ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning

The primary objective of this paper is to build classification models and strategies to identify breathing sound anomalies (wheeze, crackle) for automated diagnosis of respiratory and pulmonary diseases. In this work we propose a deep CNN-RNN model that classifies respiratory sounds based on Mel-spectrograms. We also implement a patient specific model tuning strategy that first screens respiratory patients and then builds patient specific classification models using limited patient data for reliable anomaly detection. Moreover, we devise a local log quantization strategy for model weights to reduce the memory footprint for deployment in memory constrained systems such as wearable devices. The proposed hybrid CNN-RNN model achieves a score of 66.31% on four-class classification of breathing cycles for ICBHI'17 scientific challenge respiratory sound database. When the model is re-trained with patient specific data, it produces a score of 71.81% for leave-one-out validation. The proposed weight quantization technique achieves approximate to 4x reduction in total memory cost without loss of performance. The main contribution of the paper is as follows: Firstly, the proposed model is able to achieve state of the art score on the ICBHI'17 dataset. Secondly, deep learning models are shown to successfully learn domain specific knowledge when pre-trained with breathing data and produce significantly superior performance compared to generalized models. Finally, local log quantization of trained weights is shown to be able to reduce the memory requirement significantly. This type of patient-specific re-training strategy can be very useful in developing reliable long-term automated patient monitoring systems particularly in wearable healthcare solutions.
Noise Reduction in Small-Animal PET Images Using a Multiresolution Transform

In this paper, we address the problem of denoising reconstructed small animal positron emission tomography (PET) images, based on a multiresolution approach which can be implemented with any transform such as contourlet, shearlet, curvelet, and wavelet. The PET images are analyzed and processed in the transform domain by modeling each subband as a set of different regions separated by boundaries. Homogeneous and heterogeneous regions are considered. Each region is independently processed using different filters: a linear estimator for homogeneous regions and a surface polynomial estimator for the heterogeneous region. The boundaries between the different regions are estimated using a modified edge focusing filter. The proposed approach was validated by a series of experiments. Our method achieved an overall reduction of up to 26% in the % STD of the reconstructed image of a small animal NEMA phantom. Additionally, a test on a simulated lesion showed that our method yields better contrast preservation than other state-of-the art techniques used for noise reduction. Thus, the proposed method provides a significant reduction of noise while at the same time preserving contrast and important structures such as lesions.
Double MAC on a DSP: Boosting the Performance of Convolutional Neural Networks on FPGAs

Deep learning workloads, such as convolutional neural networks (CNNs) are important due to increasingly demanding high-performance hardware acceleration. One distinguishing feature of a deep learning workload is that it is inherently resilient to small numerical errors and thus works very well with low precision hardware. We propose a novel method called double multiply-and-accumulate (MAC) to theoretically double the computation rate of CNN accelerators by packing two MAC operations into one digital signal processing block of off-the-shelf field-programmable gate arrays (FPGAs). We overcame several technical challenges by exploiting the mode of operation in the CNN accelerator. We have validated our method through FPGA synthesis and Verilog simulation, and evaluated our method by applying it to the state-of-the-art CNN accelerator. The double MAC approach used can double the computation throughput of a CNN layer. On the network level (all convolution layers combined), the performance improvement varies depending on the CNN application and FPGA size, from 14% to more than 80% over a highly optimized state-of-the-art accelerator solution, without sacrificing the output quality significantly.
Mapping Large LSTMs to FPGAs with Weight Reuse

