ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
THE CONTRIBUTION OF THE SOVIET "KINETIC" ART OF THE1960s-1970s TO THE DEVELOPMENT OF THE THEORY AND PRACTICE OF ARCHITECTURAL LIGHTING TECHNOLOGY

The paper analyses the original works in the field of architectural light and engineering belonging to the most prominent representatives of the domestic kinetic art of the 1960s (F. Infante, V.F. Koleichuk, L.N. Nusberg, as well as A. Lanin and B.M. Galeev). The conceptual basis of these works is revealed including a special attitude to light as a unique physical phenomenon and, at the same time, the most important component of a new architectural and artistic language. The practical value of the projects of special dynamic lighting of art objects, interiors of exhibition spaces, individual architectural objects and the urban environment as a whole, proposed by these authors and partly implemented by them, is revealed. It is shown in what way and for what purpose this type of synthetic art was formed at the intersection of architecture, design and engineering, which made a significant contribution to the development of both the theory and practice of architectural light engineering. In conclusion, the idea is expressed about the importance of using the considered historical experience at the present time, when every year new technical possibilities appear for creating an expressive light-colour environment, but deep content, philosophical and theoretical foundations for its design, which inspired the masters who stood at the origins of this the most important architectural and artistic direction, are lost.
CoW Bimetallic Carbide Nanocatalysts: Computational Exploration, Confined Disassembly-Assembly Synthesis and Alkaline/Seawater Hydrogen Evolution

Earth-abundant tungsten carbide exhibits potential hydrogen evolution reaction (HER) catalytic activity owing to its Pt-like d-band electronic structure, which, unfortunately, suffers from the relatively strong tungsten-hydrogen binding, deteriorating its HER performance. Herein, a catalyst design concept of incorporating late transition metal into early transition metal carbide is proposed for regulating the metal-H bonding strength and largely enhancing the HER performance, which is employed to synthesize CoW bi-metallic carbide Co6 W6 C by a "disassembly-assembly" approach in a confined environment. Such synthesized Co6 W6 C nanocatalyst features the optimal Gibbs free energy of *H intermediate and dissociation barrier energy of H2 O molecules as well by taking advantage of the electron complementary effect between Co and W species, which endows the electrocatalyst with excellent HER performance in both alkaline and seawater/alkaline electrolytes featuring especially low overpotentials, elevated current densities, and much-enhanced operation durability in comparison to commercial Pt/C catalyst. Moreover, a proof-of-concept Mg/seawater battery equipped with Co6 W6 C-2-600 as cathode offers a peak power density of 9.1 mW cm-2 and an open-circuit voltage of ≈1.71 V, concurrently realizing hydrogen production and electricity output.
Real-Time Self-Supervised Monocular Depth Estimation Without GPU

Single-image depth estimation represents a longstanding challenge in computer vision and although it is an ill-posed problem, deep learning enabled astonishing results leveraging both supervised and self-supervised training paradigms. State-of-the-art solutions achieve remarkably accurate depth estimation from a single image deploying huge deep architectures, requiring powerful dedicated hardware to run in a reasonable amount of time. This overly demanding complexity makes them unsuited for a broad category of applications requiring devices with constrained resources or memory consumption. To tackle this issue, in this paper a family of compact, yet effective CNNs for monocular depth estimation is proposed, by leveraging self-supervision from a binocular stereo rig. Our lightweight architectures, namely PyD-Net and PyD-Net2, compared to complex state-of-the-art trade a small drop in accuracy to drastically reduce the runtime and memory requirements by a factor ranging from 2x to 100x. Moreover, our networks can run real-time monocular depth estimation on a broad set of embedded or consumer devices, even not equipped with a GPU, by early stopping the inference with negligible (or no) loss in accuracy, making it ideally suited for real applications with strict constraints on hardware resources or power consumption.
Solving the robot-world hand-eye(s) calibration problem with iterative methods

