FootGPT : A Large Language Model Development Experiment on a Minimal Setting

With recent empirical observations, it has been argued that the most significant aspect of developing accurate language models may be the proper dataset content and training strategy compared to the number of neural parameters, training duration or dataset size. Following this argument, we opted to fine tune a one billion parameter size trained general purpose causal language model with a dataset curated on team statistics of the Italian football league first ten game weeks, using low rank adaptation. The limited training dataset was compiled based on a framework where a powerful commercial large language model provides distilled paragraphs and question answer pairs as intended. The training duration was kept relatively short to provide a basis for our minimal setting exploration. We share our key observations on the process related to developing a specific purpose language model which is intended to interpret soccer data with constrained resources in this article.Comment: 10 pages, 3 figure

Entity Embeddings : Perspectives Towards an Omni-Modality Era for Large Language Models

Large Language Models (LLMs) are evolving to integrate multiple modalities, such as text, image, and audio into a unified linguistic space. We envision a future direction based on this framework where conceptual entities defined in sequences of text can also be imagined as modalities. Such a formulation has the potential to overcome the cognitive and computational limitations of current models. Several illustrative examples of such potential implicit modalities are given. Along with vast promises of the hypothesized structure, expected challenges are discussed as well.Comment: 9 pages, 5 figure

Ordered Minimum Distance Bag-of-Words Approach for Aerial Object Identification

Detecting potential aerial threats like drones with computer vision is at the paramount of interest for the protection of critical locations.This type of a system should prevent efficiently the false alarms caused by non-malign objects such as birds, which intrude the image plane. In this paper, we propose an improved version of a previously presented Speeded-up Robust Feature Transform (SURF) based algorithm, referred as Ordered Minimum Distance Bag-of-Words (omidBoW) to discriminate drones, birds and background from the patches, using an extended histogram set. We show that a SURF based object recognition can be well integrated to this context and this improved algorithm can increase accuracy up to 16% compared to regular bag-ofwords approach

Generic Fourier Descriptors for Autonomous UAV Detection

With increasing number of Unmanned Aerial Vehicles (UAVs) -also known as drones- in our lives, safety and privacy concerns have arose. Especially, strategic locations such as governmental buildings, nuclear power stations etc. are under direct threat of these publicly available and easily accessible gadgets. Various methods are proposed as counter-measure, such as acoustics based detection, RF signal interception, micro-doppler RADAR etc. Computer vision based approach for detecting these threats seems as a viable solution due to various advantages. We envision an autonomous drone detection and tracking system for the protection of strategic locations. In this work, 2-dimensional scale, rotation and translation invariant Generic Fourier Descriptor (GFD) features (which are analyzed with a neural network) are used for classifying aerial targets as a drone or bird. For the training of this system, a large dataset composed of birds and drones is gathered from open sources. We have achieved up to 85.3% overall correct classification rate

Using Shape Descriptors for UAV Detection

The rapid development of Unmanned Aerial Vehicle (UAV) technology, -also known as drones- has raised concerns on the safety of critical locations such as governmental buildings, nuclear stations, crowded places etc. Computer vision based approach for detecting these threats seems as a viable solution due to various advantages. We envision an autonomous drone detection and tracking system for the protection of strategic locations. It has been reported numerous times that, one of the main challenges for aerial object recognition with computer vision is discriminating birds from the targets. In this work, we have used 2-dimensional scale, rotation and translation invariant Generic Fourier Descriptor (GFD) features and classified targets as a drone or bird by a neural network. For the training of this system, a large dataset composed of birds and drones is gathered from open sources. We have achieved up to 85.3% overall correct classification rate

Near-field radiation from nano-particles and nano-antennas illuminated with a focused beam of light

The interaction of photons with metallic nanoparticles and nanoantennas yields large enhancement and tight localization of electromagnetic fields in the vicinity of nanoparticles. In the first part of this study, the interaction of a spherical nanoparticle with focused beams of various angular spectra is investigated. This study demonstrates that the focused light can be utilized to manipulate the near-field radiation around nanoparticles. In the second part of this study, the interaction between linearly and radially polarized focused light with prolate spheroidal nanoparticles and nano-antennas is investigated. Strong and tightly localized longitudinal components of a radially polarized focused beam can excite strong plasmon modes on elongated nanoparticles such as prolate spheroids. The effect of a focused beam on parameters such as the numerical aperture of a beam and the wavelength of incident light, as well as particle geometry and composition are also studied

Prevalence of tick-borne haemoparasites in small ruminants in Turkey and diagnostic sensitivity of single-PCR and RLB

