Miracle-dz/UniFAR-FTUs huggingface dataset:
Trajectory Planning for Autonomous High-Speed Overtaking using MPC with Terminal Set Constraints

With self-driving vehicles being pushed towards the main-stream, there is an increasing motivation towards development of systems that autonomously perform manoeuvres involving combined lateral-longitudinal motion (e.g., lane-change, merge, overtake, etc.). This paper presents a situational awareness and trajectory planning framework for performing autonomous overtaking manoeuvres. A combination of a potential field-like function and reachability sets of a vehicle are used to identify safe zones on a road that the vehicle can navigate towards. These safe zones are provided to a model predictive controller as reference to generate feasible trajectories for a vehicle. The strengths of the proposed framework are: (i) it is free from non-convex collision avoidance constraints, (ii) it ensures feasibility of trajectory, and (iii) it is real-time implementable. A proof of concept simulation is shown to demonstrate the ability to plan trajectories for high-speed overtaking manoeuvres.
Guideline-based learning for standard plane extraction in 3-D echocardiography

Abstract. The extraction of six standard planes in 3-D cardiac ultrasound plays an important role in clinical examination to analyze cardiac function. A guideline-based learning method for efficient and accurate standard plane extraction is proposed. A cardiac ultrasound guideline determines appropriate operation steps for clinical examinations. The idea of guideline-based learning is incorporating machine learning approaches into each stage of the guideline. First, Hough forest with hierarchical search is applied for 3-D feature point detection. Second, initial planes are determined using anatomical regularities according to the guideline. Finally, a regression forest integrated with constraints of plane regularities is applied for refining each plane. The proposed method was evaluated on a 3-D cardiac ultrasound dataset and a synthetic dataset. Compared with other plane extraction methods, it demonstrated an improved accuracy with a significantly faster running time of 0.8 s / volume. Furthermore, it showed the proposed method was robust for a range abnormalities and image qualities, which would be seen in clinical practice.
Baicalin Weakens Staphylococcus aureus Pathogenicity by Targeting Sortase B

Staphylococcus aureus (S. aureus) is a human and other animal pathogen that contributes to the primary etiology of nosocomial pneumonia, a disease with high mortality rates and costs. Treatment of multidrug-resistant S. aureus infection is extremely challenging, and new therapeutic strategies beyond antibiotic treatment are needed. Anti-virulence agents that specifically target the molecular determinants of virulence may be a novel method for treating drug-resistant nosocomial infections. Sortase B (SrtB) is a crucial virulence factor in S. aureus and plays an important role during infection. In this study, we find that baicalin suppresses the activity of SrtB. Minimum inhibitory concentration and growth curve assays confirmed that baicalin has no anti-S. aureus properties. We performed live/dead, lactate dehydrogenase (LDH), adherence, and enzyme-linked immunosorbent assays to confirm that baicalin reduced human alveolar epithelial A549 cell injury caused by S. aureus, reduced the adherence of S. aureus to A549 cells, and significantly attenuated the inflammatory response of mouse macrophage J774 cells to S. aureus. Additionally, we were able to elucidate the binding mechanics and identify the interacting sites of baicalin and SrtB via a molecular dynamics simulation, site-directed mutagenesis, and fluorescence spectroscopy quenching. Finally, we confirmed that baicalin directly binds to the active center of SrtB, and the residues Asn92 and Tyr128 perform an important function in the interaction of SrtB and baicalin. Taken together, these data indicate that baicalin is a promising candidate to combat S. aureus infections.
A Critical Evaluation of Micro Credit Utilized by Small Farming Community : A Case Study of Mandi Baha-Ud-Din

Credit is an important tool for the farming community to get command over the use of working capital, fixed capital and consumption goods. With the dawn of green revolution, there were rapid changes in crop production technology, so credit requirements have increased for Agriculture crop production. In Pakistan, agriculture is characterized as having large number of small and marginal farms with limited financial resources. To overcome this issue various financial institutions are working in the country to fulfill the needs of farmers for better agriculture productivity. This study is purposed to ascertain the impact of credit on agriculture production, linking agriculture production function as a dependent variable with the explanatory variables like agriculture credit, seed, water, labor force and other inputs like fertilizers and pesticides. By employing Cob Doughlas production function; we can filter out the real time role of agricultural credit in changing the crop production. Times series data at District level would be used to pursue the specific objectives. The study would generate helpful policy input.
'Push-based' Strategies for Improving the Efficiency of Information Management in Design

This paper will report on how some of the barriers to effective information management in design may be overcome using a number of novel information push strategies. Information push refers to the delivery of information in anticipation of a users needs. This is in contrast to a 'pull' approach where a user requests and receives a specific piece of information. The research considers contemporary information management issues faced by designers and a review of the state-of-the-art in areas of information and computing science. It is hoped that the results of the work may provide some direction for the future of information usage scenarios in design.
BIODYNAMIC CHARACTERISTICS OF MAXIMUM SPEED DEVELOPMENT UDC 796 . 012 . 112

