ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
A Wireless Interconnection Framework for Seamless Inter and Intra-Chip Communication in Multichip Systems

Computing modules in typical datacenter nodes or server racks consist of several multicore chips either on a board or in a System-in-Package (SiP) environment. State-of-the-art inter-chip communication over wireline channels require data signals to travel from internal nets to the peripheral I/O ports and then get routed over the inter-chip channels to the I/O port of the destination chip. Following this, the data is finally routed from the I/O to internal nets of the destination chip over a wireline interconnect fabric. This multihop communication increases energy consumption while decreasing data bandwidth in a multichip system. Also, traditional I/O does not scale well with technology generations due to limitations of pitch. Moreover, intra-chip and inter-chip communication protocol within such a multichip system is often decoupled to facilitate design flexibility. However, a seamless interconnection between on-chip and off-chip data transfer can improve the communication efficiency significantly. Here, we propose the design of a seamless hybrid wired and wireless interconnection network for multichip systems with dimensions spanning up to tens of centimeters with on-chip wireless transceivers. We demonstrate with cycle accurate simulations that such a design increases the bandwidth and reduces the energy consumption in comparison to state-of-the-art wireline I/O based multichip communication.
A Hybrid Deep Model for Recognizing Arabic Handwritten Characters

Handwriting recognition for computer systems has been in research for a long time, with different researchers having an extensive variety of methods at their disposal. The problem is that most of these experiments are done in English, as it is the most spoken language in the world. But other languages such as Arabic, Mandarin, Spanish, French, and Russian also need research done on them since there are millions of people who speak them. In this work, recognizing and developing Arabic handwritten characters is proposed by cleaning the state-of-the-art Arabic dataset called Hijaa, developing Conventional Neural Network (CNN) with a hybrid model using Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) classifiers. The CNN is used for feature extraction of the Arabic character images, which are then passed on to the Machine Learning classifiers. A recognition rate of up to 96.3% for 29classes is achieved, far surpassing the already state-of-the-art results of the Hijaa dataset.
Robust Visual Odometry Leveraging Mixture of Manhattan Frames in Indoor Environments

We propose a robust RGB-Depth (RGB-D) Visual Odometry (VO) system to improve the localization performance of indoor scenes by using geometric features, including point and line features. Previous VO/Simultaneous Localization and Mapping (SLAM) algorithms estimate the low-drift camera poses with the Manhattan World (MW)/Atlanta World (AW) assumption, which limits the applications of such systems. In this paper, we divide the indoor environments into two different scenes: MW and non-MW scenes. The Manhattan scenes are modeled as a Mixture of Manhattan Frames, in which each Manhattan Frame in itself defines a Manhattan World of a specific orientation. Moreover, we provide a method to detect Manhattan Frames (MFs) using the dominant directions extracted from the parallel lines. Our approach is designed with lower computational complexity than existing techniques using planes to detect Manhattan Frame (MF). For MW scenes, we separately estimate rotational and translational motion. A novel method is proposed to estimate the drift-free rotation using MF observations, unit direction vectors of lines, and surface normal vectors. Then, the translation part is recovered from point-line tracking. In non-MW scenes, the tracked and matched dominant directions are combined with the point and line features to estimate the full 6 degree of freedom (DoF) camera poses. Additionally, we exploit the rotation constraints generated from the multi-view dominant directions observations. The constraints are combined with the reprojection errors of points and lines to refine the camera pose through local map bundle adjustment. Evaluations on both synthesized and real-world datasets demonstrate that our approach outperforms state-of-the-art methods. On synthesized datasets, average localization accuracy is 1.5 cm, which is equivalent to state-of-the-art methods. On real-world datasets, the average localization accuracy is 1.7 cm, which outperforms the state-of-the-art methods by 43%. Our time consumption is reduced by 36%.
A linear wavelet filter for parametric imaging with dynamic PET

