Miracle-dz/UniFAR-FTUs huggingface dataset:
CUP_RAM_1700046 451..469

We argue that democratic institutions influence property rights in attracting foreign direct investment (FDI) by providing: (1) a coherent logic to the property rights regime that is created in a state and (2) a legitimate way to manage conflicts that arise in dynamic economies. We expect that the marginal effect of property rights in attracting FDI has increased over time with the rate of technological dynamism. We test this using a non-nested multilevel modeling strategy with random coefficients on data from 1970 to 2009. Our results demonstrate that the effect of property rights on attracting FDI is contingent on democratic institutions and that this effect becomes more pronounced over time. This effect holds for both developing and developed countries across all regions.
Eliciting information for decision making

We consider a setting in which a principal faces a decision and asks an external expert for a recommendation as well as a probabilistic prediction about what outcomes might occur if the recommendation were implemented. The principal then follows the recommendation and observes an outcome. Finally, the principal pays the expert based on the prediction and the outcome, according to some decision scoring rule. In this paper, we ask the question: What does the class of proper decision scoring rules look like, i.e., what scoring rules incentivize the expert to honestly reveal both the action he believes to be best for the principal and the prediction for that action? We first show that in addition to an honest recommendation, proper scoring rules can only incentivize the expert to reveal the expected utility of taking the recommended action. The principal cannot strictly incentivize honest reports on other aspects of the conditional distribution over outcomes without setting poor incentives on the recommendation itself. We then characterize proper decision scoring rules as ones which give or sell the expert shares in the principal’s project. Each share pays, e.g., $1 per unit of utility obtained by the principal. Owning these shares makes the expert want to maximize the principal’s utility by giving the best-possible recommendation. Furthermore, if shares are offered at a continuum of prices, this makes the expert reveal the value of a share and therefore the expected utility of the principal conditional on following the recommendation. We extend our analysis to eliciting recommendations and predictions from multiple experts. With a few modifications, the above characterization for the single-expert case carries over. Among other implications, this characterization implies that no expert should be able to “short-sell” shares in the principal’s project and thereby profit if the project goes poorly. Inspired by our results, we finally give a detail-free auction-based mechanism for eliciting information for decision making.
A Novel Algorithm for detection of Lesions in Digitized Mammograms using Fuzzy C-Means Bootstrap with Convolutional Neural Networks

— Early recognition and analysis of breast cancer using mammogram screening decreases the breast abnormality growth mortality by around 20%. In any case, it is regularly a mind-boggling procedure to separate irregularities because of the not well-characterized edges and unpretentious appearances. This article explores a new computer aided detection system to deal with recognize the anomalies in the digital mammograms using fuzzy c-means with bootstrap method. The proposed method has been confirmed with 164 images in the mini Mammographic Image Analysis Society database (MIAS). The outcome of the experiment shows that the proposed method has a TP rate of 92.6%. The performance of algorithm is evaluated utilizing Free Receiver Operating Characteristics curve (FROC) and the results are featured. We conclude that computer aided detection system with learning classifier can feasibly help radiologists with the procedure of discriminating between lesion and normal regions.
Approximate Adder Tree Synthesis for FPGAs

Compressor trees can be used as parts of larger arithmetic circuits in application-specific accelerators. Machine learning and other application domains that are amenable to approximate arithmetic can leverage approximate accelerators constructed from approximate arithmetic components. To this end, this paper introduces an approximate 8:3 parallel counter as a building block for approximate arithmetic. This specific parallel counter was chosen due to its compatibility of the logic block architectures present in modern commercial FPGAs, in particular, fracturable lookup tables (LUTs) and dedicated carry chains. To best exploit these features, a synthesis heuristic is proposed for approximate compressor tree synthesis, which introduces a move-and-duplicate transformation to increase parallel counter utilization. While prior (exact) compressor tree synthesis heuristics relied on a library of parallel counters, the approximate compressor tree synthesis heuristic introduced here employs the approximate 8:3 parallel counter in isolation. The experimental results reported demonstrate that approximate compressor trees use fewer logic resources and logic levels, in comparison to exact compressor tree synthesized using the same basic approach. This allows for a greater number of faster arithmetic operators to fit into a fixed-size device to maximize arithmetic throughput for a given level of accuracy.
Muriella australis J. Phillipson (Chlorophyta), a new species for the algal flora of Bulgaria

