Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of levels-of-detail in Bayesian tracking framework using single RGB-D sensor

This paper propose a study of real-time human gait tracking system based on a cascaded particle filter implementation using Microsoft Kinect sensor. Our tracking system is combination of two different levels which processing both color and depth information. In the first level, we utilize color histogram to implement a coarse 2D region tracking. For the second level, we implemented two different depth feature extractions, i.e., spin image and geodesic distance for tracking extremities of the body. These two levels are combined to represent the basis for the full 3D human body tracking by obtaining a probable human body bounding box in 2D while the 3D position is obtained by incorporating available depth information. The relationship between motion and particle generation is modeled for tracking a person in the first level. For this level, we adopt the expected motion constraints for enhancing the distribution of particle at the importance sampling stage. In the implementation of the second level, 3D bounding box coordinate system is generated by synchronizing with RGB and depth video stream. In a coarse-to-fine cascaded concept, we used spin-image and geodesic depth information to track the extremities in the second level. State definition of the points of extremities is defined, and the depth-based particle filter is implemented for extremity point tracking.
Reinforcement learning, particle filters and the EM algorithm

We consider a parameter estimation problem for a Hidden Markov Model in the framework of particle filters. Using constructs from reinforcement learning for variance reduction in particle filters, a simulation based scheme is developed for estimating the partially observed log-likelihood function. A Kiefer-Wolfowitz like stochastic approximation scheme maximizes this function over the unknown parameter. The two procedures are performed on two different time scales, emulating the alternating `expectation' and `maximization' operations of the EM algorithm. Numerical experiments are presented in support of the proposed scheme.
Contextual modelling of the user browsing behaviour to identify the user's information need

In this paper, we propose a contextual modelling of the user browsing behaviour aiming to identify the user's information needs from his real-time context focusing on two parameters that are the recent interests and the temporal events that may influence the user browsing behaviour. Modelling the search behaviour of the user through these parameters gave good preliminary results which encouraged us to continue our research in this pathway.
Integrating Motivated Learning and k-Winner-Take-All to Coordinate Multi-agent Reinforcement Learning

This work addresses the coordination issue in distributed optimization problem (DOP) where multiple distinct and time-critical tasks are performed to satisfy a global objective function. The performance of these tasks has to be coordinated due to the sharing of consumable resources and the dependency on non-consumable resources. Knowing that it can be sub-optimal to predefine the performance of the tasks for large DOPs, the multi-agent reinforcement learning (MARL) framework is adopted wherein an agent is used to learn the performance of each distinct task using reinforcement learning. To coordinate MARL, we propose a novel coordination strategy integrating Motivated Learning (ML) and the k-Winner-Take-All (k-WTA) approach. The priority of the agents to the shared resources is determined using Motivated Learning in real time. Due to the finite amount of the shared resources, the k-WTA approach is used to allow for the maximum number of the most urgent tasks to execute. Agents performing tasks dependent on resources produced by other agents are coordinated using domain knowledge. Comparing our proposed contribution to the existing approaches, results from our experiments based on a 16-task DOP and a 68-task DOP show our proposed approach to be most effective in coordinating multi-agent reinforcement learning.
Congruence x employment tenure: A study with transitioning youth with disabilities

Title of Document: CONGRUENCE X EMPLOYMENT TENURE: A STUDY WITH TRANSITIONING YOUTH WITH DISABILITIES Juliana Unis Castan, Master of Arts, 2010 Directed By: Professor Ellen Fabian, Department of Counseling and Personnel Services This study examined the relationship between congruence (between individual’s inventoried career interests and subsequent job) and employment tenure for transitioning youth with disabilities successfully closed in fiscal year 2008 by the Maryland State Vocational Rehabilitation Agency (DORS) Region 6 (N = 51). The relationship between employment tenure and demographic variables (gender, ethnicity, education level, disability type, social security benefits status, and length in services) is also assessed. Data was obtained from DORS database, wage checks reports and file reviews. This study did not find support for the relationship between employment tenure and either congruence or demographic variables. Related findings include high frequency of low congruent jobs, and the majority of jobs in janitorial and service areas, part time and with hourly wage between $5.00 and $10.00. CONGRUENCE X EMPLOYMENT TENURE: A STUDY WITH TRANSITIONING YOUTH WITH DISABILITIES
Une comparaison expérimentale entre les approches PLS et LISREL

