Miracle-dz/UniFAR-FTUs huggingface dataset:
Realistic Image Degradation with Measured PSF

Training autonomous vehicles requires lots of driving sequences in all situations\cite{zhao2016}. Typically a simulation environment (software-in-the-loop, SiL) accompanies real-world test drives to systematically vary environmental parameters. A missing piece in the optical model of those SiL simulations is the sharpness, given in linear system theory by the point-spread function (PSF) of the optical system. We present a novel numerical model for the PSF of an optical system that can efficiently model both experimental measurements and lens design simulations of the PSF. The numerical basis for this model is a non-linear regression of the PSF with an artificial neural network (ANN). The novelty lies in the portability and the parameterization of this model, which allows to apply this model in basically any conceivable optical simulation scenario, e.g. inserting a measured lens into a computer game to train autonomous vehicles. We present a lens measurement series, yielding a numerical function for the PSF that depends only on the parameters defocus, field and azimuth. By convolving existing images and videos with this PSF model we apply the measured lens as a transfer function, therefore generating an image as if it were seen with the measured lens itself. Applications of this method are in any optical scenario, but we focus on the context of autonomous driving, where quality of the detection algorithms depends directly on the optical quality of the used camera system. With the parameterization of the optical model we present a method to validate the functional and safety limits of camera-based ADAS based on the real, measured lens actually used in the product.
New organising in Swedish third sector organisations

This article reports on an investigation among Swedish third sector organisations. Secretaries general or the equivalent have been interviewed concerning their views on challenges and developments in organising. The most prominent feature is the movement towards forms that complement a traditional hierarchy, the traditional form of most of the organisations. Different forms of networking, increased transparency, decentralisation and the use of modern information and communication technology are under way in many of the organisations. On the one hand, this may seem natural in light of developments in other parts of society. On the other hand, it represents a shift in the organisation of third sector organisations - away from the organised, controllable and providing a clear overview, towards the amorphous, the fuzzy and the difficult to define and overview. If it works, it can result in more productive activity in the organisations, in greater community spirit, and in a greater sharing of the workload in the organisation. However, there is a risk of unclear division of responsibility, of -unauthorised- ventures and greater diversity in goals pursued, and in a greater turnover of active members, threatening the continuity of operations and ventures.
Classification et apprentissage faiblement supervisé en acoustique halieutique

L'apprentissage statistique etablit un modele de classification probabiliste. Dans le cas supervise, ce modele est estime a partir d'un jeu de donnees labelisees, i.e. a chaque observation correspond un label. Dans le cas faiblement supervise, le label n'est pas exactement connu. Dans notre cas, seule la probabilite d'associer une observation a une classe est connue. Ainsi a chaque observation correspond un vecteur de probabilite d'affectation aux differentes classes. Les methodes developpees dans cet article sont appliquees a la reconnaissance d'objets dans une image. Nous disposons de plusieurs images contenant des objets a classifier. La verite terrain pour les images d'apprentissage est la proportion relative des classes dans chaque image. Cette proportion globale donne la probabilite d'affectation de chaque objet de l'image d'apprentissage. L'originalite de ce papier est dans l'association d'un ensemble de donnees d'apprentissage labelisees en proportion avec un modele probabiliste de classification base sur la combinaison de plusieurs modeles discriminants dont la combinaison s'effectue a l'aide d'une technique de Bagging. Deux modeles de classification (l'un Bayesien et l'autre discriminant) sont compares sur des donnees provenant de campagnes oceanographiques (l'objectif etant de reconnaitre a quelle classe d'espece est affecte un banc de poissons dans une image, la proportion des classes etant donnee par chalutage). Pour ce jeu de donnees, le modele discriminant est plus robuste au nombre de classes presentes dans l'image. L'apport du bagging est mis en evidence pour le modele discriminant.
Comparative productivity and profitability of organic and conventional tef (Eragrostis tef (Zucc.)Trotter) production under rainfed condition: Tigray, Northern Ethiopia"

1 Shire Soil Research Center, Tigray Agricultural Research Institute, P.O. Box 492, Mekelle, Ethiopia. 2 Land Resource Management and Environmental Protection Department, Mekelle University, P. O. Box 231, Mekelle, Ethiopia. 3 Dryland Crop and Horticultural Sciences Department, Mekelle University, P.O. Box 231, Mekelle, Ethiopia. 4 Natural Resource Economics Management Department, Mekelle University, P. O. Box 231, Mekelle, Ethiopia.
PERFORMANCE OF NON-PROFIT ORGANIZATIONS: EMPIRICAL CONTRASTS BETWEEN PRIVATELY AND PUBLICLY FUNDED CROATIAN HUMANITARIAN ORGANIZATIONS

