Miracle-dz/UniFAR-FTUs huggingface dataset:
Deterministic Policy Gradient With Integral Compensator for Robust Quadrotor Control

In this paper, a deep reinforcement learning-based robust control strategy for quadrotor helicopters is proposed. The quadrotor is controlled by a learned neural network which directly maps the system states to control commands in an end-to-end style. The learning algorithm is developed based on the deterministic policy gradient algorithm. By introducing an integral compensator to the actor-critic structure, the tracking accuracy and robustness have been greatly enhanced. Moreover, a two-phase learning protocol which includes both offline and online learning phase is proposed for practical implementation. An offline policy is first learned based on a simplified quadrotor model. Then, the policy is online optimized in actual flight. The proposed approach is evaluated in the flight simulator. The results demonstrate that the offline learned policy is highly robust to model errors and external disturbances. It also shows that the online learning could significantly improve the control performance.
“Who are you?” - Learning person specific classifiers from video

We investigate the problem of automatically labelling faces of characters in TV or movie material with their names, using only weak supervision from automatically-aligned subtitle and script text. Our previous work (Everingham et al. [8]) demonstrated promising results on the task, but the coverage of the method (proportion of video labelled) and generalization was limited by a restriction to frontal faces and nearest neighbour classification. In this paper we build on that method, extending the coverage greatly by the detection and recognition of characters in profile views. In addition, we make the following contributions: (i) seamless tracking, integration and recognition of profile and frontal detections, and (ii) a character specific multiple kernel classifier which is able to learn the features best able to discriminate between the characters. We report results on seven episodes of the TV series "Buffy the Vampire Slayer", demonstrating significantly increased coverage and performance with respect to previous methods on this material.
Remote sensing of forest environments : concepts and case studies

Dedication. Editor Profiles. Preface. Acknowledgements. Section 1: Data collection and pre-processing. 1. Remote sensing of forest environments, Introduction. The transition from theory to information M.A. Wulder, S.E. Franklin. 2. Selection of remotely sensed data M.A. Levsky, W.B. Cohen. 3. The roles of arial photography in forestry remote sensing image analysis R.J. Hall. 4. Indirect measurement of forest canopy structure from in situ optical sensors R.A. Fournier, et al. 5. Accuracy assessment of maps of forest condition: Statistical design and methodological considerations R.L. Czaplewski. Section 2: Common methods for data processing and information generation. 6. Geometric correction of remotely sensed images T. Toutin. 7. Radiometric image processing D.R. Peddle, et al. 8. Per-pixel analysis of forest structure: Vegetation indices, spectral mixture analysis and canopy reflectance modeling G.P. Asner, et al. 9. Extracting individual tree information: A survey of techniques for high spatial resolution imagery D.S. Culvenor. 10. Rationale and conceptual framework for classification approaches to assess forest resources and properties J. Franklin, et al. 11. Remote sensing of forests over time: Change types, methods, and opportunities Peng Gong, Bing Xu. Section 3: Case studies illustrating methods and applications for remote sensing of forests. 12. National scale forest information extraction from coarse resolution satellite data, Part 1. Data processing and mapping land cover types J. Cihlar, et al. 13. National scale forest information extraction from coarse resolution satellite data, Part 2. Forest biophysical parameters and carbon J. Cihlar, et al. 14. Regional forest land cover characterisation using medium spatial resolution satellite data Chenquan Huang, et al. 15. Modeling forest productivity using data acquired through remote sensing N.C. Coops, J.D. White. 16. Experiences in field data collection: In support of land use and land cover change classification in boreal and tropical environments G.A. Sanchez-Azofeifa, et al. 17. Estimation of foliar chemistry of western hemlock using hyperspectral data K.O. Niemann, D.G. Goodenough. 18. Using georeferenced large-scale aerial videography as a surrogate for ground validation data D. Slaymaker. 19. Tree and canopy height estimation with scanning lidar B. St-Onge, et al. 20. Remote sensing of forest environments, conclusions, challenges and opportunities S.E. Franklin, M.A. Wulder. Index.
Construction of prophage‐free and highly‐transformable Limosilactobacillus reuteri strains and their use for production of 1,3‐propanediol

