Miracle-dz/UniFAR-FTUs huggingface dataset:
Control Model of a Small Micro-class UAV Object Taking Into Account the Impact of Strong Wind

The subject of this article is to analyze and select simulation tests in the field of issues related to flight control systems for micro-class aircraft. The main purpose of the work is to develop an algorithm for the flight control system, taking into account both the speed and direction of the wind acting on the UAV, which are the key attributes that play a decisive impact on the disturbance of flight parameters and its correct performance. What is more, atmospheric conditions determined by the influence of wind can produce phenomena dangerous to aviation in the form of wind shear or blast from the back during the landing process of the aircraft. The occurrence of the above situation may be the cause of stall phenomenon, which in turn may be the cause of a dangerous aviation phenomenon (accident, incident, etc.). For the purposes of solving the research problem, the article uses a mathematical apparatus in the form of equations describing the movement of the aircraft and the forces and moments acting on it. Based on the mathematical analysis of the UAV object, in the further part of the article, an algorithm was developed to estimate the impact of wind and an analysis of measurement errors occurring during flights and their impact on the measured values, as well as the values calculated on their basis. On this basis, charts have been developed defining clearly the various relationships. In the final part of the thesis, based on the mathematical analysis, simulation tests and analysis of the results obtained, the final conclusions and observations were formulated, which are reflected in practical applications. Key-Words: Control model, micro class UAV object, equations of motion, strong wind influence
OPTIMAL AZIMUTHAL STEERING OF A FIRST-ORDER SUPERDIRECTIONAL MICROPHONE RESPONSE

An azimuth steerable first-order superdirectional microphone response can be constructed by combination of three eigenbeams (monopole and two orthogonal dipoles) and applying standard signal processing techniques. The main-lobe can be steered to a certain angle on the 2D plane, such that the desired signal is captured. Besides steering, the directional pattern can be adjusted in such a way that either a point-interferer coming from an other angle is rejected or that we get an optimal rejection of diffuse noise. However, when we want to reject both a point-interferer and obtain the best possible diffuse noise reduction, neither of these methods give optimal results. In this paper, an optimal steering method is derived to construct the first-order directional response in such a way that the point-interferer is removed with the best possible diffuse noise reduction.
DARPA’s Intra/Interchip Enhanced Cooling (ICECool) Program

The Defense Advanced Research Projects Agency (DARPA) is pursuing research working toward a new paradigm of embedded cooling for thermal management of microelectronic devices. This work was initiated in the Near Junction Thermal Transport (NJTT) thrust, which is the transitional effort to an embedded cooling paradigm, building on the advanced remote cooling developed in the Thermal Management Technologies (TMT) program. The full implementation of embedded cooling is being pursued in the two efforts of the Intra/Interchip Enhanced Cooling (ICECool) program: ICECool Fundamentals and ICECool Applications.
A Review on Parkinson’s Disease Diagnosis using Machine Learning Techniques

— Parkinson disease is a neurodegenerative disorder that affects nervous system and the root cause of it is falling rates of dopamine levels in the forebrain. It is a chronic degenerative disease with progressive illness, which means it develops new symptoms over time[20]. This happens with progressive neuronal loss in the substantia nigra of brain. People with PD cannot do their works as a normal human. Though clinical assessments considered ample amount of data that include various features, sometimes it is hard to decide whether a person is suffering from PD or not based on the type of data, feature selection methods help to solve this issue. Various methods are developed, proposed, and analyzed to detect the Parkinson disease, given the required data. This paper is a survey of predicting Parkinson disease using machine learning algorithms, various new technologies applied, and their accuracies achieved.
AnimationPak: Packing Elements with Scripted Animations

We present AnimationPak, a technique to create animated packings by arranging animated two-dimensional elements inside a static container. We represent animated elements in a three-dimensional spacetime domain, and view the animated packing problem as a three-dimensional packing in that domain. Every element is represented as a discretized spacetime mesh. In a physical simulation, meshes grow and repel each other, consuming the negative space in the container. The final animation frames are cross sections of the three-dimensional packing at a sequence of time values. The simulation trades off between the evenness of the negative space in the container, the temporal coherence of the animation, and the deformations of the elements. Elements can be guided around the container and the entire animation can be closed into a loop.
Nitrogen fixation in shallow‐water sediments: Spatial distribution and controlling factors