Robot-world, hand-eye calibration is the problem of determining the transformation between the robot end-effector and a camera, as well as the transformation between the robot base and the world coordinate system. This relationship has been modeled as , where and are unknown homogeneous transformation matrices. The successful execution of many robot manipulation tasks depends on determining these matrices accurately, and we are particularly interested in the use of calibration for use in vision tasks. In this work, we describe a collection of methods consisting of two cost function classes, three different parameterizations of rotation components, and separable versus simultaneous formulations. We explore the behavior of this collection of methods on real datasets and simulated datasets and compare to seven other state-of-the-art methods. Our collection of methods returns greater accuracy on many metrics as compared to the state-of-the-art. The collection of methods is extended to the problem of robot-world hand-multiple-eye calibration, and results are shown with two and three cameras mounted on the same robot.
Soft-Output Successive Cancellation Stack Polar Decoder

Polar coding has been ratified for employment in the 3GPP New Radio standard and several soft-decision decoders achieved comparable performance to that of the state-of-the-art successive cancellation list decoder. Aiming for further improving the performance of the soft-decision polar decoders, we propose a soft-output successive cancellation stack (SSCS) polar decoder, which jointly exploits the benefits of the depth-first search of the stack decoder and the soft information output of the belief propagation decoder. This has the substantial benefit of facilitating soft-input soft-output (SISO) decoding and seamless iterative information exchange in turbo-style receivers. As a further contribution, we intrinsically amalgamate our SSCS decoder into polar-coded large-scale multiple-input multiple-output (MIMO) systems and conceive an iterative turbo receiver, operating on the basis of logarithmic likelihood ratios (LLRs). Our simulation results show that the proposed SSCS decoder is capable of outperforming the state-of-the-art SISO polar decoders, despite requiring a lower complexity at moderate to high signal-to-noise ratios (SNRs). Additionally, compared with the non-iterative hard-output SCS decoder, our SSCS scheme attained 1.5 dB SNR gain at a bit error ratio level of 10(-5), when decoding the [256,512] polar code of a (64 x 64) MIMO system.
Probability-based approach to rectilinear Steiner tree problems

The rectilinear Steiner tree (RST) problem is of essential importance to the automatic interconnect optimization for VLSI design. In this paper, we present a class of probability-based approaches toward the best solutions under statistical sense and show their performance in comparison with the state-of-the-art algorithm. Experiments conducted on both small- and large-size problems indicate that the proposed approaches lead to promising results in terms of wire length and/or CPU time. The potential advantages with our technique are also discussed for further applications.
MaD GUI: An Open-Source Python Package for Annotation and Analysis of Time-Series Data

Developing machine learning algorithms for time-series data often requires manual annotation of the data. To do so, graphical user interfaces (GUIs) are an important component. Existing Python packages for annotation and analysis of time-series data have been developed without addressing adaptability, usability, and user experience. Therefore, we developed a generic open-source Python package focusing on adaptability, usability, and user experience. The developed package, Machine Learning and Data Analytics (MaD) GUI, enables developers to rapidly create a GUI for their specific use case. Furthermore, MaD GUI enables domain experts without programming knowledge to annotate time-series data and apply algorithms to it. We conducted a small-scale study with participants from three international universities to test the adaptability of MaD GUI by developers and to test the user interface by clinicians as representatives of domain experts. MaD GUI saves up to 75% of time in contrast to using a state-of-the-art package. In line with this, subjective ratings regarding usability and user experience show that MaD GUI is preferred over a state-of-the-art package by developers and clinicians. MaD GUI reduces the effort of developers in creating GUIs for time-series analysis and offers similar usability and user experience for clinicians as a state-of-the-art package.
Rotation-reversal invariant HOG cascade for facial expression recognition

