ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Superpixel Embedding Network

Superpixel segmentation is a fundamental computer vision technique that finds application in a multitude of high level computer vision tasks. Most state-of-the-art superpixel segmentation methods are unsupervised in nature and thus cannot fully utilize frequently occurring texture patterns or incorporate multi-scale context. In this paper, we show that superpixel segmentation can be improved by leveraging the superior modeling power of deep convolutional autoencoders in a fully unsupervised manner. We pose the superpixel segmentation problem as one of manifold learning where pixels that belong to similar texture patterns are assigned near identical embedding vectors. The proposed deep network is able to learn image-wide and dataset-wide feature patterns and the relationships between them. This knowledge is used to segment and group pixels in a way that is consistent with a more global definition of pattern coherence. Experiments demonstrate that the superpixels obtained from the embeddings learned by the proposed method outperform the state-of-the-art superpixel segmentation methods for boundary precision and recall values. Additionally, we find that semantic edges obtained from the superpixel embeddings to be significantly better than the contemporary unsupervised approaches.
Multi-modal chemical information reconstruction from images and texts for exploring the near-drug space

Identification of new chemical compounds with desired structural diversity and biological properties plays an essential role in drug discovery, yet the construction of such a potential space with elements of 'near-drug' properties is still a challenging task. In this work, we proposed a multimodal chemical information reconstruction system to automatically process, extract and align heterogeneous information from the text descriptions and structural images of chemical patents. Our key innovation lies in a heterogeneous data generator that produces cross-modality training data in the form of text descriptions and Markush structure images, from which a two-branch model with image- and text-processing units can then learn to both recognize heterogeneous chemical entities and simultaneously capture their correspondence. In particular, we have collected chemical structures from ChEMBL database and chemical patents from the European Patent Office and the US Patent and Trademark Office using keywords 'A61P, compound, structure' in the years from 2010 to 2020, and generated heterogeneous chemical information datasets with 210K structural images and 7818 annotated text snippets. Based on the reconstructed results and substituent replacement rules, structural libraries of a huge number of near-drug compounds can be generated automatically. In quantitative evaluations, our model can correctly reconstruct 97% of the molecular images into structured format and achieve an F1-score around 97-98% in the recognition of chemical entities, which demonstrated the effectiveness of our model in automatic information extraction from chemical patents, and hopefully transforming them to a user-friendly, structured molecular database enriching the near-drug space to realize the intelligent retrieval technology of chemical knowledge.
Mussel-Inspired Adhesive, Antibacterial, and Stretchable Composite Hydrogel for Wound Dressing

Hydrogels have attracted extensive attention in the field of biomedicine because of their similar structure to extracellular matrix (ECM) and good biocompatibility. However, the adhesiveness, mechanical properties, and antibacterial properties of conventional hydrogels are not satisfactory. In this study, multifunctional chitosan/polydopamine/polyacrylamide (CS/PDA/PAM) hydrogels are prepared through a nature-inspired strategy. The catechol group of polydopamine (PDA) component endows CS/PDA/PAM hydrogels with tissue adhesion and self-healing properties. The introduction of chitosan (CS) not only greatly improves antibacterial ability, but also enhances the mechanical properties of CS/PDA/PAM hydrogels. Skin wound healing experiments show that CS/PDA/PAM hydrogels could accelerate skin tissue regeneration and promote wound healing. Therefore, CS/PDA/PAM hydrogels have great potential in the application of new wound dressings.
Routing With Traffic Awareness and Link Preference in Internet of Vehicles

Considering the high mobility and uneven distribution of vehicles, an efficient routing protocol should avoid that the sent packets are forwarded within road segments with ultra-low density or serious data congestion in vehicular networks. To this end, in this paper, we propose a Traffic aware and Link Quality sensitive Routing Protocol (TLRP) for urban Internet of Vehicles (IoV). First, we design a novel routing metric, i.e., Link Transmission Quality (LTQ), to account for the impact of the number, quality and relative positions of communication links along a routing path on the network performance. Then, to adapt to the dynamic characteristics of IoV, a road weight evaluation scheme is presented to assess each road segment using the real-time traffic and link information quantified by the LTQ. Next, the path with the lowest aggregated weight is selected as the routing candidate. Extensive simulations demonstrate that our proposed protocol achieves significant performance improvements compared to the state-of-the-art protocol MM-GPSR, the typical junction-based scheme E-GyTAR, and the classic connectivity-based routing iCAR, in terms of packet delivery ratio and average transmission delay.
PEM fuel cell modeling using differential evolution

