Miracle-dz/UniFAR-FTUs huggingface dataset:
Turbulence in compact to giant H ii regions

Radial velocity fluctuations on the plane of the sky are a powerful tool for studying the turbulent dynamics of emission line regions. We conduct a systematic statistical analysis of the H α velocity field for a diverse sample of nine ${\rm H\, \small{II}}$ regions, spanning two orders of magnitude in size and luminosity, located in the Milky Way and other Local Group galaxies. By fitting a simple model to the second-order spatial structure function of velocity fluctuations, we extract three fundamental parameters: the velocity dispersion, the correlation length, and the power-law slope. We determine credibility limits for these parameters in each region, accounting for observational limitations of noise, atmospheric seeing, and the finite map size. The plane-of-sky velocity dispersion is found to be a better diagnostic of turbulent motions than the line width, especially for lower luminosity regions where the turbulence is subsonic. The correlation length of velocity fluctuations is found to be always roughly 2 per cent of the ${\rm H\, \small{II}}$ region diameter, implying that turbulence is driven on relatively small scales. No evidence is found for any steepening of the structure function in the transition from subsonic to supersonic turbulence, possibly due to the countervailing effect of projection smoothing. Ionized density fluctuations are too large to be explained by the action of the turbulence in any but the highest luminosity sources. A variety of behaviours are seen on scales larger than the correlation length, with only a minority of sources showing evidence for homogeneity on the largest scales.
Deep Q-Learning versus Proximal Policy Optimization: Performance Comparison in a Material Sorting Task

This paper presents a comparison between two well-known deep Reinforcement Learning (RL) algorithms: Deep Q-Learning (DQN) and Proximal Policy optimization (PPO) in a simulated production system. We utilize a Petri Net (PN)-based simulation environment, which was previously proposed in related work. The performance of the two algorithms is compared based on several evaluation metrics, including average percentage of correctly assembled and sorted products, average episode length, and percentage of successful episodes. The results show that PPO outperforms DQN in terms of all evaluation metrics. The study highlights the advantages of policy-based algorithms in problems with high-dimensional state and action spaces. The study contributes to the field of deep RL in context of production systems by providing insights into the effectiveness of different algorithms and their suitability for different tasks.
A Modular Test Bed for Reinforcement Learning Incorporation into Industrial Applications

This application paper explores the potential of using reinforcement learning (RL) to address the demands of Industry 4.0, including shorter time-to-market, mass customization, and batch size one production. Specifically, we present a use case in which the task is to transport and assemble goods through a model factory following predefined rules. Each simulation run involves placing a specific number of goods of random color at the entry point. The objective is to transport the goods to the assembly station, where two rivets are installed in each product, connecting the upper part to the lower part. Following the installation of rivets, blue products must be transported to the exit, while green products are to be transported to storage. The study focuses on the application of reinforcement learning techniques to address this problem and improve the efficiency of the production process.
On the Coverage of Cognitive mmWave Networks with Directional Sensing and Communication

Millimeter-waves' propagation characteristics create prospects for spatial and temporal spectrum sharing in a variety of contexts, including cognitive spectrum sharing (CSS). However, CSS along with omnidirectional sensing, is not efficient at mmWave frequencies due to their directional nature of transmission, as this limits secondary networks' ability to access the spectrum. This inspired us to create an analytical approach using stochastic geometry to examine the implications of directional cognitive sensing in mmWave networks. We explore a scenario where multiple secondary transmitter-receiver pairs coexist with a primary transmitter-receiver pair, forming a cognitive network. The positions of the secondary transmitters are modelled using a homogeneous Poisson point process (PPP) with corresponding secondary receivers located around them. A threshold on directional transmission is imposed on each secondary transmitter in order to limit its interference at the primary receiver. We derive the medium-access-probability of a secondary user along with the fraction of the secondary transmitters active at a time-instant. To understand cognition's feasibility, we derive the coverage probabilities of primary and secondary links. We provide various design insights via numerical results. For example, we investigate the interference-threshold's optimal value while ensuring coverage for both links and its dependence on various parameters. We find that directionality improves both links' performance as a key factor. Further, allowing location-aware secondary directionality can help achieve similar coverage for all secondary links.
Deep recurrent spiking neural networks capture both static and dynamic representations of the visual cortex under movie stimuli