A new species and genus Muriella australis for the algal flora of Bulgaria аre reported in this article. This rare species (known only from locus classicus in Australia, isolated from soil) was discovered also in Rhodope Mountains, Bulgaria in aerophilic coating on the bed of the fountains in village Slaveyno (Smolyan district). The diagnosis of the species was extended and expanded. A review of the species to the genus concerned was accomplished.
Variations on the Convolutional Sparse Coding Model

Over the past decade, the celebrated sparse representation model has achieved impressive results in various signal and image processing tasks. A convolutional version of this model, termed convolutional sparse coding (CSC), has been recently reintroduced and extensively studied. CSC brings a natural remedy to the limitation of typical sparse enforcing approaches of handling global and high-dimensional signals by local, patch-based, processing. While the classic field of sparse representations has been able to cater for the diverse challenges of different signal processing tasks by considering a wide range of problem formulations, almost all available algorithms that deploy the CSC model consider the same $\ell _1 - \ell _2$ problem form. As we argue in this paper, this CSC pursuit formulation is also too restrictive as it fails to explicitly exploit some local characteristics of the signal. This work expands the range of formulations for the CSC model by proposing two convex alternatives that merge global norms with local penalties and constraints. The main contribution of this work is the derivation of efficient and provably converging algorithms to solve these new sparse coding formulations.
A Maximum Power Point Tracker for Photovoltaic Arrays with Particle Swarm Optimization Technique in cooperation with Fuzzy Cognitive Networks

: The ever-increasing demand for low-cost energy and growing concern about environmental issues has generated enormous interest in the utilization of nonconventional energy sources such as the solar energy. The output power of PV arrays is always changing with weather conditions, i.e., solar irradiation and atmospheric temperature. Therefore, to maximize the efficiency of the renewable energy system, it is necessary to track the maximum power point of the PV array. In this paper, a maximum power point tracker using fuzzy set theory is presented to improve energy conversion efficiency. This method is proposed, by using a fuzzy cognitive network, which is in close cooperation with the presented fuzzy controller. The proposed approach is based on the Particle Swarm Optimization method and it is used for the determination of proper weight matrices that lead the Fuzzy Cognitive Map to desired steady states. The method gives a good maximum power operation of any PV. The advantage of the presented method is that the control system can adapt to different changes that might happen during the life cycle of the PV module.
The 2 k Neighborhoods for Grid Path Planning

Grid path planning is an important problem in AI. Its understanding has been key for the development of autonomous navigation systems. An interesting and rather surprising fact about the vast literature on this problem is that only a few neighborhoods have been used when evaluating these algorithms. Indeed, only the 4-and 8-neighborhoods are usually considered, and rarely the 16-neighborhood. This paper describes three contributions that enable the construction of eﬀective grid path planners for extended 2 k -neighborhoods; that is, neighborhoods that admit 2 k neighbors per state, where k is a parameter. First, we provide a simple recursive deﬁnition of the 2 k -neighborhood in terms of the 2 k − 1 - neighborhood. Second, we derive distance functions, for any k ≥ 2, which allow us to propose admissible heuristics that are perfect for obstacle-free grids, which generalize the well-known Manhattan and Octile distances. Third, we deﬁne the notion of canonical path for the 2 k -neighborhood; this allows us to incorporate our neighborhoods into two versions of A*, namely Canonical A* and Jump Point Search (JPS), whose performance, we show, scales well when increasing k . Our empirical evaluation shows that, when increasing k , the cost of the solution found improves substantially. Used with the 2 k -neighborhood, Canonical A* and JPS, in many conﬁgurations, are also superior to the any-angle path planner Theta ∗ both in terms of solution quality and runtime. Our planner is competitive with one implementation of the any-angle path planner, ANYA in some conﬁgurations. Our main practical conclusion is that standard, well-understood grid path planning technology may provide an eﬀective approach to any-angle grid path planning
A Parallel Image Registration Algorithm Based on a Lattice Boltzmann Model