This paper presents a novel classification framework derived from AdaBoost to classify facial expressions. The proposed framework adopts rotation-reversal invariant HOG as features. The framework is implemented by configuring the area under receiver operating characteristic curve of the weak classifier with HOG, which is a discriminative classification framework. The proposed classification framework is evaluated with three very popular and representative public databases: CK+, MMI, and AFEW. The results showed that the proposed classification framework outperforms the state-of-the-art methods.
Conjugation of cysteamine functionalized nanodiamond to gold nanoparticles for pH enhanced colorimetric detection of Cr3+ ions demonstrated by real water sample analysis

A cysteamine functionalized nanodiamond (NDC) was conjugated to gold nanoparticles (AuNPs) to deliver NDC@AuNPs and utilized in enhanced colorimetric detection of Cr3+ at pH 6 environment. The conjugation was validated using FTIR, TEM, PXRD, DLS, and zeta potential investigations. At pH 6, superior sensory response of NDC@AuNPs to Cr3+ than that of other ions was validated by UV-vis spectroscopy and colorimetric photographs. Results from UV-vis titrations displayed a linear regression from 0.01 to 0.4 µM with a LOD of 0.236 ± 0.005 nM. The particle aggregation, size variations, potential changes, and binding modes are investigated using TEM, DLS, and FTIR techniques to explore the underlying mechanisms. By adding the EDTA, sensory response is reversible up to 4 cycles. Finally, spiked real water experiments show improved sensing of Cr3+ at pH 6 via the observed recovery between 96 and 110 %, which is in good agreement with the ICP-mass data.
EndoL2H: Deep Super-Resolution for Capsule Endoscopy

Although wireless capsule endoscopy is the preferred modality for diagnosis and assessment of small bowel diseases, the poor camera resolution is a substantial limitation for both subjective and automated diagnostics. Enhanced-resolution endoscopy has shown to improve adenoma detection rate for conventional endoscopy and is likely to do the same for capsule endoscopy. In this work, we propose and quantitatively validate a novel framework to learn a mapping from low-to-high-resolution endoscopic images. We combine conditional adversarial networks with a spatial attention block to improve the resolution by up to factors of 8x, 10x, 12x, respectively. Quantitative and qualitative studies demonstrate the superiority of EndoL2H over state-of-the-art deep super- resolution methods Deep Back-Projection Networks (DBPN), Deep Residual Channel Attention Networks (RCAN) and Super Resolution Generative Adversarial Network (SRGAN). Mean Opinion Score (MOS) testswere performedby 30 gastroenterologists qualitatively assess and confirm the clinical relevance of the approach. EndoL2H is generally applicable to any endoscopic capsule system and has the potential to improve diagnosis and better harness computational approaches for polyp detection and characterization. Our code and trained models are available at https:// github. com/ CapsuleEndoscope/ EndoL2H.
Dense 3D surface reconstruction of large-scale streetscape from vehicle-borne imagery and LiDAR

Accurate and efficient three-dimensional (3D) streetscape reconstruction is the fundamental ability for an exploration vehicle to navigate safely and perform high-level tasks. Recently, remarkable progress has been made in streetscape reconstruction with visual images and light detection and ranging (LiDAR), but they have difficulties either in scaling and reconstructing large-scale outdoors or in efficient processing. To address these issues, this paper proposed an automatic method for incremental dense reconstruction of large-scale 3D streetscapes from coarse to fine at near real time. Firstly, the pose of vehicle is estimated by visual and laser odometry (VLO) and the state-of-the-art pyramid stereo matching network (PSMNet) is introduced to estimate depth information. Then, incremental dense 3D streetscape reconstruction is conducted by key-frame selection and coarse registration with local optimization. Finally, redundant and noise points are removed through multiple filtering, resulting good quality of dense reconstruction. Comprehensive experiments were undertaken to check the visual effect, trajectory pose error and multi-scale model to model cloud comparison (M3C2) based on reference trajectories and reconstructions provided by the state-of-the-art method, showing the precision, recall and F-score of sampling core points (SCPs) are over 80.42%, 71.68% and 77.19%, respectively, which verified the proposed method.
VCSEL Technology for Green Optical Interconnects