In the real world, visual stimuli received by the biological visual system are predominantly dynamic rather than static. A better understanding of how the visual cortex represents movie stimuli could provide deeper insight into the information processing mechanisms of the visual system. Although some progress has been made in modeling neural responses to natural movies with deep neural networks, the visual representations of static and dynamic information under such time-series visual stimuli remain to be further explored. In this work, considering abundant recurrent connections in the mouse visual system, we design a recurrent module based on the hierarchy of the mouse cortex and add it into Deep Spiking Neural Networks, which have been demonstrated to be a more compelling computational model for the visual cortex. Using Time-Series Representational Similarity Analysis, we measure the representational similarity between networks and mouse cortical regions under natural movie stimuli. Subsequently, we conduct a comparison of the representational similarity across recurrent/feedforward networks and image/video training tasks. Trained on the video action recognition task, recurrent SNN achieves the highest representational similarity and significantly outperforms feedforward SNN trained on the same task by 15% and the recurrent SNN trained on the image classification task by 8%. We investigate how static and dynamic representations of SNNs influence the similarity, as a way to explain the importance of these two forms of representations in biological neural coding. Taken together, our work is the first to apply deep recurrent SNNs to model the mouse visual cortex under movie stimuli and we establish that these networks are competent to capture both static and dynamic representations and make contributions to understanding the movie information processing mechanisms of the visual cortex.
Nonholonomic Motion Planning as Efficient as Piano Mover's

We present an algorithm for non-holonomic motion planning (or 'parking a car') that is as computationally efficient as a simple approach to solving the famous Piano-mover's problem, where the non-holonomic constraints are ignored. The core of the approach is a graph-discretization of the problem. The graph-discretization is provably accurate in modeling the non-holonomic constraints, and yet is nearly as small as the straightforward regular grid discretization of the Piano-mover's problem into a 3D volume of 2D position plus angular orientation. Where the Piano mover's graph has one vertex and edges to six neighbors each, we have three vertices with a total of ten edges, increasing the graph size by less than a factor of two, and this factor does not depend on spatial or angular resolution. The local edge connections are organized so that they represent globally consistent turn and straight segments. The graph can be used with Dijkstra's algorithm, A*, value iteration or any other graph algorithm. Furthermore, the graph has a structure that lends itself to processing with deterministic massive parallelism. The turn and straight curves divide the configuration space into many parallel groups. We use this to develop a customized 'kernel-style' graph processing method. It results in an N-turn planner that requires no heuristics or load balancing and is as efficient as a simple solution to the Piano mover's problem even in sequential form. In parallel form it is many times faster than the sequential processing of the graph, and can run many times a second on a consumer grade GPU while exploring a configuration space pose grid with very high spatial and angular resolution. We prove approximation quality and computational complexity and demonstrate that it is a flexible, practical, reliable, and efficient component for a production solution.
Stochastic model and kinetic Monte Carlo simulation of solute interactions with stationary and moving grain boundaries. I. Model formulation and application to one-dimensional systems

A simple stochastic model of solute drag by moving grain boundaries (GBs) is presented. Using a small number of parameters, the model describes solute interactions with GBs and captures nonlinear GB dynamics, solute saturation in the segregation atmosphere, and the breakaway from the atmosphere. The model is solved by kinetic Monte-Carlo (KMC) simulations with time-dependent transition barriers. The non-Markovian nature of the KMC process is discussed. In Part I of this work, the model is applied to planar GBs driven by an external force. The model reproduces all basic features of the solute drag effect, including the maximum of the drag force at a critical GB velocity. The force-velocity functions obtained depart from the scaling predicted by the classical models by Cahn and L\"ucke-St\"uwe, which are based on more restrictive assumptions. The paper sets the stage for Part II, in which the GB will be treated as a 2D solid-on-solid interface.
Quantification of classical and non-classical crystallization pathways in calcite precipitation