This paper describes a new filter for parametric images obtained from dynamic positron emission tomography (PET) studies. The filter is based on the wavelet transform following the heuristics of a previously published method that are here developed into a rigorous theoretical framework. It is shown that the space-time problem of modeling a dynamic PET sequence reduces to the classical one of estimation of a normal multivariate vector of independent wavelet coefficients that, under least-squares risk, can be solved by straightforward application of well established theory. From the study of the distribution of wavelet coefficients of PET images, it is inferred that a James-Stein linear estimator is more suitable for the problem than traditional nonlinear procedures that are incorporated in standard wavelet filters. This is confirmed by the superior performance of the James-Stein filter in simulation studies compared to a state-of-the-art nonlinear wavelet filter and a nonstationary filter selected from literature. Finally, the formal framework is interpreted for the practitioner's point of view and advantages and limitations of the method are discussed.
EEG Microstate Analysis and the EEG Inverse Problem Solution as a Tool for Diagnosing Cognitive Dysfunctions in Individuals Who Have Had a Mild Form of COVID-19

The term "postcovid syndrome" is firmly entrenched in medical terminology, however, many aspects of its clinical manifestations are not well understood. The aim of this work was to find the causes of the development of cognitive dysfunctions in individuals who had a mild form of SARS-CoV-2 using high-density EEG technology and solving an inverse neurophysiological problem. A dynamic study was conducted of 38 people who had COVID-19 and returned to work. Neurophysiological studies were carried out using the EGI-GES-300 system (128 channels). The descriptive characteristics of electroencephalograms were built on the method of studying the spectral density of the EEG signal on the surface of the scalp, and the dynamic characteristics of the signal were studied by fixing EEG microstates, using the method of D. Lehmann and T. Koenig (2018). In the study, a relatively new diagnostic technique for studying cognitive impairments based on the analysis of EEG microstates was implemented, which made it possible to identify signs of functional restructuring of the neuronal macronetworks of the brain and trace the characteristic adaptation of a person during the period of convalescence. The results obtained made it possible to detect a violation of the implementation of the speech function, as a violation of the perception system (ventral information flow system), as well as the connection between the fields of Wernicke's center and Broca's center (dorsal information flow system), leading to the development of communicative dysfunctions that cause characteristic clinical symptoms due to impaired perception of new information and difficulties in implementing the solution. Thus, the survey showed that SARS-Co-V2 causes objective changes in the functional activity of the brain, which are manifested by the syndrome of cognitive dysfunction and require the development of more sensitive clinical tests than currently used.
CLDA: an adversarial unsupervised domain adaptation method with classifier-level adaptation

Domain adaptation is an active and important research field in transfer learning. Unsupervised domain adaptation, which is better in line with real-world scenarios than supervised and semi-supervised domain adaptation, has attracted much attention and research. Inspired by generative adversarial networks (GANs), adversarial unsupervised domain adaptation methods are proposed in recent years, which are shown to achieve state-of-the-art performance. Existing adversarial unsupervised domain adaptation methods generally adopt feature-level adaptation to reduce the cross-domain shifts, which is shown to have some limitations in related research. In this paper, we propose a classifier-level adaptation approach to further reducing the cross-domain shifts. The classifier-level adaptation uses two different but related classifiers for source domain and target domain, different from existing adversarial unsupervised domain adaptation methods. In addition, not only domain-invariant feature representations but also auxiliary information of class labels is used to exploit the joint distribution of category information and extracted features. Based on the above-mentioned approaches, a classifier-level domain adaptation (CLDA) method is proposed. Experimental results show that the proposed CLDA method outperforms state-of-the-art unsupervised domain adaptation methods on Digits and Office-31 datasets.
GPU-based exhaustive algorithms processing kNN queries

