Miracle-dz/UniFAR-FTUs huggingface dataset:
Model-free Learning for Risk-constrained Linear Quadratic Regulator with Structured Feedback in Networked Systems

We develop a model-free learning algorithm for the infinite-horizon linear quadratic regulator (LQR) problem. Specifically, (risk) constraints and structured feedback are considered, in order to reduce the state deviation while allowing for a sparse communication graph in practice. By reformulating the dual problem as a nonconvex-concave minimax problem, we adopt the gradient descent max-oracle (GDmax), and for model-free setting, the stochastic (S)GDmax using zero-order policy gradient. By bounding the Lipschitz and smoothness constants of the LQR cost using specifically defined sublevel sets, we can design the stepsize and related parameters to establish convergence to a stationary point (at with high probability). Numerical tests in a networked microgrid control problem have validated the convergence of our proposed SGDmax algorithm while demonstrating the effectiveness of risk constraints. The SGDmax algorithm has attained a satisfactory optimality gap compared to the classical LQR control, especially for the full feedback case.
Optimising Communication Overhead in Federated Learning Using NSGA-II

Federated learning is a training paradigm according to which a server-based model is cooperatively trained using local models running on edge devices and ensuring data privacy. These devices exchange information that induces a substantial communication load, which jeopardises the functioning efficiency. The difficulty of reducing this overhead stands in achieving this without decreasing the model's efficiency (contradictory relation). To do so, many works investigated the compression of the pre/mid/post-trained models and the communication rounds, separately, although they jointly contribute to the communication overload. Our work aims at optimising communication overhead in federated learning by (I) modelling it as a multi-objective problem and (II) applying a multi-objective optimization algorithm (NSGA-II) to solve it. To the best of the author's knowledge, this is the first work that \texttt{(I)} explores the add-in that evolutionary computation could bring for solving such a problem, and \texttt{(II)} considers both the neuron and devices features together. We perform the experimentation by simulating a server/client architecture with 4 slaves. We investigate both convolutional and fully-connected neural networks with 12 and 3 layers, 887,530 and 33,400 weights, respectively. We conducted the validation on the \texttt{MNIST} dataset containing 70,000 images. The experiments have shown that our proposal could reduce communication by 99% and maintain an accuracy equal to the one obtained by the FedAvg Algorithm that uses 100% of communications.
SMORS: A soft multirotor UAV for multimodal locomotion and robust interaction

We present SMORS, the first Soft fully actuated MultirOtoR System for multimodal locomotion. Unlike conventional hexarotors, SMORS is equipped with three rigid and three continuously soft arms, with each arm hosting a propeller. We create a bridge between the fields of soft and aerial robotics by mechanically coupling the actuation of a fully actuated flying platform with the actuation of a soft robotic manipulator. Each rotor is slightly tilted, allowing for full actuation of the platform. The soft components combined with the platform's full actuation allow for a robust interaction, in the form of efficient multimodal locomotion. In this work, we present the dynamical model of the platform, derive a closed-loop control, and present simulation results fortifying the robustness of the platform under a jumping-flying maneuver. We demonstrate in simulations that our multimodal locomotion approach can be more energy-efficient than the flight with a hexarotor.
A lightweight and accurate YOLO-like network for small target detection in Aerial Imagery