Crystal precipitation from aqueous solution occurs through multiple pathways. Besides the classical ion-by-ion addition, non-classical crystallization mechanisms, such as multi-ion polymer and nano-particle attachment, could be of great significance under certain circumstances. These non-classical crystallization processes have been observed with advanced microscopy, yet detailed quantification of their contribution in mineral precipitation remains challenging. Building from paired Ca and Sr isotope observations, we develop a new theoretical framework to quantify the relative contribution of classical and non-classical crystallization pathways on the precipitation of the calcium carbonate mineral calcite, one of the most common precipitates in nature. We demonstrate that the classical (ion-by-ion) crystallization pathway alone is insufficient to account for the observed isotope behaviors and, thus, the entire calcite precipitation process. We present a new kinetic surface reaction model to incorporate the non-classical crystallization pathway. This new model, for the first time, enables the detailed characterization of the roles of classical and non-classical crystallization mechanisms in calcite precipitation. The results suggest that the relative contribution of non-classical crystallization pathways increases with saturation state and can, under high supersaturation levels, be comparable to or greater than precipitation driven by the classical crystallization pathway. The presented theoretical framework readily explains observed trace element partitioning and isotope fractionation behaviors during calcite precipitation and can be further expanded onto other mineral systems to gain insights into crystal growth mechanisms.
Adeno-Associated Virus (AAV) - Based Gene Therapies for Retinal Diseases: Where are We?

Abstract Owing to their small size and safety profiles, adeno-associated viruses (AAVs) have become the vector of choice for gene therapy applications in the retina. In addition to the naturally occurring AAVs, several engineered variants with enhanced properties are being developed for experimental and therapeutic applications. Nonetheless, there are still some challenges impeding successful application of AAVs for a broader range of retinal gene therapies. The small size of AAV particles ensures efficient tissue transduction but also limits the packaging capacity to a few kilobases. Further, AAV’s ability to cross retinal barriers is still an obstacle to pan-retinal transduction of the outer retina with tolerable doses. Lastly, despite overall safety, there have been recent reports of immune responses to AAVs in the eye. Hence, evaluation and prediction of immune responses to AAVs has come to be considered an integral part of future clinical success. This review focuses on the use of AAV in clinical trials for retinal diseases, and discusses developments of variants and novel strategies to overcome immune responses to AAVs.
Approximability and mathematical relaxations

The thesis ascertains the approximability of classic combinatorial optimization problems using mathematical relaxations. The general flavor of results in the thesis is: a problem P is hard to approximate to a factor better than one obtained from the R relaxation, unless the Unique Games Conjecture is false. Almost optimal inapproximability is shown for a wide set of problems including Metric Labeling, Max. Acyclic Subgraph, various packing and covering problems. The key new idea in this thesis is in coverting hard instances of relaxations (a.k.a integrality gap instances) into a proof of inapproximability (assuming the UGC). In most cases, the hard instances were discovered prior to this work; our results imply that these hard instances are possibly strong bottlenecks in designing approximation algorithms of better quality for these problems. For ordering problems such as Max Acyclic Subgraph and Feedback Arc Set such hard instances were previously unknown. For these problems, we construct such hard instance by using the reduction designed to prove the inapproximability. The hard instances show that all ordering problems are hard to approximate to a factor larger than the expected fraction satisfied by a random ordering: i.e., all ordering CSPs are approximation resistant. Techniques involve using mathematical relaxations to obtain local distributions, converting them into low degree functions defined over the boolean cube and using the invariance principle to analyse such function. I believe the thesis will be a good reference, both for the results proven therein, and for the framework designed in ascertaining approximability from mathematical relaxations.
MULTISYSTEMIC EMPHYSEMA (GAS BUBBLE DISEASE)-ASSOCIATED ACUTE MASS MORTALITY IN A FREE-RANGING POPULATION OF COMMON FROG (RANA TEMPORARIA) IN SWITZERLAND