State-of-the-art vertical-cavity surface-emitting laser (VCSEL) technology will be reviewed in terms of power consumption and reliability to realize the energy-efficient high-performance computing (HPC) and data centers. Intermediate wavelength (between short and long wavelength) VCSELs emitting at 1060 nm are promising candidates for the upcoming higher performance computing with energy saving.
Writer identification in handwritten musical scores with bags of notes

Writer Identification is an important task for the automatic processing of documents. However, the identification of the writer in graphical documents is still challenging. In this work, we adapt the Bag of Visual Words framework to the task of writer identification in handwritten musical scores. A vanilla implementation of this method already performs comparably to the state-of-the-art. Furthermore, we analyze the effect of two improvements of the representation: a Bhattacharyya embedding, which improves the results at virtually no extra cost, and a Fisher Vector representation that very significantly improves the results at the cost of a more complex and costly representation. Experimental evaluation shows results more than 20 points above the state-of-the-art in a new, challenging dataset. (C) 2012 Elsevier Ltd. All rights reserved.
Multimodal recognition of frustration during game-play with deep neural networks

Frustration, which is one aspect of the field of emotional recognition, is of particular interest to the video game industry as it provides information concerning each individual player's level of engagement. The use of non-invasive strategies to estimate this emotion is, therefore, a relevant line of research with a direct application to real-world scenarios. While several proposals regarding the performance of non-invasive frustration recognition can be found in literature, they usually rely on hand-crafted features and rarely exploit the potential inherent to the combination of different sources of information. This work, therefore, presents a new approach that automatically extracts meaningful descriptors from individual audio and video sources of information using Deep Neural Networks (DNN) in order to then combine them, with the objective of detecting frustration in Game-Play scenarios. More precisely, two fusion modalities, namely decision-level and feature-level, are presented and compared with state-of-the-art methods, along with different DNN architectures optimized for each type of data. Experiments performed with a real-world audiovisual benchmarking corpus revealed that the multimodal proposals introduced herein are more suitable than those of a unimodal nature, and that their performance also surpasses that of other state-of-the-art approaches, with error rate improvements of between 40% and 90%.
Sequential hybridization may have facilitated ecological transitions in the Southwestern pinyon pine syngameon

Multispecies interbreeding networks, or syngameons, have been increasingly reported in natural systems. However, the formation, structure, and maintenance of syngameons have received little attention. Through gene flow, syngameons can increase genetic diversity, facilitate the colonization of new environments, and contribute to hybrid speciation. In this study, we evaluated the history, patterns, and consequences of hybridization in a pinyon pine syngameon using morphological and genomic data to assess genetic structure, demographic history, and geographic and climatic data to determine niche differentiation. We demonstrated that Pinus edulis, a dominant species in the Southwestern US and a barometer of climate change, is a core participant in the syngameon, involved in the formation of two drought-adapted hybrid lineages including the parapatric and taxonomically controversial fallax-type. We found that species remain morphologically and genetically distinct at range cores, maintaining species boundaries while undergoing extensive gene flow in areas of sympatry at range peripheries. Our study shows that sequential hybridization may have caused relatively rapid speciation and facilitated the colonization of different niches, resulting in the rapid formation of two new lineages. Participation in the syngameon may allow adaptive traits to be introgressed across species barriers and provide the changes needed to survive future climate scenarios.
Practicing physical geography: An actor-network view of physical geography exemplified by the rock art stability index