This paper develops a differential-evolution-based solution for the problem of proton exchange membrane fuel cell stack modeling. The problem is analytically intractable and computationally hard. The present paper produces results that outperform state-of-the-art approaches on three performance metrics: solution quality corresponding to a given cost, cost of finding a solution of a given quality, and frequency of producing an optimal or near-optimal solution. (C) 2012 Elsevier Ltd. All rights reserved.
MOSFET ESD breakdown modeling and parameter extraction in advanced CMOS technologies

This paper describes an approach for modeling the breakdown and snapback behavior of state-of-the-art MOSFET structures using equivalent-circuit description. Such models are required to enable circuit-level electrostatic discharge reliability simulations, which are a major challenge for the industry nowadays. Special attention is given to,accurately describing the junction and gate leakage currents due to the increased tunneling generation in the scaled-down CMOS. Consistent parameter extraction procedures for the model parameters are described as well.
Interactive Improvements of Visual and Auditory Function for Enhancing Performance in Youth Soccer Players

We analyzed the effects of a regular training program on the health- and skill-related physical fitness (PF) of talented soccer players aged < 12 years; visual reaction time (VRT) and auditory reaction time (ART) were also assessed. In this single-group interventional study, 78 talented male youth soccer players (mean age, 9.54 years) were critically selected by the Korean Educational Development Institute and underwent a 22-week training program consisting of 16 weeks of PF and basic skill training (90 min/week) and 6 weeks of intensive training (3, 150-min sessions/week). We assessed the pre- and post-training body composition, cardiovascular endurance, muscle strength and endurance, and flexibility. We also measured power, agility, coordination and speed, passing ability, VRT, and ART. All variables improved after training. Post-training VRT correlated with ART, muscle mass, power, cardiovascular endurance, 10-m dribble time, 10-m ball touch count, and 10-m successful pass count. ART only correlated with muscle mass. ART and 10-m ball-touch count influenced VRT, and VRT influenced ART. In conclusion, the training program enhanced the PF and visual- and auditory-related reactions in talented youth soccer players. This study suggests the importance of the assessed relationships, indicating that a training program that improves these parameters enhances the players' performance.
Microlearning in Diverse Contexts: A Bibliometric Analysis

In recent years, publications on microlearning have substantially increased, as this topic has received extensive attention from scholars in the instructional design and technology discipline. To better characterize and understand microlearning, there is a need for comprehensive bibliometrics assessments of the literature on microlearning. To this end, this bibliometric study collected 208 relevant publications on microlearning from the Scopus database, published in diverse contexts. Using quantitative topic modeling and qualitative content analysis methods, we identified four major themes in these publications, namely: (1) design of microlearning; (2) implementation of microlearning as an instructional method strategy and an intervention; (3) evaluation of microlearning; and (4) the utilization of mobile devices for microlearning. Based on the study findings, we discuss the significance of the study and provide implications for research and practice, particularly in fostering rigorous inquiry on the topic of microlearning, expanding the context of research to include K-12 settings, and focusing on mobile-based microlearning.
Strategies toward Metal-Free Carbon Monoxide Prodrugs: An Update

Carbon monoxide is an important gasotransmitter in mammals, with pleiotropic therapeutic potential against a wide range of human diseases. However, clinical translation of CO is severely hampered by the lack of a reliable CO delivery form. The development of metal-free CO prodrugs is the key to resolving such delivery issues. Over the past three years, some new exciting progress has been made in this field. In this review, we highlight these advances and discuss related issues.
Phytoplankton production in Italian freshwater and marine ecosystems: State of the art and perspectives

The present work aims at evaluating the state of art of phytoplankton production research in Italy. We present a synthesis of the main results achieved in three ecosystems where primary production studies have been carried out most intensively: a large subalpine take (Lago Maggiore, LM), a shallow marine ecosystem with strong fluvial influence (the Northern Adriatic Sea, NAS). and a coastal area of the Southern Tyrrhenian Sea (the Gulf of Naples, GoN). The present yearly production values are around 150 g C m(-2) yr(-1) in LM and GoN; this ranges between 80 (offshore) and 150 g C m(-2) yr(-1) (coast) in the NAS. The temporal and spatial variations of phytoplankton production appeared, in each ecosystem. in accordance with the trophic changes. Significant correlations between production, chlorophyll. and light were generally observed for LM and for GoN. On the contrary, these parameters were poorly correlated in the NAS, hampering the use of predictive models in this ecosystem. Discrepancies between primary production and the actual phytoplankton biomass changes were observed across trophic gradients: the largest pail of the carbon that is photosynthetically produced does not seem to be transformed into new phytoplankton biomass, strongly affecting the interpretation of the production figures in the ecosystems.
Utero-cutaneous fistula after caesarean section delivery: diagnosis and management of a rare complication

