Miracle-dz/UniFAR-FTUs huggingface dataset:
Response of Forest to Climatic Events and Human Management at Fort Leavenworth, Kansas

Characteristics and temporal changes in forest cover from 1987 to 1997 were documented on the basis of remote sensing for two study forests at Fort Leavenworth, northeastern Kansas. Eight Landsat 5 Thematic Mapper (TM) datasets from the month of July cover the study period, which included a major drought in 1988-1989 and flooding along the Missouri River in 1993. Other data sources included kite aerial photographs, digital orthophotos, tree-ring cores, climatic records, and ground observations. Three study areas were evaluated from Landsat TM datasets: (1) the entire Fort Leavenworth area; (2) an upland, hardwood forest composed mainly of oaks; and (3) a bottomland, softwood forest dominated by cottonwood. Normalized Difference Vegetation Index (NDVI) values were derived from these three study sets and subjected to image differencing and principal-component analysis. The TM band 5:4 ratio was also analyzed for the two study forests. Values and trends derived from Landsat imagery were compared to data on tree-ring growth in upland oaks and regional climatic events. Annual growth of tree rings in upland oaks is tied closely to precipitation and the Palmer Drought Severity Index (PDSI); however, changes in NDVI values lag one to two years behind the onset of climatic events, particularly drought episodes. During the first year of drought (1988), vegetation cover in the upland and bottomland forests reacted in different ways: with a slight decline in the upland forest and a slight increase in the bottomland forest. The increased vegetation in the bottomland forest presumably resulted from more understory growth in dry hollows and potholes. In the second year of drought (1989), both forests suffered a marked decline in vegetation cover. NDVI values reached their minima for all categories (whole area, upland forest, and bottomland forest) in 1990, even though precipitation and tree-ring growth increased substantially that year. We conclude that changes in Landsat-derived NDVI values are out of phase with climatic events and variations in tree-ring growth for both upland and bottomland forests in northeastern Kansas and northwestern Missouri. Overall change (1987 to 1997) for NDVI values is down slightly for all categories of evaluation. This probably reflects reduced precipitation throughout the study period compared to the long-term average. Changes in vegetation took place mainly on the forest margins. Such changes are thought to result from microclimatic stress at forest edges. The bottomland study forest also was impacted by severe flooding in 1993. Routine human activities may have resulted in minor changes along the margins of both study forests. The bottomland forest was affected by intentional burning of the adjacent prairie in April 2000. Cottonwood trees at the forest edge were killed or injured by the prairie fire, which penetrated the forest understory some distance.
Modularizing Human Motion into Actions and Behaviors

This thesis proposal addresses the problems of modularizing humanoid robot control and representing human activity. We address the problem through the creation of basis behaviors called perceptual-motor primitives. Perceptual-motor primitives serve as a substrate for linking a humanoid’s ability to perceive human activities and perform those activities. Thus, by using perceptual-motor primitives, we address both perception and control with the same underlying, synergistic structuring of motion. Furthermore, these primitives provide a mapping between perception and control that allows for imitation, the process of learning new movement patterns and skills by observation. This thesis is primarily concerned with a methodology for automatically deriving such perceptual-motor primitives from human motion-capture data, in an off-line data-driven fashion. Non-linear dimensionality reduction (DR) is used to iteratively transform a motion-capture data set into reduced spaces from which actions, and subsequently behaviors, can be extracted, while also accounting for spatio-temporal dependencies in the motion-capture data. A behavior in this context is an ordered set of component actions. After the initial DR iteration, motions generated by the same underlying action are grouped into units of action and generalized into action primitives. In further DR iterations, motions generated by the same underlying behavior are grouped into units of behavior and generalized into behavior machines. We validate and evaluate the above approach to automated derivation of action primitives and behavior machines in several ways. First, the methodology is evaluated for multiple sets of human motion-capture data that span a variety of free-space upperbody movement from activities such as dancing and athletics. Second, the approach is evaluated by comparing the results of primitive derivation for manually-segmented and automatically-segmented motion-capture data. Third, the approach is validated based on its ability to: i) reconstruct slight variations of original capture motion, ii) control a simulated humanoid robot using the derived actions and behaviors, and iii) produce actions and behaviors comparable to a priori knowledge of activities performed in the training motion data. Finally, the dissertation will include an evaluation of the utility of the approach in the context of imitation for classifying and reconstructing human activity. viii
Concomitant Release of Ventral Tegmental Acetylcholine and Accumbal Dopamine by Ghrelin in Rats