The lactic acid bacterium Limosilactobacillus reuteri (formerly Lactobacillus reuteri) is a desirable host for the production of 1,3‐propanediol (1,3‐PDO) from glycerol when 1,3‐PDO is used in the food or cosmetic industry. However, the production is hindered by strain instability, causing cell lysis, and difficult gene manipulation. This study reveals that the stability of L. reuteri DSM 20016 and its 1,3‐PDO production, especially in the alcohol dehydrogenases (ADHs)‐deletion mutants, are greatly enhanced after the deletion of two prophages (Φ3 and Φ4) present in the L. reuteri's chromosome. The resulting phage‐free and ADHs‐deletion mutant could produce >825 mM 1,3‐PDO in 48 h without cell lysis at the theoretical maximum yield on glucose of ~2 mol/mol. Compared to the wild‐type strain, the mutant exhibited a 45.2% increase in 1,3‐PDO production titer and a 2.1‐fold increase in yield. In addition, this study reports that the transformation efficiency of L. reuteri Δadh2Δadh6 mutant strains were greatly enhanced by >300‐fold after the deletion of prophage Φ3, probably due to the removal of a restriction‐modification (RM) system which resides in the phage genome. With improved stability and higher transformation efficiency, recombinant L. reuteri DSM 20016 Δadh2Δadh6ΔΦ3ΔΦ4 can be a more reliable and amenable host for industrial applications.
Combining SAR interferometry and the equation of continuity to estimate the three-dimensional glacier surface-velocity vector

Abstract Until now, an assumption of surface-parallel glacier flow has been used to express the vertical velocity component in terms of the horizontal velocity vector, permitting all three velocity components to be determined from synthetic aperture radar interferometry. We discuss this assumption, which neglects the influence of the local mass balance and a possible contribution to the vertical velocity arising if the glacier is not in steady state. We find that the mass-balance contribution to the vertical surface velocity is not always negligible as compared to the surface-slope contribution. Moreover, the vertical velocity contribution arising if the ice sheet is not in steady state can be significant. We apply the principle of mass conservation to derive an equation relating the vertical surface velocity to the horizontal velocity vector. This equation, valid for both steady-state and non-steady-state conditions, depends on the ice-thickness distribution. Replacing the surface-parallel-flow assumption with a correct relationship between the surface velocity components requires knowledge of additional quantities such as surface mass balance or ice thickness.
Infrared Aerothermal Nonuniform Correction via Deep Multiscale Residual Network

In the infrared focal plane arrays imaging systems, the temperature-dependent nonuniformity effects severely degrade the image quality. In this letter, we propose a very deep convolutional neural network for unified infrared aerothermal nonuniform correction. Our network is built with the multiscale and residual training. The multiscale subnetworks utilize the multiscale property in the images, and the long–short-term residual learning contributes to the information propagation. Compared with the previous methods, the proposed method is more robust to various nonuniform artifacts and more efficient at processing time. Experimental results validate the superiority of our method for infrared nonuniform correction.
Learning in the Credit Card Market

Agents with more experience make better choices. We measure learning dynamics using a panel with four million monthly credit card statements. We study add-on fees, specifically cash advance, late payment, and overlimit fees. New credit card accounts generate fee payments of $15 per month. Through negative feedback -- i.e. paying a fee -- consumers learn to avoid triggering future fees. Paying a fee last month reduces the likelihood of paying a fee in the current month by about 40%. Controlling for account fixed effects, monthly fee payments fall by 75% during the first three years of account life. We find that learning is not monotonic. Knowledge effectively depreciates about 10% per month, implying that learning displays a strong recency effect.
China's Participation in Anti-Piracy Operations off the Horn of Africa: Drivers and Implications