Long-Short Term Memory (LSTM) can retain memory and learn from data sequences. It gives state-of-the-art accuracy in many applications such as speech recognition, natural language processing and video classifications. Field-Programmable Gate Arrays (FPGAs) have been used to speed up the inference of LSTMs, but FPGA-based LSTM accelerators are limited by the size of on-chip memory and the bandwidth of external memory on FPGA boards. We propose a novel hardware architecture to overcome data dependency and a new blocking-batching strategy to reuse the LSTM weights fetched from external memory to optimize the performance of systems with size-limited on-chip memory for large machine learning models. Evaluation results show that our architecture can achieve 20.8 GOPS/W, which is among the highest for the FPGA-based LSTM designs storing weights in off-chip memory. Our design achieves 1.65 times higher performance-per-watt efficiency and 2.48 times higher performance-per-DSP efficiency when compared with the current state-of-the-art designs of LSTM using weights stored in off-chip memory. Compared with CPU and GPU implementations, our FPGA implementation is 23.7 and 1.3 times faster while consuming 208 and 19.2 times lower energy respectively, which shows that our approach enables large LSTM systems to be processed efficiently on FPGAs with high performance and low power consumption.
Semi-Supervised Unmixing of Hyperspectral Data via Spectral-Spatial Factorization

Due to the limited spatial resolution of hyperspectral sensors, each pixel in hyperspectral image often consists of several components, called endmembers. Hyperspectral unmixing aims at extracting these endmembers and corresponding fractional abundances from the hyperspectral image (HSI) data. With the availability of spectral libraries, semi-supervised unmixing which estimates the abundance from given endmember matrix, have become more and more popular. General semi-supervised methods take advantage of the sparsity constraint on the abundance matrix and consider the pixels as independent trials. However, the spatial information for example the correlation between pixels often cannot be taken into consideration. In this paper, we derive a semi-supervised hyperspectral image unmixing algorithm which handles both spectral and spatial prior efficiently using matrix factorization. The abundance matrix is recast as a multiplication of two variables, in which the spectral and spatial priors are captured respectively. Numerical tests in both simulated and real datasets show that compared to state-of-the-art unmixing algorithms, the proposed spectral-spatial factorization method has lower computation cost, better unmixing results, and is more robust to regularization parameter selection.
Modern Details in Meaningful Architecture

Architecture is the art of shaping space, system, and technology. A close relationship is established between the building and its user, as the facility provides shelter and communicates with the inhabitants by meanings encoded in the form. The reception of architecture occurs through an ideological narrative and the quality of construction and material solutions. Contemporary pro-environmental postulates exert an increasingly clear influence on how architecture is shaped, especially on its aesthetic and semantic solutions. In this context, the article refers to the interdependence between art and technology in shaping the architecture of meanings through detail. The work aims to expand qualitative research on shaping contemporary detail in the context of pro-environmental trends in architecture. The detail was selected based on its clear message-its meaning provides the leading feature of the structure, both in technical (engineering solutions) and semantic (narrative) terms. The article provides an attempt to answer the question of how a semantic detail should be shaped, with the account to contemporary concepts on sustainable development architecture. A synthetic-comparative methodology was adopted; specific groups of completed objects were analyzed in the context of the indicated topics. The conclusions from this part of the work constitute case study guidelines, which was conducted on the example of an original project.
Contrast-Attentive Thoracic Disease Recognition With Dual-Weighting Graph Reasoning

Automatic thoracic disease diagnosis is a rising research topic in the medical imaging community, with many potential applications. However, the inconsistent appearances and high complexities of various lesions in chest X-rays currently hinder the development of a reliable and robust intelligent diagnosis system. Attending to the high-probability abnormal regions and exploiting the priori of a related knowledge graph offers one promising route to addressing these issues. As such, in this paper, we propose two contrastive abnormal attention models and a dual-weighting graph convolution to improve the performance of thoracic multi-disease recognition. First, a left-right lung contrastive network is designed to learn intra-attentive abnormal features to better identify the most common thoracic diseases, whose lesions rarely appear in both sides symmetrically. Moreover, an inter-contrastive abnormal attention model aims to compare the query scan with multiple anchor scans without lesions to compute the abnormal attention map. Once the intra- and inter-contrastive attentions are weighted over the features, in addition to the basic visual spatial convolution, a chest radiology graph is constructed for dual-weighting graph reasoning. Extensive experiments on the public NIH ChestX-ray and CheXpert datasets show that our model achieves consistent improvements over the state-of-the-art methods both on thoracic disease identification and localization.
Trastuzumab-based near-infrared photoimmunotherapy in xenograft mouse of breast cancer

