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Jakub Lemiesz

Publications and source records attributed to Jakub Lemiesz.

5 recordsLinked to original sources
arXiv2026

EdgeSketch: Efficient Analysis of Massive Graph Streams

We introduce EdgeSketch, a compact graph representation for efficient analysis of massive graph streams. EdgeSketch provides unbiased estimators for key graph properties with controllable variance and supports implementing graph algorithms on the stored summary directly. It is constructed in a fully streaming manner, requiring a single pass over the edge stream, while offline analysis relies solely on the sketch. We evaluate the proposed approach on two representative applications: community detection via the Louvain method and graph reconstruction through node similarity estimation. Experiments demonstrate substantial memory savings and runtime improvements over both lossless representations and prior sketching approaches, while maintaining reliable accuracy.

cs.DS↗
arXiv2021

SUPPNet: Neural network for stellar spectrum normalisation

Precise continuum normalisation of merged échelle spectra is a demanding task necessary for various detailed spectroscopic analyses. Automatic methods have limited effectiveness due to the variety of features present in the spectra of stars. This complexity often leads to the necessity of manual normalisation which is a time demanding task. The aim of this work is to develop a fully automated normalisation tool that works with order-merged spectra and offers flexible manual fine-tuning, if necessary. The core of the proposed method uses the novel fully convolutional deep neural network (SUPP Network) that was trained to predict a pseudo-continuum. The post-processing step uses smoothing splines that gives access to regressed knots useful for optional manual corrections. The active learning technique was applied to deal with possible biases that may arise from training with synthetic spectra and to extend the applicability of the proposed method to features absent in this kind of spectra. The developed normalisation method was tested with high-resolution spectra of stars having spectral types from O to G, and gave root mean squared (RMS) error over the set of test stars equal $0.0128$ in the spectral range from $3900\,Å$ to $7000\,Å$ and $0.0081$ in the range from $4200\,Å$ to $7000\,Å$. Experiments with synthetic spectra give RMS of the order of $0.0050$. The proposed method gives results comparable to careful manual normalisation. Additionally, this approach is general and can be used in other fields of astronomy where background modelling or trend removal is a part of data processing. The algorithm is available online at https://git.io/JqJhf.

astro-ph.IM↗
arXiv2016

$λ$-persistant CSMA: a radio-channel access protocol

This paper presents an algorithm that improves channel-access statistics for wireless medium. The proposed modification of the standard CSMA algorithm is analytically shown to yield better results and simulation results are given to support this claim.

cs.NI↗
arXiv2016

A Jamming-Resilient MAC-layer Device Identification for Internet of Things

In a number of practical scenarios a wireless device needs to mark its presence, for instance, to some access point. That enables the access point to assign the device its transmission slot or update the count of the network nodes. Many protocols can achieve exactly this result. In this paper, our goal is to show how that can be done in the simplest messaging model, the so-called beeping model. Consequently, we constrain our design so that the station does not send any modulated information in the signal and the receiver actually does not need to demodulate/decode it. We are interested in sending just a short signal, so called 'beep'. Moreover, we want to design such protocol that is resilient to random interference and enables us to identify devices which are sending the signal, as opposed to only note their presence. To do that, we leverage temporal correlations of a sequence of beeps issued by a device, as if the time-moments when they happen come from a pre-defined probability distribution, that is the fingerpring of the device.

cs.NI↗