Ghrelin, an orexigenic peptide, regulates energy balance specifically via hypothalamic circuits. Growing evidence suggest that ghrelin increases the incentive value of motivated behaviours via activation of the cholinergic-dopaminergic reward link. It encompasses the cholinergic afferent projection from the laterodorsal tegmental area (LDTg) to the dopaminergic cells of the ventral tegmental area (VTA) and the mesolimbic dopamine system projecting from the VTA to nucleus accumbens (N.Acc.). Ghrelin receptors (GHS-R1A) are expressed in these reward nodes and ghrelin administration into the LDTg increases accumbal dopamine, an effect involving nicotinic acetylcholine receptors in the VTA. The present series of experiments were undertaken directly to test this hypothesis. Here we show that ghrelin, administered peripherally or locally into the LDTg concomitantly increases ventral tegmental acetylcholine as well as accumbal dopamine release. A GHS-R1A antagonist blocks this synchronous neurotransmitter release induced by peripheral ghrelin. In addition, local perfusion of the unselective nicotinic antagonist mecamylamine into the VTA blocks the ability of ghrelin (administered into the LDTg) to increase N.Acc. -dopamine, but not VTA-acetylcholine. Collectively our data indicate that ghrelin activates the LDTg causing a release of acetylcholine in the VTA, which in turn activates local nicotinic acetylcholine receptors causing a release of accumbal dopamine. Given that a dysfunction in the cholinergic-dopaminergic reward system is involved in addictive behaviours, including compulsive overeating and alcohol use disorder, and that hyperghrelinemia is associated with such addictive behaviours, ghrelin-responsive circuits may serve as a novel pharmacological target for treatment of alcohol use disorder as well as binge eating.
Complete Genome Sequences of pLMA1 and pLMA7, Two Large Linear Plasmids of Micrococcus Strains Isolated from a High-Altitude Lake in Argentina

ABSTRACT The two linear plasmids pLMA1 (109,112 bp) and pLMA7 (82,075 bp) from Micrococcus strains were isolated from a high-altitude lake in the Argentinean Puna, sequenced, and annotated. These extrachromosomal elements are probably conjugative and harbor genes potentially involved in coping with the harsh conditions in such extreme environments.
An industrial robotic knowledge representation for kit building applications

The IEEE RAS Ontologies for Robotics and Automation Working Group is dedicated to developing a methodology for knowledge representation and reasoning in robotics and automation. As part of this working group, the Industrial Robots sub-group is tasked with studying industrial applications of the ontology. One of the first areas of interest for this subgroup is the area of kit building or kitting. This is a process that brings parts that will be used in assembly operations together in a kit and then moves the kit to the assembly area where the parts are used in the final assembly. This paper examines the knowledge representations that have been developed and implemented for the kitting problem.
Model-based systems analysis using CSIM18

Achieving the best possible cost performance for a system is often the goal of the manager of that system. This is true for large systems (such as an entire railroad system) as well as for small systems (such as a computerized departmental transaction processing system). One technique for achieving this management goal is to develop and use an operational model of the system. This paper describes model-based systems analysis using simulation models of systems. This technique for finding and addressing performance related problems is widely used in a variety of industries. In many cases, the resulting improvements in cost-performance characteristics for the system can be significant.
On-demand gateway broadcast scheme for connecting mobile ad hoc networks to the Internet

Gateway discovery algorithm is a fundamental protocol for interconnecting mobile ad hoc network (MANET) with the Internet. In most existing schemes, each gateway node broadcasts gateway advertisements to announce its presence. The decision of when to emit advertisements can influence the performance of the network. Traditional gateway discovery schemes adopt the method of periodically emitting advertisements with a time interval. However, this method does not fulfill the actual needs of the source nodes. This paper proposes a novel adaptive scheme for gateway discovery, in which the gateway broadcasts advertisements only on-demand instead of periodic emission. In order to obtain the network's actual demands for gateway advertisement, routes to the gateway are monitored. In particular, if any route is predicted to be broken, the source node requires fresh gateway advertisements to update routes, and then the gateway will be triggered to broadcast to fulfill such demands. We study the performance of on-demand gateway discovery scheme by a comparison approach. The results show that the proposed adaptive gateway discovery scheme greatly outperforms the conventional solutions: it is capable of achieving higher packet delivery ratio and lower end-to-end delay, while minimizing the routing overhead.
Thrombin–aptamer recognition: a revealed ambiguity

Aptamers are structured oligonucleotides that recognize molecular targets and can function as direct protein inhibitors. The best-known example is the thrombin-binding aptamer, TBA, a single-stranded 15-mer DNA that inhibits the activity of thrombin, the key enzyme of coagulation cascade. TBA folds as a G-quadruplex structure, as proved by its NMR structure. The X-ray structure of the complex between TBA and human α-thrombin was solved at 2.9-Å resolution, but did not provide details of the aptamer conformation and the interactions with the protein molecule. TBA is rapidly processed by nucleases. To improve the properties of TBA, a number of modified analogs have been produced. In particular, a modified TBA containing a 5′-5′ polarity inversion site, mTBA, has higher stability and higher affinity toward thrombin with respect to TBA, although it has a lower inhibitory activity. We present the crystal structure of the thrombin–mTBA complex at 2.15-Å resolution; the resulting model eventually provides a clear picture of thrombin–aptamers interaction, and also highlights the structural bases of the different properties of TBA and mTBA. Our findings open the way for a rational design of modified aptamers with improved potency as anticoagulant drugs.
A Theory of Learning and Generalization