Image registration is a key pre-procedure for high level image processing. However, taking into consideration the complexity and accuracy of the algorithm, the image registration algorithm always has high time complexity. To speed up the registration algorithm, parallel computation is a relevant strategy. Parallelizing the algorithm by implementing Lattice Boltzmann method (LBM) seems a good candidate. In consequence, this paper proposes a novel parallel LBM based model (LB model) for image registration. The main idea of our method consists in simulating the convection diffusion equation through a LB model with an ad hoc collision term. By applying our method on computed tomography angiography images (CTA images), Magnet Resonance images (MR images), natural scene image and artificial images, our model proves to be faster than classical methods and achieves accurate registration. In the continuity of 2D image registration model, the LB model is extended to 3D volume registration providing excellent results in domain such as medical imaging. Our method can run on massively parallel architectures, ranging from embedded field programmable gate arrays (FPGAs) and digital signal processors (DSPs) up to graphics processing units (GPUs).
MODELLING OF H2 DISPERSION AND COMBUSTION PHENOMENA USING CFD CODES

Computational Fluid Dynamics codes are increasingly being considered for safety assessment demonstrations in many industrial fields as tools to model accidental phenomena and to design mitigation (risk reducing) systems. Thus, they naturally complement experimental programmes which may be expensive to run or difficult to set up. However, to trust numerical simulations, the validity of the codes must be firmly established, and a certain number of error sources (user effect, modelling errors, discretization errors, etc) reduced to the minimum. Code validation and establishment of “best practice guidelines” in the application of simulation tools to hydrogen safety assessment are some of the objectives pursued by the HYSAFE Network of Excellence. This paper will contribute to these goals by describing some of the validation efforts that CEA is making in the areas of release, dispersion, combustion and mitigation, thereby proposing the outline of a validation matrix for hydrogen safety problems.
Identifying countries at risk of fiscal crises: High‐debt developed countries

How large can debt get before triggering a crisis? Since debt is the expected present value of future primary surpluses, the answer depends on a countryâ€™s technical and political ability to raise future primary surpluses. However, countries do not raise the primary surplus to its peak and maintain the peak forever, the assumption implicit in the standard practice of setting maximum debt at the present value of the peak surplus. We estimate fiscal feedback rules for seven high-debt developed countries and find an increase in debt creates a sustained increase in the primary surplus, with the primary surplus reaching a peak in the future. Therefore, our implied debt limit is much lower than the standard measure. We estimate debt limits following the global financial crisis in 2008 and find substantial heterogeneity. We separate countries into risk categories based on fiscal space. Greece and Portugal eroded their fiscal space several years, prior to their fiscal crises, placing them in the highest risk category and predicting the crises that followed. Canada and Belgium maintained large enough fiscal space to achieve safe status. Other countries reduced fiscal space, with France and Spain eroding fiscal space in 2014, warning of future crises.
Embryonic development of a mountainous fish species Garra cambodgiensis (Tirant, 1883) in southern Thailand