In this paper we contrast different aspects of non-profit organizations’ performance from the perspective of their funding sources. Based on previous empirical results indicating that sources of funding influence fundraising performance, we extend the existing research to the analysis of overall non-profit organizational performance. We use the social constructivist approach to non-profit performance to discuss contrasts between privately and publicly funded humanitarian organizations in Croatia. Some differences in growth- and development-oriented measures of non-profit performance for these groups are identified and discussed in the context of future regional research.
HUMAN ROBOT INTERACTION ON GESTURE CONTROLLED DRONE: METHODS OF GESTURE ACTION RECOGNITION by

Title of thesis: HUMAN ROBOT INTERACTION ON GESTURE CONTROLLED DRONE: METHODS OF GESTURE ACTION RECOGNITION Siqin Li Master of Science, 2018 Thesis directed by: Professor Yiannis Aloimonos Department of Computer Science Today, the interaction between robots and human is mostly based on remote controller. However this interaction could be more natural, just like we humans interact with each others through speech, body movements, facial expressions, and so on. We propose gesture and body language as an alternative to interact with robots, particularly Unmanned Aerial Vehicles(UAVs) also known as drones. In this thesis, we developed action recognition methods for the interaction with drones. Specifically, we developed approaches to recognize human gestures for the communication with drones. Automatic detection and classification of dynamic actions in real-world system intended for human robot interaction is challenging because: 1) there is large variation in how people perform actions, making detection and classification difficult; 2) the system must work online in order to avoid a noticeable delay in the interaction between the human and the drone. In this work, we address these challenges through the combination of a real-time skeleton detection library and deep learning techniques. Our methods perform dynamic actions or gestures classification from skeleton data.
Bilateral Filter Extension for Removal of Universal Noise

In this paper, I propose an extension of a bilateral filter that can additionally remove impulse noise. The algorithm involves mainly detection of the noise and classifying it based on an efficient detector. Based on the detection, the proposed filter is applied for noise removal. The ascending median vector value (AMVV) is the main feature of noise detection. From the mean value, a threshold median is calculated for each pixel. Each pixel may be affected by Gaussian or impulse noise. There also remains the possibility that the pixel is noise free. By subtracting the threshold value form each pixel, the noise can be identified. A bilateral filter normally removes Gaussian noise. By applying the extension it can be switched to another mode, i.e. for removing impulse noise as well.
An incremental updating method for the kanonymous dataset using a similar-B-tree

K-anonymity is an effective method to prevent linking attacks and protect privacy. Although the k-anonymous dataset guarantees privacy, it must be constantly updated because the original dataset updates occasionally after a version of k-anonymous dataset has been exposed. So, how to update the k-anonymous dataset simultaneously when the original dataset has been updated becomes an urgent problem. To solve this problem, according to the mapping relation between tuples of the original dataset and k-anonymous dataset, a kind of tree structure similar to a B-tree is proposed, so that the update operations on the original dataset can be converted into the corresponding operations of leaf node in the similar-B-tree. Based on this, an incremental updating method for the k-anonymous dataset using a similar-B-tree is presented. This method can reflect the changes in the original dataset to the k-anonymous dataset in time, and it can greatly improve the update efficiency of the k-anonymous dataset.
Leptogenesis in the Universe

Leptogenesis is a class of scenarios in which the cosmic baryon asymmetry originates from an initial lepton asymmetry generated in the decays of heavy sterile neutrinos in the early Universe. We explain why leptogenesis is an appealing mechanism for baryogenesis. We review its motivations and the basic ingredients and describe subclasses of effects, like those of lepton flavours, spectator processes, scatterings, finite temperature corrections, the role of the heavier sterile neutrinos, and quantum corrections. We then address leptogenesis in supersymmetric scenarios, as well as some other popular variations of the basic leptogenesis framework.
Power Efficient Successive Approximation Registers

In recent years, there has been a growing need for Successive Approximation Register (SAR) Analogto-Digital Converter in medical application such as pacemaker. The demand for long battery life-time in these applications poses the requirement for designing ultra-low power SAR ADCs. The project involves custom design and implementation of 8 bit successive approximation ADC for low power and low frequency signals. The ADC would comprise of a SAR Logic, DAC, a comparator and a sample and hold circuit. The DAC would be designed to reduce the systematic and the symmetric errors using the hierarchical symmetrical switching. The main motivation is to implement design of capacitor array DAC and achieve high speed with medium resolution using 60nm technology. The advantage is matching of capacitor can be achieved better then resistor. The market and cost analysis says current SAR ADC has better future. Here we are using spice backend design Tool.
Increasing Value Generation Using a Hierarchical Simulation-Based SCOR Framework

