ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Fairness and Efficiency in Stochastic Buffer-Aided Relay-Assisted MEC

This letter studies hierarchical Mobile Edge Computing (MEC) with multiple sources, multiple buffer-aided relays, and one Higher-level computing Node (HN). The relays may or may not have computation resources and therefore, they randomly decide to process the received tasks or to offload them to the HN. We consider the delay in the transmission and computation buffers, and propose a novel Fairness and Efficiency Cost Function (FECF) as the weighted sum of the maximum Average Response Time (ART) and the overall system ART. We formulate a novel problem to minimize the FECF while keeping the system queues stable, by jointly optimizing the offloading probabilities at the relays and the capacities of the virtual machines at the HN. We prove that the problem is a multi-convex optimization problem and propose an effective solution method by alternating optimization. Simulation results demonstrate the effectiveness of the different variants of the proposed scheme in the scenarios with server-less and server-empowered relays and provide important insights regarding the fairness and efficiency in terms of the ART.
ASPD-Net: Self-aligned part mask for improving text-based person re-identification with adversarial representation learning

Text-based person re-identification aims to retrieve images of the corresponding person from a large visual database according to a natural language description. When it comes to visual local information extraction, most of the state-of-the-art methods adopt either a strict uniform strategy which can be too rough to catch local details properly, or pre-processing with external cues which may suffer from the deviations of the pre-trained model and the large computation consumption. In this paper, we proposed an Adversarial Self -aligned Part Detecting Network (ASPD-Net) model which extracts and combines multi-granular visual and textual features. A novel Self-aligned Part Mask Module was presented to autonomously learn the information of human body parts, and obtain visual local features in a soft-attention manner by using K Self-aligned Part Mask Detectors. Regarding the main model branches as a generator, a discriminator is employed to determine whether the representation vector comes from the visual modality or the textual modality. With Adversarial Loss training, ASPD-Net can learn more robust representations, as long as it successfully tricks the discriminator. Experimental results demonstrate that the proposed ASPD-Net outperforms the previous methods and achieves the state-of-the-art performance on the CUHK-PEDES and RSTPReid datasets.
Psychological Well-Being of Prospective Counselors from the Faith-Based Educational Institution in the COVID-19 Outbreak

This study aims to analyze the Psychological Well-Being among prospective counselors from the Faith-Based Educational Institution during the COVID-19 pandemic. The approach of this study was quantitative with a descriptive method. The Psychological Well-Being among prospective counselors is at a high classification level, namely 84%. The components of Psychological Well-Being that are above the total average score are Positive Relationship with Other People and components of Self-Growth.
Complete mitochondrial genome sequence of Chestnut-flanked white-eye (Zosterops erythropleurus)

The Chestnut-flanked white-eye (Zosterops erythropleurus) is a species of family Zosteropidae, which is distributed widely in the world. In the present study, the complete mitochondrial genome sequence of Chestnut-flanked white-eye was determined. It has a total length of 17 811 bp, and contains 13 protein-coding genes, 22 tRNA genes, 2 ribosome RNA genes and 2 control regions. The total base composition was 30.2% for A, 31.0% for C, 14.2% for G and 24.6% for T. The phylogenetic tree of Chestnut-flanked white-eye and 13 other species belonging to the order Passeriformes was built. The molecular data presented here will be useful to study the evolutionary relationships and genetic diversity of Chestnut-flanked white-eye.
CATtalk: An IoT-Based Interactive Art Development Platform

Interactive art has been significant advanced by the Internet of Things (IoT) and cyber physical interaction technologies, which enables the participants to engage with the art devices. Several tools and platforms have been proposed to create the art devices. However, the interactive artworks are typically developed with these art devices in ad hoc approaches, and the artists need to spend significant programming efforts to integrate the art devices. This paper proposes CATtalk, a platform to create and maintain interactive artworks. The novel idea is to treat all art devices in an interactive artwork as IoT devices that can be transparently reused by reconfiguration in CATtalk. Therefore, the artworks developed independently by individual artists can be quickly integrated to create new interactive applications. Through CATtalk's no-code and low-code mechanisms, the artists can manipulate CATtalk with little or no programing efforts. CATtalk offers a built-in mechanism so that any person in the audience can play with an interactive artwork with his/her smartphone. We also conduct analytic analysis, simulation and measurements to ensure that the interactive art performance in cross-country remote stages are not affected by the communications delays. In our measurements, the average local and remote communication delays are about 0.01 and 0.05 seconds, respectively. If the art performance is designed such that the average delay between two actions of a local (remote) performer is longer than 0.1 seconds, then the probability of out-of-sequence actions is less than 0.01%. That is, the local dancer should perform slower than the remote dancer. Such delay analysis for remote interactive art performance has not been conducted in the literature.
Digital design and manufacturing on the cloud: A review of software and services

