Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Guaranteed Finite-Time Non-Affine Control of Waverider Vehicles Without Function-Approximation

Waverider Vehicles (WVs) show great potential in air and space transportation. This article aims at exploring finite-time prescribed performance control (FPPC) for WVs, being able to guarantee the spurred performance. Firstly, the dynamics of WVs is decomposed as the velocity subsystem and the altitude subsystem of non-affine formulations. Then, a new type of back-stepping controller without any approximation/estimation is devised based on FPPC, such that all tracking errors satisfy spurred finite-time prescribed performance. Moreover, the closed-loop stability and the guarantee of prescribed performance are proved via Lyapunov synthesis. The special contribution is that no fuzzy/neural approximation/estimation is required for the resistance of unknown dynamics, yielding a low computation and complexity design. Finally, compared simulation results with practical examples are presented to validate the superiority.
Limiting Distributions for Path Lengths in Recursive Trees

The depth of insertion and the internal path length of recursive trees are studied. Luc Devroye has recently shown that the depth of insertion in recursive trees is asymptotically normal. We give a direct alternative elementary proof of this fact. Furthermore, via the theory of martingales, we show that In, the internal path length of a recursive tree of order n, converges to a limiting distribution. In fact, we show that there exists a random variable I such that (In – n In n)/n→I almost surely and in quadratic mean, as n → α. The method admits, in passing, the calculation of the first two moments of In.
Impact of Infrastructure on Economic Growth: A Panel Data Approach Using PMG Estimator

Growth theory asserts that infrastructure investments promote economic growth by improving the quality of life and increasing private sector productivity . Transport services, water utility services and telecommunication services provide better facilities to attract FDI (foreign direct investment) and increase productivity across sectors. The aim of this article is to analyze whether transport infrastructure investments have a strong effect on the economic growth. It also attempts to analyze the differential impact of each type of infrastructural spending on economic growth. Our data set covers annual data from 1993 to 2015 period for 15 OECD countries (Austria, Turkey, Czech Republic, Spain, Finland, Japan, Germany, Ireland, Italy, France, Korea, Mexico, Netherlands, Poland, U.K) and China. In this study, we employ a Pool Mean Group (PMG) estimator to find long run and short run relations between the variables. Output elasticity of air transport is found to be positive and significant at five percent level and there exists a long run relationship between GDP per capita and other explanatory variables such as transport infrastructure indicators, gross capital formation and labor force. The crowding- out hypothesis is also supported by coefficients on county specific results. Our data set includes infrastructure variables such as Railways, (million passenger-km), Air transport, (freight, million ton-km), Individuals using the Internet (% of population).
Low frequency modes in strongly coupled magnetized dusty plasmas

Low frequency modes in strongly coupled magnetized dusty plasmas are investigated based on the generalized hydrodynamic model for dust particles. A longitudinal hybrid-like mode in the perpendicular direction exists with weak damping. Especially, a peculiar transverse shear mode is found in the parallel direction due to strong coupling in the magnetized dusty plasma. Its group velocity is proportional to the wave vector, analogous to a torsional vibration mode in a solid.
ROSAT observations of cataclysmic variables: A search for the boundary layer emission

Many cataclysmic variables (CVs) and low-mass X-ray binaries show He II line emission at wavelength 1640 and wavelength 4686. As recombination lines of an ion requiring 55 eV for ionization, these lines cannot be attributed to gravitational heating in the disk but could be due to reprocessed 55-280 eV X-rays which may be produced in a boundary layer at the disk/star surface. We undertook a search for this soft component using the Positon Sensitve Proportional Counter (PSPC) on ROSAT. The photon fluxes we observe are not sufficient to produce the He II wavelength 1640 fluxes observed by IUE. If we assume that the X-ray luminosity is due to emission from a boundary layer, then the observed quiescent luminosity is one-quarter of the total accretion luminosity as determined by optical and ultraviolet observations rather than the one-half that is predicted by some theories. An additional very soft component (e.g. an approximately equal 10-20 eV blackbody) could provide sufficient photons in the 0.055-0.100 keV band to produce the observed He II fluxes and contribute the additional luminosity from the boundary layer while remaining hidden to the PSPC and IUE. Alternatively, the high He II line fluxes could be evidence for high-temperature collisionally ionized material. We also find evidence for line emission at approximately equal 0.9 keV consistent with L-shell emission from highly ionized iron.
Response of rove‐beetle (Staphylinidae) assemblages to the cumulative effect of wildfire and linear footprint in boreal treed peatlands