Les modeles d'equations structurelles sont utilises en marketing specialement dans les etudes de satisfaction. Nous presentons les principaux concepts des modeles d'equations structurelles. Une comparaison sur le meme jeu de donnees entre PLS (Partial Least Squares) et LISREL (LInear Structural RELationships) est realisee. Les aspects theoriques sont verifies : PLS favorise le modele externe et LISREL le modele interne. Abstract: Structural equation modeling is used in marketing, especially for consumer satisfaction. We present the main concepts of structural equation modeling. A comparison between PLS (Partial Least Squares) and LISREL (LInear Structural RELationships), is realized on the same data. The theoretical aspects are verified: PLS favours the outer model and LISREL inner model.
Impaired neuronal operation through aberrant intrinsic plasticity in epilepsy

Patients with temporal lobe epilepsy often display cognitive comorbidity with recurrent seizures. However, the cellular mechanisms underlying the impairment of neuronal information processing remain poorly understood in temporal lobe epilepsy. Within the hippocampal formation neuronal networks undergo major reorganization, including the sprouting of mossy fibers in the dentate gyrus; they establish aberrant recurrent synapses between dentate granule cells and operate via postsynaptic kainate receptors. In this report, we tested the hypothesis that this aberrant local circuit alters information processing of perforant path inputs constituting the major excitatory afferent pathway from entorhinal cortex to dentate granule cells.
Settlement size and fertility in the Nordic countries

While the variation in childbearing patterns across countries and between socio-economic groups within a country has been studied in detail, less is known about the differences in fertility patterns across settlements within a country. Using aggregate and individual-level register data, we examine fertility variation across settlements in Denmark, Finland, Norway, and Sweden. We observe a significant variation in fertility level by settlement size in all four of these Nordic countries—the larger the settlement, the lower the fertility. Second, the variation in fertility level has decreased over time, but significant differences in fertility between settlements of different size persist. Third, the timing of childbearing also varies across settlements—the larger the settlement, the later the peak of fertility. Fourth, our analysis of parity-specific fertility in Sweden shows that the major socio-economic characteristics of women account for only a small portion of fertility variation across settlements.
Access to health care: the role of a community based health insurance in Kenya

Background Out-of-pocket payments create financial barriers to health care access. There is an increasing interest in the role of community based health insurance schemes in improving equity and access of the poor to essential health care. The aim of this study was to assess the impact of Jamii Bora Health Insurance on access to health care among the urban poor. Methods Data was obtained from the household health interview survey in Kibera and Mathare slums, which consisted of 420 respondents, aged 18 and above who were registered as members of Jamii Bora Trust. The members of Jamii Bora Trust were divided into two groups the insured and the non-insured. Results In total, 17.9% respondents were hospitalized and women (19.6%) were more likely to be admitted than men (14.7%). Those in the poorest quintile had the highest probability of admission (18.1%). Those with secondary school education, large household size, and aged 50 and above also had slightly greater probability of admission (p<0.25). 86% of admissions among the insured respondents were covered JBHI and those in the poorest quintile were more likely to use the JBHI benefit. Results from the logistic regression revealed that the probability of being admitted, whether overall admission or admission covered by the JBHI benefit was determined by the presence of chronic condition (p<0.01). Conclusion Utilization and take up of the JBHI benefits was high. Overall, JBHI favoured the members in the lower income quintiles who were more likely to use health care services covered by the JBHI scheme.
Predicting semantically linkable knowledge in developer online forums via convolutional neural network

Consider a question and its answers in Stack Overflow as a knowledge unit. Knowledge units often contain semantically relevant knowledge, and thus linkable for different purposes, such as duplicate questions, directly linkable for problem solving, indirectly linkable for related information. Recognising different classes of linkable knowledge would support more targeted information needs when users search or explore the knowledge base. Existing methods focus on binary relatedness (i.e., related or not), and are not robust to recognize different classes of semantic relatedness when linkable knowledge units share few words in common (i.e., have lexical gap). In this paper, we formulate the problem of predicting semantically linkable knowledge units as a multiclass classification problem, and solve the problem using deep learning techniques. To overcome the lexical gap issue, we adopt neural language model (word embeddings) and convolutional neural network (CNN) to capture word- and document-level semantics of knowledge units. Instead of using human-engineered classifier features which are hard to design for informal user-generated content, we exploit large amounts of different types of user-created knowledge-unit links to train the CNN to learn the most informative wordlevel and document-level features for the multiclass classification task. Our evaluation shows that our deep-learning based approach significantly and consistently outperforms traditional methods using traditional word representations and human-engineered classifier features.
UvA-DARE ( Digital Academic Repository ) Evidence for Countercyclical Risk Aversion : An Experiment with Financial Professionals

