ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Epoch Extraction from Telephonic Speech Signal using Stockwell Transform

Speech is produced by exciting time-varying vocal tract with time-varying impulse-like excitations called epochs. In the literature, epoch extraction methods performed well on clean speech, but, detecting epoch locations from the band-limited signal like telephonic speech is difficult due to loss of information at low frequencies. This paper proposes a Stockwell transform (S-Transform)-based method that can find epochs accurately from the telephonic speech. The frequency-dependent Gaussian window and localization capabilities of S-Transform will reduce the effect of the bandpass nature of the telephonic channel. The telephonic channel is simulated using a 300- 3400 Hz bandpass filter. The proposed method is evaluated on five speakers data, namely BDL, SLT, JMK, KED, and RAB, from CMU arctic database. The results are compared with the state-of-the-art methods for both clean speech and telephonic speech. The proposed method produced comparable results with existing methods on clean speech but has shown an improvement of 4.68% over state-of-the-art methods.
Mixed-Sandwich Titanium(III) Qubits on Au(111): Electron Delocalization Ruled by Molecular Packing

Organometallic sandwich complexes are versatile molecular systems that have been recently employed for single-molecule manipulation and spin sensing experiments. Among related organometallic compounds, the mixed-sandwich S = 1/2 complex (η8-cyclooctatetraene)(η5-cyclopentadienyl)titanium, here [CpTi(cot)], has attracted interest as a spin qubit because of the long coherence time. Here the structural and chemical properties of [CpTi(cot)] on Au(111) are investigated at the monolayer level by experimental and computational methods. Scanning tunneling microscopy suggests that adsorption occurs in two molecular orientations, lying and standing, with a 3:1 ratio. XPS data evidence that a fraction of the molecules undergo partial electron transfer to gold, while our computational analysis suggests that only the standing molecules experience charge delocalization toward the surface. Such a phenomenon depends on intermolecular interactions that stabilize the molecular packing in the monolayer. This orientation-dependent molecule-surface hybridization opens exciting perspectives for selective control of the molecule-substrate spin delocalization in hybrid interfaces.
YADA: you always dream again for better object detection

Object detection has been attracting a lot of attention from the computer vision community. It has a wide range of practical applications ranging from the traditional use such as image annotation to modern uses such as self-driving vehicles, robotics, surveillance systems, and augmented reality. Recently, deep learning has significantly improved the state-of-the-art performance of the object detection task. Many works explore various deep network structures to improve the performance. However, the impact of training data is still not well investigated. Although some works focus on data augmentation and data synthesis, there is no guarantee that they are effective for the training process. In this paper, we propose a novel framework addressing the problem of generating relevant data and how to use them effectively. We apply lucid data synthesizing which generates data by mining hard examples and embedding them to the same context locations. Further, we utilize a dual-level deep network leveraged with these generated data to effectively detect hard objects in images. Extensive experiments on two benchmarks, PASCAL VOC and KITTI, demonstrate the superiority of our approach over the state-of-the-art methods.
Assessing Undergraduates' Perception of Risks Related to Body Art in Italy: The SUPeRBA Multicenter Cross-Sectional Study

Tattooing and piercing may lead to health complications. The present multicenter cross-sectional study aimed to assess awareness and knowledge of health risks related to body art and to identify their possible determinants among a large sample of undergraduates in Italy. A web-based questionnaire collecting information on socio-demographic characteristics, awareness, knowledge, and some potential predictors was administered to undergraduates attending twelve Italian universities. The level of knowledge was expressed as the number of correct answers (0-11 for tattooing, 0-14 for piercing). A total of 2985 participants (mean age 23.15 +/- 3.99, 73.9% F) participated in the study. Although 95.4% of the respondents were aware of possible health consequences of body art, a low level of specific knowledge was registered for both tattooing (mean number of correct answers 5.38 +/- 2.39) and piercing (5.93 +/- 3.12) consequences. Lower knowledge was associated with the attendance of non-life science course and with lower duration of academic education for both tattoo and piercing. Lower knowledge of tattooing risks was related with commuter status, while lower knowledge of piercing risks was associated with lower father's education. These findings highlight the need to enhance information campaigns targeted to youths to increase their awareness of possible health risk of body art.
Ultra-Tight Host-Guest Binding with Exceptionally Strong Positive Cooperativity