Despite the breakthrough deep learning performances achieved for automatic object detection, small target detection is still a challenging problem, especially when looking at fast and accurate solutions suitable for mobile or edge applications. In this work we present YOLO-S, a simple, fast and efficient network for small target detection. The architecture exploits a small feature extractor based on Darknet20, as well as skip connection, via both bypass and concatenation, and reshape-passthrough layer to alleviate the vanishing gradient problem, promote feature reuse across network and combine low-level positional information with more meaningful high-level information. To verify the performances of YOLO-S, we build"AIRES", a novel dataset for cAr detectIon fRom hElicopter imageS acquired in Europe, and set up experiments on both AIRES and VEDAI datasets, benchmarking this architecture with four baseline detectors. Furthermore, in order to handle efficiently the issue of data insufficiency and domain gap when dealing with a transfer learning strategy, we introduce a transitional learning task over a combined dataset based on DOTAv2 and VEDAI and demonstrate that can enhance the overall accuracy with respect to more general features transferred from COCO data. YOLO-S is from 25% to 50% faster than YOLOv3 and only 15-25% slower than Tiny-YOLOv3, outperforming also YOLOv3 in terms of accuracy in a wide range of experiments. Further simulations performed on SARD dataset demonstrate also its applicability to different scenarios such as for search and rescue operations. Besides, YOLO-S has an 87% decrease of parameter size and almost one half FLOPs of YOLOv3, making practical the deployment for low-power industrial applications.
Automating Cryptographic Protocol Language Generation from Structured Specifications

Security of cryptographic protocols can be analysed by creating a model in a formal language and verifying the model in a tool. All such tools focus on the last part of the analysis, verification, and the interpretation of the specification is only explained in papers. Rather, we focus on the interpretation and modelling part by presenting a tool to aid the cryptographer throughout the process and automatically generating code in a target language. We adopt a data-centric approach where the protocol design is stored in a structured way rather than as textual specifications. Previous work shows how this approach facilitates the interpretation to a single language (for Tamarin) which required aftermath modifications. By improving the expressiveness of the specification data structure we extend the tool to export to an additional formal language, ProVerif, as well as a C++ fully running implementation. Furthermore, we extend the plugins to verify correctness in ProVerif and executability lemmas in Tamarin. In this paper we model the Diffie-Hellman key exchange, which is traditionally used as a case study; a demo is also provided for other commonly studied protocols, Needham-Schroeder and Needham-Schroeder-Lowe.
Serum adenosine deaminase activity: A novel test for early diagnosis of pulmonary tuberculosis - What about efficacy?

Background: Pulmonary tuberculosis is a tremendous public health problem, increasing significantly, especially in developing countries. Getting a TB-free globe appears to be not more than a dream. Mycobacterial culturing is the gold standard for precise diagnosis but requires six-week time, in the meantime, patients remain a source of inadvertent disease dissemination in the community. The unavailability of sputum also poses a challenge several times. As adenosine deaminase level measurement is easy, rapid and independent of a sputum sample, this study was conducted to determine the diagnostic efficacy of adenosine deaminase activity for pulmonary tuberculosis.Patients and methods: This prospective study was conducted at Pulmonology-OPD, Gulab Devi Chest Hospital, Lahore, from 01-01-2019 to 30-06-2019. A total of 300 sputum smear-positive patients with clinical and radiological evidence consistent with diagnosis of pulmonary tuberculosis and 30 normal participants without any clinical, radiological or haematological evidence of tuberculosis were included. While, smear-negative patients and those having any evidence of hepatic disease, hematopoietic malignancy, infectious mononucleosis, typhoid or pregnancy were excluded. The serum ADA level of each participant was determined. All patients were subjected to AFB culture. Diagnostic accuracy was determined by considering sputum culture as a gold standard.Results: At cut-off value of >15 IU/L, a sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy of 98.30%, 100.00%, 100.00%, 85.71%, 98.46% respectively were defined. Conclusion: Serum ADA level, having excellent sensitivity and specificity, can diagnose pulmonary tuberculosis even if sputum sample is not available.
The Board Structure and Dividend Payout Policy: Empirical Evidence from BIST 100, Turkey

There are various theories regarding the secret of dividend preference over other alternatives. The most notable of these dividend theories is the agency theory. Dividends play a vital role in mitigating agency costs between shareholders and managers. This study is distinct from others since it includes more separate board structure variables with long study periods using a panel econometric model. Hence, the present study aims to investigate the links between board attributes and dividends payout policy of listed industrial companies of Turkey from 2011 to 2019. Data were collected from the sampled companies that were listed on the Borsa Istanbul BIST 100 indexes as of December 31, 2019, from Finnet (data provider). The study employed the panel Tobit model and found a significant positive effect of board size on dividend payout policy and a significant negative impact of board meeting frequency on the payout policy of listed companies in Turkey.
System Architecture for Brain-Computer Interface based on Machine Learning and Internet of Things