Abstract Cumulative effects of anthropogenic and natural disturbances have become increasingly relevant in the context of biodiversity conservation. Oil and gas (OG) exploration and extraction activities have created thousands of kilometers of linear footprints in boreal ecosystems of Alberta, Canada. Among these disturbances, seismic lines (narrow corridors cut through the forest) are one of the most common footprints and have become a significant landscape feature influencing the maintenance of forest interior habitats and biodiversity. Wildfire is a common stand‐replacing natural disturbance in the boreal forest, and as such, it is hypothesized that its effects can mitigate the linear footprint associated with OG exploration, but only a few studies have examined its effectiveness. We studied the short‐term (1 year post‐fire) response of rove‐beetle assemblages to the combined effects of wildfire and linear footprint in forest, edge, and seismic line habitats at burned and unburned peatlands along the southwest perimeter of the 2016 Horse River wildfire (Fort McMurray). While rove‐beetle species richness was higher in seismic lines in both the burned and unburned habitats compared with the adjacent peatland, diversity was greater only in seismic lines of burned areas. Abundance was lower in the burned adjacent peatland but similarly higher in the remaining habitats. Assemblage composition on seismic lines was significantly different from that in the adjacent forest and edge habitats within both burned and unburned sites. Moreover, species composition in burned seismic lines was different from either unburned lines or burned forest and edge. Euaesthethus laeviusculus and Gabrius picipennis were indicator species of burned line habitats, are sensitive to post‐fire landscapes and can occupy wet habitats with moss cover more efficiently than when these habitats are surrounded by unburned forest. Although these results are based on short‐term responses, they suggest that wildfire did not reduce the linear footprint, and instead, the cumulative effect of these two disturbances had a more complex influence on rove‐beetle recovery at the landscape level than for other invertebrates. Therefore, continued monitoring of these sites can become useful to evaluate changes over time and to better understand longer‐term biodiversity responses to the cumulative effects of wildfire and linear disturbances in boreal treed peatlands, given the long‐lasting effect of such disturbances.
A message-passing approach to random constraint satisfaction problems with growing domains

Message-passing algorithms based on belief propagation (BP) are implemented on a random constraint satisfaction problem (CSP) referred to as model RB, which is a prototype of hard random CSPs with growing domain size. In model RB, the number of candidate discrete values (the domain size) of each variable increases polynomially with the variable number N of the problem formula. Although the satisfiability threshold of model RB is exactly known, finding solutions for a single problem formula is quite challenging and attempts have been limited to cases of N ∼ 102. In this paper, we propose two different kinds of message-passing algorithms guided by BP for this problem. Numerical simulations demonstrate that these algorithms allow us to find a solution for random formulas of model RB with constraint tightness slightly less than pcr, the threshold value for the satisfiability phase transition. To evaluate the performance of these algorithms, we also provide a local search algorithm (random walk) as a comparison. Besides this, the simulated time dependence of the problem size N and the entropy of the variables for growing domain size are discussed.
Dietary supplements in neurological diseases and brain aging

Summary A healthy diet shapes a healthy mind. Diet quality has a strong association with brain health. Diet influences the onset and consequences of neurological diseases, and dietary factors may influence mental health at individual and population level. The link between unhealthy diet, impaired cognitive function and neurodegenerative diseases indicates that adopting a healthy diet would ultimately afford prevention and management of neurological diseases and brain aging. Neurodegenerative diseases are of multifactorial origin and result in progressive loss of neuronal function in the brain, leading to cognitive impairment and motoneuron disorders. The so-called Mediterranean diet (MedDiet) with its healthy ingredients rich in antioxidant, anti-inflammatory, immune, neuroprotective, antidepressant, antistress and senolytic activity plays an essential role in the prevention and management of neurological diseases and inhibits cognitive decline in neurodegenerative diseases such as Alzheimer’s, Parkinson’s and Huntington’s diseases. The MedDiet also modulates the gut-brain axis by promoting a diversity of gut microbiota. In view of the importance of diet in neurological diseases management, this review focuses on the dietary components, natural compounds and medicinal plants that have proven beneficial in neurological diseases and for brain health. Among them, polyphenols, omega-3 fatty acids, B vitamins and several ayurvedic herbs have promising beneficial effects.
Conservative treatment of glassy cell cervical cancer: A case report

Glassy cell carcinoma (GCC) constitutes a rare yet histologically aggressive subtype of cervical cancer known for its rapid proliferation and high risk of recurrence and metastasis. Due to its low prevalence, the medical literature lacks large retrospective and prospective studies, and thus, no standardized management has been defined. The recommended treatment for GCC is radical hysterectomy with bilateral pelvic lymphadenectomy; however, since it mainly affects young women of reproductive age, data in the literature suggest conservative management, such as radical trachelectomy. The present report describes the cases of 2 young patients treated conservatively with pelvic lymphadenectomy and radical trachelectomy for early-stage GCC of the cervix. The first patient was a 37-year-old patient who presented a 15-mm GCC tumor [International Federation of Gynecology and Obstetrics (FIGO) stage IB1], and the second patient was a 23-year-old patient who presented a 14-mm GCC tumor (FIGO stage IB1). The first patient presented early vaginal recurrence 3 months postoperatively, which was treated with concomitant pelvic chemoradiation (45 Gy) followed by vaginal and uterine brachytherapy (15 Gy). The second patient presented internal iliac nodal recurrence 1 year after treatment, which was treated by carboplatin-paclitaxel-Avastin-based chemotherapy, followed by laparoscopic paraaortic lymph node dissection and pelvic chemoradiation (45 Gy). Both patients were tumor-free after 5 and 6 years, respectively. Due to the aggressiveness of GCC of the cervix and its high risk of recurrence, conservative treatment should be considered cautiously and should not be the standard of care.
Effects of Checkpointing and Queueing on Program Performance