Nitrogenase activity (NA) in shallow‐water (< 1 m) sediments was investigated at 60 randomly selected sites along a 150 km stretch on the brackish‐water Swedish west coast, without targeting any specific type of sediments, such as microbial mats. Benthic nitrogen (N) fixation and diazotrophs (nifH genes) were found at all sites, regardless of the presence of cyanobacterial or microbial mats. The majority of sites showed N fixation rates between 0.03 and 1 mmol N m−2 d−1. These rates were similar to those of benthic denitrification previously measured in the area. Maximum rates up to 3.4 mmol N m−2 d−1 were measured. A structural equation model was used to investigate direct and indirect effects of biogeochemical and physical factors on NA. Number of nifH genes had the largest direct positive influence on NA, whereas increasing wave exposure had an indirect negative effect on NA through its influence on the diazotrophic abundance. Increased salinity, previously been shown to suppress NA in coastal waters, was found to directly stimulate benthic N fixation, likely by generating favorable conditions for diazotrophic sulfate‐reducing bacteria. Our field data confirmed previously observed negative effects of dissolved inorganic nitrogen on NA, which have so far mainly been experimentally studied. Both NA rates and the number of nifH genes correlated positively with pore‐water dissolved inorganic phosphorus concentrations. These findings show that the potential for N fixation in illuminated sediments can be considerable, stretching beyond cyanobacterial mats, being controlled by complex interactions between biotic and abiotic factors.
A Clustering Algorithm for Early Prediction of Controversial Reddit A Clustering Algorithm for Early Prediction of Controversial Reddit Posts Posts

Social curation platforms like Reddit are rich with user interactions such as comments, upvotes, and downvotes. Predicting these interactions before they happen is an interesting computational challenge and can be used for a variety of tasks, ranging from content moderation to personality prediction. Given the vast amount of information posted on these sites, it’s important to develop models that can simplify this prediction task. In this paper, we present a simple clustering algorithm that helps predict the controversiality of a Reddit post using the user’s proﬁle information, their past contributions on Reddit, and the sentiment expressed in their post. On average, introducing the cluster to the prediction task improved the accuracy of the prediction by over 20 percent, with F1 scores of 0.95 (micro) and 0.7 (macro). The classiﬁer performs better than a majority predictor. The results also show that the overwhelming majority of users are inactive and when they do post, they post non-controversial content.
Human Adenovirus Type 55 Distribution, Regional Persistence, and Genetic Variability

Human adenovirus type 55 (HAdV-55) causes acute respiratory disease of variable severity and has become an emergent threat in both civilian and military populations. HAdV-55 infection is endemic to China and South Korea, but data from other regions and time periods are needed for comprehensive assessment of HAdV-55 prevalence from a global perspective. In this study, we subjected HAdV-55 isolates from various countries collected during 1969–2018 to whole-genome sequencing, genomic and proteomic comparison, and phylogenetic analyses. The results show worldwide distribution of HAdV-55; recent strains share a high degree of genomic homogeneity. Distinct strains circulated regionally for several years, suggesting persistent local transmission. Several cases of sporadic introduction of certain strains to other countries were documented. Among the identified amino acid mutations distinguishing HAdV-55 strains, some have potential impact on essential viral functions and may affect infectivity and transmission.
Three Approaches to Learning TLINKs in TimeML

TimeML (Pustejovsky et al. 2005) (www.timeml.org) is an annotation scheme for markup of events, times, and their temporal relations in news articles. There has been a considerable amount of research activity related to this scheme. In this paper, we focus on the problem of learning temporal relations (called TLINKs) in TimeML. We draw attention to two problems found in earlier work by (Mani et al. 2006). The first problem is a bug in vector generation (duplicate vectors were included). The second problem is the fact that the evaluation scheme was somewhat unrealistic; simply evaluating on a perrelation basis across relations drawn from all documents doesn’t tell us how well we’re doing on each document. This paper addresses these two problems.
Wireless Power Transfer to Intra-abdominal Implants Using an Anatomically Adaptive Around-the-Body Loop Antenna

We studied the possibility of wireless powering, the intra-abdominal implants using an anatomically adaptive around-the-body loop antenna. The analysis includes the wireless link characterization and modelling using an anatomical and a simplified homogenous body model. We categorically analyze the power transfer link for four body sizes ranging from 21 to 36 inches of radial dimensions with an implant depth of 48.5 to 101 mm with promising received power level. In this assessment, the transducer power gain of the wireless power transfer link varied from −2.65 to −10.84 dB.
Evaluating Methods for Counting Cryptic Carnivores