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Hydrologic Uncertainty for Bayesian Probabilistic Forecasting Model Based on BP ANN

The Bayesian forecasting system (BFS) consists of three components which can be deal with independently. Considering the fact that the quantitative rainfall forecasting has not been fully developed in all catchment areas in China, the emphasis is given to the hydrologic uncertainty for Bayesian probabilistic forecasting. The procedure of determining the prior density and likelihood functions associated with hydrologic uncertainty is very complicated and there is a requirement to assume a linear and normal distribution within the framework of BFS. These pose severe limitation to its practical application to real-life situations. In this paper, a new prior density and likelihood function model is developed with BP artificial neural network (ANN) to study the hydrologic uncertainty of short-term reservoir stage forecasts based on the BFS framework. Markov chain Monte Carlo (MCMC) method is employed to solve the posterior distribution and statistics of reservoir stage. A case study is presented to investigate and illustrate these approaches using 3 hours rainfall-runoff data from the ShuangPai Reservoir in China. The results show that Bayesian probabilistic forecasting model based on BP ANN not only increases forecasting precision greatly but also offers more information for flood control, which makes it possible for decision makers consider the uncertainty of hydrologic forecasting during decisionmaking and estimate risks of different decisions quantitatively.
Visual Landmark-Based Localization for MAVs Using Incremental Feature Updates

Micro aerial vehicles (MAVs) are gaining importance as image acquisition tools in urban environments, where areas of interest are often close to buildings and to the ground. While GPS is still the most widely used sensor for outdoor localization, urban applications motivate the change towards visual localization. We present a framework based on metric, geo-referenced visual landmarks, which can be obtained by taking images with a consumer camera at ground level. Visual landmarks serve as prior knowledge to the MAV and allow robust, high-accuracy localization in urban environments. The issue of differing camera views in higher altitudes is reduced by incremental feature updates, a novel technique which boosts the performance by 30% in comparison to previous work, facilitates long-term operation, and results in a localization rate of 83%. We validate the visual pose estimation in-flight by comparison to IMU and GPS data, and evaluate our positioning accuracy with respect to differential GPS.
Smart traffic online system (STOS): Presenting road networks with time-weighted graphs

Driving direction traffic Systems are becoming needed systems among many Geographical Information systems (GIS's) by many kinds of users. Yahoo maps, MapQuest, and Google Earth are examples of online Internet applications. Vehicles GPS or what is known as navigation systems are also examples. Calculating the best driving path (shorter in distance or faster in time) between two addresses is subject to many factors. Some of these factors are distances, road situation, road traffic and others. This paper presents the driving traffic problem and a solution for finding an efficient path between two points (addresses) having a number factors. It discusses spatial databases, current solutions, and finally presents a smart solution that is an extension previous solution using A∗ (an artificial intelligent algorithm) and A∗Traffic (a variation of A∗, proposed in a previous paper). The new solution uses time as the main factor in the graph representing the road network.
Complications of fluoroscopically guided extraforaminal cervical nerve blocks. An analysis of 1036 injections.

BACKGROUND A number of serious complications associated with fluoroscopically guided extraforaminal cervical nerve blocks have been reported in the literature. The purpose of the present study was to determine the rate of complications associated with these blocks and to determine whether needle positioning during the procedure affected the prevalence of complications at one institution. METHODS Between October 1999 and June 2003, we performed 1036 fluoroscopically guided extraforaminal cervical nerve blocks in 844 patients. Plain radiographs documenting the procedure were made as part of the standard quality-assurance protocol. An independent observer who was uninvolved with the procedures reviewed a prospectively kept database on all patients. We subsequently reviewed the patient records to identify complications. RESULTS There were no catastrophic complications such as vessel damage, paralysis, or death. Overall, fourteen patients (1.66%) had a minor complication in association with the procedure. With the numbers available, the rate of complications associated with pdeep injection (798 blocks) was not significantly different from that associated with shallow injection (238 blocks) (1.89% compared with 0.84%). However, the rate of complications associated with anterior placement of the needle tip (thirty-three blocks) was higher than that associated with ideal placement of the needle tip (904 blocks) (6.06% compared with 1.55%) (p = 0.04). CONCLUSIONS No catastrophic complications occurred in this series of 1036 nerve blocks. We found that the medial-lateral needle depth as seen on frontal-view radiographs was not associated with complications, although the anterior positioning of the needle as seen on lateral-view radiographs was associated with minor complications. Our results suggest that, with our technique, cervical nerve blocks are relatively safe procedures.
Velocity consensus and formation shape control using distance-only measurements

