various application areas has become a recent inclination of many machine learning practitioners due to their impressive performance. Research trends show that the state-of-the-art networks are getting deeper and deeper and such networks have shown significant performance increase. Deeper and larger neural networks imply the increase in computational intensity and memory footprint. This is particularly a problem for inference-based applications on resource constrained computing platforms. On the other hand, field-programmable gate arrays (FPGAs) are becoming a promising choice in giving hardware solutions for most deep learning implementations due to their high-performance and low-power features. With the rapid formation of various state-of-the-art CNN architectures, a flexible CNN hardware processor that can handle different CNN architectures and yet customize itself to achieve higher resource efficiency and optimum performance is critically important. In this paper, a novel and highly flexible DCNN processor, MulNet, is proposed. MulNet can be used to process most regular state-of-the-art CNN variants aiming at maximizing resource utilization of a target device. A processing core with multiplier and without multiplier is employed to achieve that. We formulated optimum fixed-point quantization format for MulNet by analyzing layer-by-layer quantization error. We also created a power-of-2 quantization for multiplier-free (MF) processing core of MulNet. Both quantizations significantly reduced the memory space needed and the logic consumption in the target device. We utilized Xilinx Zynq SoCs to leverage the one die hybrid (CPU and FPGA) architecture. We devised a scheme that utilizes Zynq processing system (PS) for memory intensive layers and the Zynq programmable logic (PL) for computationally intensive layers. We implemented modified LeNet, CIFAR-10 full, ConvNet processor (CNP), MPCNN, and AlexNet to evaluate MulNet. Our architecture with MF processing cores shows the promising result, by saving 36%-72% on-chip memory and 10%-44% DSP48 IPs, compared to the architecture with cores implemented using the multiplier. Comparison with the state of the art showed a very promising 25-40x DSP48 and 25-29x on-chip memory reduction with up to 136.9-GOP/s performance and 88.49-GOP/s/W power efficiency. Hence, our results demonstrate that the proposed architecture can be very expedient for resource constrained devices.
Assessing expert system-assisted literature reviews with a case study

Given the large numbers of publications in software engineering, frequent literature reviews are required to keep current on work in specific areas. One tedious work in literature reviews is to find relevant studies amongst thousands of non-relevant search results. In theory, expert systems can assist in finding relevant work but those systems have primarily been tested in simulations rather than in application to actual literature reviews. Hence, few researchers have faith in such expert systems. Accordingly, using a realistic case study, this paper assesses how well our state-of-the-art expert system can help with literature reviews. The assessed literature review aimed at identifying test case prioritization techniques for automated UI testing, specifically from 8,349 papers on IEEE Xplore. This corpus was studied with an expert system that incorporates an incrementally updated human-in-the-loop active learning tool. Using that expert system, in three hours, we found 242 relevant papers from which we identified 12 techniques representing the stateof-the-art in test case prioritization when source code information is not available. These results were then validated by six other graduate students manually exploring the same corpus. Without the expert system, this task would have required 53 h and would have found 27 additional papers. That is, our expert system achieved 90% recall with 6% of the human effort cost when compared to a conventional manual method. Significantly, the same 12 state-of-the-art test case prioritization techniques were identified by both the expert system and the manual method. That is, the 27 papers missed by the expert system would not have changed the conclusion of the literature review. Hence, if this result generalizes, it endorses the use of our expert system to assist in literature reviews.
Compressed Collective Sparse-Sketch for Distributed Data-Parallel Training of Deep Learning Models

Distributed data-parallel training (DDP) is prevalent in large-scale deep learning. To increase the training throughput and scalability, high-performance collective communication methods such as AllReduce have recently proliferated for DDP use. However, these approaches require long communication periods with increasing model sizes. Collective communication transmits many sparse gradient values that can be efficiently compressed to reduce the required training time. State-of-the-art compression approaches do not provide mergeable compression for AllReduce and lack convergence bounds. We present a sparse sketch reducer (S2Reducer), a sparsity-preserving sketch based collective communication method. S2Reducer preserves gradient sparsity and reduces communication costs via a bitmap informed count sketch structure and adapts to efficient AllReduce operators. We tune the count sketch organization to minimize the hash conflicts in a fixed-size budget. We prove that our method has the same convergence rate as vanilla data-parallel training and a much smaller communication overhead than those of state-of-the-art methods. We implement a GPU-accelerated S2Reducer for the Ring All-Reduce-based DDP system. We perform extensive evaluations against four state-of-the-art methods across seven deep learning models. Our results show that S2Reducer converges to the same accuracy as that of state-of-the-art approaches while reducing the sparse communication overhead by up to 86% and achieving a speedup of up to 3.5x in distributed training.
Critical Current Distributions of Recent Bi-2212 Round Wires

Bi2Sr2CaCu2O8+x (Bi-2212) is the only high-field, high-temperature superconductor (HTS) capable of reaching a critical current density J(c) (16T, 4.2 K) of 6500A center dot mm(-2) in the highly desirable round wire (RW) form. However, state-of-the-art Bi-2212 conductors still have a critical current density (J(c)) to depairing current density (J(d)) ratio around 20 to 30 times lower than that of state-of-the-art Nb-Ti or REBCO. Previously, we have shown that recent improvements in Bi-2212 RW Jc are due to improved connectivity associated with optimization of the heat treatment process, and most recently due to a transition to a finer and more uniform powder manufactured by Engi-Mat. One quantitative measure of connectivity may be the critical current (I-c) distribution, since the local I-c in a wire can vary along the length due to variable vortex-microstructure interactions and to factors such as filament shape variations, grain-to-grain connectivity variations and blocking secondary phase distributions. Modeling the experimental V-I transition measured on a low resistance shunt as a complex sum of voltage contributions of individual filament and wire sub-sections allows a numerical extraction of the I-c distribution from the d(2)V/dI(2) treatment of the V-I curves. Here we compare similar to 0.1 m length I-c distributions of Bi-2212 RWs with recent state-of-the-art very high-J(c) Engi-Mat powder and lower J(c) and older Nexans granulate powder. We do find that the I-c spread for Bi-2212 wires is about twice the relative standard of high-J(c) Nb-Ti well below H-irr. We do not yet see any obvious contribution of the Bi-2212 anisotropy to the I-c distribution and are rather encouraged that these Bi-2212 round wires show relative I-c distributions not too far from high-J(c) Nb-Ti wires.
Clinical characteristics of steroid-responsive but dependent chronic graft-versus-host disease: a multicenter retrospective analysis