This study examined the artificial breeding and embryonic development of a mountainous fish species Garra cambodgiensis (Tirant, 1883) found in Promlok waterfall in Khaoluang National park, Phromkhiri district, Nakhon Si Thammarat province. The fishes were collected from June 2017January 2018 and kept in aquaria. Afterward, the brood males and females were selected and injected with buserelin (LHRHa) (10 μg/kg body weight) and domperidone (10 mg/kg body weight). After the injections, both females and males were kept together in the water at a proportion of 3 females: 1 male. The fertilization of eggs started after 4 hours and 30 minutes. The fertilized eggs were greenish-grey and semibuoyant. After fertilization, one blastodisc turned into 2 equalsized blastomeres, and then each cell divided into 4, 8, 16, 32, 64 cells respectively. The morula stage went to the blastula stage in about 3hr and 28 min, to gastrula stage in about 5 hr and 11 min, and to the somite stage in about 7 hr and 7 min. The optical vesicles and auditory vesicles developed after approximately 8 hr and 27 min, and 10 hr and 30 min, respectively. After approximately 12 hours and 58 minutes of fertilization, hatching of eggs occurred. Nowadays, the numbers of G. Cambodgienesis are declining rapidly in southern Thailand due to several environmental and anthropogenic reasons. Therefore, it is very important to conserve the populations of G. Cambodgienesis. Artificial breeding could be an effective way to conserve and restore this fish in their natural habitat in southern Thailand.
Online LiDAR-SLAM for Legged Robots with Robust Registration and Deep-Learned Loop Closure

In this paper, we present a 3D factor-graph LiDAR-SLAM system which incorporates a state-of-the-art deeply learned feature-based loop closure detector to enable a legged robot to localize and map in industrial environments. Point clouds are accumulated using an inertial-kinematic state estimator before being aligned using ICP registration. To close loops we use a loop proposal mechanism which matches individual segments between clouds. We trained a descriptor offline to match these segments. The efficiency of our method comes from carefully designing the network architecture to minimize the number of parameters such that this deep learning method can be deployed in real-time using only the CPU of a legged robot, a major contribution of this work. The set of odometry and loop closure factors are updated using pose graph optimization. Finally we present an efficient risk alignment prediction method which verifies the reliability of the registrations. Experimental results at an industrial facility demonstrated the robustness and flexibility of our system, including autonomous following paths derived from the SLAM map.
Socio-economic Risk Factors of Spontaneous Preterm Birth Among Saudi Women: A Case-Control Study

Preterm birth (PTB), before 37 completed gestational weeks, is considered as the leading cause of under 5-year mortality. According to the reports, PTB was globally responsible for over one million child deaths in 2015 (1). It is also responsible for lifelong morbidity including neurodevelopmental disabilities (2). The risk factors for PTB vary in different communities. Medical conditions including previous PTB, sociodemographic factors such as maternal age, ethnicity, and consanguineous marriage, as well as psychological risk factors including depression and anxiety were found to have a strong association with PTB (3,4). Exploring the risk factors and prevention of PTB is regarded as an important public health intervention. Such a program contributes to reducing under-5 mortality and disabilities and the cost associated with the management of immediate and long-term complications. Many measures are suggested for PTB prevention and its complications. For example, medical interventions such as administering corticosteroids to women at the risk of delivering preterm to reduce respiratory complications and smoking cessation, avoid secondhand tobacco smoke, optimize mother’s weight prior to pregnancy, and increase the intake of fruits and vegetables (5,6). The epidemiology PTB differs in various communities. Hence, considering the importance of robust data on PTB incidence and risk factors, healthcare planners and providers are advised to implement preventive measures in this respect. The current study sought to investigate the socio-economic risk factors associated with spontaneous preterm birth (SPTB) in Riyadh.
One nuclei at a time : FISHing and staining to understand chromosome composition and nuclear activity in the rhizarian Corallomyxa tenera Microbial Diversity 2018 Miniproject Report