Checkpointing is a well-known procedure to reduce the execution time of programs in the presence of failures. If a failure occurs, the work done on the program since the last checkpoint is lost and therefore must be repeated in order to continue the correct execution of the program. A new model is developed for the analysis of the distribution of the program execution time. The questions of whether checkpointing is beneficial and the optimal checkpointing rate are addressed. A queueing analysis is performed to obtain the distribution of the response time of a program in a queueing environment.
"just like therapy!": Investigating the Potential of Storytelling in Online Postpartum Depression Communities

One in seven women encounter postpartum depression upon transitioning to motherhood. Many of them frequently seek social support in online support groups. We conducted a mixed-methods formative research to assess the potential of digital storytelling on those online platforms. We observed that mothers acquire social support from online groups through storytelling. We present design recommendations for online postpartum depression (PPD) communities to utilize storytelling in fostering emotional support and providing relevant information and education through storytelling.
On the triggering of an isolated magnetic substorm: A theory

A high frequency electromagnetic instability of non resonant type is proposed, driven by cross current as a source for the onset of substorm expansion phase and the field line reconnection beyond 15R/subE/. By including a normal component of the tail field, B/subZ/, it is found that the instability can be quenched provided B/subZ/ exceeds certain threshold value -1.8x10$sup 6$rootTe/ lambda gauss (0.06$gamma$, for typical values in the neutral sheet). (AIP)
Criterion for the resemblance between the mother and the model distribution

If the probability distribution model aims to approximate the hidden mother distribution, it is imperative to establish a useful criterion for the resemblance between the mother and the model distributions. This study proposes a criterion that measures the Hellinger distance between discretized (quantized) samples from both distributions. Unlike information criteria such as AIC, this criterion does not require the probability density function of the model distribution, which cannot be explicitly obtained for a complicated model such as a deep learning machine. Second, it can draw a positive conclusion (i.e., both distributions are sufficiently close) under a given threshold, whereas a statistical hypothesis test, such as the Kolmogorov-Smirnov test, cannot genuinely lead to a positive conclusion when the hypothesis is accepted. In this study, we establish a reasonable threshold for the criterion deduced from the Bayes error rate and also present the asymptotic bias of the estimator of the criterion. From these results, a reasonable and easy-to-use criterion is established that can be directly calculated from the two sets of samples from both distributions.
Manifold Learning for Dimensionality Reduction: Quantum Isomap algorithm

Isomap algorithm is a representative manifold learning algorithm. The algorithm simpliﬁes the data analysis process and is widely used in neuroimaging, spectral analysis and other ﬁelds. However, the classic Isomap algorithm becomes unwieldy when dealing with large data sets. Our object is to accelerate the classical algorithm with quantum computing, and propose the quantum Isomap algorithm. The algorithm consists of two sub-algorithms. The ﬁrst one is the quantum Floyd algorithm, which calculates the shortest distance for any two nodes. The other is quantum Isomap algorithm based on quantum Floyd algorithm, which ﬁnds a low-dimensional representation for the original high-dimensional data. Finally, we analyze that the quantum Floyd algorithm achieves exponential speedup without sampling. In addition, the time complexity of quantum Isomap algorithm is O ( dNpolylogN ) . Both algorithms reduce the time complexity of classical algorithms.
A Smoothing Riemannian Gradient Algorithm Framework for Nonsmooth Optimization Problem on Manifold

To ﬁnd a generalized ǫ -stationarity point of a nonconvex and nonsmooth minimization problem on Riemannian manifold embedding in Euclidean space, we develop a family of Riemannian gradient type methods based on Moreau envelope with a decreasing sequence of smoothing parameters, namely Riemannian smoothing gradient and Riemannian stochastic smoothing gradient methods. We prove that, the proposed methods have the iteration complexity of O ( ǫ − 3 ) and O ( ǫ − 5 ), respectively. To our knowledge, these are the ﬁrst results on the computational complexity of the kind of algorithms.
Overview Of Satellite Image Recognition Models