—Brain functions are required to be read for curing neurological illness. Brain-Computer Interface (BCI) connects the brain to the digital world for brain signals receiving, recording, processing, and comprehending. With a Brain-Computer Interface (BCI), the information from the user’s brain is fed into actuation devices, which then carry out the actions programmed into them. The Internet of Things (IoT) has made it possible to connect a wide range of everyday devices. Asynchronous BCIs can benefit from an improved system architecture proposed in this paper. Individuals with severe motor impairments will particularly get benefit from this feature. Control commands were translated using a rule-based translation algorithm in traditional BCI systems, which relied only on EEG recordings of brain signals. Examining BCI technology’s various and cross-disciplinary applications, this argument produces speculative conclusions about how BCI instruments combined with machine learning algorithms could affect the forthcoming procedures and practices. Compressive sensing and neural networks are used to compress and reconstruct ECoG data presented in this article. The neural networks are used to combine the classifier outputs adaptively based on the feedback. A stochastic gradient descent solver is employed to generate a multi-layer perceptron regressor. An example network is shown to take a 50% compression ratio and 89% reconstruction accuracy after training with real-world, medium-sized datasets as shown in this paper.
Revisiting the Rate-Dependent Mechanical Response of Typical Silicon Structures via Molecular Dynamics

Strain rate is a critical parameter in the mechanical application of nano-devices. A comparative atomistic study on both perfect monocrystalline silicon crystal and silicon nanowire was performed to investigate how the strain rate affects the mechanical response of these silicon structures. Using a rate response model, the strain rate sensitivity and the critical strain rate of two structures were given. The rate-dependent dislocation activities in the fracture process were also discussed, from which the dislocation nucleation and motion were found to play an important role in the low strain rate deformations. Finally, through the comparison of five equivalent stresses, the von Mises stress was verified as a robust yield criterion of the two silicon structures under the strain rate effects.
Principles or Practice? The Impact of Natural Resource Management on Farmer Well-being and Social Connectedness

Abstract Ensuring natural resource management (NRM) positively impacts the quality of life of a farmer is important to farmer mental health and persistence with adoption. Evidence suggests the well-being co-benefits of NRM are related to either ‘doing’ NRM practices or applying socioecological systems (SES) principles to guide NRM. We examine the relationship between a common NRM practice—tree planting, and farmer well-being, psychological distress, and social connectedness. Using hierarchical regression models, we test to see if these relationships remain with the addition of a measure of SES principles. Results show that while NRM practice was associated with improved social connectedness, only SES principles were associated with well-being. Neither NRM practice nor SES principles were associated with psychological distress. Both NRM practices and SES principles have the potential to positively impact farmers' lives and ensuring NRM programs have co-benefits to farmer well-being may help to encourage long-term engagement.
Molecular Characterization of Toxoplasma gondii in Cats and Its Zoonotic Potential for Public Health Significance