Cooperativity plays a critical role in self-assembly and molecular recognition. A rigid aromatic oligoamide macrocycle with a cyclodirectional backbone binds with DABCO-based cationic guests in a 2 : 1 ratio in high affinities (Ktotal ≈1013 M-2 ) in the highly polar DMF. The host-guest binding also exhibits exceptionally strong positive cooperativity quantified by interaction factors α that are among the largest for synthetic host-guest systems. The unusually strong positive cooperativity, revealed by isothermal titration calorimetry (ITC) and fully corroborated by mass spectrometry, NMR and computational studies, is driven by guest-induced stacking of the macrocycles and stabilization from the alkyl end chains of the guests, interactions that appear upon binding the second macrocycle. With its tight binding driven by extraordinary positive cooperativity, this host-guest system provides a tunable platform for studying molecular interactions and for constructing stable supramolecular assemblies.
Contrastive and Selective Hidden Embeddings for Medical Image Segmentation

Medical image segmentation is fundamental and essential for the analysis of medical images. Although prevalent success has been achieved by convolutional neural networks (CNN), challenges are encountered in the domain of medical image analysis by two aspects: 1) lack of discriminative features to handle similar textures of distinct structures and 2) lack of selective features for potential blurred boundaries in medical images. In this paper, we extend the concept of contrastive learning (CL) to the segmentation task to learn more discriminative representation. Specifically, we propose a novel patch-dragsaw contrastive regularization (PDCR) to perform patch-level tugging and repulsing. In addition, a new structure, namely uncertainty-aware feature re- weighting block (UAFR), is designed to address the potential high uncertainty regions in the feature maps and serves as a better feature re- weighting. Our proposed method achieves state-of-the-art results across 8 public datasets from 6 domains. Besides, the method also demonstrates robustness in the limited-data scenario. The code is publicly available at https://github.com/lzh19961031/PDCR_UAFR-MIShttps://github.com/lzh19961031/PDCR_UAFR-MIS.
Diffusion basis functions decomposition for estimating white matter intravoxel fiber geometry

In this paper, we present a new formulation for recovering the fiber tract geometry within a voxel from diffusion weighted magnetic resonance imaging (MRI) data, in the presence of single or multiple neuronal fibers. To this end, we define a discrete set of diffusion basis functions. The intravoxel information is recovered at voxels containing fiber crossings or bifurcations via the use of a linear combination of the above mentioned basis functions. Then, the parametric representation of the intravoxel fiber geometry is a discrete mixture of Gaussians. Our synthetic experiments depict several advantages by using this discrete schema: the approach uses a small number of diffusion weighted images (23) and relatively small b values (1250 s/mm(2)), i.e., the intravoxel information can be inferred at a fraction of the acquisition time required for datasets involving a large number of diffusion gradient orientations. Moreover our method is robust in the presence of more than two fibers within a voxel, improving the state-of-the-art of such parametric models. We present two algorithmic solutions to our formulation: by solving a linear program or by minimizing a quadratic cost function (both with non-negativity constraints). Such minimizations are efficiently achieved with standard iterative deterministic algorithms. Finally, we present results of applying the algorithms to synthetic as well as real data.
Application Research of Virtual Reality Technology in Ocean Environmental Art Design

With the progress of the information age, many scientific and technological products have changed people's lives. In the field of environmental art design, the emergence of computer technology and realistic simulation technology has broken the traditional environmental art design mode and accelerated the environmental art design scheme and its application based on virtual reality technology. This paper introduces realistic simulation technology and its practical application and probes into the profound impact brought by realistic simulation technology in the specific design process of environmental art design so as to provide some inspirations on ideas of environmental art design. The paper suggests the designers to lower the cost of the project design to a certain extent, avoid artificial design error, improve quality and efficiency of environmental art design, and most importantly enhance the resource utilization rate of the environment art design projects and then improve the economic benefits of substantial project in operation process. In addition, it is worthwhile to promote such technology in relative industries.
Recipes for Resilience: Engaging Caribbean Youth in Climate Action and Food Heritage through Stories and Song

This paper presents findings from the Recipes for Resilience project, an international, interdisciplinary collaboration between Caribbean and UK scholars of history, geography, anthropology, cultural studies, development studies, ethnobotany, and climate-risk studies, and the research partners, the Caribbean Youth Environment Network. The purpose of the project was to investigate how agrifood heritage may be mobilized in creative ways to engage Caribbean youth in climate action and justice. The project utilized arts and humanities methods, such as storytelling, songwriting, online games, and brief research-led talks, culminating in the co-created song: "Food and Resistance for Climate Resilience". The results of the project provide evidence that climate action requires arts and humanities methods to appeal to youth, as opposed to purely fact-based or scientific forms of climate communication. We conclude that co-creative methods such as music and storytelling can inspire youth to engage in climate action, in this case through a (re)valuation culinary and agricultural heritage.
Correlation between Redox Potential and Solvation Structure in Biphasic Electrolytes for Li Metal Batteries