Chronic graft-versus-host disease (cGVHD) is a long-term complication of allogeneic hematopoietic stem cell transplantation. The clinical importance of long-term corticosteroid dependency in steroid-responsive cGVHD is undetermined. We retrospectively reviewed the data of 120 consecutive patients who received systemic steroid therapy for cGVHD between January 2007 and December 2018 at three institutions. Among patients with steroid-responsive cGVHD, those who successfully tapered off corticosteroids within 1 year were defined as the early withdrawal group (EW-cGVHD) and others were defined as the dependent group (Dp-cGVHD). Twenty-six patients were classified as EW-cGVHD and 55 as Dp-cGVHD. The proportion of men was significantly higher and performance status was significantly better in EW-cGVHD. The 5-year overall survival and cGVHD recurrence-free survival rates were significantly higher in EW-cGVHD than Dp-cGVHD (96% vs. 68%, p = 0.017 and 84% vs. 41%, p = 0.002, respectively). While the relapse-free survival rate did not differ significantly (84% vs. 65%, p = 0.15), the proportion of patients requiring readmission, mainly due to cGVHD recurrence or infection, was significantly increased in Dp-cGVHD (38% vs. 84%, p < 0.001). In summary, steroid dependency in cGVHD for more than 1 year was significantly associated with poor transplant outcomes.
Long noncoding RNA Pvt1 promotes the proliferation and migration of Schwann cells by sponging microRNA-214 and targeting c-Jun following peripheral nerve injury

Research has shown that long-chain noncoding RNAs (lncRNAs) are involved in the regulation of a variety of biological processes, including peripheral nerve regeneration, in part by acting as competing endogenous RNAs. c-Jun plays a key role in the repair of peripheral nerve injury. However, the precise underlying mechanism of c-Jun remains unclear. In this study, we performed microarray and bioinformatics analysis of mouse crush-injured sciatic nerves and found that the lncRNA Pvt1 was overexpressed in Schwann cells after peripheral nerve injury. Mechanistic studies revealed that Pvt1 increased c-Jun expression through sponging miRNA-214. We overexpressed Pvt1 in Schwann cells cultured in vitro and found that the proliferation and migration of Schwann cells were enhanced, and overexpression of miRNA-214 counteracted the effects of Pvt1 overexpression on Schwann cell proliferation and migration. We conducted in vivo analyses and injected Schwann cells overexpressing Pvt1 into injured sciatic nerves of mice. Schwann cells overexpressing Pvt1 enhanced the regeneration of injured sciatic nerves following peripheral nerve injury and the locomotor function of mice was improved. Our findings reveal the role of lncRNAs in the repair of peripheral nerve injury and highlight lncRNA Pvt1 as a novel potential treatment target for peripheral nerve injury.
Low-Rank Modeling of Local k-Space Neighborhoods (LORAKS) for Constrained MRI

Recent theoretical results on low-rank matrix reconstruction have inspired significant interest in low-rank modeling of MRI images. Existing approaches have focused on higher-dimensional scenarios with data available from multiple channels, timepoints, or image contrasts. The present work demonstrates that single-channel, single-contrast, single-timepoint k-space data can also be mapped to low-rank matrices when the image has limited spatial support or slowly varying phase. Based on this, we develop a novel and flexible framework for constrained image reconstruction that uses low-rank matrix modeling of local k-space neighborhoods (LORAKS). A new regularization penalty and corresponding algorithm for promoting low-rank are also introduced. The potential of LORAKS is demonstrated with simulated and experimental data for a range of denoising and sparse-sampling applications. LORAKS is also compared against state-of-the-art methods like homodyne reconstruction, l(1)-norm minimization, and total variation minimization, and is demonstrated to have distinct features and advantages. In addition, while calibration-based support and phase constraints are commonly used in existing methods, the LORAKS framework enables calibrationless use of these constraints.
Thermoresponsive Random Poly(ether urethanes) with Tailorable LCSTs for Anticancer Drug Delivery

A new class of thermoresponsive random polyurethanes is successfully synthesized and characterized. Poly(ethylene glycol) diol (Mn = 1500 Da) and 2,2-dimethylolpropionic acid are reacted with isophorone diisocyanate in the presence of methane sulfonic acid catalyst. It is found that these polyurethanes are thermoresponsive in aqueous media and manifest a lower critical solution temperature (LCST) that can be easily tuned from 30 °C to 70 °C by increasing the poly(ethylene glycol) content. Their sharp LCST transitions make these random polyurethanes ideal candidates for stimuli-responsive drug delivery applications. To that end, the ability of these systems to efficiently sequester doxorubicin (up to 36 wt%) by means of a sonication/dialysis method is successfully demonstrated. Additionally, it is also demonstrated that accelerated doxorubicin release kinetics from the nanoparticles can be attained above the LCST.