Miracle-dz/UniFAR-FTUs huggingface dataset:
On the development of a hole filling algorithm for triangular meshes

Abstract Hole‐filling is a process to repair the holes in a triangular model and to keep the topology accurate and preserve the smoothness of the original surface as new meshes are added. We propose a hole‐filling algorithm by fitting the vertices in the vicinity of a hole into a B‐spline surface and plan new vertices on the u‐v domain corresponding to such a surface. The main advantage of such a method is that the u‐v plane, instead of a 3D plane conventionally used, can better be used for the planning of new vertices distributed appropriately on the hole, especially for curved holes with steep slopes. The points on the u‐v plane are mapped onto the B‐spline surface to yield 3D vertices. Since the surface does not pass through the boundary vertices of the hole completely, a modification algorithm based on the local moving least squares method is proposed to modify the 3D vertices. Successful examples are presented to illustrate the feasibility of the proposed algorithm.
Portfolio Choice and Trading in a Large 401(k) Plan

We study nearly 7,000 retirement accounts during the April 1994-August 1998 period. Several interesting patterns emerge. Most asset allocations are extreme (either 100 percent or zero percent in equities) and there is inertia in asset allocations. Equity allocations are higher for males, married investors, and for investors with higher earnings and more seniority on the job; equity allocations are lower for older investors. There is very limited portfolio reshuffling, in sharp contrast to discount brokerage accounts. Daily changes in equity allocations correlate only weakly with same-day equity returns and do not correlate with future equity returns.
Bekentenissen van een Decaan

Tussen Augustinus en Rousseau is de zelfbeschouwing verschoven van Belijdenis naar Bekentenis, van een theocratische context naar een psychologische. Het is de vraag of het daaruit resulterende egocentrisme van de moderne tijd vooruitgang impliceert. De zelfbeschouwing die hier naar aanleiding van een tienjarig faculteitsdecanaat wordt gepresenteerd is dan ook niet op het eigen ik gericht, maar op de ‘hogere’ (maar overigens niet-theocratische) context: de relatie tussen academisch bestuur, wetenschap en authenticiteit. Aan de hand van de zeven hoofdzonden zullen voorbeelden worden besproken van menselijke tekortkomingen, die ook in het wetenschappelijk bedrijf, zowel als in het academisch bestuur, onvermijdelijk een rol spelen. Hoe is het mogelijk korte termijn belangen terzijde te schuiven en wetenschappelijke waarden en intellectuele authenticiteit voorrang te geven?
Data based dynamical modeling of vision observed small robots

Until recently the control and navigation of mobile robots was mainly performed based on exteroceptive sensoring. Recent investigation on the cooperation of multiple robotic agents with centralized control, has ushered in small and less sophisticated robots that are observed by a global vision system. This paper presents a modeling methodology that enables accurate prediction of such small robot's position and orientation from data measured using the global vision system. Results of several experiments show that the model is both accurate and robust. Another relevant aspect of the work is the ability to determine the limitations likely to be found in centralized control of agents with little or no local "intelligence".
Robust Calculation of Ego-Vehicle Corridors for Vision-Based DAS

An important component of driver assistance systems (DAS) is lane detection, and has been studied since the 1990s. However, improving and generalizing lane detection solutions remains to be a challenging task until recently. A (physical) lane is defined by road boundaries or various kinds of lane marks, and this is only partially applicable for modeling the space an ego-vehicle is able to driving in. This paper proposes a concept of a (virtual) corridor for modeling this space. A corridor depends on information available about the motion of the ego-vehicle, as well as about the (physical) lane. This paper suggests robust corridor detection using hypothesis testing based on maximum a posterior (MAP) estimation. Then, boundary selection and road patch extension are applied as post-processing. Furthermore, a simple but efficient corridor tracking method is also discussed. This paper also informs the readers about experiments using images of some challenging road situations illustrating the usefulness of the proposed corridor detection and tracking scheme.
Policing in the 21st Century: What Works and What Doesn't

This presentation provides some general observations and specific suggestions about policing in Australia into the 21st Century. I make three general observations about policing in Australia: that the police continue to erroneously regard 'quality of life' concerns as 'soft policing'; that there continues to be an unacceptable distance between the police and the communities that they serve; and that the police are under-resourced in many aspects of information technology. These problems with policing in Australia come, I believe, from a mis-guided adherence to traditional policing strategies that we know don't work. For example, we know that strategies such as rapid response, random patrolling and reactive arrests are out-dated, they fail to reduce crime problems, and scarce police resources would be better used focusing on other, more contemporary crime control strategies. I conclude that the police would increase their effectiveness (in terms of controlling and preventing crime) if they adopted strategies that we know work. For example, we know that the police are highly effective when they adopt crime control strategies such as third party policing, problem-oriented policing and directed patrolling. These contemporary strategies are explicated and discussed.
Multispectral Face Recognition in Texture Space

