Combinations of drugs have demonstrated potential therapeutic results in controlling the progression of cancer, with a possible decrease in toxicity and unfavorable side efects. Experimental screening is no longer fea...
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Coronavirus disease (COVID-19), an effect of the SARS-CoV-2 virus, is an emerging infectious disease that infects humans due to interspecies transmission. Typically, Spike (S) protein plays a crucial role in entering ...
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Human speech can be characterized by different components, including semantic content, speaker identity and prosodic information. Significant progress has been made in disentangling representations for semantic conten...
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Dear editor, Security experts have been fighting against cybercriminals for many years and existing research shows that this battle will continue. Malicious software has no remorse when it targets different organizati...
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Dear editor, Security experts have been fighting against cybercriminals for many years and existing research shows that this battle will continue. Malicious software has no remorse when it targets different organizations, regardless of its forms [1]. Ransomware [2] has caused serious issues in different industries, especially in healthcare. The existing report shows that 34% of ransomware is targeting healthcare organizations. Nowadays, criminals prefer crypto-jacking over ransomware (which also relies on cryptocurrency for anonymous ransom payments).
Current Sundanese stemmers either ignore reduplication words or define rules to handle only affixes. There is a significant amount of reduplication words in the Sundanese language. Because of that, it is impossible to...
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Irregular boundaries in image stitching naturally occur due to freely moving *** deal with this problem,existing methods focus on optimizing mesh warping to make boundaries regular using the traditional explicit ***,p...
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Irregular boundaries in image stitching naturally occur due to freely moving *** deal with this problem,existing methods focus on optimizing mesh warping to make boundaries regular using the traditional explicit ***,previous methods always depend on hand-crafted features(e.g.,keypoints and line segments).Thus,failures often happen in overlapping regions without distinctive *** this paper,we address this problem by proposing RecStitchNet,a reasonable and effective network for image stitching with rectangular *** that both stitching and imposing rectangularity are non-trivial tasks in the learning-based framework,we propose a three-step progressive learning based strategy,which not only simplifies this task,but gradually achieves a good balance between stitching and imposing *** the first step,we perform initial stitching by a pre-trained state-of-the-art image stitching model,to produce initially warped stitching results without considering the boundary ***,we use a regression network with a comprehensive objective regarding mesh,perception,and shape to further encourage the stitched meshes to have rectangular boundaries with high content ***,we propose an unsupervised instance-wise optimization strategy to refine the stitched meshes iteratively,which can effectively improve the stitching results in terms of feature alignment,as well as boundary and structure *** to the lack of stitching datasets and the difficulty of label generation,we propose to generate a stitching dataset with rectangular stitched images as pseudo-ground-truth labels,and the performance upper bound induced from the it can be broken by our unsupervised *** and quantitative results and evaluations demonstrate the advantages of our method over the state-of-the-art.
Combinations of drugs have demonstrated potential therapeutic results in controlling the progression of cancer, with a possible decrease in toxicity and unfavorable side efects. Experimental screening is no longer fea...
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Combinations of drugs have demonstrated potential therapeutic results in controlling the progression of cancer, with a possible decrease in toxicity and unfavorable side efects. Experimental screening is no longer feasible due to the vast number of possible medication combinations. As a result, the scientific community is becoming more and more interested in creating computational models that can quickly and precisely identify prospective drug combinations. Our methodology employs therapeutically significant drugs and cell line properties in machine learning techniques. Our suggested stacked machine learning model outperforms all other machine learning models taken into consideration. It employs Random Forest and XGBoost as base learners and Logistic Regression as a meta learner. The results of our research underscore the remarkable efficacy of our approach. Not only does it address the complexities of the task at hand, but it also showcases its potential to enhance predictive accuracy within this domain. The comparative analysis reveals that our model exhibits a noteworthy performance improvement across multiple evaluation metrics. Our findings represent a significant step forward in the quest for improved cancer treatment strategies.
Due to the wide availability of big data, institutions and companies are currently concentrating on developing highly effective recommender systems for their users. Traditional recommender systems use standard informa...
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FC Portugal, a simulated humanoid soccer team from the universities of Porto and Aveiro, achieved the third consecutive victory in the main competition of the 2024 RoboCup 3D Simulation League. The team registered 16 ...
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The monitoring of a patient's heart rate (HR) is critical in the diagnosis of diseases. In the detection of sleep disorders, it also plays an important role. Several techniques have been proposed, including using ...
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