From the Publisher: How does it differ from first edition? Includes new material on: * support vector machines (SVM's), * fat shattering dimensions * applications to neural network learning, * learning with dependent samples generated by beta-mixing process, * connections between system identification and learning theory * probabilistic solution of "intractable" problems in robust control and matrix theory using randomised algorithms. In addition, solutions to some open problems posed in the first edition are included, and new open problems are added. The author is a respected authority in the field of control and systems theory. This new edition, with substantial new material, takes account of important new developments in the theory of learning. It also deals extensively with the theory of learning control systems, which has now reached a level of maturity comparable to that of learning of neural networks. The book is written in a manner that would suit self-study and contains comprehensive references. The chapters are also written to be as autonomous as possible and contain updated open problems to enhance further research and self-study.
The Effects of Data Compression on SAR Change Detection

The performance of coherent and non-coherent change detection algorithms is evaluated using complex SAR data that have been processed with various data compression approaches; the hope is that it may be possible to achieve higher compression ratios than could be achieved using classical image compression approaches such as BAQ (block adaptive quantization). BAQ compression is typically applied to raw (I,Q) SAR phase-history data, and our studies show that to obtain reasonably good coherent change detection (CCD) performance from a baseline CCD algorithm, BAQ compression requires at least 4-bit quantization for each of the I and Q phase-history data samples; since our original full-precision data is 8-bits I and 8-bits Q, the best compression ratio (CR) that could be achieved using BAQ compression was a factor of 2. Our goal is to increase the amount of compression while achieving the same quality of change detection using more sophisticated wavelet-based approaches such as ―compressive sensing‖ or ―set partitioning‖ (SPIHT). This paper demonstrates a wavelet-based compressive sensing approach that gives CR = 3 with comparable CCD performance; we also demonstrate a wavelet-based SPIHT approach that gives CR = 4 with comparable CCD performance.
Mechanism of stochastic resonance enhancement in neuronal models driven by 1/f noise.

Noise can assist neurons in the detection of weak signals via a mechanism known as stochastic resonance (SR). In a previous study [Phys. Lett. A 243, 281 (1998)], we showed that when colored noise with 1/f(beta) spectrum is added to the FitzHugh-Nagumo (FHN) neuronal model, the optimal noise variance for SR could be minimized with beta approximately 1. In this study, we investigate analytically how the noise color (beta) affects the SR profile in a linearized version of the FHN model. We demonstrate that the aforementioned effect of 1/f noise is related to the dynamical characteristics of the model neuron, i.e., the refractory period, the low-pass filtering effect of the membrane capacitance, and the high-pass filtering effect of the recovery variable.
Efficient Distributed MPLS P2MP Fast Reroute

in the IETF to support various applications such as IP multicast over MPLS with sub-LSP fast reroute. Fast reroute was designed to meet the needs of real-time applications. It computes and creates backup paths in advance, and re-directs traffic quickly upon failure. To meet a restoration target, sufficient backup capacity is necessary. In this paper, we provide a novel and efficient distributed P2MP fast reroute solution that includes an efficient P2MP backup routing tree, a distributed bandwidth management method, and an optimal backup path selection algorithm. To assess the quality of our solution, the restoration ratio (backup bandwidth over service bandwidth) is compared via simulation with the IETF sub-LSP fast reroute and another published local repair methods. Simulation results show that our proposed solution significantly reduces the restoration ratio from 3.5 to about 0.8.
Roads Impact on Poverty Reduction A Case Study of Cameroon

Many investments in infrastructure are built on the belief that they will ineluctably lead to poverty reduction and income generation. This has e ntail d massive aid-financed projects in roads in developing countries. However the lack of robust evaluations and a comprehensive theoretical framework could raise questions about c urrent strategies in Sub-Saharan Africa. Using the second Cameroonian national household sur vey (Enquête Camerounaise Auprès des Ménages II, 2001) and the Cameroon case study, this paper demonstrates that investing uniformly in tarred roads in Africa is likely to ha ve a much lower impact on poverty than expected. Isolation from a tarred road is found to have no direct impact on consumption expenditures in Cameroon. The only impact is an ind irect one in the access to labor activities. This paper reasserts the fact that access to roads is only one factor contributing to poverty reduction (and not necessarily the most important i n many cases). Considering that increase in non-farming activities is the main driver for pover ty eduction in rural Africa, the results contribute to the idea that emphasis on road invest m n s should be given to locations where non-farming activities could be developed, which do es mean that the last mile in rural areas should not be probably a road.
Personal identification based on feature extraction using motions of a reduced set of joints