In recent years, industrial nations around the globe have invested heavily in new technologies, software, and services to advance digital design and manufacturing using cyber-physical systems, data analytics, and high-performance computing. Many of these initiatives, such as cloud-based design and manufacturing, fall under the umbrella of what has become known as Industry 4.0 or Industrial Internet and are often hailed as pillars of a new industrial revolution. While an increasing number of companies are developing or already offer commercial cloud-based software packages and services for digital design and manufacturing, little work has been reported on providing a review of the state of the art of these commercial software and services as well as identifying research gaps in this field. The objective of this paper is to present a state-of-the-art review of digital design and manufacturing software and services that are currently available on the cloud. The focus of this paper is on assessing to what extent engineering design, engineering analysis, manufacturing, and production across all phases of the product development lifecycles can already be performed based on the software and services accessed through the cloud. In addition, the key capabilities and benefits of these software packages and services are discussed. Based on the assessment of the core features of commercial software and services, it can be concluded that almost all phases of product realization can be conducted through digital design and manufacturing software and services on the cloud. Finally, existing research gaps and related challenges to overcome are identified. The state-of-the-art review serves to provide a technology guide for decision makers in their efforts to select suitable cloud-based software and services as alternatives to existing in-house resources as well as to recommend new research areas.
Minimizing flowtime in a flowshop scheduling problem with a biased random-key genetic algorithm

In this paper, we advance the state of the art for solving the Permutation Flowshop Scheduling Problem with total flowtime minimization. For this purpose, we propose a Biased Random-Key Genetic Algorithm (BRKGA) introducing on it a new feature called shaking. With the shaking, instead to full reset the population to escape from local optima, the shaking procedure perturbs all individuals from the elite set and resets the remaining population. We compare results for the standard and the shaking BRKGA with results from the Iterated Greedy Search, the Iterated Local Search, and a commercial mixed integer programming solver, in 120 traditional instances. For all algorithms, we use warm start solutions produced by the state-of-the-art Beam-Search procedure. Computational experiments show the efficiency of proposed BRKGA, in addition to identify lower and upper bounds, as well as some optimal values, among the solutions. (C) 2019 Elsevier Ltd. All rights reserved.
Agro-pastoral rituals and shaman dances of Dahongyan rock painting, Guizhou, Southwestern China, new investigations

Guizhou was for a long time the wildest and the most enclave province of South China, its ethnographic, historical, archaeological and prehistoric researches are recent, especially that of the rock paintings (Wang and Luo, 1989; Cao, 2004; He, 2006; Yu, 2009). Dahongyan, or the Great Red Cliff, is the first rock art locality known in the Province. The research program is conducted by Cao Bo, vice-director of the Guizhou Provincial Culture Relics and Archaeology Institute. In 2012, a cooperation has developed with the Mountain Resources Institute, Academy of Sciences of Guizhou, and the Department of Prehistory of the Natural Museum of Natural History, Paris, France. The first description of the rock paintings has been presented at the IFRAO (International Federation of Rock Art Organizations) world Congress in July 2014, at Guiyang (Cao et al., 2014). The meeting was co-organized with the People's Government of Guiyang Municipality and the Rock Art Research Association of China (RARAC). The preliminary studies of some frescoes put in light ritual shamanic practices from agro-pastoral peoples before rice cultivation, in relation with animal spirits symbolized by the buffalo and the python. Three scenes are described with new observations and interpreted in the historical context of ethnic minorities and their cultural traditions devoid of written language. They have been named "The shaman dance", "The dance lesson" and "The meeting".
Two tree-based methods for the waterfall

The waterfall transform is a hierarchical segmentation technique based on the watershed transform from the field of mathematical morphology. Watershed-based techniques are useful in numerous fields ranging from image segmentation to cell-and-portal generation for games. The waterfall helps mitigate the problem of over-segmentation that commonly occurs when applying the basic watershed transform. It can also be used as a core part of a method for constructing image partition forests, a tree-based, multiscale representation of an image. The best existing method for the waterfall is fast and effective, but our experience has been that it is not as straightforward to implement as might be desired. Furthermore, it does not deal consistently with the issue of non-minimal plateaux. This paper therefore proposes two new tree-based methods for the waterfall. Both are easier to implement than the existing state-of-the-art, and in our implementations, both were faster by a constant factor. The Simplified Waterfall (SW) method focuses on simplicity and ease of implementation; the Balanced Waterfall (BW) method focuses on robust handling of non-minimal plateaux. We perform experiments on both 2D and 3D images to contrast the new methods with each other and with the existing state-of-the-art, and show that both achieve a noticeable speed-up whilst producing similar results. (C) 2014 Elsevier Ltd. All rights reserved.
Synthesized pansharpening using curvelet transform and adaptive neuro-fuzzy inference system