Abstract Numerous techniques have been proposed to estimate carnivore abundance and density, but few have been validated against populations of known size. We used a density estimate established by intensive monitoring of a population of radiotagged leopards (Panthera pardus) with a detection probability of 1.0 to evaluate efficacy of track counts and camera-trap surveys as population estimators. We calculated densities from track counts using 2 methods and compared performance of 10 methods for calculating the effectively sampled area for camera-trapping data. Compared to our reference density (7.33 ± 0.44 leopards/100 km2), camera-trapping generally produced more accurate but less precise estimates than did track counts. The most accurate result (6.97 ± 1.88 leopards/100 km2) came from camera-trap data with a sampled area buffered by a boundary strip representing the mean maximum distance moved by leopards outside the survey area (MMDMOSA) established by telemetry. However, contrary to recent suggestions, the traditional method of using half the mean maximum distance moved from photographic recaptures did not result in gross overestimates of population density (6.56 ± 1.92 leopards/100 km2) but rather displayed the next best performance after MMDMOSA. The only track-count method comparable to reference density employed a capture–recapture framework applied to data when individuals were identified from their tracks (6.45 ± 1.43 leopards/100 km2) but the underlying assumptions of this technique limit more widespread application. Our results demonstrate that if applied correctly, camera-trap surveys represent the best balance of rigor and cost-effectiveness for estimating abundance and density of cryptic carnivore species that can be identified individually.
IMPACTS OF HOUSING DEVELOPMENTS ON WOOD THRUSH NESTING SUCCESS IN HARDWOOD FOREST FRAGMENTS

Abstract We studied the impacts of low density, exurban housing developments on Wood Thrushes (Hylocichla mustelina) breeding in small forest fragments in two regions of rural southern Ontario. In both regions, Wood Thrushes breeding in woodlots with embedded houses (housing penetrating the forest border) experienced significantly higher rates of parasitism by Brown-headed Cowbirds (Molothrus ater) than Wood Thrushes breeding in woodlots with adjacent houses (houses within 100 m of the forest edge), or undeveloped woodlots (no houses within 100 m of the forest edge). Wood Thrushes breeding in Peterborough area woodlots with embedded or adjacent houses experienced significantly increased rates of nest predation compared to Wood Thrushes breeding in undeveloped woodlots. This increased nest predation resulted in significant reductions in seasonal productivity in developed woodlots. No increase in nest predation was experienced by Wood Thrushes nesting in developed woodlots in the Ottawa region. The effects of housing developments appear to be region-specific and may depend on other factors influencing the overall abundance of cowbirds. Impactos de la Construcción de Viviendas en el Éxito de Nidificación de Hylocichla mustelina en Fragmentos de Bosque Resumen. Estudiamos los impactos de la construcción en baja densidad de viviendas peri-urbanas sobre individuos de Hylocichla mustelina que se encontraron criando en fragmentos pequeños de bosque en dos regiones rurales del sur de Ontario. En ambas regiones, los individuos de H. mustelina que se reprodujeron en bosques donde había casas inmersas (que penetraban el borde del bosque) experimentaron tasas de parasitismo por Molothrus ater significativamente mayores que los individuos criando en bosques con casas adyacentes (dispuestas a menos de 100 m del borde del bosque), o en bosques no alterados por la presencia de casas (a más de 100 m del borde del bosque). Los individuos de H. mustelina que se encontraron criando en áreas boscosas de Peterborough, donde las casas estaban adentro o adyacentes al bosque, experimentaron incrementos significativos en las tasas de depredación de nidos comparados con individuos que criaron en bosques no alterados. Este incremento en la depredación de nidos llevó a reducciones significativas en la productividad estacional en los bosques con viviendas. No registramos un incremento en la depredación de nidos de H. mustelina en bosques con viviendas en la región de Ottawa. Los efectos de la construcción de viviendas parecen estar relacionados de modo específico con la región y podrían depender de otros factores que influencian la abundancia de M. ater.
Row Width and Seeding Rate Performance of Indeterminate, Semideterminate, and Determinate Soybean

In the northern-soybean [Glycine max. (L.) Mer. ]-producing areas of the USA and Canada, indeterminate (IND) soybean cultivars predominate. Over the last several years, soybean cultivars with semideterminate (SDT) or determinate (DET) stem termination type have been developed, however, basic agronomic information is limited for these types in the Maturity Group I to II soybean growing areas of southern Ontario. For this study, six cultivars (two per stem type) were seeded at 10-, 20-, and 30-in. -row widths and 160000, 240000 and 320000 seeds/acre seeding rates in 1983, 1984, and 1985
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data

Deep generative models often perform poorly in real-world applications due to the heterogeneity of natural data sets. Heterogeneity arises from data containing different types of features (categorical, ordinal, continuous, etc.) and features of the same type having different marginal distributions. We propose an extension of variational autoencoders (VAEs) called VAEM to handle such heterogeneous data. VAEM is a deep generative model that is trained in a two stage manner such that the first stage provides a more uniform representation of the data to the second stage, thereby sidestepping the problems caused by heterogeneous data. We provide extensions of VAEM to handle partially observed data, and demonstrate its performance in data generation, missing data prediction and sequential feature selection tasks. Our results show that VAEM broadens the range of real-world applications where deep generative models can be successfully deployed.
Constrained Combinatorial Optimization with Reinforcement Learning