The activity of lithium ions in electrolytes depends on their solvation structures. However, the understanding of changes in Li+ activity is still elusive in terms of interactions between lithium ions and solvent molecules. Herein, the chelating effect of lithium ion by forming [Li(15C5)]+ gives rise to a decrease in Li+ activity, leading to the negative potential shift of Li metal anode. Moreover, weakly solvating lithium ions in ionic liquids, such as [Li(TFSI)2 ]- (TFSI = bis(trifluoromethanesulfonyl)imide), increase in Li+ activity, resulting in the positive potential shift of LiFePO4 cathode. This allows the development of innovative high energy density Li metal batteries, such as 3.8 V class Li | LiFePO4 cells, along with introducing stable biphasic electrolytes. In addition, correlation between Li+ activity, cell potential shift, and Li+ solvation structure is investigated by comparing solvated Li+ ions with carbonate solvents, chelated Li+ ions with cyclic and linear ethers, and weakly solvating Li+ ions in ionic liquids. These findings elucidate a broader understanding of the complex origin of Li+ activity and provide an opportunity to achieve high energy density lithium metal batteries.
Investigating the impact of frame rate towards robust human action recognition

Human action recognition from videos is very important for visual analytics. Due to increasing abundance of diverse video content in the era of big data, research on human action recognition has recently shifted towards more challenging and realistic settings. Frame rate is one of key issues in diverse and realistic video settings. While there have been several evaluation studies investigating different aspects of action recognition such as different visual descriptors, the frame rate issue has been seldom addressed in the literature. Therefore, in this paper, we investigate the impact of frame rate on human action recognition with several state-of-the-art approaches and three benchmark data sets. Our experimental results indicate that those state-of-the-art approaches are not robust to the variations of frame rate. As a result, more robust visual features and advanced learning algorithms are required to further improve human action recognition performance towards its more practical deployments. In addition, we investigate key frame selection techniques for choosing a set of suitable frames from an action sequence for action recognition. Promising results indicate that well designed key-frame selection methods can produce a set of representative frames and eventually reduce the impact of frame rate on the performance of human action recognition. (C) 2015 Elsevier B.V. All rights reserved.
Continuous-Time Delta-Sigma ADCs With Improved Interferer Rejection

This paper reviews and analyzes the state of the art of Delta-Sigma modulators for receiver applications. Receiver ADCs require not only steadily increased bandwidth, low power and area consumption, but especially face strong interferer signals. These are outside the band of interest, but they most often determine the overall receiver dynamic range and linearity requirements. In order to avoid or at least relax explicit filtering in front of the ADC, Delta-Sigma architectures have been presented with improved robustness to interferers by filtering. This paper intends to review the motivation, to give a tutorial of the state of the art, as well as to introduce a deeper analysis for DSM with interferer robustness. For this, an introduction to the requirements of Delta-Sigma modulators in receivers is given, and a method is introduced which allows to analyze and to scale the internal states meeting the requirements of the interferer scenarios. The paper additionally analyzes state-of-the-art techniques of modulators with improved interferer rejection in order to outline more clearly their possibilities and drawbacks.
Differential attentional responding in caesarean versus vaginally delivered infants

Little is known about the role that the birth experience plays in brain and cognitive development. Recent research has suggested that birth experience influences the development of the somatosensory cortex, an area involved in spatial attention to sensory information. In this study, we explored whether differences in spatial attention would occur in infants who had different birth experiences, as occurs for caesarean versus vaginal delivery. Three-month-old infants performed either a spatial cueing task or a visual expectation task. We showed that caesarean-delivered infants' stimulus-driven, reflexive attention was slowed relative to vaginally delivered infants', whereas their cognitively driven, voluntary attention was unaffected. Thus, types of birth experience influence at least one form of infants' attention, and possibly any cognitive process that relies on spatial attention. This study also suggests that birth experience influences the initial state of brain functioning and, consequently, should be considered in our understanding of brain development.
NAS-Based CNN Channel Pruning for Remote Sensing Scene Classification

Recently, convolutional neural network (CNN)-based remote sensing scene classification has achieved great success. However, the prohibitively expensive computation and storage requirements of state-of-the-art models have hindered the deployment of CNNs on on- board platforms. In this letter, we propose a differentiable neural architecture search (NAS)-based channel pruning method to automatically prune the CNN models. In the proposed method, the importance of each output channel is measured by a trainable score. The scores are optimized by an NAS method to search a good-performance pruned structure. After the search process, a global score threshold is adopted to derive the pruned model. A cost-awareness loss is proposed for the search process to encourage the floating-point operation (FLOP) compression ratio of the pruned model coverage to a desired value. We apply the proposed method to ResNet-34 and VGG-16 to verify the performance. The NWPU-RESISC-45 and UC Merced Land-Use (UCM) datasets are used for the performance evaluation. A comparison with state-of-the-art pruning methods demonstrates that the proposed method can achieve competitive performance with a similar reduction in FLOP.
Sea Surface Temperature Forecasting With Ensemble of Stacked Deep Neural Networks