Image fusion is an important technique in remote sensing to improve visual interpretation and classification. Pansharpening is the procedure of fusing panchromatic (PAN) and multispectral images to produce high spatial and spectral resolution images. Synthesized pansharpening is performed on Linear Imaging Self-Scanning Sensor III and Advanced Wide Field Sensor data, which are freely available and provided by the National Remote Sensing Center. The Adaptive Neuro-Fuzzy Inference System (ANFIS) in multiscale transform domain for multisensor image fusion application is evaluated. The state-of-the-art method has been evaluated by various quality metrics. The computational cost of ANFIS with wavelet, contourlet, shearlet, and curvelet transform is investigated. This study proves that curvelet with ANFIS-based fusion technique outperformed state-of-the-art techniques. The application will be used to incorporate the missing spectral information in the high spatial resolution PAN image to identify objects, highlighting the regions clearly. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)
A paleolimnological context of ecological vulnerability for the freshwater ecosystems of Sable Island National Park Reserve, Canada

Protected areas require long-term monitoring to understand the influence and extent of ecosystem stress to inform management and conservation decisions. As long-term data are not always available, paleolimnological methods offer a way of extending our knowledge of past environmental conditions necessary to use as context for remediation. Here, we examine four sediment cores and additional surface sediments from 14 ponds located on Sable Island National Park Reserve Canada (SINPR), where long-term ecological changes and vulnerability to disturbance are not well defined. We develop a paleolimnological approach to assessing environmental vulnerability through the use of biological indicators (Diptera: Chironomidae), where shifts in the environment are inferred by shifts in chironomid assemblages over time. Analysis of surface sediments show four distinct assemblage types reflecting four different habitat conditions; primarily represented by the presence of Glyptotendipes, Chironomus, Microtendipes, and Dicrotendipes. Differences in habitat conditions through time based on these results are then compared to biostratigraphic analysis of sediment cores from four of the ponds. We found that two ponds had large shifts in chironomids assemblages that were associated with changes in habitat over time, while two others that were not as exposed to the influence of erosion and influx of sand dunes did not. Our findings established a baseline of historical change in SINPR, broadening the scope of long-term monitoring, which is essential for defining goals for management and conservation of the ecological integrity of Sable Island.
Extrachromosomal circular DNA: Current status and future prospects

Extrachromosomal circular DNA (eccDNA) is a double-stranded DNA molecule found in various organisms, including humans. In the past few decades, the research on eccDNA has mainly focused on cancers and their associated diseases. Advancements in modern omics technologies have reinvigorated research on eccDNA and shed light on the role of these molecules in a range of diseases and normal cell phenotypes. In this review, we first summarize the formation of eccDNA and its modes of action in eukaryotic cells. We then outline eccDNA as a disease biomarker and reveal its regulatory mechanism. We finally discuss the future prospects of eccDNA, including basic research and clinical application. Thus, with the deepening of understanding and exploration of eccDNAs, they hold great promise in future biomedical research and clinical translational application.
Artificial intelligence-based creative thinking skill analysis model using human-computer interaction in art design teaching

Due to the rapid growth of network technology, its openness and sharing capabilities and interaction have helped change the art education model for the better. Increased science has been presented to students who spend a lot of time utilizing technology. They similarly did greater mathematical abilities. IT has a good effect on student learning must be included in classroom education. Art education must shift from a teacher-centred to a student-centred approach, and students' passive reception of knowledge must be altered. Students' academic enthusiasm and awareness must be fostered, and their ability to locate knowledge resources must be enhanced. Using pedagogical content knowledge (PCK) in visual art education as a benchmark and this study examines art teachers' PCK proficiency in this area. Social learning theory is applied in the art education curriculum through observational learning based on the Artificial Intelligence-based Creative Thinking Skills Analysis Model (AI-CTSAM). An analytical hierarchy process (AHP) and a grey clustering-based performance analysis model are established to enhance AI's effectiveness in art instruction. Most of the methods to assess clustering performance exist in two types in which one is irrelevant actions requiring labels of foundation truth. Examples are Adjusted Rand, Fowlkes-Mallows, Mutual information scores, Homogeneity, Completeness, and V measurements. Model-based cluster analysis is a new classification approach for investigating population heterogeneity utilizing a limited multivariate mixture. Students can describe, analyze, interpret, and evaluate artwork through the visual art education curriculum.
Scholarship on urban Africa's water crisis narratives: the state of the art