Utero-cutaneous fistula is an extremely rare condition characterized by an abnormal communication between the anterior wall of the uterus and the abdominal wall. The causes include multiple caesarean sections, incomplete hysterorrhaphy, miscarriages, uterine cavity revision, retention of placental material after delivery, use of drains, post-operative infections, or injuries. Herein, we report a case of a 38-year-old female, who underwent caesarean section 42 days earlier and presented to the emergency room complaining of fever, abdominal pain, and purulent discharge from the abdominal wall from 6 days. Her medical history included 2 previous term caesarean section deliveries and an hysteroscopic polypectomy 2 years earlier. A pelvic computed tomography scan with contrast medium showed fluid/super-fluid phlogistic collection reported at the anterior wall of the uterus with a continuous solution of the uterine wall itself. Magnetic resonance imaging demonstrated the presence of a probable hyperintense fistula, extended for 30 mm and 16 mm of thickness, which ended in the subcutaneous area with an abscess joint without continuous solution with the skin. A laparotomic surgical procedure was successfully performed. Histopathology confirmed the surgical suspect of utero-cutaneous fistula. Although utero-cutaneous fistula is an extremely rare complication, it should be considered if after caesarean section delivery signs and symptoms of skin inflammation and/or infection persist.
Incremental Mining of High Utility Patterns in One Phase by Absence and Legacy-Based Pruning

Mining high utility patterns in dynamic databases is an important data mining task. While a naive approach is to mine a newly updated database in its entirety, the state-of-the-art mining algorithms all take an incremental approach. However, the existing incremental algorithms either take a two-phase paradigm that generates a large number of candidates that causes scalability issues or employ a vertical data structure that incurs a large number of join operations that leads to efficiency issues. To address the challenges with the existing incremental algorithms, this paper proposes a new algorithm incremental direct discovery of high utility patterns (Id(2)HUP+). Id(2)HUP+ adapts a one-phase paradigm by improving the relevance-based pruning and upper-bound-based pruning proposes a novel data structure for a quick update of dynamic databases and proposes the absence-based pruning and legacy-based pruning dedicated to incremental mining. The extensive experiments show that our algorithm is up to 1-3 orders of magnitude more efficient than the state-of-the-art algorithms, and is the most scalable algorithm.
Presence of antibodies against Mycobacterium avium subspecies paratuberculosis in Brazilian high-producing dairy herds

This study aimed to determine the presence of antibodies against Mycobacterium avium subspecies paratuberculosis (MAP) in high-producing dairy cows, the presence of the pathogen in the feces, and the risk factors associated with the disease. Blood and fecal samples were collected from 708 dairy cows over 2 years from 54 herds located in five municipalities of Paraná, Brazil. The serum samples were evaluated for the presence of antibodies against MAP using enzyme-linked immunosorbent assay (ELISA). Fecal samples from 100 cows (69 seropositive and 31 seronegative) were assessed using real-time PCR (qPCR) for IS900 of MAP. The herd prevalence of antibodies against MAP was 61.1% (33/54; 95% CI 46.88-74.08), ranging from 12.5 to 80% across the municipalities, and the prevalence in the animals was 9.8% (69/708; 95% CI 7.77-12.15); it ranged from 0 to 87.5% per herd. Only one of the 69 (1.45%) fecal samples from the seropositive cows was positive for the qPCR. The factors associated with the occurrence of paratuberculosis in herds were the use of compost barn system and the type of bed, whereas only the type of bed was associated with the infection of cows. The only risk factor (OR = 2.45; 95% CI 1.03-5.85) associated with the occurrence of paratuberculosis was the introduction of animals purchased from other dairy farms. The prevalence of active infection was low; however, our results demonstrate the presence of MAP in high-producing dairy herds in Paraná state, Brazil.
Video Synchronization With Bit-Rate Signals and Correntropy Function

We propose an approach for the synchronization of video streams using correntropy. Essentially, the time offset is calculated on the basis of the instantaneous transfer rates of the video streams that are extracted in the form of a univariate signal known as variable bit-rate (VBR). The state-of-the-art approach uses a window segmentation strategy that is based on consensual zero-mean normalized cross-correlation (ZNCC). This strategy has an elevated computational complexity, making its application to synchronizing online data streaming difficult. Hence, our proposal uses a different window strategy that, together with the correntropy function, allows the synchronization to be performed for online applications. This provides equivalent synchronization scores with a rapid offset determination as the streams come into the system. The efficiency of our approach has been verified through experiments that demonstrate its viability with values that are as precise as those obtained by ZNCC. The proposed approach scored 81% in time reference classification against the equivalent 81% of the state-of-the-art approach, requiring much less computational power.
Environmental monitoring of sacred artworks - A case study for the search for an index of correlation between particle concentration and mass of fine dust