The purpose of the present review article is to bring together some of the most important findings from the field of maximum speed development from the aspect of biomechanical, motor and neuro-muscular factors. Maximum speed is a complex biomotor ability, which manifests itself in real sports situations and is an important generator of the success of athletes in various sports disciplines. Efficiency of maximum speed is defined in terms of frequency and the length of one's stride. Both parametres are mutually dependent; they also depend on the processes of the central regulation of the motor stereotype. From the biomechanical point of view, a running stride as a basic structural unit depends on the eccentric-concentric muscular cycle of take-off action. Utilisation of elastic strength in the muscular-tendon complex and pre-activation of m. gastrocnemius is highly important in this segment. Maximum speed is a very limited hereditary biomotor ability which is characterized by a reduced possibility for controlling movement. The cerebellum, coactivation of muscles in the kinetic chain and the frequency of activation of motor units play important roles in controlling the activation of agonists and antagonists. The primary goal of training is to create an optimal model of the motor stereotype in the zone of maximum speed. Such a process has to be long-term and methodical.
A method to fabricate small features on scaffolds for tissue engineering via selective laser sintering

Purpose: Selective laser sintering (SLS) is a rapid pro- totyping technique applied to produce tissue-engineer- ing scaffolds from powder materials. The standard scanning technique, however, often produces struts of extensive thickness, which means fabrication of high- ly porous scaffolds with small overall dimensions is quite difficult. Nevertheless, this study aims to overcome this shortfall. Design/methodology/approach: To this end, three scanning methods were evaluated in terms of minimum feature size and freedom of design, using a test polyamide (PA) material. Polycaprolactone (PCL) was then employed to create highly porous 3D scaffolds using the preferred scanning me- thod to produce thin struts. Findings: While in normal scanning mode some features were well above the laser spot diameter, strut thicknesses below the laser spot diameter were achieved when using the “outline scan” function for PA material. Those achieved for PCL were slightly higher and in the 500-800 ? m range, with an average pore size of 400 µm. Investigations on the properties of the scaffolds revealed an effective compression modulus of the PCL scaffold of 6.5 MPa. Furthermore, there was no change in physical or che- mical properties when the scaffolds were stored in a physiological environment for 7 weeks. Originality/ value: Though SLS is considered as a fabrication te- chnique for tissue engineering scaffolds, actually pro- duced scaffolds did not comply with porosity requirements and limitations of the SLS process in produ- cing features at the size of the laser beam spot have not been discussed. The present paper shows the capabilities of the SLS process based on two materials and presents a method to minimize feature size in scaffolds.
Locally Differentially-Private Randomized Response for Discrete Distribution Learning

We consider a setup in which confidential i.i. d. samples $X_1,\dotsc,X_n$ from an unknown finite-support distribution $\boldsymbol{p}$ are passed through $n$ copies of a discrete privatization channel (a.k.a. mechanism) producing outputs $Y_1,\dotsc,Y_n$. The channel law guarantees a local differential privacy of $\epsilon$. Subject to a prescribed privacy level $\epsilon$, the optimal channel should be designed such that an estimate of the source distribution based on the channel outputs $Y_1,\dotsc,Y_n$ converges as fast as possible to the exact value $\boldsymbol{p}$. For this purpose we study the convergence to zero of three distribution distance metrics: $f$-divergence, mean-squared error and total variation. We derive the respective normalized first-order terms of convergence (as $n\to\infty$), which for a given target privacy $\epsilon$ represent a rule-of-thumb factor by which the sample size must be augmented so as to achieve the same estimation accuracy as that of a non-randomizing channel. We formulate the privacy-fidelity trade-off problem as being that of minimizing said first-order term under a privacy constraint $\epsilon$. We further identify a scalar quantity that captures the essence of this trade-off, and prove bounds and data-processing inequalities on this quantity. For some specific instances of the privacy-fidelity trade-off problem, we derive inner and outer bounds on the optimal trade-off curve.
Historical DNA from museum type specimens clarifies diversity of Asian leaf turtles (Cyclemys)