This paper explores the use of a new pedagogy, the rock art stability index (RASI), to engender deeper understanding of weathering science concepts by students. Owing to its dynamic nature, RASI represents a quintessential actor network for weathering science, because it links task in the landscape with an active material practice and an alternative materialistic world-view recently called for in positivistic science, to create place. Using concept maps as an assessment tool, 571 college undergraduate students and 13 junior high school integrated science students (ages 12-13) were evaluated for increased learning potential between pre- and post-field experiences. Further, this article demonstrates that when students use RASI to learn the fundamental complex science of weathering they make in-depth connections between weathering form and process not achieved through traditional, positivistic weathering pedagogy. We argue that RASI draws upon inherent actor networks which allow students to link weathering form and process to an animate conceptualization of landscape. Conceptualizing landscape as sentient actor networks removes weathering science disciplinary connections and their inherent pedagogic practices. Our focus in this paper is not to challenge weathering ontology and epistemology, but rather to argue that there is a need for a pedagogical paradigm shift in weathering science.
PolarPose: Single-Stage Multi-Person Pose Estimation in Polar Coordinates

Regression based multi-person pose estimation receives increasing attention because of its promising potential in achieving realtime inference. However, the challenges in long-range 2D offset regression have restricted the regression accuracy, leading to a considerable performance gap compared with heatmap based methods. This paper tackles the challenge of long-range regression through simplifying the 2D offset regression to a classification task. We present a simple yet effective method, named PolarPose, to perform 2D regression in Polar coordinate. Through transforming the 2D offset regression in Cartesian coordinate to quantized orientation classification and 1D length estimation in the Polar coordinate, PolarPose effectively simplifies the regression task, making the framework easier to optimize. Moreover, to further boost the keypoint localization accuracy in PolarPose, we propose a multi-center regression to relieve the quantization error during orientation quantization. The resulting PolarPose framework is able to regress the keypoint offsets in a more reliable way, and achieves more accurate keypoint localization. Tested with the single-model and single scale setting, PolarPose achieves the AP of 70.2% on COCO test-dev dataset, outperforming the state-of-the-art regression based methods. PolarPose also achieves promising efficiency, e.g., 71.5% AP at 21.5FPS and 68.5%AP at 24.2FPS and 65.5%AP at 27.2FPS on COCO val2017 dataset, faster than current state-of-the-art.
Low power & mobile hardware accelerators for deep convolutional neural networks

This article provides a comprehensive review of recent developments in the field of computational hardware for mobile low power machine learning hardware accelerators. The article provides an introduction to neural networks, convolutional neural networks and details recent developments in state of the art deep convolutional neural networks. The key considerations in the design of low power hardware accelerators are discussed with reference to a conceptual system. Strategies for reducing the energy cost of memory access and computation in state of the art hardware accelerators are detailed. This includes techniques such as dataflow, reduced precision, model compression and sparsity. Recent reported digital mobile accelerators for deep convolutional neural networks with power consumptions of less than 3.3 W are observed to have 4x-20x better efficiency than the reference GPU accelerator at 16-bit precision, and can achieve 20x-1171x better efficiency at less than 4-bit precision. Efficiency improvements of 20x-1171x over a GPU is observed for reported mobile accelerators with reduced precision.
A Tensor Factorization Method for 3-D Super Resolution With Application to Dental CT

Available super-resolution techniques for 3-D images are either computationally inefficient prior-knowledge-based iterative techniques or deep learning methods which require a large database of known low-resolution and high-resolution image pairs. A recently introduced tensor-factorization-based approach offers a fast solution without the use of known image pairs or strict prior assumptions. In this paper, this factorization framework is investigated for single image resolution enhancement with an offline estimate of the system point spread function. The technique is applied to 3-D cone beam computed tomography for dental image resolution enhancement. To demonstrate the efficiency of our method, it is compared to a recent state-of-the-art iterative technique using low-rank and total variation regularizations. In contrast to this comparative technique, the proposed reconstruction technique gives a 2-order-of-magnitude improvement in running time-2 min compared to 2 h for a dental volume of 282 x 266 x 392 voxels. Furthermore, it also offers slightly improved quantitative results ( peak signal-to-noise ratio and segmentation quality). Another advantage of the presented technique is the low number of hyperparameters. As demonstrated in this paper, the framework is not sensitive to small changes in its parameters, proposing an ease of use.