Abstract : In December 2008, the navy of the People's Republic of China (People's Liberation Army Navy, or PLAN) deployed a task force, made up of two guided missile destroyers and a supply ship, to participate in international antipiracy operations off the coast of Somalia, in the Gulf of Aden/Horn of Africa (HoA) region. This deployment marked the first time in modern history that China's navy has engaged in an operational mission outside of its claimed territorial waters. The PLAN's participation in international anti-piracy operations has been viewed by many in the United States and elsewhere as an indication of China's apparent willingness to take on a larger military role on the global stage.
Evaluation of SMOTE for High-Dimensional Class-Imbalanced Microarray Data

Synthetic Minority Oversampling TEchnique (SMOTE) is a popular oversampling method that was proposed to improve random oversampling but its behavior on high-dimensional data has not been thoroughly investigated. In this paper we evaluate the performance of SMOTE on high-dimensional data, using gene expression microarray data. We observe that SMOTE does not attenuate the bias towards the classification in the majority class for most classifiers, and it is less effective than random undersampling. SMOTE is beneficial for k-NN classifiers based on the Euclidean distance if the number of variables is reduced performing some type of variable selection and the benefit is larger if more neighbors are used. If the variable selection is not performed than the k-NN classification is counter intuitively biased towards the minority class, so SMOTE for k-NN without variable selection should not be used in practice.
Check-list of Andalusia (S. Spain) Seaweeds. I Phaeophyceae

Check-list of Andalusia (S. Spain) seaweeds. I. Phaeophyceae. Check-list of the Phaeophyceae from the Andalusian coast, based on literature records (from 1807 to 1995), is presented. Taxa are listed by provinces. Catalogue contains 108 species and infraspecific taxa accepted under current taxonomy.
Using Hardware Counters to Automatically Improve Memory Performance

In this paper, we introduce a profile-driven online page migration scheme and investigate its impact on the performance of multithreaded applications. We use lightweight, inexpensive plug-in hardware counters to profile the memory access behavior of an application, and then migrate pages to memory local to the most frequently accessing processor. Using the Dyninst runtime instrumentation combined with hardware counters, we were able to add page migration capabilities to the system without having to modify the operating system kernel, or to re-compile application programs. This approach reduced the total number of non-local memory accesses of applications by up to 90%. Even on a system with small remote to local memory access latency rations, this resulted in up to 16% improvement in execution time.
Eradication of Helicobacter pylori Infection Improves Levodopa Action, Clinical Symptoms and Quality of Life in Patients with Parkinson's Disease

Background Previous studies have demonstrated a higher prevalence of Helicobacter pylori (H. pylori) infection in patients with Parkinson's disease (PD) compared to controls. H. pylori infection affects levodopa absorption and its eradication significantly improves clinical response to levodopa. Here, we studied the prevalence of H. pylori infection and its eradication effects among our PD patients. Methods A prospective study involving idiopathic PD patients on levodopa therapy. 13C-urea breath test (UBT) was used to detect H. pylori. UBT-positive patients were given standard eradication therapy and followed up at 6 and 12 weeks in an open label single arm design. Repeat UBT was performed at 12 weeks. The UPDRS, PD NMQ, PD NMSS and PDQ-39 were administered at baseline and post-eradication (6 and 12 weeks). Levodopa ‘onset’ time and ON-duration were recorded. Results Of 82 patients recruited, 27 (32.9%) had positive UBT. H. pylori-positive patients had significantly poorer total UPDRS (p = 0.005) and PDQ39 (p<0.0001) scores compared to H. pylori-negative patients. At 12 weeks post-eradication, the mean levodopa onset time shortened by 14 minutes (p = 0.011). The mean ON duration time increased by 56 minutes at week 6 (p = 0.041) and 38 minutes at week 12 (p = 0.035). The total UPDRS scores (p<0.0001), scores for parts II (p = 0.001), III (p<0.0001) and IV (p = 0.009) were significantly better. The total PDQ-39 scores (p = 0.001) and subdomains mobility (p = 0.002), ADL (p = 0.001), emotional well being (p = 0.026) and stigma (p = 0.034) significantly improved. The PD NMSQ did not show significant improvement. Conclusions H. pylori eradication improved levodopa onset time, ON duration, motor severity and quality of life parameters. Screening and eradication of H. pylori is inexpensive and should be recommended in PD patients, particularly those with erratic response to levodopa. Trial Registration ClinicalTrials.gov NCT02112812
Summary : National Hospital Discharge Survey