Near-infrared photoimmunotherapy (NIR-PIT) is a novel form of cancer treatment using conjugates of antibody against overexpressed antigens in cancers and photoabsorber IRDye700DX. HER2 is overexpressed in various cancers, for which molecular targeted therapy such as trastuzumab has been developed. The present study investigated the efficacy potential of HER2-targeted NIR-PIT using trastuzumab-IRDye700DX conjugate (Tra-IR700) in HER2-positive breast cancer. We first examined the reactivity of Tra-IR700 and the cytotoxicity of NIR-PIT in vitro. HER2-positive BT-474 and SK-BR-3 cells and HER2-negative BT-20 cells were used. Tra-IR700 fluorescence was only observed in HER2-positive breast cancer cell lines, and the fluorescence was localized to the cell surface. Furthermore, HER2-positive breast cancer cell lines treated with NIR-PIT showed swelling and blebbing shortly after irradiation, and eventually increased PI-positive dead cells. Next, tumor accumulation of Tra-IR700 and tumor damage by NIR-PIT were examined in vivo. Tra-IR700 was administered intravenously to a xenograft model in which BT-474 cells were implanted subcutaneously in BALB/c nude mice. Tra-IR700 fluorescence was the highest in tumor tissue 1 day after administration, and the fluorescence was localized to the cell membrane of tumor cells. At this time point, NIR-PIT resulted in diffuse necrosis of tumor tissues 1 day after irradiation. These results suggest that NIR-PIT with Tra-IR700 induces a highly selective therapeutic effect in a HER2-positive breast cancer model. NIR-PIT using Tra-IR700 is expected to be a novel treatment for HER2-positive cancers, including breast cancer.
Improving Detection Accuracy of Memristor-Based Bio-Signal Sensing Platform

Recently a novel neuronal activity sensor exploiting the intrinsic thresholded integrator capabilities of memristor devices has been proposed. Extracellular potentials captured by a standard bio-signal acquisition platform are fed into a memristive device which reacts to the input by changing its resistive state (RS) only when the signal ampitude exceeds a threshold. Thus, significant peaks in the neural signal can be stored as non-volatile changes in memristor resistive state whilst noise is effectively suppressed. However, as a memristor is subjected to increasing numbers of supra-threshold stimuli during practical operation, it accumulates (RS) changes and eventually saturates. This leads to severely reduced neural activity detection capabilities. In this work we explore different signal processing and memristor operating procedure strategies in order to improve the detection rate of significant neuronal activity events. We analyse the data obtained from a single-memristive device biased with a reference neural recording and observe that performance can be improved markedly by a) increasing the frequency at which the memristor is reset to an initial resistive state where it is known to be highly responsive, b) appropriately preconditioning the input waveform through application of gain and offset in order to optimally exploit the intrinsic device behaviour. All results are validated by benchmarking obtained spike detection performance against a state-of-the-art template matching system utilising computationally-heavy, multi-dimensional, principal component analysis.
The use of ketamine as an induction agent for anesthesia in pulmonary thromboendarterectomy surgery: A case series

Pulmonary thromboendarterectomy (PTE) surgery is the treatment of choice for patients with chronic thromboembolic pulmonary hypertension (CTEPH). The induction of anesthesia in patients with severe pulmonary hypertension (PHT) can be challenging, with a risk of cardiovascular collapse. The administration of ketamine in patients with PHT is controversial, with some recommendations contraindicating its use. However, ketamine has been used safely in children with severe PHT. We present a retrospective case series of adult patients with severe PHT presenting for PTE surgery, using intravenous ketamine as a co-induction anesthetic agent.
A novel cost effective demosaicing approach