Abstract: In April 2020, nearly 5,000 free-ranging common frogs (Rana temporaria) were found dead on the surface of the water in a retention pond in the Swiss Alps. Macroscopic and microscopic lesions revealed multisystem emphysema, affecting multiple organs. The most severe lesions were seen in the skin, eyes, and blood vessels of internal organs and were secondary to the sudden massive distension of the skin and other affected organs. All frogs had similar lesions consistent with those described associated with gas bubble disease. No obvious pre-existing conditions potentially priming the occurrence of the observed lesions could be detected. All the examined frogs were negative by PCR for Batrachochytrium dendrobatidis, Ranavirus and Ranid Herpesvirus 3 (now Batravirus ranidallo 3). The proposed etiology is considered to be an undetermined physical event, leading to an abrupt change in the molecular or physical characteristics of the water (namely pressure and oxygen or other gas supersaturation), resulting in the occurrence of the observed lesions in the frogs. No obvious pumping system malfunction was recorded in the Mägisalp ponds before the mass mortality, but a sudden and temporary undetected change in the water flow, which then quickly rebalanced, cannot be excluded. Other hypotheses include weather conditions, such as lightning strikes in the water, or a device detonating in the water.
Reliability of major bleeding events in UK routine data versus clinical trial adjudicated follow-up data

Objective To assess how reliable UK routine data are for ascertaining major bleeding events compared with adjudicated follow-up. Methods The ASCEND (A Study of Cardiovascular Events iN Diabetes) primary prevention trial randomised 15 480 UK people with diabetes to aspirin versus matching placebo. The primary safety outcome was major bleeding (including intracranial haemorrhage, sight-threatening eye bleeding, serious gastrointestinal bleeding and other major bleeding (epistaxis, haemoptysis, haematuria, vaginal and other bleeding)) ascertained by direct-participant mail-based follow-up, with >90% of outcomes undergoing adjudication. Nearly all participants were linked to routinely collected hospitalisation and death data (ie, routine data). An algorithm categorised bleeding events from routine data as major/minor. Kappa statistics were used to assess agreement between data sources, and randomised comparisons were re-run using routine data. Results When adjudicated follow-up and routine data were compared, there was agreement for 318 major bleeding events, with routine data identifying 281 additional-potential events, and not identifying 241 participant-reported events (kappa 0.53, 95% CI 0.49 to 0.57). Repeating ASCEND’s randomised comparisons using routine data only found estimated relative and absolute effects of allocation to aspirin versus placebo on major bleeding similar to adjudicated follow-up (adjudicated follow-up: aspirin 314 (4.1%) vs placebo 245 (3.2%); rate ratio (RR) 1.29, 95% CI 1.09 to 1.52; absolute excess +6.3/5000 person-years (mean SE±2.1); vs routine data: 327 (4.2%) vs 272 (3.5%); RR 1.21, 95% CI 1.03 to 1.41; absolute excess +5.0/5000 (±2.2)). Conclusions Analyses of the ASCEND randomised trial found that major bleeding events ascertained via UK routine data sources provided relative and absolute treatment effects similar to adjudicated follow-up. Trial registration number ISRCTN60635500; NCT00135226.
Numerical recipes for investigating endemic equilibria of age-structured SIR epidemics