This work introduces the use of LBP like texture descriptors for efficient multispectral face recognition. LBP has been widely used in visible spectrum face recognition. This work extend its use to non visible spectrums (active and passive infrared spectrums). Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) descriptors are used. Also a simple differential LTP descriptor (DLT) is introduced. The proposed texture space is less sensitive to noise, illumination change and facial expressions. These characteristics make it a good candidate for efficient multispectral face recognition. Linear and non linear dimensionality reduction techniques are introduced and used for performance evaluation of multispectral face recognition in the texture space. The obtained results show that the use of the proposed texture descriptors permit to achieve high recognition rates in multispectral face recognition.
Effects of multiple vein microjoints on the mechanical behaviour of dragonfly wings: numerical modelling

Dragonfly wings are known as biological composites with high morphological complexity. They mainly consist of a network of rigid veins and flexible membranes, and enable insects to perform various flight manoeuvres. Although several studies have been done on the aerodynamic performance of Odonata wings and the mechanisms involved in their deformations, little is known about the influence of vein joints on the passive deformability of the wings in flight. In this article, we present the first three-dimensional finite-element models of five different vein joint combinations observed in Odonata wings. The results from the analysis of the models subjected to uniform pressures on their dorsal and ventral surfaces indicate the influence of spike-associated vein joints on the dorsoventral asymmetry of wing deformation. Our study also supports the idea that a single vein joint may result in different angular deformations when it is surrounded by different joint types. The developed numerical models also enabled us to simulate the camber formation and stress distribution in the models. The computational data further provide deeper insights into the functional role of resilin patches and spikes in vein joint structures. This study might help to more realistically model the complex structure of insect wings in order to design more efficient bioinspired micro-air vehicles in future.
First-principles calculations of magnetic interactions in correlated systems

We present a novel approach to calculate the effective exchange interaction parameters based on the realistic electronic structure of correlated magnetic crystals in local approach with the frequency dependent self energy. The analog of ``local force theorem'' in the density functional theory is proven for highly correlated systems. The expressions for effective exchange parameters, Dzialoshinskii- Moriya interaction, and magnetic anisotropy are derived. The first-principle calculations of magnetic excitation spectrum for ferromagnetic iron, with the local correlation effects from the numerically exact QMC-scheme is presented.
Entrepreneurial Leadership: what is it and how should it be taught?

We offer a comprehensive review of the literature relating to entrepreneurial leadership, noting that there are diverse understandings of the concept and little exploration of how best to teach it. We next present empirical data from a survey of teaching practices at 51 HEIs in the UK that indicate little explicit teaching of entrepreneurial leadership. Drawing on this literature and data, we make recommendations for the design of teaching materials that emphasise the relevance of leadership in entrepreneurship education and of entrepreneurship in leadership education.
Active authentication by mouse movements

We present an application for authenticating users with mouse movements. We extract the features from nine paths between seven squares displayed consecutively on the screen. Although the paths among the squares are fixed, user can not get familiar to the paths by the selection algorithm of next path. This prevents similar results from different users. We experimented on improving the success rate, by selecting the optimal time interval between mouse movement points and refining features to obtain valid data. Experiment's results presented a false acceptance rate (FAR) of 5.9% and a false reject rate (FRR) of 5.9%.
Consensus clustering of gene expression microarray data using genetic algorithms

This work presents a new consensus clustering method for gene expression microarray data based on a genetic algorithm. Using two datasets { DA and DB { as input, the genetic algorithm examines puta- tive partitions for the samples in DA, selecting biomarkers that support such partitions. The biomarkers are then used to build a classifler which is used in DB to determine its samples classes. The genetic algorithm is guided by an objective function that takes into account the accuracy of classiflcation in both datasets, the number of biomarkers that support the partition, and the distribution of the samples across the classes for each dataset. To illustrate the method, two whole-genome breast cancer instances from difierent sources were used. In this application, the results indicate that the method could be used to flnd unknown subtypes of dis- eases supported by biomarkers presenting similar gene expression proflles across platforms. Moreover, even though this initial study was restricted to two datasets and two classes, the method can be easily extended to consider both more datasets and classes. The deluge of data that occurred after the introduction of the microarray tech- nology imposed a series of methodological problems in terms of producing rele- vant and statistically sound results. Given the amount of data publicly available, the use of a single dataset is no longer acceptable to justify new discoveries. So, any new hypothesis needs to be based on two or more groups of patients, which might imply comparing microarrays from difierent laboratories, and obtained using difierent technologies. This work addresses the problem of unsupervised clustering using information from two or more datasets, aiming at producing a consensus clustering of the samples. Towards this goal, among the most common unsupervised clustering techniques, we must cite principal component analysis, self-organizing maps, hierarchical clustering, k-means, metaheuristics (e.g. tabu search, genetic and memetic algorithms), etc (1,2). A problem that arises is that microarray data is highly heterogeneous and noisy, and in general, difierent techniques will flnd difierent conflgurations of clusters. In addition, clusters found using a speciflc dataset most times are not
I Know Where You Live: Inferring Details of People's Lives by Visualizing Publicly Shared Location Data