We are part of extraordinary times, global recession coupled with increased competition; high costs and decreasing demand have changed the dynamics of supply chain management (SCM). In response, many organizations have fast-tracked changes to corporate-level strategies to reduce costs and maintain profit margins and have not considered the long-term impact these decisions have on more operational-level SCM activities. This has resulted in a renewed focus on customer value and the economic and behavioral systems of the supply chain, or more accurately, the value chain. The Supply Chain Operations Reference (SCOR) model increases the integration organizations have within their supply chains and increases alignment between different hierarchical strategies. Simulation techniques, in particular discrete-event simulation (DES) and system dynamics (SD) are proven techniques in improving SCM corporate and operational decision making processes. This paper presents a framework that integrates SCOR with DES-SD modeling approaches in order to improve the performance of inventory management in a leading tire distribution center in Ireland. This integration shows an effective method to evaluate order strategies, enhance throughput and increase value generation within supply chain networks.
Design Considerations of Artificial Mangrove Embankments for Mitigating Coastal Floods – Adapting to Sea-level Rise and Long-term Subsidence

Abstract. Mangrove plantation belts are expected to act as natural infrastructural buffers against coastal hazards. However, their performance will not endure over time if the platform is not appropriately designed. In fact, despite massive funds dedicated to the rehabilitation of mangrove forests, the long-term survival rates of mangroves are generally low. This paper investigates the function of mangrove embankments in attenuating the amplitudes of ocean tides through a coupled numerical model that reproduces shallow-water wave propagations under the progress of soil consolidation. The developed model is capable of simulating tidal propagation over an artificial embankment, which will inevitably change its ground surface elevation with the passage of time because of sea-level rise, land subsidence, vegetation growth and sediment accretion. A parametric analysis demonstrates that high tides could be effectively mitigated only if the embankment is appropriately designed to maintain an equilibrium state among these multiple influences over the long term. On the other hand, an embankment designed without considering geomorphological transitions will become submerged under the rising sea level, resulting in no significant effect on tidal damping. Therefore, the artificial mangrove embankment must be carefully designed to function not only during the initial stage of its lifetime but also over time, to avoid system failure in the future.
Inflation Risk in Corporate Bonds

type="main"> We argue that corporate bond yields reflect fears of debt deflation. When debt is nominal, unexpectedly low inflation increases real liabilities and default risk. In a real business cycle model with optimal but infrequent capital structure choice, more uncertain or procyclical inflation leads to quantitatively important increases in corporate log yields in excess of default-free log yields. A panel of credit spread indexes from six developed countries shows that credit spreads rise by 14 basis points if inflation volatility or the inflation-stock correlation increases by one standard deviation.
AGB and post-AGB stars

Intermediate mass stars (1-8 M ) evolve along the Asymptotic Giant Branch after completion of hydrogen and helium core burning. At the tip they lose for several ten to hundred thousand years copious amounts of mass and exhibit various forms of variability, as for example large-amplitude variations with periods of one to five years. In the oxygen- rich circumstellar envelopes powerful OH, H2O and SiO masers may operate. Part of the AGB population is converted to carbon stars. During the latest phases of AGB evolution the mass loss rates approach several 10 5 M yr 1 , so that the stars become invisible in the optical and in part in the near infrared. On departure from the AGB a fundamental transition in the mass loss process is taking place changing from a spherically symmetric outflow on the AGB to axi-symmetric or point- symmetric geometries. While the mass loss rates decrease to 10 8 M yr 1 , the velocities are strongly increasing. Stars are now in their "post-AGB" phase. Mounting evidence is gathered that during the very latest phase of AGB evolution and during the post-AGB phase, evolution proceeds on very short timescales, which in extreme cases is comparable to the working life of an astronomer.
Some leading causes of emerging rural poultry small and medium enterprises failure in South Africa

This study examines the factors that cause failures of many South African rural small and medium enterprises (SMEs) in poultry business. The research method was qualitative phenomenology using in-depth, semi-structured interviews to explore the factors considered by rural chicken SMEs owners to be causes of their business difficulties. The study revealed that these entrepreneurships knew chicken business only from the home-grown chickens, and the local poultry was usually providing only meat. Compared to the local chickens that were cheap to acquire and nurture, the commercial ones had to be purchased, nourished with purchased foods, supported with electricity light at night, and still, some died. It was much cheaper to raise indigenous chickens. The results indicate that rural poultry SMEs owners did not know that buying a commercial chick carries benefits that range from eggs, feathers, meat, and other chicken products. Also, after purchasing each chick, there are more additional investments needed to maintain the market standard. The study enlightens chicken entrepreneurs that they should, first, learn the insight of business, the poultry industry, and the context of the business they wish to pursue.
non-metropolitan Counties of Culpeper, Fauquier, Madison, Orange and Rappahannock in Northern Virginia (NOVA). These counties are adjacent to the larger Washington DC metropolitan area including Loudoun County, Prince William (Prince William County, Manassas and Manassas Park Cities), Fairfax (Fairf

