Computer Science

A Survey on Syntactic Pattern Recognition Methods in Bioinformatics

Mariusz Flasiński — Sun, 10 Mar 2024 00:00:00 +0100

Formal tools and models of syntactic pattern recognition which are used in bioinformatics are introduced and characterized in the paper. They include, among others: stochastic (string) grammars and automata, hidden Markov models, programmed grammars, attributed grammars, stochastic tree grammars, Tree Adjoining Grammars (TAGs), algebraic dynamic programming, NLC- and NCE-type graph grammars, and algebraic graph transformation systems. The survey of applications of these formal tools and models in bioinformatics is presented.

Using Deep Neural Networks to Improve the Precision of Fast-Sampled Particle Timing Detectors

Sun, 10 Mar 2024 00:00:00 +0100

Measurements from particle timing detectors are often affected by the time walk effect caused by statistical fluctuations in the charge deposited by passing particles. The constant fraction discriminator (CFD) algorithm is frequently used to mitigate this effect both in test setups and in running experiments, such as the CMS-PPS system at the CERN’s LHC. The CFD is simple and effective but does not leverage all voltage samples in a time series. Its performance could be enhanced with deep neural networks, which are commonly used for time series analysis, including computing the particle arrival time. We evaluated various neural network architectures using data acquired at the test beam facility in the DESY-II synchrotron, where a precise MCP (MicroChannel Plate) detector was installed in addition to PPS diamond timing detectors. MCP measurements were used as a reference to train the networks and compare the results with the standard CFD method. Ultimately, we improved the timing precision by 8% to 23%, depending on the detector's readout channel. The best results were obtained using a UNet-based model, which outperformed classical convolutional networks and the multilayer perceptron.

Machine Learning based Event Reconstruction for the MUonE Experiment

Miłosz Zdybał, Marcin Kucharczyk, Marcin Wolter — Sun, 10 Mar 2024 00:00:00 +0100

As currently operating high energy physics experiments produce a huge amount of data, new methods of fast and efficient event reconstruction are necessary to handle the immense load. Storing the unprocessed data is not feasible, forcing experiments to process the data online employing the algorithms of quality provided for the offline analysis, but within strict time constraints. In the MUonE experiment the machine learning based event reconstruction techniques are being implemented and tested in order to provide efficient online reduction of data and to maximize the statistical power of the final physics measurement.

Generalizing Clustering Inferences with ML Augmentation of Ordinal Survey Data

Bhupendera Kumar, Rajeev Kumar — Sun, 10 Mar 2024 00:00:00 +0100

In this paper, we attempt to generalize the ability to achieve quality inferences of survey data for a larger population through data augmentation and unification. Data augmentation techniques have proven effective in enhancing models' performance by expanding the dataset's size. We employ ML data augmentation, unification, and clustering techniques. First, we augment the \textit{limited} survey data size using data augmentation technique(s). Next, we carry out data unification, followed by clustering for inferencing.

We took two benchmark survey datasets to demonstrate the effectiveness of augmentation and unification. One is on features of students to be entrepreneurs, and the second is breast cancer survey data. We compare the results of the inference obtained from the raw survey data and the newly converted data. The results of this study indicate that the machine learning approach, data augmentation with the unification of data followed by clustering, can be beneficial for generalizing the inferences drawn from the survey data.

Efficient Selection Methods in Evolutionary Algorithms

Jarosław Tomasz Stańczak — Sun, 10 Mar 2024 00:00:00 +0100

Evolutionary algorithms mimic some elements of the theory of evolution. The survival of individuals and the possibility of producing offspring play a huge role in the process of natural evolution. This process is called a natural selection.
This mechanism is responsible for eliminating poor population members and gives the possibility of development for good ones. The evolutionary algorithm - an instance of evolution in the computer environment also requires a selection method, a computer version of natural selection. Widely used standard selection methods applied in evolutionary algorithms are usually derived from nature and prefer competition, randomness and some kind of ``fight'' among individuals. But computer environment is quite different from nature. Computer populations of individuals are usually small, they easily suffer from a premature convergence to local extremes. To avoid this drawback, computer selection methods must have different features than natural selection. In the computer selection methods randomness, fight and competition should be controlled or influenced to operate to the desired extent. Several new methods of individual selection are proposed in this work: several kinds of mixed selection, an interval selection and a taboo selection. Also advantages of passing them into the evolutionary algorithm are shown, using examples based on searching for the maximum α-clique problem and traditional TSP in comparison with traditionally considered as very efficient tournament selection, considered ineffective proportional (roulette) selection and similar classical methods.

The Most Current Solutions using Virtual-Reality-Based Methods in Cardiac Surgery -- A Survey

Sun, 10 Mar 2024 00:00:00 +0100

There is a widespread belief that VR technologies can provide controlled, multi-sensory, interactive 3D stimulus environments that engage patients in interventions and measure, record and motivate required human performance. In order to investigate state-of-the-art and associated occupations we provided a careful review of 6 leading medical and technical bibliometric databases. Despite the apparent popularity of the topic of VR use in cardiac surgery, only 47 articles published between 2002 and 2022 met the inclusion criteria. Based on them VR-based solutions in cardiac surgery are useful both for medical specialists and for the patients themselves. The new lifestyle required from cardiac surgery patients is easier to implement thanks to VR-based educational and motivational tools. However, it is necessary to develop the above-mentioned tools and compare their effectiveness with Augmented Reality (AR). With the aforementioned reasons, interdisciplinary collaboration between scientists, clinicians and engineers is necessary.