This research measures human performance in inferring the functional types (i.e., home, work, leisure and transport) of locations in geo-location data using different visual representations of the data (textual, static and animated visualizations) along with different amounts of data (1, 3 or 5 day(s)). We first collected real life geo-location data from tweets. We then asked the data owners to tag their location points, resulting in ground truth data. Using this dataset we conducted an empirical study involving 45 participants to analyze how accurately they could infer the functional location of the original data owners under different conditions, i.e., three data representations, three data densities and four location types. The study results indicate that while visual techniques perform better than textual ones, the functional locations of human activities can be inferred with a relatively high accuracy even using only textual representations and a low density of location points. Workplace was more easily inferred than home while transport was the functional location with the highest accuracy. Our results also showed that it was easier to infer functional locations from data exhibiting more stable and consistent mobility patterns, which are thus more vulnerable to privacy disclosures. We discuss the implications of our findings in the context of privacy preservation and provide guidelines to users and companies to help preserve and safeguard people's privacy.
Implications of Changing Temperatures on the Growth, Fecundity and Survival of Intermediate Host Snails of Schistosomiasis: A Systematic Review

Climate change has been predicted to increase the global mean temperature and to alter the ecological interactions among organisms. These changes may play critical roles in influencing the life history traits of the intermediate hosts (IHs). This review focused on studies and disease models that evaluate the potential effect of temperature rise on the ecology of IH snails and the development of parasites within them. The main focus was on IH snails of schistosome parasites that cause schistosomiasis in humans. A literature search was conducted on Google Scholar, EBSCOhost and PubMed databases using predefined medical subject heading terms, Boolean operators and truncation symbols in combinations with direct key words. The final synthesis included nineteen published articles. The studies reviewed indicated that temperature rise may alter the distribution, optimal conditions for breeding, growth and survival of IH snails which may eventually increase the spread and/or transmission of schistosomiasis. The literature also confirmed that the life history traits of IH snails and their interaction with the schistosome parasites are affected by temperature and hence a change in climate may have profound outcomes on the population size of snails, parasite density and disease epidemiology. We concluded that understanding the impact of temperature on the growth, fecundity and survival of IH snails may broaden the knowledge on the possible effects of climate change and hence inform schistosomiasis control programmes.
Broadband Explicit Time Domain Model for Human Skin Permittivity

Multiterm Debye models, working well in the frequency and time domains, are established to describe the frequency-dependent behavior of complex dielectric properties of dry and wet human skin. The forearm data measured from 10 Hz to 100 GHz, accompanied by the genetic algorithm, are used to find the best multiterm Debye parameters for the models. These broadband models are very helpful and straightforward to investigate the interaction of transient signals such as Gaussian pulses with human skin as a dispersive medium in the time domain. The direct finite difference time domain formulation is used to propagate a Gaussian pulse through each of these media. According to this time domain analysis, the optimized perfectly matched layer and skin depth versus frequency are calculated for every skin model. Good agreement is observed between the calculated and measurement data of skin depths.
Analyzing the Anterior Knee Laxity During Passive Flexion

—Anterior-posterior knee laxity in the sagittal plane indicates the functional state of cruciate ligaments. Particularly, the anterior cruciate ligament or ACL is damaged frequently during strenuous activities like in sports. Following injury and treatment, not only a significant percentage of patients are not able to return to their pre-injury level activity, but also they continue to have knee related complications in medium to long term period. Therefore, there is a need for better understanding of the knee. In the present study, anterior laxity of the knee that is related to the function of the ACL, is analyzed during passive flexion of the joint. The knee was modelled in the sagittal plane using anatomical data and material properties to represent the joint structures mathematically and simulate the joint motion during 0–120 o flexion while no muscle force or external load was applied. The ACL was represented as bundles of non-linear elastic fibers. A laxity test with 130N anteriorly directed external force on the tibia was simulated at several flexion angles. The results from model calculations showed agreement with experimental observations from literature. 130N anterior force translated the tibia anterior to the femur non-linearly throughout the flexion range. Due to the applied force, fibers of the ACL slackened or stretched differently depending on their location of insertion as well as on flexion angle. For example, the most anterior fiber stretched at all flexion positions while the most posterior fiber stretched at low or high flexion. Anterior bundle of the ACL contributed significantly in resisting the external force while the posterior bundle contributed mainly near extension or in high flexion. The analysis has relevance to ACL-reconstruction and ACL rehabilitation.
Targeted In Situ Gene Correction of Dysfunctional APOE Alleles to Produce Atheroprotective Plasma ApoE3 Protein