1.1 Study Area This study examines the work related commuting patterns of residents in the five non-metropolitan Counties of Culpeper, Fauquier, Madison, Orange and Rappahannock in Northern Virginia (NOVA). These counties are adjacent to the larger Washington DC metropolitan area including Loudoun County, Prince William (Prince William County, Manassas and Manassas Park Cities), Fairfax (Fairfax County, Fairfax and Falls Church Cities), Alexandria City and Arlington County (Figure 1-1).
Feeding behaviour traits recorded during gestation are heritable even though feed intake itself is restricted

Data obtained from electronic sow feeders during the gestation period were used to investigate feed intake and feeding behaviour traits from 3785 predominantly (89.9%) F1 sows. Estimates of heritability, permanent environmental effect of the sow and phenotypic variance were obtained for seven distinct time periods during gestation for average feed intake (AFI), daily time spent eating (AFT), rate of feed consumption (AFR), the number of feeding events (AFE) and total born (TB) piglets. As expected, heritability estimates for feed intake traits such as AFI1-AFI7 were not different from zero, which can be explained by the restricted feed allocation (rather than ad libitum feeding). In the same time periods, heritabilities for the amount of time sows spent eating were low to moderate: lowest at the beginning (0.12±0.03) and at the end of gestation (0.16±0.04) and highest in the middle of gestation (range: 0.16 to 0.27). The same pattern was found for the rate of feed consumption. Further investigation of these feeding behaviour traits is warranted with respect to their associations with reproductive performance outcomes, given that they represent potential limitations to sows’ ability to consistently meet their nutritional requirements over time.
Effects of climate change on fish reproduction and early life history stages

Seasonal change in temperature has a profound effect on reproduction in fish. Increasing temperatures cue reproductive development in spring-spawning species, and falling temperatures stimulate reproduction in autumn-spawners. Elevated temperatures truncate spring spawning, and delay autumn spawning. Temperature increases will affect reproduction, but the nature of these effects will depend on the period and amplitude of the increase and range from phase-shifting of spawning to complete inhibition of reproduction. This latter effect will be most marked in species that are constrained in their capacity to shift geographic range. Studies from a range of taxa, habitats and temperature ranges all show inhibitory effects of elevated temperature albeit about different environmental set points. The effects are generated through the endocrine system, particularly through the inhibition of ovarian oestrogen production. Larval fishes are usually more sensitive than adults to environmental fluctuations, and might be especially vulnerable to climate change. In addition to direct effects on embryonic duration and egg survival, temperature also influences size at hatching, developmental rate, pelagic larval duration and survival. A companion effect of marine climate change is ocean acidification, which may pose a significant threat through its capacity to alter larval behaviour and impair sensory capabilities. This in turn impacts on population replenishment and connectivity patterns of marine fishes.
Unbiased Diffusion to Escape through Small Windows: Assessing the Applicability of the Reduction to Effective One-Dimension Description in a Spherical Cavity

This study is devoted to unbiased motion of a point Brownian particle that escapes from a spherical cavity through a round hole. Effective one-dimensional description in terms of the generalized Fick-Jacobs equation is used to derive a formula which gives the mean first-passage time as a function of the geometric parameters for any value of a, where a is the hole’s radius. This is our main result and is given in equation (19). This result is a generalization of the Hill’s formula, which is restricted to small values of a.
Pattern Evaluation Proved To Be Important For Decision Support in Data Warehouse

It is the mobile sensor network or satellite that is observing and obtaining the data pattern. The study focuses on spatio-temporal data and events pattern for the data mining and for data warehouse. There is a repository or database where sensor, satellite inputs or data are collected for analysis. The depiction of satellite or mobile sensor and network leads new path for patterns, data mining and data warehouse. From the remote, the data obtained by using the mobile sensor, satellite so that data mining technique perform for finding desirable patterns. The study introduces moving point object data and algorithm as an advance research. The existing concept of moving point object based flock and leadership algorithm is exercising with uninterrupted data movement. It instructs that data moving in interrupted state. Present study focuses on an implementation of moving point object based Flock and Leadership algorithm. The algorithm is functioning with moving point object data but interrupted condition. The interrupted data produced abnormal result because the input data is captured in interrupted state. From the inputs the study observes or collects spatial, temporal based elements or properties. There will be Spatial Temporal an event pattern. After obtaining data by using mobile sensor network or satellite network. The patterns are essential and useful which is desirable. The study instructing that patterns evaluation proved to be important for decision support in data warehouse. Further the study focuses on significance of pattern and its evaluation. There are events like earthquake, flood, desert events that are observed or obtained as an input of the work. They were abnormal in input stage but the data mining process has made data proper and obtained proper resulting data. On the basis of the selected pattern or data there can be a decision. The decision can be forecasting for earthquake, flood, or desert events. Finally the study observes the performance of pattern in all the stages of the study and has obtained the result of its importance and performance of the pattern for the decision support system in data warehouse.