Background: Tick-borne haemoparasitic diseases (TBHDs), caused by Theileria, Babesia, Anaplasma and Ehrlichia, are common in regions of the world where the distributions of host, pathogen and vector overlap. Many of these diseases threaten livestock production and some also represent a concern to human public health. The primary aim of this study was to determine the prevalence of the above-mentioned pathogens in a large number of blood samples (n = 1979) collected from sheep (n = 1727) and goats (n = 252) in Turkey. A secondary aim was to assess the diagnostic sensitivity of a number of species-specific polymerase chain reaction (PCR) tests and the reverse line blotting (RLB) assay. DNA samples were screened using species-specific PCR for the presence of Theileria ovis, Theileria sp. MK, T. lestoquardi, T. uilenbergi, T. luwenshuni, Babesia ovis, Anaplasma ovis and A. phagocytophilum while RLB was undertaken to test for the presence of all known Theileria, Babesia, Anaplasma and Ehrlichia species. The diagnostic sensitivity of these two approaches was then compared in terms of their ability to detect single species and mixed infections. Results: Overall, 84 and 74.43% of the small ruminants sampled were identified as hosting one or more pathogen(s) by species-specific PCR and RLB respectively. The presence of Theileria sp. OT1, T. luwenshuni and T. uilenbergi in Turkey was revealed for the first time while the presence of Babesia motasi, B. crassa and T. separata in Turkish small ruminants was confirmed using molecular methods. A high prevalence of mixed infection was evident, with PCR and RLB approaches indicating that 52.24 and 35.42% of animals were co-infected with multiple species, respectively. More than 80% of the mixed infections contained T. ovis and/or A. ovis. The RLB approach was found to be capable of detecting mixed infections with species such as Theileria sp. OT1, Theileria sp. OT3, T. separata, B. crassa and Babesia spp. Conclusion: The results indicated that pathogens causing TBHDs are highly prevalent in sheep and goats in Turkey. The diagnostic sensitivity of species-specific single PCR was generally higher than that of RLB. However, the latter approach was still capable of identifying a high proportion of individuals containing mixed-species infections. The use of species-specific single PCR is recommended to accurately estimate pathogen prevalence and to identify co-infected hosts

Simultaneous ipsilateral proximal interphalangeal and metacarpophalangeal dislocation of the fifth phalanx: A case report

We propose, analyze and demonstrate the optoelectronic phase-locking of optical waves whose frequencies are chirped continuously and rapidly with time. The optical waves are derived from a common optoelectronic swept-frequency laser based on a semiconductor laser in a negative feedback loop, with a precisely linear frequency chirp of 400 GHz in 2 ms. In contrast to monochromatic waves, a differential delay between two linearly chirped optical waves results in a mutual frequency difference, and an acoustooptic frequency shifter is therefore used to phase-lock the two waves. We demonstrate and characterize homodyne and heterodyne optical phase-locked loops with rapidly chirped waves, and show the ability to precisely control the phase of the chirped optical waveform using a digital electronic oscillator. A loop bandwidth of ∼ 60 kHz, and a residual phase error variance of < 0.01 rad^2 between the chirped waves is obtained. Further, we demonstrate the simultaneous phase-locking of two optical paths to a common master waveform, and the ability to electronically control the resultant two-element optical phased array. The results of this work enable coherent power combining of high-power fiber amplifiers—where a rapidly chirping seed laser reduces stimulated Brillouin scattering—and electronic beam steering of chirped optical waves

Correlation of [18F]FDG PET activity with expressions of Ki-67 in non-small-cell lung cancer

Background: Lung carcinoma is the most commonly diagnosed cancer throughout the world and is the leading cause of cancer-related deaths. Non-small cell lung cancer (NSCLC) accounts for up to 80% of newly diagnosed lung cancer cases. This study aimed to investigate the relationship between Ki-67 proliferation index (PI) and the maximum standardized uptake value (SUVmax) obtained from [18F]FDG PET/CT in NSCLCs and whether prognosis was predicted with SUVmax values.Material and methods: This retrospective study included biopsy and resection materials of 41 patients, who were examined in the pathology laboratory of Konya Training and Research Hospital between January 2010 and December 2019, and diagnosed with NSCLC, and whose [18F]FDG PET/CT images were present.Results: There was no significant difference between histopathological subtypes in terms of age (p = 0.077), Ki-67 PI (p = 0.454), and SUVmax (p = 0.143). No correlation was observed between Ki-67 PI and SUVmax values obtained from [18F]FDG PET/CT (p = 0.338, r = 0.153). There was no significant correlation between Ki-67 PI and tumor diameter (p = 0.531). The SUVmax value was found to be lower (12.78 ± 6.14) in tumors measuring ≤ 2.5 in diameter and higher (18.46 ± 7.81) in tumors measuring &gt; 2.5 cm (p = 0.027). Metastases not proven histopathologically but detected in [18F]FDG PET/CT were found to have no significant correlation with Ki-67 and SUVmax values (p = 0.881, p = 0.837).Conclusions: This study showed that there was no significant relationship between Ki-67 PI and SUVmax value obtained from [18F]FDG PET/CT in NSCLC tumors