Species boundaries in Asian leaf turtles of the genus Cyclemys are difficult to define on the basis of morphology, primarily because many populations exhibit considerable ontogenetic variation in shell and head coloration. Two recent molecular phylogenetic hypotheses of Cyclemys species relationships, based largely on market and pet-trade samples of uncertain provenance, were highly incongruent. We used historical DNA methods to sequence fragments of the mitochondrial cytochrome b gene from eight type specimens of Cyclemys (including one collected by Alfred Russel Wallace), and phylogenetically placed these type sequences into the context of published cytochrome b variation. Our phylogenetic hypothesis supports the recognition of four named species (Cyclemys atripons, Cyclemys dentata, Cyclemys oldhamii, and C. pulchristriata), as well as a fifth species of unknown geographical provenance obtained from the Hong Kong pet trade. The type sequences show that previous molecular phylogenetic studies were hampered by misidentifications, supporting the notion that Cyclemys of unknown provenance are not reliably identified to species solely on the basis of morphology. © 2008 The Linnean Society of London, Biological Journal of the Linnean Society, 2008, 94, 131‐141. ADDITIONAL KEYWORDS: Geoemydidae ‐ historical DNA ‐ museum specimens ‐ species delimitation.
Otolith elemental evidence for spatial structuring in a temperate reef fish population

Elemental fingerprints of calcified structures can provide useful insight into disper- sal, structure and connectivity of fish and invertebrate populations, but an assumption must be made that fingerprints reflect local physico-chemical conditions at the time of accretion. Physio- logical control of elemental uptake and temporal variability in environmental conditions can con- found interpretation of spatial patterns. Multiple lines of evidence suggest that a commercially important temperate reef fish Parapercis colias (Forster 1801) (Pinguipedidae) in the New Zealand fjords forms discrete subpopulations at the mesoscale (10 to 20 km). We investigate otolith micro- chemistry as a tool to elucidate population structure and connectivity in the southern fjords. Con- centrations of 12 elements (Li, B, Mg, P, S, Ca, Mn, Cu, Zn, Sr, Ba and Pb) were measured at the otolith edge (the most recently accreted region) using laser ablation inductively coupled plasma mass spectrometry (ICP-MS). Demographic and temporal variability were accounted for before spatial patterns were investigated. P:Ca, Sr:Ca and Ba:Ca ratios were positively correlated while Li:Ca was negatively correlated with age. Inter-annual variability in signatures within a single site was widespread. Independent of demography, fingerprints differed between inner and outer fjord subpopulations in BradshawThompson, Breaksea and Dusky Sounds but not in Doubtful Sound or Long SoundPreservation Inlet. Solution and laser ablation ICP-MS were similarly able to dis- criminate between subpopulations of fish in a subset for which one sagitta was analysed by each technique. The assumption that fingerprints represent environmental conditions at the time of accretion is supported by a relocation study between habitats in Doubtful Sound. These results have important implications for temperate reef fish management and support the adoption of spa- tially explicit measures such as those in place in the New Zealand fjords.
Efficient inference for time-varying behavior during learning

The process of learning new behaviors over time is a problem of great interest in both neuroscience and artificial intelligence. However, most standard analyses of animal training data either treat behavior as fixed or track only coarse performance statistics (e.g., accuracy, bias), providing limited insight into the evolution of the policies governing behavior. To overcome these limitations, we propose a dynamic psychophysical model that efficiently tracks trial-to-trial changes in behavior over the course of training. Our model consists of a dynamic logistic regression model, parametrized by a set of time-varying weights that express dependence on sensory stimuli as well as task-irrelevant covariates, such as stimulus, choice, and answer history. Our implementation scales to large behavioral datasets, allowing us to infer 500K parameters (e.g., 10 weights over 50K trials) in minutes on a desktop computer. We optimize hyperparameters governing how rapidly each weight evolves over time using the decoupled Laplace approximation, an efficient method for maximizing marginal likelihood in non-conjugate models. To illustrate performance, we apply our method to psychophysical data from both rats and human subjects learning a delayed sensory discrimination task. The model successfully tracks the psychophysical weights of rats over the course of training, capturing day-to-day and trial-to-trial fluctuations that underlie changes in performance, choice bias, and dependencies on task history. Finally, we investigate why rats frequently make mistakes on easy trials, and suggest that apparent lapses can be explained by sub-optimal weighting of known task covariates.
Unstructured-Grid CFD Algorithms on Many-Core Architectures

