Miracle-dz/UniFAR-FTUs huggingface dataset:
Dijagnoza metaboličke inzulinske rezistencije Diagnosis of the Metabolic Insulin Resistance

Sažetak Termin metabolička inzulinska rezistencija upućuje na nesposobnost inzulina da proizvede svoj uobičajeni biološki učinak u koncentraciji koja je učinkovita u tzv. zdravih pojedinaca. Metabolička inzulinska rezistencija može se razviti vezano uza sve aspekte djelovanja inzulina. Na prisutnost metaboličke inzulinske rezistencije moramo posumnjati u osoba s obiteljskom anamnezom pozitivnom na šećernu bolest u bliskih rođaka, bolesnica s gestacijskom šećernom bolešću ili sindromom policističnih jajnika te u osoba s oštećenom tolerancijom glukoze, arterijskom hipertenzijom, dislipidemijom, povišenim vrijednostima jetrenih enzima te u debelih osoba, poglavito onih s abdominalnim tipom pretilosti. S obzirom na to da se inzulinska rezistencija često javlja značajno prije nego što se pojavi ili ustanovi neko od gore spomenutih stanja, identifi ciranje i liječenje inzulinske rezistencije u općoj populaciji ima određeno preventivno značenje. U radu je opisano nekoliko metoda za praćenje prisutnosti i stupnja inzulinske rezistencije: clamp tehnika, FSIVGTT (Frequently sampled intravenous glucose tolerance test), CIGMA (Continuous infusion of glucose with model assessment) te HOMA (homeostasis model assessment). Danas najčešće rabljena metoda HOMA jednostavna je za procjenu funkcije beta-stanica i stupnja inzulinske rezistencije iz bazalnih vrijednosti glukoze i koncentracije inzulina, odnosno Cpeptida.
Dynamical evolution of massive perturbers in realistic multicomponent galaxy models I: implementation and validation

Galaxies are self-gravitating structures composed by several components encompassing spherical, axial and triaxial symmetry. Although real systems feature heterogeneous components whose properties are intimately connected, semi-analytical approaches often exploit the linearity of the Poisson's equation to represent the potential and mass distribution of a multi-component galaxy as the sum of the individual components. In this work, we expand the semi-analytical framework developed in Bonetti et al. (2020) by including both a detailed implementation of the gravitational potential of exponential disc (modelled with a ${\rm sech}^2$ and an exponential vertical profile) and an accurate prescription for the dynamical friction experienced by massive perturbers in composite galaxy models featuring rotating disc structures. Such improvements allow us to evolve arbitrary orbits either within or outside the galactic disc plane. We validate the results obtained by our numerical model against public semi-analytical codes as well as full N-body simulations, finding that our model is in excellent agreement to the codes it is compared with. The ability to reproduce the relevant physical processes responsible for the evolution of massive perturber orbits and its computational efficiency make our framework perfectly suited for large parameter-space exploration studies.
Multimodal Speech Recognition with Unstructured Audio Masking

Visual context has been shown to be useful for automatic speech recognition (ASR) systems when the speech signal is noisy or corrupted. Previous work, however, has only demonstrated the utility of visual context in an unrealistic setting, where a fixed set of words are systematically masked in the audio. In this paper, we simulate a more realistic masking scenario during model training, called RandWordMask, where the masking can occur for any word segment. Our experiments on the Flickr 8K Audio Captions Corpus show that multimodal ASR can generalize to recover different types of masked words in this unstructured masking setting. Moreover, our analysis shows that our models are capable of attending to the visual signal when the audio signal is corrupted. These results show that multimodal ASR systems can leverage the visual signal in more generalized noisy scenarios.
Image Captioning with Visual Object Representations Grounded in the Textual Modality

We present our work in progress exploring the possibilities of a shared embedding space between textual and visual modality. Leveraging the textual nature of object detection labels and the hypothetical expressiveness of extracted visual object representations, we propose an approach opposite to the current trend, grounding of the representations in the word embedding space of the captioning system instead of grounding words or sentences in their associated images. Based on the previous work, we apply additional grounding losses to the image captioning training objective aiming to force visual object representations to create more heterogeneous clusters based on their class label and copy a semantic structure of the word embedding space. In addition, we provide an analysis of the learned object vector space projection and its impact on the IC system performance. With only slight change in performance, grounded models reach the stopping criterion during training faster than the unconstrained model, needing about two to three times less training updates. Additionally, an improvement in structural correlation between the word embeddings and both original and projected object vectors suggests that the grounding is actually mutual.
Bridging the Gap Between Markowitz Planning and Deep Reinforcement Learning