During 1992, an estimated 31.0 million inpatients, excluding newborn infants, were discharged from short-stay non-Federal hospitals in the United States. These patients used 190,4 million days of inpatient hospital care. The discharge rate was 122.1 discharges per 1,000 civilian population and the average length of stay was 6.2 days. These and other statistics presented in this report are based on data collected by means of the National Hospital Discharge Survey (NT-IDS),a continuous survey that has been conducted by the National Center for Health Statistics (NCHS) since 1965. In 1992, data were abstracted from the medical records of approximately 274,000 patients discharged from 494 short-stay non-Federal hospitals. Beginning in 19SS, a new three-stage stratified sample design was put in operation, A brief
Predicting the Dynamic Parameters of Multiphase Flow in CFD (Dam-Break Simulation) Using Artificial Intelligence-(Cascading Deployment)

Multiphase flow of oil, gas, and water occurs in a reservoir’s underground formation and also within the associated downstream pipeline and structures. Computer simulations of such phenomena are essential in order to achieve the behavior of parameters including but not limited to evolution of phase fractions, temperature, velocity, pressure, and flow regimes. However, within the oil and gas industry, due to the highly complex nature of such phenomena seen in unconventional assets, an accurate and fast calculation of the aforementioned parameters has not been successful using numerical simulation techniques, i.e., computational fluid dynamic (CFD). In this study, a fast-track data-driven method based on artificial intelligence (AI) is designed, applied, and investigated in one of the most well-known multiphase flow problems. This problem is a two-dimensional dam-break that consists of a rectangular tank with the fluid column at the left side of the tank behind the gate. Initially, the gate is opened, which leads to the collapse of the column of fluid and generates a complex flow structure, including water and captured bubbles. The necessary data were obtained from the experience and partially used in our fast-track data-driven model. We built our models using Levenberg Marquardt algorithm in a feed-forward back propagation technique. We combined our model with stochastic optimization in a way that it decreased the absolute error accumulated in following time-steps compared to numerical computation. First, we observed that our models predicted the dynamic behavior of multiphase flow at each time-step with higher speed, and hence lowered the run time when compared to the CFD numerical simulation. To be exact, the computations of our models were more than one hundred times faster than the CFD model, an order of 8 h to minutes using our models. Second, the accuracy of our predictions was within the limit of 10% in cascading condition compared to the numerical simulation. This was acceptable considering its application in underground formations with highly complex fluid flow phenomena. Our models help all engineering aspects of the oil and gas industry from drilling and well design to the future prediction of an efficient production.
Estimativa da diversidade de variedades tradicionais de feijão-caupi do Acre com marcadores SSR