The subject of this paper is the analysis of the equibria of a SIR type epidemic model, which is taken as a case study among the wide family of dynamical systems of infinite dimension. For this class of systems both the determination of the stationary solutions and the analysis of their local asymptotic stability are often unattainable theoretically, thus requiring the application of existing numerical tools and/or the development of new ones. Therefore, rather than devoting our attention to the SIR model's features, its biological and physical interpretation or its theoretical mathematical analysis, the main purpose here is to discuss how to study its equilibria numerically, especially as far as their stability is concerned. To this end, we briefly analyze the construction and solution of the system of nonlinear algebraic equations leading to the stationary solutions, and then concentrate on two numerical recipes for approximating the stability determining values known as the characteristic roots. An algorithm for the purpose is given in full detail. Two applications are presented and discussed in order to show the kind of results that can be obtained with these tools.
MitoNEET prevents iron overload‐induced insulin resistance in H9c2 cells through regulation of mitochondrial iron

Iron overload (IO) induces insulin resistance in H9c2 cardiomyoblast cells. Here, we used H9c2 cells overexpressing MitoNEET to examine the potential for protection against iron accumulation in the mitochondria and subsequent insulin resistance. In control H9c2 cells, IO was observed to increase mitochondrial iron content, reactive oxygen species (ROS) production, mitochondrial fission, and reduced insulin‐stimulated Akt and ERK1/2 phosphorylation. IO did not significantly affect mitophagy, or mitochondrial content, however, an increase in peroxisome‐proliferator‐activated receptor gamma coactivator 1 alpha (PGC1α) protein expression, a key regulator of mitochondrial biogenesis, was observed. MitoNEET overexpression was able to attenuate the effects of IO on mitochondrial iron content, reactive oxygen species, mitochondrial fission, and insulin signaling. MitoNEET overexpression also upregulated levels of PGC1α protein. The mitochondria‐targeted antioxidant, Skq1, prevented IO‐induced ROS production and insulin resistance in control cells, indicating mitochondrial ROS plays a causal role in the onset of insulin resistance. The selective mitochondrial fission inhibitor, Mdivi‐1, prevented IO‐induced mitochondrial fission, however, it did not alleviate IO‐induced insulin resistance. Collectively, IO causes insulin resistance in H9c2 cardiomyoblasts and this can be averted by reduction of mitochondrial iron accumulation and ROS production by overexpression of the MitoNEET protein.
Singer's high formant associated with different larynx position in styles of singing

This paper is a further presentation and discussion of the author's voice research and previous paper and film on singer's (high) formant associated with different larynx position in styles of singing. The main results and points are : 1. The singer's high formant pattern varies with similar characteristic (two to four high and often compact energy peaks, including a possible extra peak fe, in the bright frequency range); 2. The fe is also found in high laryngeal singing and does not necessarily appear as a typical acoustic feature in all professional singing voice; 3. Sundberg's lowered larynx interpretation does not explain the extra formant, singer's formant, and voice quality; 4. Sundberg's different definitions of singer's formant are not inclusive of the various formant patterns of singing associated with different laryngeal position and different types ; 5. The author also suggests a definition for the singer's formant; 6. The low laryngeal position could be yet regarded as a distinctive feature for the covered singing voice though not for the trained singing voice; 7. The source characteristics and the interaction between the source and the supra/sub glottal systems are possibly different in styles of singing associated with various laryngeal positions.
Leader-following consensus for discrete-time descriptor multi-agent systems with observer-based protocols

In this paper, the leader-following consensus problem of discrete-time descriptor multi-agent systems is considered. Each agent’s dynamics is modelled by a discrete-time linear descriptor system, and the interaction topology among the agents is assumed to be directed. To solve the multi-agent consensus problem, three different architectures of connecting observers and controllers are used to construct the consensus protocols. A kind of modified generalized algebraic Riccati equation is provided to design the protocol’s gain matrices. Based on graph theory, matrix theory and the Lyapunov method, some sufficient convergence conditions are established to guarantee the descriptor multi-agent system achieves consensus. Finally, a simulation example is provided to illustrate the obtained results.