While researchers in the asset management industry have mostly focused on techniques based on financial and risk planning techniques like Markowitz efficient frontier, minimum variance, maximum diversification or equal risk parity , in parallel, another community in machine learning has started working on reinforcement learning and more particularly deep reinforcement learning to solve other decision making problems for challenging task like autonomous driving , robot learning, and on a more conceptual side games solving like Go. This paper aims to bridge the gap between these two approaches by showing Deep Reinforcement Learning (DRL) techniques can shed new lights on portfolio allocation thanks to a more general optimization setting that casts portfolio allocation as an optimal control problem that is not just a one-step optimization, but rather a continuous control optimization with a delayed reward. The advantages are numerous: (i) DRL maps directly market conditions to actions by design and hence should adapt to changing environment , (ii) DRL does not rely on any traditional financial risk assumptions like that risk is represented by variance, (iii) DRL can incorporate additional data and be a multi inputs method as opposed to more traditional optimization methods. We present on an experiment some encouraging results using convolution networks.
A Comprehensive Overview on 5G-and-Beyond Networks With UAVs: From Communications to Sensing and Intelligence

Due to the advancements in cellular technologies and the dense deployment of cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the fifth-generation (5G) and beyond cellular networks is a promising solution to achieve safe UAV operation as well as enabling diversified applications with mission-specific payload data delivery. In particular, 5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in three-dimensional (3D) space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference. Besides the requirement of high-performance wireless communications, the ability to support effective and efficient sensing as well as network intelligence is also essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting aerial and ground users. In this paper, we provide a comprehensive overview of the latest research efforts on integrating UAVs into cellular networks, with an emphasis on how to exploit advanced techniques (e.g., intelligent reflecting surface, short packet transmission, energy harvesting, joint communication and radar sensing, and edge intelligence) to meet the diversified service requirements of next-generation wireless systems. Moreover, we highlight important directions for further investigation in future work.
Classification, Evolution, and the Nature of Biology

Acknowledgements 1. Introduction 2. Patterns of classification 3. Patterns of phylogeny 4. Homology and the evidence for evolution 5. Geological and geographical evidence 6. Methods of classification: the development of taxonomy 7. Methods of classification: phenetics and cladistics 8. Methods of classification: the current debate 9. Classification and the reconstruction of phylogeny 10. Is systematics independent? 11. Mechanisms of evolution: Darwinism and its rivals 12. Mechanisms of evolution: the synthetic theory 13. Scientific knowledge 14. Philosophy and biology References Author index Subject index.
A dual formulation of wavefield reconstruction inversion for large-scale seismic inversion

Many of the seismic inversion techniques currently proposed that focus on robustness with respect to the background model choice are not apt to large-scale 3D applications, and the methods that are computationally feasible for industrial problems, such as full waveform inversion, are notoriously limited by convergence stagnation and require adequate starting models. We propose a novel solution that is both scalable and less sensitive to starting models or inaccurate parameters (such as anisotropy) that are typically kept fixed during inversion. It is based on a dual reformulation of the classical wavefield reconstruction inversion, whose empirical robustness with respect to these issues is well documented in the literature. While the classical version is not suited to 3D, as it leverages expensive frequency-domain solvers for the wave equation, our proposal allows the deployment of state-of-the-art time-domain finite-difference methods, and is potentially mature for industrial-scale problems.
A method to evaluate the reliability of social media data for social network analysis

In order to study the effects of Online Social Network (OSN) activity on real-world offline events, researchers need access to OSN data, the reliability of which has particular implications for social network analysis. This relates not only to the completeness of any collected dataset, but also to constructing meaningful social and information networks from them. In this multidisciplinary study, we consider the question of constructing traditional social networks from OSN data and then present a measurement case study showing how the reliability of OSN data affects social network analyses. To this end we developed a systematic comparison methodology, which we applied to two parallel datasets we collected from Twitter. We found considerable differences in datasets collected with different tools and that these variations significantly alter the results of subsequent analyses. Our results lead to a set of guidelines for researchers planning to collect online data streams to infer social networks.
Blockchain Based Decentralized Cyber Attack Detection for Large Scale Power Systems

Large scale power systems are comprised of regional utilities with IIoT enabled assets that stream sensor readings in real time. In order to detect cyberattacks, the globally acquired, real time sensor data needs to be analyzed in a centralized fashion. However, owing to operational constraints, such a centralized sharing mechanism turns out to be a major obstacle. In this paper, we propose a blockchain based decentralized framework for detecting coordinated replay attacks with full privacy of sensor data. We develop a Bayesian inference mechanism employing locally reported attack probabilities that is tailor made for a blockchain framework. We compare our framework to a traditional decentralized algorithm based on the broadcast gossip framework both theoretically as well as empirically. With the help of experiments on a private Ethereum blockchain, we show that our approach achieves good detection quality and significantly outperforms gossip driven approaches in terms of accuracy, timeliness and scalability.
Hector: Using Untrusted Browsers to Provision Web Applications