Water crises present a global water governance challenge. To date, scholarship has tended to focus on technological and policy-based solutions, while ignoring the influence of narratives on public buy-in during such crises. Africa is expected to become hotter and drier in future, while its cities experience high levels of informal population growth and inequality. These factors combine to make African cities particularly vulnerable to times of water stress. The aim in this paper is to investigate the state of the 'art' on narratives framing domestic water use in African cities during periods of acute water stress and 'crises', using a systematic literature review of peer-reviewed academic journal articles. The findings revealed a small population of recently published papers that engage critically with state-generated narratives framing the crisis, limited to case studies on Cape Town and Windhoek. We recommend, however, a greater critical engagement with the anti-establishment narratives that can flourish during periods of acute water stress, and tend to be inflammatory and divisive in nature.
A statistical framework for few-shot action recognition

Along with the exponential growth of online video creation platforms such as Tik Tok and Instagram, state of the art research involving quick and effective action/gesture recognition remains crucial. This work addresses the challenge of classifying short video clips, using a domain-specific feature design approach, capable of performing significantly well using as little as one training example per action. The method is based on Gunner Farneback's dense optical flow (GF-OF) estimation strategy, Gaussian mixture models, and information divergence. We first aim to obtain accurate representations of the human movements/actions by clustering the results given by GF-OF using K-means method of vector quantization. We then proceed by representing the result of one instance of each action by a Gaussian mixture model. Furthermore, using Kullback-Leibler divergence (KL-divergence), we attempt to find similarities between the trained actions and the ones in the test videos. Classification is done by matching each test video to the trained action with the highest similarity (a.k.a lowest KL-divergence). We have performed experiments on the KTH and Weizmann Human Action datasets using One-Shot and K-Shot learning approaches, and the results reveal the discriminative nature of our proposed methodology in comparison with state-of-the-art techniques.
Case report: Langerhans cell histiocytosis of the temporal bone in children: Challenging diagnosis of a rare disease with some pitfalls

A 4-year-old girl was admitted to hospital with disturbance of balance. After being questioned, parents remembered an otitis with effusion 3 months earlier. CT-scans revealed destruction of both temporal bones. Initial biopsy showed granulomatous, necrotic inflammation, which led to comprehensive differential diagnoses. A second tissue sample confirmed Langerhans cell histiocytosis.
The Essential Role of Water Molecules in the Reaction Mechanism of Protein O-Fucosyltransferase 2

Protein O-fucosyltransferase 2 (PoFUT2) is an inverting glycosyltransferase (GT) that fucosylates thrombospondin repeats (TSRs) from group 1 and 2. PoFUT2 recognizes a large and diverse number of TSRs through a dynamic network of water-mediated interactions. By X-ray structural studies of C. elegans PoFUT2 complexed to a TSR of group 2, we demonstrate that this GT recognizes similarly the 3D structure of TSRs from both groups 1 and 2. Its active site is highly exposed to the solvent, suggesting that water molecules might also play an essential role in the fucosylation mechanism. We applied QM/MM methods using human PoFUT2 as a model, and found that HsPoFUT2 follows a classical SN 2 reaction mechanism in which water molecules contribute to a great extent in facilitating the release of the leaving pyrophosphate unit, causing the H transfer from the acceptor nucleophile (Thr/Ser) to the catalytic base, which is the last event in the reaction. This demonstrates the importance of water molecules not only in recognition of the ligands but also in catalysis.
SAR Time-Series Despeckling via Nonlocal Total Variation Regularized Robust PCA

Through the development of synthetic aperture radar (SAR) technology, it is now possible to observe dynamic processes on the earth with fine temporal resolution by forming SAR time series. Nonetheless, such sequential images remain difficult to interpret due to the speckle effect. Despeckling them is further complicated by outliers caused by abrupt changes in weather conditions or the appearance of objects. In spite of the fact that many state-of-the-art methods can achieve excellent filtering performances over stable areas, they often result in artifacts in those areas where outliers existed at the time of acquisition. To simultaneously mitigate the speckle noise and extract outliers, we propose a novel SAR time-series despeckling method based on nonlocal total variation (TV) regularized robust principle component analysis (RPCA), which is termed SAR-NL-TVRPCA. By comparing it to other state-of-the-art methods, the effectiveness of its despeckling has been validated in real data experiments. Furthermore, the extracted outliers can provide insights into abrupt changes occurring throughout the observation period, which provides byproducts for further analysis.