This paper presents a framework to tackle constrained combinatorial optimization problems using deep Reinforcement Learning (RL). To this end, we extend the Neural Combinatorial Optimization (NCO) theory in order to deal with constraints in its formulation. Notably, we propose defining constrained combinatorial problems as fully observable Constrained Markov Decision Processes (CMDP). In that context, the solution is iteratively constructed based on interactions with the environment. The model, in addition to the reward signal, relies on penalty signals generated from constraint dissatisfaction to infer a policy that acts as a heuristic algorithm. Moreover, having access to the complete state representation during the optimization process allows us to rely on memory-less architectures, enhancing the results obtained in previous sequence-to-sequence approaches. Conducted experiments on the constrained Job Shop and Resource Allocation problems prove the superiority of the proposal for computing rapid solutions when compared to classical heuristic, metaheuristic, and Constraint Programming (CP) solvers.
Identification of thirteen up-expressed sequence tags from Monascus pilosus mutant MK-1

Monascus pilosus MK-1 is a mutant of M. pilosus IFO4520, which is used to brew the red yeast rice for food industry as functional food additives or food colorants. The obvious phenotypes of the mutant MK-1 showed a high productivity of lovastatin and red pigments, slow growth of the fungal mycelia comparing with the wild type IFO4520. Through thesuppression subtractive hybridization, we identified 30 up-expressed sequence tags (up-EST) (AB193486 to AB193498) from the mutant MK-1. Among them, 10 of the up-ESTs were homologues of the known-functional genes. Two up-ESTs (AB193498 and AB193494) are the transcript factor-related gene homologues, and they were homologous with the phosphatidylinositol-4-phosphate 5-kinase and the transcription factor respectively. Three up-ESTs (AB193487, AB193496 and AB193497) were homologous with pyruvate carboxylase, glyceraldehydes-3-phosphate dehydrogenase and ATP-dependent protease respectively, and the three genes are involved in the carbon metabolism. Four up-ESTs (AB193486, AB193488, AB193491 and AB193495) were homologous with the ADP-ribosylation factors. An up-EST (AB193490) was a homologue of penicillin-binding protein. These up-ESTs might indicate to explain the phenotypic differences between the wild type IFO4520 and the mutant MK-1. Key words: Monascus pilosus, lovastatin, EST, red pigment.
Provably Efficient Causal Reinforcement Learning with Confounded Observational Data

Empowered by expressive function approximators such as neural networks, deep reinforcement learning (DRL) achieves tremendous empirical successes. However, learning expressive function approximators requires collecting a large dataset (interventional data) by interacting with the environment. Such a lack of sample efficiency prohibits the application of DRL to critical scenarios, e.g., autonomous driving and personalized medicine, since trial and error in the online setting is often unsafe and even unethical. In this paper, we study how to incorporate the dataset (observational data) collected offline, which is often abundantly available in practice, to improve the sample efficiency in the online setting. To incorporate the possibly confounded observational data, we propose the deconfounded optimistic value iteration (DOVI) algorithm, which incorporates the confounded observational data in a provably efficient manner. More specifically, DOVI explicitly adjusts for the confounding bias in the observational data, where the confounders are partially observed or unobserved. In both cases, such adjustments allow us to construct the bonus based on a notion of information gain, which takes into account the amount of information acquired from the offline setting. In particular, we prove that the regret of DOVI is smaller than the optimal regret achievable in the pure online setting by a multiplicative factor, which decreases towards zero when the confounded observational data are more informative upon the adjustments. Our algorithm and analysis serve as a step towards causal reinforcement learning.
Biomass and production of small and large free‐living and attached bacteria in Lake Constance1

Cell numbers, biomass, and secondary production of small (0.2-l pm) and large (1.0-3.0 pm) free-living and attached bacteria were studied in Lake Constance. Biomass as well as production rates exhibited large fluctuations vertically and seasonally. When averaged over a year, small freeliving bacteria were most important for recycling of organic matter since they comprised 77% of total cell numbers and 66% of bacterial secondary production. Large free-living bacteria contained roughly half of the total bacterial biomass because of their relatively larger cell size. Although attached bacteria usually were of minor importance, they occasionally comprised about half of total bacterial production. During the phytoplankton spring bloom when bacterial production reached its maximum, 60-80% of all free-living bacteria were active as shown by autoradiography. During that period a high portion of detrital particles was colonized by metabolically active cells. The data suggest that biomass, cell size, and production of free-living bacteria are controlled by substrate input and grazing of either heterotrophic microflagellates or crustacean zooplankton. In contrast, biomass and production of attached bacteria appear to be predominantly controlled by substrate input of readily decomposable particulate organic matter.