In this article, the analysis of existing models of satellite image recognition was carried out, the problems in the field of satellite image recognition as a source of information were considered and analyzed, deep learning methods were compared, and existing image recognition methods were analyzed. The results obtained will be used as a basis for the prospective development of a fire recognition model based on satellite images and the use of recognition results as input data for a cognitive model of forecasting the macro-economic situation based on fuzzy cognitive maps.
Generating Real-Time Strategy Game Units Using Search-Based Procedural Content Generation and Monte Carlo Tree Search

Real-Time Strategy (RTS) game unit generation is an unexplored area of Procedural Content Generation (PCG) research, which leaves the question of how to automatically generate interesting and balanced units unanswered. Creating unique and balanced units can be a difficult task when designing an RTS game, even for humans. Having an automated method of designing units could help developers speed up the creation process as well as find new ideas. In this work we propose a method of generating balanced and useful RTS units. We draw on Search-Based PCG and a fitness function based on Monte Carlo Tree Search (MCTS). We present ten units generated by our system designed to be used in the game microRTS, as well as results demonstrating that these units are unique, useful, and balanced.
Stochastic Observability and Filter Stability under Several Criteria

Despite being a foundational concept of modern systems theory, there have been few studies on observability of non-linear stochastic systems under partial observations. In this paper, we introduce a definition of observability for stochastic non-linear dynamical systems which involves an explicit functional characterization. To justify its operational use, we establish that this definition implies filter stability under mild continuity conditions: an incorrectly initialized non-linear filter is said to be stable if the filter eventually corrects itself with the arrival of new measurement information. Numerous examples are presented and a detailed comparison with the literature is reported. We also establish implications for various criteria for filter stability under several notions of convergence such as weak convergence, total variation, and relative entropy. These findings are connected to robustness and approximations in partially observed stochastic control.
Machine Learning Assisted Inverse Design of Microresonators

The high demand for fabricating microresonators with desired optical properties has led to various techniques to optimize geometries, mode structures, nonlinearities, and dispersion. Depending on applications, the dispersion in such resonators counters their optical nonlinearities and influences the intracavity optical dynamics. In this paper, we demonstrate the use of a machine learning (ML) algorithm as a tool to determine the geometry of microresonators from their dispersion profiles. The training dataset with ∼460 samples is generated by finite element simulations and the model is experimentally verified using integrated silicon nitride microresonators. Two ML algorithms are compared along with suitable hyperparameter tuning, out of which Random Forest yields the best results. The average error on the simulated data is well below 15%.
Modified correlation criterion for digital image correlation considering the effect of lighting variations in deformation measurements

Abstract. The Newton-Raphson (N-R) algorithm based on the sum of squared differences (SSD) function used in the digital image correlation (DIC) may obtain unreliable results in deformation measurements because light source variation is not taken into account. Here, a modified N-R algorithm that includes the effect of light source variations on the measurement results is presented. Instead of involving the mean value and square sum of the gray value of the object, variables were employed in the proposed algorithm. Thus, we refer to it as variables-based sum of squared differences (VSSD) function. VSSD has been validated by comparing the computer generated speckle images, which indicates that it could give rise to more accurate results in calculating deformation than SSD when the surrounding light source changes. In addition, VSSD is compared with the zero-normalized sum of squared differences (ZNSSD) function that is often used to remove the light source variation. The results show that VSSD can achieve the same accuracy within in less time compared to ZNSSD.
Determination of superexchange interactions for the CuO$_2$ chains in LiCu$_2$O$_2$

Starting from \textit{ab-initio} calculations, we derive a five-band Hubbard model to describe the CuO$_2$ chains of LiCu$_2$O$_2$. This model is further simplified to a low-energy effective Heisenberg model with nearest-neighbor (NN) $J_1$, and next-nearest-neighbor (NNN) $J_2$ interactions, combining perturbation theory, exact diagonalization calculations and Density Matrix Renormalization Group results. For realistic parameters we find the corresponding values of these interactions. The obtained effective model is consistent with a spiral-magnetic ground state as experimentally observed. Using symmetry arguments, the spiral state is a sufficient condition for the ferroelectricity observed in the system.
THREAT/crawl: a Trainable, Highly-Reusable, and Extensible Automated Method and Tool to Crawl Criminal Underground Forums

Collecting data on underground criminal communities is highly valuable both for security research and security operations. Unfortunately these communities live within a constellation of diverse online forums that are difficult to infiltrate, may adopt crawling monitoring countermeasures, and require the development of ad-hoc scrapers for each different community, making the endeavour increasingly technically challenging, and potentially expensive. To address this problem we propose THREAT/crawl, a method and prototype tool for a highly reusable crawler that can learn a wide range of (arbitrary) forum structures, can remain under-the-radar during the crawling activity and can be extended and configured at the user will. We showcase THREAT/crawl capabilities and provide prime evaluation of our prototype against a range of active, live, underground communities.