Efficient kNN search, or k-nearest neighbors search, is useful, among other fields, in multimedia information retrieval, data mining and pattern recognition problems. A distance function determines how similar the objects are to a given kNN query object. As finding the distance between any given pair of objects (i.e., high-dimensional vectors) is known to be a computationally expensive operation, using parallel computation techniques is an effective way of reducing running times to acceptable values in large databases. In the present work, we offer novel GPU approaches to solving kNN (k-nearest neighbor) queries using exhaustive algorithms based on the Selection Sort, Quicksort and state-of-the-art algorithms. We show that the best approach depends on the k value of the kNN query and achieve a speedup up to 86.4 better than the sequential counterpart. We also propose a multi-core algorithm to be used as reference for the experiments and a hybrid algorithm which combines the proposed algorithms with a state-of-the-art heaps-based method, in which the best performance is obtained with high k values. We also extend our algorithms to be able to deal with large databases that do not fit in GPU memory and whose performance does not deteriorate as database size increases.
Progressive Joint Modeling in Unsupervised Single-Channel Overlapped Speech Recognition

Unsupervised single-channel overlapped speech recognition is one of the hardest problems in automatic speech recognition (ASR). Permutation invariant training (PIT) is a state of the art model-based approach, which applies a single neural network to solve this single-input, multiple-output modeling problem. We propose to advance the current state of the art by imposing a modular structure on the neural network, applying a progressive pretraining regimen, and improving the objective function with transfer learning and a discriminative training criterion. The modular structure splits the problem into three subtasks: frame-wise interpreting, utterance-level speaker tracing, and speech recognition. The pretraining regimen uses these modules to solve progressively harder tasks. Transfer learning leverages parallel clean speech to improve the training targets for the network. Our discriminative training formulation is a modification of standard formulations that also penalizes competing outputs of the system. Experiments are conducted on the artificial overlapped switchboard and hub5e-swb dataset. The proposed framework achieves over 30% relative improvement of word error rate over both a strong jointly trained system, PIT for ASR, and a separately optimized system, PIT for speech separation with clean speech ASR model. The improvement comes from better model generalization, training efficiency, and the sequence level linguistic knowledge integration.
Urban Public Space as a Didactic Platform: Raising Awareness of Climate Change through Experiencing Arts

This paper investigates the meanings of urban public space, both as a didactic platform and as a way to spread awareness of climate change through art. What are the roles of public space? How do artworks intervene in urban public space? How can public art contribute to "sustainability" issues? I have argued that the intervention of art in urban public space offers effective ways of developing climate change art, which is understood to be an educator. Public space can be categorized into three different types: everyday, social, and symbolic spaces. These can be used as a platform for opening discussion and learning about the increased issues of the global crisis in contemporary society. I have drawn upon the representative case studies about climate change to explore how they intervene in urban public space and how they engage viewers to spread awareness, which is one of the fundamental aspects of this paper. It also stimulates viewers' perceptions and awareness of a more sustainable future through phenomenological and emotional experiences. Thus, this paper contributes to the understanding and knowledge of the relationship between art and public space with respect to raising awareness about climate change and considering how art intervenes in urban public space to create an eco-didactic platform.
Fast Georeferenced Aerial Image Stitching With Absolute Rotation Averaging and Planar- Restricted Pose Graph

Accurate digital orthophoto map generation from high-resolution aerial images is important in various applications. Compared with the existing commercial software and the current state-of-the-art mosaicing systems, a novel fast georeferenced orthophoto mosaicing framework is proposed in this study. The framework can adapt to the challenging requirements of high-accuracy orthoimage generations with relatively fast speed, even if the overlap rate is low. We provide appearance and spatial correlation-constrained fast low-overlap neighbor candidate query and matching. On the basis of GPS information, we introduce an absolute position and rotation-averaging strategy for global pose initialization, which is essential for the high convergence and efficiency of nonconvex pose optimization of every image. We also propose a planar-restricted global pose graph optimization method. The optimization is extremely efficient and robust considering that point clouds are parameterized to planes. Finally, we apply a matching graph-based exposure compensation and region reduction algorithm for large-scale and high-resolution image fusion with high efficiency and novel precision. Experimental results demonstrate that our method can achieve the state-of-the-art performance while maintaining high precision and robustness.
Symbolic territories in pre-Magdalenian art?