Toxoplasmosis is a globally distributed disease of warm-blooded animals. It is caused by the opportunistic parasite Toxoplasma gondii (T. gondii). One-third of the global human population is believed to be infected with T. gondii. Cats serve as final host of T. gondii and are the main source of contamination of soil and water. This study aimed to detect genotypes of T. gondii in cats. Fecal samples (n = 400) were collected from districts of South Punjab (Khanewal and Sahiwal), and were processed by polymerase chain reaction (PCR) followed by sequencing and phylogenetic analysis. The obtained oligonucleotide sequences (T. gondii) were submitted to the GenBank database, and the evolutionary tree was constructed using MEGA-X software. Seven fecal samples (3.5%) from cats were positive. Five out of thirteen fecal samples (38.46%) found to be positive for T. gondii with microscopy were confirmed by PCR. After phylogenetic analysis with 3 clonal types and atypical strains, isolates of T. gondii in current study were more closely linked to a typical strain (AF249696). Besides genotyping from cats, seroprevalence from humans and ruminants is still considered to be the best and easiest way to identify the Toxoplasma. Blood samples were collected from sheep and goats (n = 2000 each), and human blood samples (n = 400) were collected from the same vicinity. Seroprevalence was determined using a commercial enzyme-linked immunosorbent assay (ELISA) kit. In Khanewal, the blood samples of 292 goats (29.2%) and 265 sheep (26.5%), and 6 fecal samples from cats (3%) were positive. Out of 200 human blood samples, 52 were positive, with a seroprevalence of 26%. In the Sahiwal district, the blood samples from 49 humans, 235 sheep and 348 goats were positive, with seroprevalence of 24.5%, 23.5% and 34.8%, respectively. The present study revealed the current circulating genotype of T. gondii from cats in the districts Khanewal and Sahiwal and the seroprevalence of the organism in small ruminants and humans living in the same vicinity. Further genotype analyses of the organism from ruminants and humans are needed.
Quantum Biology Research Meets Pathophysiology and Therapeutic Mechanisms: A Biomedical Perspective

The recent advances of quantum biology suggest a potential role in biomedical research. Studies related to electromagnetic fields, proton pumping in mitochondrial respiratory chain, quantum theory of T-cell receptor (TCR)-degeneracy, theories on biophotons, pyrophosphates or tubulin as possible carriers for neural information, and quantum properties of ions and protons, might be useful for understanding mechanisms of some serious immune, cardiovascular, and neural pathologies for which classic biomedical research, based on biochemical approach, is struggling to find new therapeutic strategies. A breakthrough in medical knowledge is therefore needed in order to improve the understanding of the complex interactions among various systems and organs typical of such pathologies. In particular, problems related to immune system over-activation, to the role of autonomic nervous system (ANS) dysfunction in the obstructive sleep apnea (OSA) syndrome, to the clinical consequences of ion channels dysfunction and inherited cardiac diseases, could benefit from the new perspective provided by quantum biology advancement. Overall, quantum biology might provide a promising biophysical theoretic system, on which to base pathophysiology understanding and hopefully therapeutic strategies. With the present work, authors hope to open a constructive and multidisciplinary debate on this important topic.
SME efficiency in transforming regional business research and innovation investments into innovative sales output

ABSTRACT Based on data provided by the Regional Innovation Scoreboard on 23 capital and 184 non-capital regions in Europe, slacks-based models of data envelopment analysis (DEA) reveal that the efficiency by which business research and innovation inputs are converted at regional-level aggregated innovative sales output in small and medium-sized enterprises was significantly lower in capital regions in the period 2006–14. In view of efficiency maximization, a majority of the capital regions overinvest in non-research and development innovation activities, are over-specialized in knowledge-intensive industries, and fall behind in converting research and innovation inputs in intermediary intellectual property outcomes.
Tunable Properties via Composition Modulations of Poly(vinyl alcohol)/Xanthan Gum/Oxalic Acid Hydrogels

The design of hydrogel networks with tuned properties is essential for new innovative biomedical materials. Herein, poly(vinyl alcohol) and xanthan gum were used to develop hydrogels by the freeze/thaw cycles method in the presence of oxalic acid as a crosslinker. The structure and morphology of the obtained hydrogels were investigated by means of scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, and swelling behavior. The SEM analysis revealed that the surface morphology was mostly affected by the blending ratio between the two components, namely, poly(vinyl alcohol) and xanthan gum. From the swelling study, it was observed that the presence of oxalic acid influenced the hydrophilicity of blends. The hydrogels based on poly(vinyl alcohol) without xanthan gum led to structures with a smaller pore diameter, a lower swelling degree in pH 7.4 buffer solution, and a higher elastic modulus. The antimicrobial activity of the prepared hydrogels was tested and the results showed that the hydrogels conferred antibacterial activity against Gram positive bacteria (Staphylococcus aureus 25923 ATCC) and Gram negative bacteria (Escherichia coli 25922 ATCC).
The benefits, risks and alternatives of mitochondrial replacement therapy – bringing proportionality into public policy debate