The State of Acre is an important source of diversity of cupi bean, Vigna unguiculata (L.) Walp. , with small farmers being the main responsible for the maintenance and conservation of this crop. Although it is of great importance for improvement, the genetic diversity of cowpea has not yet been properly determined and explored. Molecular markers are efficient tools to reliably estimate the differences between genotypes of the same species. The objective of this study was to determine, through microsatellite markers, the genetic variability of traditional varieties of cowpea cultivated in Acre. Seeds of 53 genotypes were collected in the main producing regions of this culture. The collected genotypes were seeded and cultivated in pots until the production of fully expanded leaves for DNA extraction. Five microsatellite loci were used. The data on the presence and absence of the bands of each marker were used to calculate the jaccard index. Multivariate analyzes were performed to verify the grouping of genotypes by the UPGMA, Tocher Optimization, modified Tocher and principal coordinates methods. Genetic distances ranged from 0.00 to 0.81. There was high variability among the 53 genotypes, which were separated into 44 distinct groups. One genotype pair was identified as a duplicate. The variability among cowpea genotypes is high. The divergent genotypes identified in this study can be indicated to compose cowpea genetic improvement programs.
Perception of the Arctic as Tourism Destination and Perspectives of its Utilization by Tourism Industry in Finnish Lapland

of Thesis School of Tourism and Hospitality Management Degree Programme in Tourism Abstract of Thesis Abstract of Thesis Author Mikhail Malinin Year 2014 Commissioned by Thesis title Number of pages Arctic Power Research Center Perception of the Arctic as Tourism Destination and Perspectives of its Utilization by Tourism Industry in Finnish Lapland 42 + 8 The aim of this thesis project was to analyse how the notion and brand image of the Arctic is perceived and understood, and how it could be exploited by tourism industry stakeholders in case of Finnish Lapland. The main focus was put on determining what associations people have about the Arctic, what images and attributes are most popular and how the Arctic is perceived as a tourism destination. The study also evaluated what tourism products and services are most expected and how high the level of preparedness to travel to the Arctic is. In order to find answers to the research question a quantitative survey was conducted with the help of structured questionnaires. The questionnaire was given to foreign visitors in Rovaniemi, Finland and to other people who do not live in the Arctic. The questions in the questionnaire inquired about the different aspects of perception of the Arctic as tourism destination: images associated with it, level of awareness of the existing tourism network, most expected tourism services, general level of preparedness to travel and possible factors which could hinder the travel. A qualitative interview was conducted with Ilona Mettiäinen, a researcher, who works at the Arctic Research center in Rovaniemi, and whose expertise is related to this topic. The findings indicate that this image is dominated by classic images of the high Arctic: icebergs, snow and ice. At the same time, there is a growing awareness about the local peoples, interest in their culture, handicrafts and food. However, the image of the Arctic is not very diversified. Most expected tourism products and services are mainly nature-related, but also those related to handicrafts, local cuisine, saunas and spa services are prominent, which implies that the possible service range which can impact the general image of the Arctic is quite wide. The most popular reason, seen as capable to hinder the travel is the high transportation costs, which can also serve as a future benefit for non-high Arctic areas.
Column subset selection, matrix factorization, and eigenvalue optimization

Given a fixed matrix, the problem of column subset selection requests a column submatrix that has favorable spectral properties. Most research from the algorithms and numerical linear algebra communities focuses on a variant called rank-revealing QR, which seeks a well-conditioned collection of columns that spans the (numerical) range of the matrix. The functional analysis literature contains another strand of work on column selection whose algorithmic implications have not been explored. In particular, a celebrated result of Bourgain and Tzafriri demonstrates that each matrix with normalized columns contains a large column submatrix that is exceptionally well conditioned. Unfortunately, standard proofs of this result cannot be regarded as algorithmic. This paper presents a randomized, polynomial-time algorithm that produces the submatrix promised by Bourgain and Tzafriri. The method involves random sampling of columns, followed by a matrix factorization that exposes the well-conditioned subset of columns. This factorization, which is due to Grothendieck, is regarded as a central tool in modern functional analysis. The primary novelty in this work is an algorithm, based on eigenvalue minimization, for constructing the Grothendieck factorization. These ideas also result in an approximation algorithm for the (∞, 1) norm of a matrix, which is generally NP-hard to compute exactly. As an added bonus, this work reveals a surprising connection between matrix factorization and the famous maxcut semidefinite program.