Oceanic temperature has a great impact on global climate and worldwide ecosystems, as its anomalies have been shown to have a direct impact on atmospheric anomalies. The major parameter for measuring the thermal energy of oceans is the sea surface temperature (SST). SST prediction plays an essential role in climatology and ocean-related studies. However, SST prediction is challenging due to the involvement of complex and nonlinear sea thermodynamic factors. To address this challenge, we design a novel ensemble of two stacked deep neural networks (DNNs) that uses air temperature, in addition to water temperature, to improve the SST prediction accuracy. To train our model and compare its accuracy with the state-of-the-art, we employ two well-known datasets from the national oceanic and atmospheric administration as well as the international Argo project. Using DNNs, our proposed method is capable of automatically extracting required features from the input time series and utilizing them internally to provide a highly accurate SST prediction that outperforms state-of-the-art models.
State-of-the-art magnetic resonance imaging sequences for pediatric body imaging

Longer examination time, need for anesthesia in smaller children and the inability of most children to hold their breath are major limitations of MRI in pediatric body imaging. Fortunately, with technical advances, many new and upcoming MRI sequences are overcoming these limitations. Advances in data acquisition and k-space sampling methods have enabled sequences with improved temporal and spatial resolution, and minimal artifacts. Sequences to minimize movement artifacts mainly utilize radial k-space filling, and examples include the stack-of-stars method for T1-weighted imaging and the periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER)/BLADE method for T2-weighted imaging. Similarly, the sequences with improved temporal resolution and the ability to obtain multiple phases in a single breath-hold in dynamic imaging mainly use some form of partial k-space filling method. New sequences use a variable combination of data sampling methods like compressed sensing, golden-angle radial k-space filling, parallel imaging and partial k-space filling to achieve free-breathing, faster sequences that could be useful for pediatric abdominal and thoracic imaging. Simultaneous multi-slice method has improved diffusion-weighted imaging (DWI) with reduction in scan time and artifacts. In this review, we provide an overview of data sampling methods like parallel imaging, compressed sensing, radial k-space sampling, partial k-space sampling and simultaneous multi-slice. This is followed by newer available and upcoming sequences for T1-, T2- and DWI based on these other advances. We also discuss the Dixon method and newer approaches to reducing metal artifacts.
On the possibility of observing polarisation of vacuum in a magnetic field

The possibility of detecting birefringence induced by a magnetic field in vacuum is analysed. It is shown that the state of the art of the laser measuring technique allows one to hope for setting up successful experiments.
Blind image deblurring using elastic-net based rank prior

In this paper, we propose a new image prior for blind image deblurring. The proposed prior exploits similar patches of an image and it is based on an elastic-net regularization of singular values. We quantitatively verify that it favors clear images over blurred images. This property is able to facilitate the kernel estimation in the conventional maximum a posterior (MAP) framework. Based on this prior, we develop an efficient optimization method to solve the proposed model. The proposed method does not require any complex filtering strategies to select salient edges which are critical to the state-of-the-art deblurring algorithms. We also extend the prior to deal with non-uniform image deblurring problem. Quantitative and qualitative experimental evaluations demonstrate that the proposed algorithm performs favorably against the state-of-the-art deblurring methods.
Decorrelating Feature Spaces for Learning General-Purpose Audio Representations

Inspired by the recent progress in self-supervised learning for computer vision, in this paper, through the DeLoRes (Decorrelating latent spaces for Low Resource audio representation learning) framework, we introduce two new general-purpose audio representation learning approaches, the DeLoRes-S and DeLoRes-M. Our main objective is to make our network learn representations in a resource-constrained setting (both data and compute) that can generalize well across a diverse set of downstream tasks. Inspired by the Barlow Twins objective function, we propose learning embeddings invariant to distortions of an input audio sample while ensuring that they contain non-redundant information about the sample. We call this the DeLoRes learning framework, which we employ in different fashions with the DeLoRes-S and DeLoRes-M. In our experiments, we learn audio representations with less than half the number of model parameters and 10% audio samples compared to state-of-the-art algorithms to achieve state-of-the-art results on 7 out of 11 tasks on linear evaluation and 4 out of 11 tasks in the finetuning setup. In addition to being simple and intuitive, our pre-training procedure is amenable to compute through its inherent nature of construction. Furthermore, we conduct extensive ablation studies on our training algorithm, model architecture, and results and make all our code and pre-trained models publicly available(1).