The present work concerns the study of the concentration of Total Suspended Powders (TSP) mainly from uncombusted residues of smoke candles or incense. The aim is identifying the most critical situations for the preservation and conservation in situ of works of sacred art, normally exposed to the veneration of the faithful. These peculiar types of works of art cannot be placed out of context. This because the artifact is an object of worship for many years, it is often object to physical contati by the faithful, subjected to frequent expositions and microclimate changes. Also it suffers an intensive smoking derived from incense and votive candles. The experimental analysis carried out by the authors concerned the monitoring of TSP in the niche of the SS. Crucifix in the Church of St. Papino in Milazzo (ME). The niche, as well as the wooden statue of the Crucified contained, includes a great variety of objects of different nature: bone relics, scrolls, tapestries, precious votive offerings. At present all this precious material is been pouring in clear degradation. The monitoring of TSP was performed using a particle counter and a extractor plug with a collection filter for the gravimetric test. Tools have provided quantitative data of the number of particles per unit volume and the value of particles mass per unit volume, respectively. Then it was possible to determine a correlation marker between the two different instrumental control methods. Scanning electronic microscope analysis, applied to the samples collected, also provided detailed indication on the nature of various inorganic components stored on filters. The research results will lead to the definition of new screening equipment useful for particulate matter filtering or suppression, specifically in churches. These devices can be validated by subsequent and more detailed monitoring surveys.
PushBox: Making Use of Every Bit of Time to Accelerate Completion of Data-Parallel Jobs

To minimize a job's completion time, we need to minimize the completion time of its final stage's last task. Scheduling of machine slots and networks largely dominates the variable part of each task's duration. Finding an optimal schedule is NP-hard even for offline and simplified scenarios. Previous work does lead to improved performance with various strategies. State-of-the-art task placement and network scheduling efforts are largely disjunctive. Without joint optimization, they are sub-optimal and myopic in many scenarios. Task placement usually treats the network as a black box. Thus, we use prioritized bandwidth allocation among tasks making the network both predictable and efficient to achieve joint scheduling. With this feature, joint scheduling can be transformed into a special bin-packing problem. Over this minimal yet power-enough abstraction, we propose PushBox to schedule data-parallel jobs in multi-tenant clusters. When designing the joint scheduling algorithm, we not only embrace the wisdom of prior art but also respect administrators' fairness intent, which is so far largely ignored. We implement PushBox on Hadoop 3. PushBox performs persistently well on both a small testbed and a trace-driven simulator.
Sources of Joy In Medical Educators as Described by the PERMA Model

Phenomenon: Burnout is prevalent amongst long-practicing physicians. For medical educators, it has deleterious effects not only on the educator themselves, but also the students they are teaching. Though significant research has focused on factors associated with burnout, there is limited understanding of its counter: how physicians, particularly medical educators, derive joy from their work. Approach: This qualitative study included 15 highly-rated clinician educators in Internal Medicine who took part in individual semi-structured interviews. Participants were invited to discuss their sources of professional joy. After transcription, we used thematic content analysis: 50 themes were identified. Themes were then coded using the domains of the PERMA (Positive Emotion, Engagement, Relationships, Meaning, and Accomplishment) model of positive psychology, assigning each theme a best fit domain. Forty-five themes were mapped into the PERMA model. Findings: When describing professional joy, highly-rated clinician educators displayed high levels of overlap with all domains of the PERMA model. Interaction with the learner was a prominent source of professional joy, particularly within Positive Emotion, Engagement, and Relationship domains. Insights: Our findings indicate that the PERMA model appropriately defines the sources of professional joy for these educators. Future research could employ this model to identify targets for interventions aimed at amplifying joy at work for this group.
Utilizing Sub-topic Units for Patent Prior-Art Search

One of the defining challenges in patent prior-art search is the problem of representing a long, technical document as a query. Previously work on this problem has concentrated on single query representations of the patent application. In the following paper, we describe an approach which uses multiple query representations generated from semantically coherent passages extracted from patent documents. We validate our technique in an experiment using the CLEF-IP 2011 patent search collection. Our system achieves statistically significant improvements over various state-of-art query generation techniques.
Long-Term Visual Localization Revisited

Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing conditions, including day-night changes, as well as weather and seasonal variations, while providing highly accurate six degree-of-freedom (6DOF) camera pose estimates. In this paper, we extend three publicly available datasets containing images captured under a wide variety of viewing conditions, but lacking camera pose information, with ground truth pose information, making evaluation of the impact of various factors on 6DOF camera pose estimation accuracy possible. We also discuss the performance of state-of-the-art localization approaches on these datasets. Additionally, we release around half of the poses for all conditions, and keep the remaining half private as a test set, in the hopes that this will stimulate research on long-term visual localization, learned local image features, and related research areas. Our datasets are available at visuallocalization.net, where we are also hosting a benchmarking server for automatic evaluation of results on the test set. The presented state-of-the-art results are to a large degree based on submissions to our server.