The legacy of specialists in Upper Paleolithic art shows a common point: a more or less clear separation between Magdalenian art and earlier symbolic manifestations. One of principal difficulty is due to little data firmly dated in the chronology for the "ancient" periods, even if recent studies precise chronological framework. There is a variability of the symbolic traditions from the advent of monumental art in Europe, and there are graphic elements crossing regional limits and asking the question of real symbolic territories existence. The different thematic choices also allows to raise territorial kinships between various caves and various regions. The object of this paper is to define where these rich and varied symbolic records appear, and how graphic traditions are distributed in the Western European Paleolithic area, throughout these 15 000 years. To provide some elements of response to this question, we will draw on the one hand, on the formal approaches in the figures, and on the other - on the thematic range used by Paleolithics. (c) 2017 Published by Elsevier Ltd.
Crystal Facet-Modulated WO3 Nanoplate Photoanode for Photoelectrochemical Glyoxal Semi-oxidation into Glyoxylic Acid

Transforming glyoxal to value-added glyoxylic acid (GA) is highly desirable but challenging due to the uncontrollable over-oxidation. In this work, we report on a first demonstration of semi-oxidation of glyoxal with high selectivity (86.5%) and activity on WO3 nanoplate photoanode through the photoelectrochemical strategy. The optimization of reactivity was achieved via crystal facet regulation, showing a satisfactory GA production rate of 308.4 mmol m-2 h-2, 84.0% faradaic efficiency, and 4.3% total solar-to-glyoxylic acid efficiency on WO3 with enriched {200} facets at 1.6 V versus RHE. WO3 with a high {200} facet ratio exhibits more efficient electron-hole transfer kinetics, resulting in the facilitated formation of hydroxyl radicals (•OH) and glyoxal radicals. Meanwhile, the theoretical calculation results indicate that the high selectivity and activity come from the strong adsorption ability for glyoxal and the low reaction energy for glyoxal radical generation on the (200) facets of WO3. Moreover, the high energy demand toward oxalic acid production on WO3 leads to the exciting semi-oxidation process.
Occlusion Boundary: A Formal Definition & Its Detection via Deep Exploration of Context

Occlusion boundaries contain rich perceptual information about the underlying scene structure and provide important cues in many visual perception-related tasks such as object recognition, segmentation, motion estimation, scene understanding, and autonomous navigation. However, there is no formal definition of occlusion boundaries in the literature, and state-of-the-art occlusion boundary detection is still suboptimal. With this in mind, in this paper we propose a formal definition of occlusion boundaries for related studies. Further, based on a novel idea, we develop two concrete approaches with different characteristics to detect occlusion boundaries in video sequences via enhanced exploration of contextual information (e.g, local structural boundary patterns, observations from surrounding regions, and temporal context) with deep models and conditional random fields. Experimental evaluations of our methods on two challenging occlusion boundary benchmarks (CMU and VSB100) demonstrate that our detectors significantly outperform the current state-of-the-art. Finally, we empirically assess the roles of several important components of the proposed detectors to validate the rationale behind these approaches.
High-Voltage Integrated Circuits: History, State of the Art, and Future Prospects

High-voltage ICs (HVICs) are used in many applications, including ac/dc conversion, off-line LED lighting, and gate drivers for power modules. This paper describes the technologies most commonly used in commercial HVICs, including junction-isolation, thin silicon-on-insulator (SOI), and thick SOI approaches. Emerging technologies such as thin silicon membrane are also discussed.
Circular Dichroism of Amino Acids: Following the Structural Formation of Phenylalanine