A unique color interpolation approach for digital still cameras is introduced and described in terms of color vectors. A new difference plane model is introduced and used in conjunction with the proposed here correction process. This avoids edge blurring while improving on the color appearance of previous methods. Experimental results indicate that the proposed method exhibits superior performance over state of-the-art color interpolation methods(1).
Microplastics in the first-year sea ice of the Novik Bay, Sea of Japan

Sea ice is heavily contaminated with microplastics particles (MPs, <5 mm). First-year sea ice cores (38-41 cm thick) were taken in the beginning of spring in a narrow populated bay of the Sea of Japan. Two ice cores were examined (layer-by-layer, excluding surface) for MPs content: one using μ-FTIR for 25-300 μm (SMPs), and another one - with visual+Raman identification for 300-5000 μm particles (LMPs). The integral (25-5000 μm) bulk mean abundance of MPs was found to be 428 items/L of meltwater, with fibers making 19 % in SMPs size range and 59 % in LMPs. Integral mean mass of MPs was estimated in 34.6 mg/L, with 99.6 % contribution from fragments of LMPs. Comparison with simple fragmentation models confirms deficit of SMPs (especially of fibers in size range 150-300 μm), suggested to result from their leakage with brine. Multivariate statistical analysis indicates strong positive correlation of large fiber (>300 μm) counts and ice salinity.
A comparison of methods for sketch-based 3D shape retrieval

Sketch-based 3D shape retrieval has become an important research topic in content-based 3D object retrieval. To foster this research area, two Shape Retrieval Contest (SHREC) tracks on this topic have been organized by us in 2012 and 2013 based on a small-scale and large-scale benchmarks, respectively. Six and five (nine in total) distinct sketch-based 3D shape retrieval method have competed each other in these two contests, respectively. To measure and compare the performance of the top participating and other existing promising sketch-based 3D shape retrieval methods and solicit the state-of-the-art approaches, we perform a more comprehensive comparison of fifteen best (four top participating algorithms and eleven additional state-of-the-art methods) retrieval methods by completing the evaluation of each method on both benchmarks. The benchmarks, results, and evaluation tools for the two tracks are publicly available on our websites [1,2]. (C) 2013 Elsevier Ltd. All rights reserved.
AmbiKraf

This paper presents, AmbiKraf, a non-emissive fabric display that subtly animates patterns on common fabrics. We use thermochromic inks and peltier semiconductor elements to achieve this technology. With this technology we have produced numerous prototypes from animated wall paintings to pixilated fabric displays. The ability of this technology to subtly and ubiquitously change the color of the fabric itself has made us able to merge different fields and technologies with AmbiKraf. In addition, with an animated room divider screen, Ambikraf merged its technology with Japanese Byobu art to tighten the gap between traditional arts and contemporary technologies. Through this AmbiKraf Byobu art installation and other installations, we discuss the impact of this technology as a ubiquitous fabric display. With focus to improvements of some limitations of the existing system, we present our future vision that enables us to merge this technology into more applications fields thus making this technology a platform for ubiquitous interactions on our daily peripherals.
Patient-Centered Discussions About Disease Progression, Symptom, and Treatment Burden in Chronic Obstructive Pulmonary Disease Could Facilitate the Integration of End-of-Life Discussions in the Disease Trajectory: Patient, Clinician, and Literature Perspectives: A Multimethod Approach