Web applications are on the rise and rapidly evolve into more and more mature replacements for their native counterparts. This disruptive trend is mainly driven by the attainment of platform-independence and instant deployability. On top of this, web browsers offer the opportunity for seamless browser-to-browser communication for distributed interaction. In this paper, we present Hector, a novel web application framework that transforms web browsers into a distributed application-centric computing platform. Hector enables offloading application logic to users, thereby improving user experience with lower latencies while generating less costs for service providers. Following the programming paradigm of Function-as-a-Service, applications are decomposed into functions so they can be managed efficiently and deployed in a responsive, scalable and lightweight fashion. In case of client-side resource shortage or unresponsive clients, execution falls back to a traditional cloud-based infrastructure. Hector combines WebAssembly for multi-language computations at near-native speed, WebRTC for browser-to-browser communication and trusted execution as provided by the Intel Software Guard Extensions so browsers can trust each other's computations. We evaluate Hector by implementing a digital assistant as well as a recommendation system. Our evaluation shows that Hector achieves lower end-user latencies while generating less costs than traditional deployments. Additionally, we show that Hector scales linearly with increasing client numbers and can cope well with unresponsive clients.
Making restoration history: reconsidering Aldo Leopold's Arboretum dedication speeches

Scholarship surrounding Aldo Leopold's involvement in the University of Wisconsin Arboretum in Madison, Wisconsin, has often pointed to Leopold's 1934 dedication speech as a significant historical artifact. While reflecting both Arboretum history and the development of Leopold's thought and influence, the speech may also have been the first publically articulated rationale for ecological restoration in the modern era. This scholarly personal essay describes my 50‐year relationship with both Leopold and the Arboretum, and my recent research that offers a new historical interpretation of Leopold's speech. Two versions of the speech exist; tensions between them have been discussed in at least two professional journals. What I learned from examining the archival record indicates that we really don't know for sure what Leopold said that June morning of 1934. What we can be sure of, however, is that the dedication speech usually attributed to Leopold, by such able scholars as Curt Meine and Baird Callicott, was not the one Leopold actually delivered. What many historians continue to refer to as Aldo Leopold's 1934 Arboretum dedication speech was actually written for another Wisconsin audience. As I explored this mystery, I rediscovered Leopold's passion for ecological restoration and his commitment to educate a diverse, Depression‐era public about its goals and purposes. For Leopold, restoration involved not only the expression of social and ecological values, but also public critique of the destructive social forces that make restoration necessary. Embodying social critique and ecological science, Leopold continues to model for us personal and professional roles as public ecological citizens dedicated to land.
Effect of feed energy levels on semen quality and freezability of young Murrah buffalo bulls

Effect of feed energy levels on semen quality and freezability of Murrah buffalo young bulls was investigated. Eighteen bulls (age ~24 mo and bwt ~428 kg) were randomly allotted to three groups of six in each. Group 1 was kept as control and bulls in this group were fed as per NRC (2001) recommendations, while feed of Group 2 and Group 3 bullswas same as in control andadditional 10% and 20% higher energy was provided through molasses to bulls of Group 2 and Group 3, respectively. Monthly feedintake of every bull was recorded. Semen was collected using AV technique at weekly interval; reaction time of bulls was recorded at collection. Ejaculate volume (ml), mass motility (0 to 5 scale), individual sperm motility (%), sperm concentration (m/ml); percent viability, acrosome integrity and morphological abnormality in fresh semen and post thaw sperm motility, viability and acrosome integrity in frozen semen were evaluated with microscopic methods. Results showed that the DM intake was higher (P < 0.01) in Group 3 than Group 2 and control. Reaction time (second) was the lowest (P < 0.01) in Group 2 followed by Group 1 and Group 3. Sperm concentration (m/ml) and mass motility was found to be higher (P < 0.05) in Group 2 than Group 3 and control. Similar trend (P < 0.05) of percent sperm motility, viability and acrosome integrity amongthe groups was observed. Whereas, significantly (P < 0.05) higher percent sperm abnormality was obtained in Group 3 followed by Group 1 and Group 2. Post-thaw percent sperm motility, viability and acrosome integrity were significantly higher (P < 0.05) in Group 2 than control and Group 3. From this study it can be concluded that 10% higher energy provided to the bulls in Group 2 than the control improved quality significantly of fresh and frozen semenin comparison to the control and other treatment group.
Synaptophysin is a β-Amyloid Target that Regulates Synaptic Plasticity and Seizure Susceptibility in an Alzhiemer’s Model