In the eld of computational uid dynamics (CFD), the NavierStokes equations are often solved using an unstructured-grid approach to accommodate geometric complexity. Furthermore, turbulent ows encountered in aerospace applications generally require highly anisotropic meshes, driving the need for implicit solution methodologies to e ciently solve the discrete equations. These approaches require frequent construction and solution of large, tightly-coupled systems of block-sparse linear equations. We explore the transition of two representative CFD kernels from a coarse-grained MPI-based model originally developed for multicore systems to a shared-memory model suitable for many-core platforms. Results for the Intel Xeon Phi Knights Landing, NVIDIA Pascal P100, and NVIDIA Volta V100 architectures are compared with the aforementioned MPI-based implementation for the multicore Intel Xeon Broadwell (BWL) processor. We observe substantial speedups over BWL as well as higher performance per dollar MSRP and performance per watt for the many-core architectures. ACM Reference format: Aaron Walden, Eric J. Nielsen, Mohammad Zubair, Jason Orender, John C. Linford, Izaak Beekman, Samuel Khuvis, Sameer S. Shende, Justin P. Luitjens, and John G. Wohlbier. 2017. Unstructured-Grid CFD Algorithms on Many-Core Architectures. In Proceedings of The International Conference for High Performance Computing, Networking, Storage and Analysis, Denver, CO, November 12–17, 2017 (SC ’17), 2 pages. DOI: 10.1145/nnnnnnn.nnnnnnn ∗Also with Old Dominion University. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro t or commercial advantage and that copies bear this notice and the full citation on the rst page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior speci c permission and/or a fee. Request permissions from permissions@acm.org.
Yield performance of Ganoderma lucidum (Fr.) Karst cultivation on substrates containing different protein and carbohydrate sources

This study was conducted to investigate the effects of different dosages of molasses as a carbohydrate source and corn-gluten meal as a protein source on the yield of Ganoderma lucidum and to improve yield on solid state medium. Three dosages (1, 2 and 3%) of gluten meal or molasses were added to sawdust and bran based medium. Results showed that yield was increased significantly when 1% molasses and gluten meal were added to substrate media. Molasses was found to be the best supplement as a carbohydrate source compared to gluten which is rich in protein.
Impact of stimulus configuration on steady state visual evoked potentials (SSVEP) response

We investigate the impact of configuration of multistimuli presented in computer monitor to steady-state visual evoked potential response. The configuration of stimuli is defined by three parameters-the size of stimuli, the separation distance between the stimuli and the layout. Two 4 by 4 checkerboards in twelve configurations were presented to the subjects. 9 subjects participated in this study. Subjects’ electroencephalography (EEG) data was off-line analyzed by using Fast Fourier Transform (FFT). The mean classification rates of configuration with bigger size and larger separation distance is higher than those configurations with smaller size and shorter separation distance. These results suggest that the stimulus size is the most important parameter of three, followed by the separation distance and layout.
SURF: Understanding and Predicting Urban Convection and Haze

Urbanization modifies atmospheric energy and moisture balances, forming distinct features [e.g., urban heat islands (UHIs) and enhanced or decreased precipitation]. These produce significant challenges to science and society, including rapid and intense flooding, heat waves strengthened by UHIs, and air pollutant haze. The Study of Urban Impacts on Rainfall and Fog/Haze (SURF) has brought together international expertise on observations and modeling, meteorology and atmospheric chemistry, and research and operational forecasting. The SURF overall science objective is a better understanding of urban, terrain, convection, and aerosol interactions for improved forecast accuracy. Specific objectives include a) promoting cooperative international research to improve understanding of urban summer convective precipitation and winter particulate episodes via extensive field studies, b) improving high-resolution urban weather and air quality forecast models, and c) enhancing urban weather forecasts for societal applications (e.g., health, energy, hydrologic, climate change, air quality, planning, and emergency response management). Preliminary SURF observational and modeling results are shown (i.e., turbulent PBL structure, bifurcating thunderstorms, haze events, urban canopy model development, and model forecast evaluation).
The genetic legacy of 50 years of desert bighorn sheep translocations

Conservation biologists have increasingly used translocations to mitigate population declines and restore locally extirpated populations. Genetic data can guide the selection of source populations for translocations and help evaluate restoration success. Bighorn sheep (Ovis canadensis) are a managed big game species that suffered widespread population extirpations across western North America throughout the early 1900s. Subsequent translocation programs have successfully re‐established many formally extirpated bighorn herds, but most of these programs pre‐date genetically informed management practices. The state of Nevada presents a particularly well‐documented case of decline followed by restoration of extirpated herds. Desert bighorn sheep (O. c. nelsoni) populations declined to less than 3,000 individuals restricted to remnant herds in the Mojave Desert and a few locations in the Great Basin Desert. Beginning in 1968, the Nevada Department of Wildlife translocated ~2,000 individuals from remnant populations to restore previously extirpated areas, possibly establishing herds with mixed ancestries. Here, we examined genetic diversity and structure among remnant herds and the genetic consequences of translocation from these herds using a genotyping‐by‐sequencing approach to genotype 17,095 loci in 303 desert bighorn sheep. We found a signal of population genetic structure among remnant Mojave Desert populations, even across geographically proximate mountain ranges. Further, we found evidence of a genetically distinct, potential relict herd from a previously hypothesized Great Basin lineage of desert bighorn sheep. The genetic structure of source herds was clearly reflected in translocated populations. In most cases, herds retained genetic evidence of multiple translocation events and subsequent admixture when founded from multiple remnant source herds. Our results add to a growing literature on how population genomic data can be used to guide and monitor restoration programs.