Background: Patients with chronic obstructive pulmonary disease (COPD) seldom discuss preferences for future care/treatments with clinicians. The lack of discussions prevents the delivery of care grounded on patient preferences. Instead, treatments become increasingly burdensome as disease progresses and patients approach the end of life. Objective: Identify current and best practice in initiating and conducting conversations about future and palliative care, by integrating data from multiple sources. Design: Multiphasic study where the findings of a systematic literature review and qualitative interviews were combined and synthesized using a triangulation protocol. Setting/Participants: Thirty-three patients with COPD and 14 clinicians from multiple backgrounds were recruited in the United Kingdom. Results: Clinicians' and patients' poor understanding about palliative care and COPD, difficulties in timing and initiating discussions, and service rationing were the main factors for late discussions. Divergent perspectives between patients and clinicians about palliative care discussions often prevented their start. Instead, early and gradual patient-centered discussions on treatment choices, symptom, and treatment burden were recommended by patients, clinicians, and the literature. Earlier patient-centered discussions may reduce their emotional impact and enable patients to participate fully, while enabling clinicians to provide timely and accurate information on illness progression and appropriate self-management techniques. Conclusion: Current approaches toward palliative care discussions in COPD do not guarantee that patients' preferences are met. Early and gradual patient-centered discussions may enable patients to fully express their care preferences as they evolve over time, while minimizing the impact of symptom and treatment burden.
Threshold temperature scaling: Heuristic to address temperature and power issues in MPSoCs

In this article, we propose a scheduling based temperature and power-aware heuristic for multi-core systems. Instead of using a fixed value of temperature threshold in load balancing techniques, the proposed heuristic suggests adjusting the value of threshold temperature as a function of the workload. As the workload reduces, the value of the threshold is lowered accordingly and vice versa. Lowering the value of threshold temperature decreases the temperature peaks, temperature spatial, and temporal gradients. Furthermore, it also reduces the total power consumed by the processing unit. The effectiveness of the approach is evaluated in a simulation-based environment that includes a scheduling and a thermal modeling tool. For evaluation, the heuristic is integrated with a state of the art load balancing technique based on global scheduling and a comparison is performed with the popular thermal-aware techniques. The results analysis shows that the use of proposed heuristic lowers the temperature peak by up to 7 degrees C, temperature spatial gradients by up to 35%, temporal gradient by 65%, and power utilization by up to 5.5% when compared with the state-of-the-art thermal balancing techniques and predictive thermalaware models. (c) 2020 Elsevier B.V. All rights reserved.
The economics of CCS: Why have CCS technologies not had an international breakthrough?

Eleven years on since the United Nations' Intergovernmental Panel on Climate Change was awarded the Nobel Peace Prize in recognition of its efforts in combating climate change, fossil fuels remain the most dominant global energy source. As the total replacement of fossil fuel energy is not expected to take place immediately in the near future, the International Energy Agency has repeatedly declared carbon capture and sequestration (CCS) as a key technology for mitigating climate change. However, CCS lacks the scale required for substantial reduction in carbon dioxide emissions from fossil fuel power generation. Even though CCS is one of the key technologies for mitigating climate change, why has this technology not had an international breakthrough? To shed light on this question, this paper employs a simple model of energy generation, scrutinizes the economic drivers of CCS based on the analytical results, and discusses the possible obstacles that can prevent a widespread rollout of the technology. This is followed by a state-of-the-art in literature pertaining to the economics of CCS, and a discussion that points to a dichotomy between the economic theory and reality. The study concludes with some policy suggestions and directions for future research.
Methods for Simultaneous Robot-World-Hand-Eye Calibration: A Comparative Study

In this paper, we propose two novel methods for robot-world-hand-eye calibration and provide a comparative analysis against six state-of-the-art methods. We examine the calibration problem from two alternative geometrical interpretations, called hand-eye' and robot-world-hand-eye', respectively. The study analyses the effects of specifying the objective function as pose error or reprojection error minimization problem. We provide three real and three simulated datasets with rendered images as part of the study. In addition, we propose a robotic arm error modeling approach to be used along with the simulated datasets for generating a realistic response. The tests on simulated data are performed in both ideal cases and with pseudo-realistic robotic arm pose and visual noise. Our methods show significant improvement and robustness on many metrics in various scenarios compared to state-of-the-art methods.