Corallomyxa tenera is a marine amoeba with tens to hundreds of nuclei that are transported around an expanding and contracting reticulated network. Whether or not the nuclei are genetically differentiated across this network remains unknown. Similarly, whether or not the nuclei are in synchrony across their growth cycles or have nuclear autonomy is not well understood. Elucidating the differentiation of nuclei across the C. tenera network, not only allows us to understand how processes in this large network are organized, but also provides insight into unique evolutionary strategies that has allowed this single cell to form networks large enough to be seen by the naked eye. Introduction: Microbial eukaryotes harbor incredible morphological and associated genetic diversity that remains understudied and underappreciated. Corallomyxa tenerathe focus of this studyis a cultivable marine amoeba within the Rhizarian supergroup that can grow from a single cyst or many cysts into a large anastomosing reticulate network (Tekle et al. 2007). The cell expands, contracts, and hunts prey in response to environmental and biological stimuli (Lagostina 2015). Additionally, at times small parts of the network can break off into smaller amoebae but can communicate and fuse again with the network later on. This extreme morphological and physiological complexity poses an interesting biological challenge and begs the question: what evolutionary strategies has Corallomyxa tenera employed to cozy into such a unique and dynamic cellular lifestyle? Perhaps one particular trait that Corallomyxa tenera has benefited from is it’s multinuclearity. Despite the fact that multinuclearity is widespread across divergent lineages of eukaryotes (Parfrey et al. 2008), little investment has been made in understanding the influence of multinuclearity on ecology and evolution, especially in non-model systems. Yet, multinuclearity can perceivably have many advantages, such as increasing localized mRNA production and limiting transport of proteins. Importantly for larger syncytia, cellular labor can also be divided spatially or functionally among nuclei. For instance, the genetic identity of nuclei within multinucleated cells can vary greatly, often depending on gene content, ploidy level, and sexual and asexual reproduction. In fungi, genetically diverse nuclei have been known to enhance phenotypic plasticity (Roper et al. 2011) and may also play a role in fungal virulence (Ma et al. 2010). In this study, I seek to understand whether the non-model microbial eukaryote Corallomyxa tenera has differences in chromosome composition among its nuclei and whether or not the nuclei are autonomous or synchronous
Spatial distribution of cells over the process of biofilm formation in a Methylophilus strain

Understanding spatial organization of individual cells in biofilms is challenging and only recent studies started to clarify this aspect for only a few model organisms. Therefore, when considering the wide diversity of biofilm-forming species in the environment, this information is even less understood. Here, we characterize the process of biofilm formation for a methanol-oxidizing bacterium isolated from Trunk River. Sequencing of the 16S rDNA and phylogenetic analyses identified the bacterium as Methylophilus sp strain LM11. Individual cells in mature biofilms are usually evenly spaced 1.5-3.5 μm from each other and seem to be mainly vertically oriented in the biofilm matrix. This pattern of organization is consistent in biofilms formed in glass-chambers slides and non-shaking tubes, indicating reproducibility under different methods. Future analysis of cells spatial organization for other species should reveal if this is a phenomenon unique to Methylophilus sp. or if this is a general pattern that can be found in other biofilm-forming species.
DESIGNING EXPERIMENTS TO DETERMINE THE COEFFICIENTS OF ACTIV ATED SLUDGE MODELS

A calibration technique comprised of an experimental procedure and a curve fitting mathematical method has been proposed in order to estimate the coefficients of the IA WPRC activated sludge model restricted to oxic reactions. A continuous activated sludge plant having two in-series biodegradation reactors is suggested for the experiment. This plant is operated by reducing, in steps, the frrst reactor volume. It was found that this experimental procedure enables scanning of a significant part of the kinetics contained in the model. The curve fitting mathematical method was an identification algorithm based on recursive non-linear Kalman mter. In order to assess the validity of the calibration technique, simulated results of measurable variables, generated from presumed real coefficients, have been employed as real results by the identification algorithm. The effect of several erroneous initial estimates of the coefficients, which are required to start the identification, has been investigated. It was found that the proposed calibration technique gave rise to acceptable identifications of the real coefficients independent of the initial estimate used. The convergence rate for every coefficient, however, depends on the initial estimate. Those rates were properly related to the differences observed between the real results and the predictions obtained with the initial estimates for the measurable variables.