Alzheimer’s disease (AD), a condition characterized by cognitive deficits and progressive loss of memory, is causally linked to the short amyloid peptide Aβ42, which disrupts normal neurotransmission1,2. Neurotransmitter (NT) release from synaptic vesicles (SV) requires coordinated binding of the conserved core secretory machinery comprised of the soluble NSF attachment protein receptor (vSNARE) synaptobrevin 2 (VAMP2) on the SV and the cognate tSNAREs on the plasma membrane. Synaptophysin (SYP) is the most abundant SV protein3 and the major pre-fusion binding partner of VAMP24. A major challenge in understanding the etiology and prevention of AD is determining the proteins directly targeted by Aβ42 and elucidating if these targets mediate disease phenotypes. Here we demonstrate that Aβ42 binds to SYP with picomolar affinity and disrupts the SYP/VAMP2 complex resulting in inhibition of both neurotransmitter release and synaptic plasticity. While functionally redundant paralogs of SYP have masked its critical activity in knockout studies5,6, we now demonstrate a profound seizure susceptibility phenotype in SYP knockout mice that is recapitulated in an AD model mouse. Our studies imply a subtle yet critical role for SYP in the synaptic vesicle cycle and the etiology of AD.
Fast accuracy estimation of deep learning based multi-class musical source separation

Music source separation represents the task of extracting all the instruments from a given song. Recent breakthroughs on this challenge have gravitated around a single dataset, MUSDB, only limited to four instrument classes. Larger datasets and more instruments are costly and time-consuming in collecting data and training deep neural networks (DNNs). In this work, we propose a fast method to evaluate the separability of instruments in any dataset without training and tuning a DNN.This separability measure helps to select appropriate samples for the efficient training of neural networks. Based on the oracle principle with an ideal ratio mask, our approach is an excellent proxy to estimate the separation performances of state-of-the-art deep learning approaches such as TasNet or Open-Unmix.Our results contribute to revealing two essential points for audio source separation: 1) the ideal ratio mask, although light and straightforward, provides an accurate measure of the audio separability performance of recent neural nets, and 2) new end-to-end learning methods such as Tasnet, that operate directly on waveforms, are, in fact, internally building a Time-Frequency (TF) representation, so that they encounter the same limitations as the TF based-methods when separating audio pattern overlapping in the TF plane.
A Nesterov's Accelerated quasi-Newton method for Global Routing using Deep Reinforcement Learning

Deep Q-learning method is one of the most popularly used deep reinforcement learning algorithms which uses deep neural networks to approximate the estimation of the action-value function. Training of the deep Q-network (DQN) is usually restricted to first order gradient based methods. This paper attempts to accelerate the training of deep Q-networks by introducing a second order Nesterov's accelerated quasi-Newton method. We evaluate the performance of the proposed method on deep reinforcement learning using double DQNs for global routing. The results show that the proposed method can obtain better routing solutions compared to the DQNs trained with first order Adam and RMSprop methods.
Mesenchymal Paradigm: Implications in Morphogenesis

A micro-anatomic study of mesenchyme involvement in the morphogenesis of human biosystem is intended. The research was carried out on 80 sagittal serial sections taken from a 12-week human fetus. Spatial distribution of mesenchyme within the gut tube, urogenital and endocrine systems was analyzed. The results are based on the assumption that there are two forms of mesenchyme if one takes into consideration the embryonic origin: mesenchyme originating from mesoderm (meso-mesenchyme) and mesenchyme originating from ectoderm (ectomesenchyme) within the neural crest. We believe that in the morphogenesis of human biosystem, the mesenchyme can be induced byphenotype changes or it can act as an inductor in other structures. The significance of mesenchyme space distribution and of its phenotype changes in pathology is discussed.
Phylogeny of deep-level relationships within Euglenozoa based on combined small subunit and large subunit ribosomal DNA sequences

67 INTRODUCTION.67 MATERIALS AND METHODS . 70 RESULTS AND DISCUSSIONS . 72 CONCLUSION . 78 ACKNOWLEDGEMENTS . 79 REFERENCES.79 4 CONCLUSION.90 LSU rDNA IS A USEFUL MOLECULAR MARKER.92 KINETOPLASTIDS ARE AT THE BASE OF EUGLENOZOA.93 PLACE-HOLDER EUGLENOZOA IN THE TOL.94 ORPHAN TAXON PETALOMONAS CANTUSCYGNI . 94 REFERENCES.94