Support Vector Machines (SVMs) are known to be consistent and robust for classification regression if they based on a Lipschitz continuous loss function bounded kernel with dense separable reproducing Hilbert space.These facts even true in the context unbounded output spaces, target f is integrable respect marginal distribution of input variable X Y has finite first absolute moment.The latter assumption clearly excludes distributions heavy tails, e.g., several stable or some extreme value...

Environmental monitoring of microplastics (MP) contamination has become an area great research interest, given potential hazards associated with human ingestion MP. In this context, determination MP concentration is essential. However, cheap, rapid, and accurate quantification remains a challenge to date. This study proposes deep learning-based image segmentation method that properly distinguishes fluorescent from other elements in microscopy image. A total nine different learning models,...

Protein therapeutics play an important role in controlling the functions and activities of disease-causing proteins modern medicine. Despite protein having several advantages over traditional small-molecule therapeutics, further development has been hindered by drug complexity delivery issues. However, recent progress deep learning-based structure prediction approaches, such as AlphaFold2, opens new opportunities to exploit these macro-biomolecules for highly specialised design inhibit,...

Abstract RNA–protein interactions are crucial for diverse biological processes. In prokaryotes, enable adaptive immunity through CRISPR-Cas systems. These defence systems utilize CRISPR RNA (crRNA) templates acquired from past infections to destroy foreign genetic elements crRNA-mediated nuclease activities of Cas proteins. Thanks the programmability and specificity systems, CRISPR-based antimicrobials have potential be repurposed as new types antibiotics. Unlike traditional antibiotics,...

Although supervised learning has been highly successful in improving the state-of-the-art domain of image-based computer vision past, margin improvement diminished significantly recent years, indicating that a plateau is sight. Meanwhile, use self-supervised (SSL) for purpose natural language processing (NLP) seen tremendous successes during past couple with this new paradigm yielding powerful models. Inspired by excellent results obtained field NLP, methods rely on clustering, contrastive...

Given their substantial success in addressing a wide range of computer vision challenges, Convolutional Neural Networks (CNNs) are increasingly being used smart home applications, with many these applications relying on the automatic recognition human activities. In this context, low-power radar devices have recently gained popularity as recording sensors, given that usage allows mitigating number privacy concerns, key issue when making use conventional video cameras. Another concern is...

Different algorithms for object dimension measurement have recently been proposed, leveraging the power of RGB-D cameras. However, most these are not suitable deployment in logistics industry: they only able to deal with non-moving objects and/or usage multiple cameras introduces time delays. In this paper, we introduce Box-Scan, a novel algorithm that enables real-time box conveyor systems using single camera. We discuss industrial setting which our needs operate, as well prototype...

This paper presents our approach for the MuSe-Personalization sub-challenge of fourth Multimodal Sentiment Analysis Challenge (MuSe 2023), with goal detecting human stress levels through multimodal sentiment analysis. We leverage and enhance a Transformer-encoder model, integrating improvements that mitigate issues related to memory leakage segmentation faults. propose novel feature extraction techniques, including pose based on joint pair distance self-supervised learning-based audio using...

Class Activation Mapping (CAM) can be used to obtain a visual understanding of the predictions made by Convolutional Neural Networks (CNNs), facilitating qualitative insight into these neural networks when they are, for instance, purpose medical image analysis. In this paper, we investigate what extent CAM also enables quantitative CNN-based classification models through creation segmentation masks out class activation maps, hereby targeting use case brain tumor classification. To that end,...

Machine vision technology is moving more and towards a three-dimensional approach, plant phenotyping following this trend. However, despite its potential, the complexity of analysis 3D representations has been main bottleneck hindering wider deployment phenotyping. In review we provide an overview typical steps for processing plants, to offer potential users first gateway into application, stimulate further development. We focus on applications where goal measure characteristics single...

We propose a new approach to the model reduction of biochemical reaction networks governed by various types enzyme kinetics rate laws with non-autocatalytic reactions, each which can be reversible or irreversible. This method extends for previously proposed Rao et al. proceeds step-wise in number complexes Kron weighted Laplacian corresponding complex graph network. The main idea current manuscript is based on rewriting mathematical network as consisting linkage classes that contain more...

Trypanosomiasis, a neglected tropical disease (NTD), challenges communities in sub-Saharan Africa and Latin America. The World Health Organization underscores the need for practical, field-adaptable diagnostics rapid screening tools to address negative impact of NTDs. While artificial intelligence has shown promising results screening, lack curated datasets impedes progress. In response this challenge, we developed Tryp dataset, comprising microscopy images unstained thick blood smears...

Given the success of online shopping platforms and e-commerce technology, there is an increasing necessity to quickly safely package different types items. Addressing this requires technology accurately measure items at a high speed. Existing studies, however, lack in terms reproducibility diversity measured. In paper, we present novel approach for item measurement, targeting automated packaging systems that make use belt conveyors. particular, leveraging scenario-driven automata-based...

We illustrate a new technique for the model reduction of biochemical reaction networks governed by any kind enzyme kinetics. Previously Rao et al. proposed method various reversible and irreversible extend ideas show how number variables in mathematical chemical network can be reduced using initial data from network. Moreover, outgoing fluxes forward direction remain unchanged compared to original one. then apply our an example, which encountered serious limitations.