Mitochondrial replacement therapy (MRT) utilises nuclear transfer technology to replace defective mitochondria with healthy ones and thereby minimise the risk of a mitochondrial disease passing from a mother to her child. It promises much but comes with ethical controversy, significant risk of harm and many unknowns. Forming a position on MRT requires accurate information about the current state of knowledge, and an appreciation of the ethical issues at stake. Ethical deliberations will vary depending on the framework used. There are in principle objections to MRT on the grounds of direct harm to human embryos and germline genetic modification. But even without these objections MRT can be weighed in terms of the balance between risks and benefits, alternatives and uncertainties. This paper explores the evidence and lays out the relevant issues to assist such a deliberation.
Spectrum of BRCA1/2 Mutations in Romanian Breast and Ovarian Cancer Patients

Background: About 10,000 women are diagnosed with breast cancer and about 2000 women are diagnosed with ovarian cancer each year in Romania. There is an insufficient number of genetic studies in the Romanian population to identify patients at high risk of inherited breast and ovarian cancer. Methods: We evaluated 250 women of Romanian ethnicity with BC and 240 women of Romanian ethnicity with ovarian cancer for the presence of damaging germline mutations in breast cancer genes 1 and 2 (BRCA1 and BRCA2, respectively) using Next-Generation Sequencing (NGS) technology. Results: Of the 250 breast cancer patients, 47 carried a disease-predisposing BRCA mutation (30 patients (63.83%) with a BRCA1 mutation and 17 patients (36.17%) with a BRCA2 mutation). Of the 240 ovarian cancer patients, 60 carried a BRCA mutation (43 patients (72%) with a BRCA1 mutation and 17 patients (28%) with a BRCA2 mutation). In the BRCA1 gene, we identified 18 variants (4 in both patient groups (ovarian and breast cancer patients), 1 mutation variant in the BC patient group, and 13 mutation variants in the ovarian cancer patient group). In the BRCA2 gene, we identified 17 variants (1 variant in both ovarian and breast cancer patients, 6 distinct variants in BC patients, and 10 distinct variants in ovarian cancer patients). The prevailing mutation variants identified were c.3607C>T (BRCA1) (18 cases) followed by c.5266dupC (BRCA1) (17 cases) and c.9371A>T (BRCA2) (12 cases). The most prevalent mutation, BRCA1 c.3607C>T, which is less common in the Romanian population, was mainly associated with triple-negative BC and ovarian serous adenocarcinoma. Conclusion: The results of our analysis may help to establish specific variants of BRCA mutations in the Romanian population and identify individuals at high risk of hereditary breast and ovarian cancer syndrome by genetic testing.
Multiple Hydrophone Arrays based Underwater Localization with Matching Field Processing

—Matched ﬁeld processing technology (MFP) is a general passive localization method for underwater sound source due to its advantages in ultra-long distance positioning. In this paper, assume the total number of hydrophones remains unchanged, a single hydrophone array is divided into multiple hydrophone sub-arrays for independent positioning, and the positioning results of sub-arrays are fused to reduce the impact of noise and improve the robustness of the positioning system. Based on the traditional Bartlett processor, we derive the formula for average positioning error which varies with signal to noise ratio ( SNR ) and the number of hydrophones. The formula is used to decide the optimal structure of sub-arrays, i.e., the number of sub-arrays and the number of hydrophones in each sub- array. Experiments and simulations proves that multiple sub-arrays can improve the positioning accuracy compared with the single hydrophone array in the noisy environment. The average positioning errors produced by the experiments are consistent with the numerical ones based on the theoretical analysis.