This paper proposes a strategy to achieve velocity consensus and simultaneous formation shape control in a multiagent formation using distance-only measurements. Since with agents executing arbitrary motions, distance-only measurement cannot provide enough information for our objectives, we postulate that agents engage in a combination of circular motion and linear motion. When energy saving is the first priority, the linear motion component should dominate. On the other hand, when measurement accuracy is the first priority, the circular motion must be more prominent. Such motion allows agents to localize their neighbors (which is important for shape control) and to determine the relative velocity of the linear motion components. Simulation results are also provided to illustrate the performance of the control algorithm when the measurements are corrupted by noise.
Bringing robotics into formal education using the Thymio open source hardware robot

Mobile robots are valuable tools for education because of both the enthusiasm they raise and the multidisciplinary nature of robotic technology. Mobile robots give access to a wide range of fields, such as complex mechanics, sensors, wireless transmission, mathematics, and computer science, among others. As mobile robots sense the environment and take actions based on their perception, they seem to display intentions of their own [1]. This impression of intelligence, the permeating presence of robots in science fiction, and their projected use in our society give a sense of touching the future. Despite their potential as educational tools, robots are still not as widespread in schools as they could be. Among the possible reasons, we believe that the following five play a key role: 1) Although many research projects are developing innovative and interesting educational robots, few reach sufficient maturity to become distributed and accessible to schools. 2) A versatile robot performing interesting behaviors is a complex piece of technology and therefore expensive. This prevents most schools, which have a limited budget for equipment, from acquiring educational robots. 3) Introducing robotic tools into teaching activities requires investment in time and training for the teachers [2]. Therefore, to be accepted by teachers, robots must be both accessible with minimal effort and accompanied by well-prepared educational material shared among colleagues. 4) Robot construction, use, and programming is often perceived as a boyish activity in our Western society [3],
On a Linearization Principle for Nonlinear p-mean Subdivision Schemes

In a recent preprint [6], the authors consider, motivated by nonlinear signal processing, a family of nonlinear subdivision operators based on interpolation-imputation of p-means. A linearization principle was proposed which, when applied to these nonlinear operators, led to a corresponding family of linear subdivision operators. Based on a number of numerical experiments, the authors conjectured that the critical Holder regularity of any p-mean subdivision operator is exactly the same as that of the corresponding linear subdivision operator. (The latter can be determined analytically by standard techniques in the linear theory.) In the same article the conjecture was proved in the case of (p, L, d) = (1, 1, 3), i.e. triadic subdivision based on median interpolation-imputation by quadratics. In this note, we prove the conjecture in the case of (p, L, d) = (∞, 1, 3). The method of proof is by and large similar to that in the p = 1 case; in particular the dynamical behaviors of three maps acting on the real projective plane P have to be studied. The main difference is that in the p = ∞ case the convergence of an infinite product has to be established during a final stage of the proof.
IMAGE LOCALIZATION IN SATELLITE IMAGERY WITH FEATURE-BASED INDEXING

This paper presents a method to index ortho-map databases with image-based features and search a map database for regions that match query images of unknown scales and rotations. The proposed method uses image-based features to index the 2D map locations. Image feature extractors normally generate features with location, orientation, shape, and a descriptor for normalized image patch. In a map database, the geographical location, orientation and shape of the image features can be recovered with a reasonable local planarity assumptions. The paper makes use of a visual word based recognition scheme and extends it by adding geographical dimensions to the visual words and use them to index 2D locations in a map grid.An indexing-friendly scoring system is defined to measure the similarity of query and database images which represent unit tiles of the complete map. The implemented scoring algorithm can efficiently give the matching scores between a query image and all possible database images. Upon searching a new approximately orthogonal image, a set of scaling and rotations are first selected, and the visual words are transformed and matched against the database. The best locations along with scales and rotations are determined from the query results of the different set of transformed visual words. Experiments show a high success rate and high speed in searching map databases for aerial images from different datasets.