Circular dichroism (CD) is frequently used to assess the secondary structure of peptides and proteins, whereas less attention has been given to their building blocks, that is, single amino acids, as they do not possess a secondary structure. Here, we follow the CD signal of amino acids and reveal that several acids exhibit a unique CD pattern as a function of their concentration. Accordingly, we propose an eight-level classification of the CD signal of the various amino acids. Special focus is given to the CD pattern of phenylalanine (Phe), for which we observe the formation of an ultra-narrow CD peak (full width at high maximum of only 5 nm). This CD peak can be attributed to the formation of Phe-based chiral structural features. Further support for the formation of an ordered structure is given by using NMR, and the additional self-assembly process of Phe to tubular structures.
Ultralow-Latency VLSI Architecture Based on a Linear Approximation Method for Computing Nth Roots of Floating-Point Numbers

State-of-the-art approaches that perform root computations based on the COordinate Rotation Digital Computer (CORDIC) algorithm suffer from high latency in performing multiple iterations. Therefore, root computations based on the CORDIC algorithm cannot meet the strict latency requirements of some applications. In this paper, we propose a methodology for performing Nth root computations on floating-point numbers based on the piecewise linear (PWL) approximation method. The proposed method divides an Nth root computation into several subtasks approximated by the PWL algorithm. It determines the widest segments of the subtasks and the smallest fractional width needed to satisfy the predefined maximum relative error Max_ Err(r). Our design is coded in Verilog HDL and synthesized under TSMC 40 nm CMOS technology. The synthesized results show that our design can reach the highest frequency of 2.703 GHz with an area consumption of 2608.84 mu m(2) and a power consumption of 2.4476 mW. Compared with one state-of-the-art architecture, our design saves 91.60%, 89.84%, and 63.33% of the area, power, and latency @1.89GHz frequency, respectively, while reducing Max_Err(r) by 57.30%. In addition, it saves 94.52%, 92.68%, and 73.17% of the area, power, and delay @1.89GHz frequency, respectively, and reduces Max_ Err(r) by 1.65% when compared with the other state-of-the-art design.
Phase II Trial of Reduced-Intensity Busulfan/Clofarabine Conditioning with Allogeneic Hematopoietic Stem Cell Transplantation for Patients with Acute Myeloid Leukemia, Myelodysplastic Syndromes, and Acute Lymphoid Leukemia

Clofarabine has potent antileukemia activity and its inclusion in reduced-intensity conditioning (RIC) allogeneic hematopoietic stem cell transplantation (HSCT) for acute leukemia could potentially improve outcomes. We conducted a phase II study of busulfan (.8 mg/kg i.v. twice daily on days -5, -4, -3, and -2) with clofarabine (40 mg/m(2) i.v. daily on days -5, -4, -3, and -2) conditioning before allogeneic 8/8 HLA-matched related or unrelated HSCT. The primary endpoint was donor neutrophil engraftment by day +40. Secondary endpoints included nonrelapse mortality (NRM), acute and chronic graft-versus-host disease (GVHD), progression-free survival (PFS), and overall survival (OS). Thirty-four patients (acute myeloid leukemia [AML], n = 25; myelodysplastic syndromes, n = 5; and acute lymphoid leukemia, n = 4) were enrolled. Day 40+ engraftment with donor chimerism was achieved in 33 of 34 patients with 1 patient dying before count recovery. Day 100 and 1-year NRM were 5.9% (95% confidence interval [CI], 1.0 to 17.4) and 24% (95% CI, 11 to 39), respectively. The 2-year relapse rate was 26% (95% CI, 13 to 42). Cumulative incidences of acute and chronic GVHD were 21% and 44%, respectively. The 2-year PFS was 50% (95% CI, 32 to 65) and OS was 56% (95% CI, 38 to 71). For patients with AML in first complete remission, 2-year PFS and OS were both 82% (95% CI, 55 to 94). RIC with busulfan and clofarabine leads to successful engraftment with acceptable rates of NRM and GVHD.