It is required to construct a practical scheme that can obtain personal properties accurately based on gait analysis because it can work under severe conditions. The authors tackle this task and have proposed a feature extraction scheme based on joint motions. However, it is not clear that our scheme is feasible because the scheme uses 25 joints all of that cannot be estimated accurately in severe conditions based on only visible images for feature extraction. To show that our scheme has a potential for practical applications, the classification accuracy of personal identification is evaluated under practical conditions where the number of joints available for feature extraction is small. Experimental results show that the classification accuracy is 81.58% if the number of joints is 10 and the accuracy keeps 79.82% even if the number of joints is reduced to 6.
Model-based Bayesian Reinforcement Learning in Partially Observable Domains

Bayesian reinforcement learning in partially observable domains is notoriously difficult, in part due to the unknown form of the beliefs and the optimal value function. We show that beliefs represented by mixtures of products of Dirichlet distributions are closed under belief updates for factored domains. Belief monitoring algorithms that use this mixture representation are proposed. We also show that the optimal value function is a linear combination of products of Dirichlets for factored domains. Finally, we extend BEETLE, which is a point-based value iteration algorithm for Bayesian RL in fully observable domains, to partially observable domains.
From workaholism to burnout: psychological capital as a mediator

Purpose The purpose of this paper is to examine the relationships among workaholism, psychological capital (PsyCap), and burnout, as well as investigate the potential mediating effect of PsyCap on the relationship between workaholism and burnout. Design/methodology/approach Data on workaholism, PsyCap, and burnout were collected, through administration of an online survey, from 400 faculty and staff at a large Southeastern university. Findings Workaholism is negatively related to PsyCap and is positively related to burnout, while PsyCap is negatively correlated with burnout. Moreover, while tested in a mediation model, PsyCap appeared as a significant mediating variable for all three dimensions of burnout (i.e. emotional exhaustion, depersonalization, and personal accomplishment). Research limitations/implications Future researchers may benefit from examining a sample outside an educational institution. Also, both workaholism and burnout may be better suited for studies utilizing longitudinal designs. That said, the data reveal information about the process whereby tendencies indicative of heavy work investment lead to burnout within employees. That is, workaholism leads to lower PsyCap, which then results in burnout. Practical implications The results suggest that employers may benefit from considering the negative effects of heavy work investment and maintaining a culture that discourages such behavior. It might also be beneficial for employers to ensure that employees have a heightened PsyCap and, therefore, are safeguarded against burnout. Originality/value This is the first study to examine the influence that employee PsyCap has on the development of workaholism and burnout by examining its role as a mediator.
Design of a Hybrid Visuo-Inertial Smart Sensor

In this paper, a smart sensor dedicated to image and inertial sensing is proposed. This work considers a system with a CMOS imager and an inertial set composed by 3 linear accelerometers and 3 gyrometers. One of the most original aspect of this approach is the use of a System On Chip implemented in a FPGA to manage the whole system. With its structure, the system proposes a high degree of versatility and allows the implementation of parallel image and inertial processing algorithms. In order to illustrate our approach, first results consisting in a depth estimation of a target are proposed.
DIRECT ENCODING EVOLUTIONARY LEARNING ALGORITHM FOR MULTILAYER MORPHOLOGICAL PERCEPTRON

This paper presents a method based on evolutionary computation to train multilayer morphological perceptron (MLMP). The algorithm calculates network parameters such as its connection weights, pre-synaptic and postsynaptic values for a given network topology. Morphological perceptron are a new type of feed-forward artificial neural network based on lattice algebra which can be used for pattern classification. The representation scheme is based on a tree data structure which the algorithm to perform operations such as crossover by replacing or switching whole nodes between parent. Adaptive mutation is used as the genetic algorithm approaches convergence to fine tune network parameters final values. The algorithm uses a special fitness function based on the mean square error of the pattern classification and introduces a penalty function to reduce the number of redundant neurons in the resulting neural network.
Play, Game, World: Anatomy Of A Videogame

This paper is part of an experimental approach aimed to study the nature of videogames. We will focus on videogames rules in order to try to understand the anatomy of a videogame. Being inspired by the methodology that Propp used for his classification of Russian fairy tales, we have cleared out recurrent diagrams within rules of videogames. We then analyzed these rules diagrams by using the definition of a game drawn by Salen and Zimmerman, which led us to propose a definition for the nature of gameplay. Through an additional analysis, we will be able to propose a typology of videogames rules which extends the typology proposed by Frasca.