Cardiovascular disease is the leading worldwide cause of death. Apolipoprotein E (ApoE) is a 34-kDa circulating glycoprotein, secreted by the liver and macrophages with pleiotropic antiatherogenic functions and hence a candidate to treat hypercholesterolaemia and atherosclerosis. Here, we describe atheroprotective properties of ApoE, though also potential proatherogenic actions, and the prevalence of dysfunctional isoforms, outline conventional gene transfer strategies, and then focus on gene correction therapeutics that can repair defective APOE alleles. In particular, we discuss the possibility and potential benefit of applying in combination two technical advances to repair aberrant APOE genes: (i) an engineered endonuclease to introduce a double-strand break (DSB) in exon 4, which contains the common, but dysfunctional, ε2 and ε4 alleles; (ii) an efficient and selectable template for homologous recombination (HR) repair, namely, an adeno-associated viral (AAV) vector, which harbours wild-type APOE sequence. This technology is applicable ex vivo, for example to target haematopoietic or induced pluripotent stem cells, and also for in vivo hepatic gene targeting. It is to be hoped that such emerging technology will eventually translate to patient therapy to reduce CVD risk.
An Industry-Equilibrium Analysis of the LeChatelier Principle

By considering firms operating in a perfectly- or monopolisticallycompetitive industry with free entry, we show that well-established results on the celebrated LeChatelier principle (LCP) do not extend into an endogenous competitive environment. For instance, labour demand may be more elastic in the short run (where capital is fixed) than in the long run even if capital and labour are either complements or substitutes in profits. This may also be true locally at a point of long-run equilibrium. A novel insight is that industry-equilibrium effects introduce an asymmetry such that the LCP may hold for wage increases but not for wage decreases. These results are important for the interpretation of estimated labour-demand elasticities. Finally, we show that the LCP may hold for the total industry labour demand in situations where it does not hold for the labour demand of individual firms.
A Novel Method on Incremental Information Acquisition for Deep Web

Deep Web is autonomous, independently updating, and its data are always in a state of frequent update. However, the user always hopes to obtain the newest content in the current Web database. Different from previous research, this paper wants to emphasize the importance of updating frequency in the study of Deep Web information acquisition. And, an approach on incremental information acquisition based on logical reinforcement learning has been proposed. Then, we find in our research that under the same condition of constraint resources, the novel approach can improve the freshness of data, discovery efficiency of new data and the service quality of Deep Web information integration.
HC-Search: Learning Heuristics and Cost Functions for Structured Prediction

Structured prediction is the problem of learning a function from structured inputs to structured outputs with prototypical examples being part-of-speech tagging and image labeling. Inspired by the recent successes of search-based structured prediction, we introduce a new framework for structured prediction called {\em HC-Search}. Given a structured input, the framework uses a search procedure guided by a learned heuristic H to uncover high quality candidate outputs and then uses a separate learned cost function C to select a final prediction among those outputs. We can decompose the regret of the overall approach into the loss due to H not leading to high quality outputs, and the loss due to C not selecting the best among the generated outputs. Guided by this decomposition, we minimize the overall regret in a greedy stage-wise manner by first training H to quickly uncover high quality outputs via imitation learning, and then training C to correctly rank the outputs generated via H according to their true losses. Experiments on several benchmark domains show that our approach significantly outperforms the state-of-the-art methods.
Vision-based autonomous landing system for unmanned aerial vehicle: A survey

Recently, there has been growing interest in developing unmanned aircraft system (UAS) based on visual sensors. During the whole autonomous assignment, the landing procedure is one of the most dangerous and challenging process. For most of unmanned aircraft vehicle, visual sensors are the basic equipment, which are also widely used during the landing maneuver. This paper first presents the main research groups involved in the development of vision-based autonomous landing systems. Then it discusses the detail of each algorithms and systems in different categories. The goal of this paper is to review the state-of-the-art vision-based autonomous landing methods that captures all milestones and seminal works. These algorithms and systems are classified into different categories. Finally, the paper highlights challenges in this research field.