Iskanje po repozitoriju

A+ | A- | | SLO | ENG

Opis: Multiple myeloma (MM) is a cancer of plasma cells, defined by complex genetics and extensive intra- and inter-patient heterogeneity. Despite improved patient survival driven by a plethora of treatment options, the disease remains incurable. Molecularly-guided precision medicine to individualize treatment strategies in MM has had limited success, in part due to the genetic and molecular complexity of the disease. Functional precision medicine, a complementary approach in which patient treatment is guided by the ex vivo drug response of patient cells, has not yet been evaluated for MM systematically.
Ključne besede: mutliple myeloma, hematology, precision medicine, microscopy, deep learning, phenotyping, oncology, proteotype
Objavljeno v RUNG: 11.11.2024; Ogledov: 212; Prenosov: 2
Povezava na datoteko
Gradivo ima več datotek! Več...

Opis: Dark matter (DM), known to be a dominant matter component in the Universe, has been searched for extensively, yet remains undetected. One of the promising avenues of detecting a DM signal is to observe the so called ’DM sub-halos’ within our galaxy. These sub-halos, which are numerous within the Milky Way, are formed by the clustering of DM, as predicted by cosmological simulations, and most of them lack baryonic matter counterparts, making them challenging to detect. How- ever, the annihilation or decay of Weakly Interacting Massive Particles (WIMPs), a leading candidate for DM, within these sub-halos is expected to produce very high-energy (VHE) photons (called gamma-rays) at TeV energies, offering possible indirect DM detection. In this thesis, we focus on the Galactic Plane Survey (GPS) of the Cherenkov Tele- scope Array Observatory (CTAO), an upcoming ground-based gamma-ray obser- vatory, which promises unprecedented sensitivity and resolution in the detection of cosmic gamma-ray sources in the ∼ 30 GeV to ∼ 100 TeV energy range. As dark sub-halos are expected to appear as unidentified (point) sources in the CTAO GPS data, we employ a machine learning (ML)-based approach, the AutoSour- ceID framework, leveraging U-shaped networks (U-Nets) and Laplacian of Gaus- sian (LoG) filter, for automatic source detection and localization, and apply it to simulated GPS data. We establish detection thresholds for U-Nets trained on dif- ferently scaled counts (counts, square root or log of counts) and identify which approach offers best results (in terms of flux sensitivity and location accuracy). Our findings suggest that using log-scaled counts yields a factor of 1.7 lower flux threshold compared to counts alone. In addition, we compare our ML outcomes with traditional methods; however, this comparison is not straightforward, as ML and traditional approaches fundamentally differ in their methodologies and un- derlying assumptions. Nevertheless, The flux threshold obtained using log-scaled counts is comparable to that of the traditional likelihood-based detection method implemented in the Gammapy library, although further study is needed to estab- lish a more definitive comparison. These preliminary results also suggest that the flux threshold for detecting 90% of true sources with the ML approach is approx- imately two times lower than the sensitivity reported for the GPS in the CTAO publication. Although these results are not directly comparable due to differences in methodology, they hint that ML methods may offer superior performance in certain scenarios. Furthermore, we discuss the implications of our results on the sensitivity to DM sub-halos, improving it by a factor of 4, highlighting the possi- bility of detecting at least one sub-halo with a cross section approximately ⟨σv⟩ = 2.4 × 10−23 cm3 /s.
Ključne besede: Cherenkov Telescope Array Observatory, dark matter, sub-halos, machine learning, gamma-rays, master's thesis
Objavljeno v RUNG: 06.09.2024; Ogledov: 589; Prenosov: 12
Celotno besedilo (5,39 MB)

Ključne besede: fractals, fish, machine learning
Objavljeno v RUNG: 15.04.2024; Ogledov: 1391; Prenosov: 5
Povezava na datoteko
Gradivo ima več datotek! Več...

Opis: This paper is about the first automated systems developed to recognize identity. While automated recognition in the twenty-first century is widely associated with images of the human face, its roots are to be found in attempts to visualize identity in other, non-figural types of trace left by human bodies, ranging as widely as shadows, astrological signs, handwriting, the prints left by palms and fingers and the acoustics of the human voice. This paper investigates one such system of recognition as it emerged from within the telecommunications industry context in the midcentury U.S. Ostensibly built to reduce human labor and cable bandwidth, Bell Labs developed three different phone devices in the 1950s to photograph, formalize and analyze the sounds of speech as they traveled through the telephony system. And while the device called “Audrey” indeed succeeded in recognizing spoken digits, it was its failure to recognize the speech contents without prior awareness of the identity of the speaker, that is to distinguish between the individuality of the speaking “medium” and their intended meaning, that arguably made the experiment a landmark in the history of machine-driven recognition. Accounting for the “noise” made by the body and the environment from which sound emanated into the device, which the lab’s technicians defined as ranging from “speech defects” to “inflection” and “background interference” proved more important than phonetic analysis in determining the intended message of given speech spectogram. Similarly to a range of experiments with noise by formalist filmmakers such as Tony Conrad, John Cage, Kurt Kren and others, it was on the principle of contingency and irreproducible uniqueness that Bell Lab technicians sought to train machine-driven intelligence.
Ključne besede: History of computer science, machine learning, Bell Labs, history of telecommunications, sound studies
Objavljeno v RUNG: 19.02.2024; Ogledov: 1303; Prenosov: 8
Celotno besedilo (31,80 MB)

Opis: We report on the updated results on the search for photon-like-induced events in the data, collected by Telescope Array's Surface Detectors during the last 14 years. In order to search for photon-like-induced events, we trained a neural network on Monte-Carlo simulated data to distinguish between the proton-induced and photon-induced air showers. Both reconstructed composition-sensitive parameters and raw signals registered by the Surface Detectors are used as input data for the neural network. The classification threshold was optimized to provide the strongest possible constraint on the photons' flux.
Ključne besede: Telescope Array, indirect detection, surface detection, ground array, ultra-high energy, cosmic rays, photons, neural network, machine learning
Objavljeno v RUNG: 09.10.2023; Ogledov: 1889; Prenosov: 8
Celotno besedilo (543,46 KB)
Gradivo ima več datotek! Več...