Miracle-dz/UniFAR-FTUs huggingface dataset:
Orientation estimation fusing a downward looking camera and inertial sensors for a hovering UAV

Nowadays, a particular kind of UAVs (Unmanned Aerial Vehicle) known as quadcopters or quadrotors have become very popular. This is mainly due to their reduced size and high maneuverability which allow them to operate in indoor environments. The requirement for orientation estimation in these vehicles is twofold: for low level stability control, and for high level navigation and motion planning. Orientation estimation is usually carried out fusing measurements of different sensors including inertial sensor, magnetic compass, sonar, GPS, camera, etc. As is known, GPS signal is not available in indoor environments and the Earth's magnetic field is highly disturbed by ferromagnetic structures. In the present work we describe a new approach for quadrotor orientation estimation fusing inertial measurements with a downward looking camera. Inertial sensor are used for orientation estimation based on the gravity vector, and the camera provides information related to the heading direction. The camera heading or yaw angle estimation is based on spectral features extracted from the floor plane. Experimental results show the performance of the presented approach applied to a hovering UAV.
Creation and use of a Talairach‐compatible atlas for accurate, automated, nonlinear intersubject registration, and analysis of functional imaging data

Spatial normalization in functional imaging can encompass various processes, including nonlinear warping to correct for intersubject differences, linear transformations to correct for identifiable head movements, and data detrending to remove residual motion correlated artifacts. We describe the use of AIR to create a custom, site‐specific, normal averaged brain atlas that can be used to map T2 weighted echo‐planar images and coplanar functional images directly into a Talairach‐compatible space. We also discuss extraction of characteristic descriptors from sets of linear transformation matrices describing head movements in a functional imaging series. Scores for these descriptors, derived using principal components analysis with singular value decomposition, can be treated as confounds associated with each individual image in the series and systematically removed prior to voxel‐by‐voxel statistical analysis. Hum. Brain Mapping 8:73–79, 1999. © 1999 Wiley‐Liss, Inc.
Statement Minas Gjoka 1 Overview

1 Overview My research interests are in the general areas of networking and distributed systems. Nowadays the Internet is ubiquitous and there are a number of killer applications that run on it. Some examples include Peer-to-Peer systems that enable video streaming (Skype) and file sharing (BitTorrent); Online Social Networks (Facebook, Twitter) and e-mail (Gmail, Hotmail) that facilitate information sharing between users. Many users spend the majority of their time in these systems and generate a large amount of the traffic on the Internet. My research is focused on such internet systems and can be split into the following three subareas. • measurement techniques and analysis of large-scale data produced by internet systems. • network modeling and simulation of the processes that take place within internet systems • design and implementation of networked systems, addressing challenges such as scalability or incentive mechanisms. Below I provide a brief background and describe my research contributions in each subarea.
MAR Elements and Transposons for Improved Transgene Integration and Expression

Reliable and long-term expression of transgenes remain significant challenges for gene therapy and biotechnology applications, especially when antibiotic selection procedures are not applicable. In this context, transposons represent attractive gene transfer vectors because of their ability to promote efficient genomic integration in a variety of mammalian cell types. However, expression from genome-integrating vectors may be inhibited by variable gene transcription and/or silencing events. In this study, we assessed whether inclusion of two epigenetic control elements, the human Matrix Attachment Region (MAR) 1–68 and X-29, in a piggyBac transposon vector, may lead to more reliable and efficient expression in CHO cells. We found that addition of the MAR 1–68 at the center of the transposon did not interfere with transposition frequency, and transgene expressing cells could be readily detected from the total cell population without antibiotic selection. Inclusion of the MAR led to higher transgene expression per integrated copy, and reliable expression could be obtained from as few as 2–4 genomic copies of the MAR-containing transposon vector. The MAR X-29-containing transposons was found to mediate elevated expression of therapeutic proteins in polyclonal or monoclonal CHO cell populations using a transposable vector devoid of selection gene. Overall, we conclude that MAR and transposable vectors can be used to improve transgene expression from few genomic transposition events, which may be useful when expression from a low number of integrated transgene copies must be obtained and/or when antibiotic selection cannot be applied.
OUR PROPOSITION 5 . 1 The Conversion Process

In this paper, we have presented a new approach of programming languages inter-conversion which can be applied to all types of programming languages. The idea is about implementation of the intermediate language for inter-conversion. This language can be used to store the logic of the program in an algorithmic format without disturbing the structure of the original program. This paper also discusses the major advantages and challenges associated with this conversion approach. We have also performed a theoretical case study on the conversion of code written in the C++ programming language to Java.
Compact Multi-Band Bandpass Filters With Mixed Electric and Magnetic Coupling Using Multiple-Mode Resonator

In this paper, a multiple stubs loaded ring resonator (MSLRR) is proposed to design directly coupled multi-band bandpass filters (BPFs) with mixed electric and magnetic coupling (MEMC). The proposed MSLRR exhibits multiple-mode resonant behavior. The increased number of loaded stubs excite many more useful resonant modes, but these resonant modes can be still independently controlled. As examples, a dual-band BPF, a tri-band BPF, a quad-band BPF, and a quint-band BPF using different types of MSLRRs are designed and fabricated. The passband frequencies and return losses (RLs) of these multi-band BPFs can be independently controlled. A dual-mode open loop resonator is then introduced in the quint-band BPF to enhance the sixth resonant mode of the MSLRR to produce the sixth passband so that a sext-band BPF is also presented. Multiple transmission zeros due to the cancelling effect of MEMC and virtual grounds in the MSLRR can be observed around the passbands resulting in sharp passband selectivity and high band-to-band isolation. Moreover, all of the fabricated multi-band BPFs have compact sizes, good RLs, and low insertion losses. Good agreements are observed between the simulated and measured results.
An information systems approach to support decision makers selecting group decision processes

This paper describes a support system for group decision-making. The system is based on a database of typical decision cases and also an underlying model of the group decision process. The model serves to organize the way users interact with the database, exploring, analysing and selecting cases. Currently the database has 75 group decision cases.
Verification State-Space Reduction through Restricted Parsing Environments

We discuss the potential for significant reduction in size and complexity of verification tasks for input-handling software when such software is constructed according to Lang Sec principles, i.e., Is designed as a recognizer for a particular language of valid inputs and is compiled for a suitably limited computational model no stronger than needed for the recognition task. We introduce Crema, a programming language and restricted execution environment of sub-Turing power, and conduct a case study to estimate and compare the respective sizes of verification tasks for the qmail SMTP parsing code fragments when executed natively vs in Crema -- using LLVM and KLEE. We also study the application of the same principles to the verification of reference monitors.
Efficient and Dynamic Simplification of Line Drawings

In this paper we present a pipeline for rendering dynamic 2D/3D line drawings efficiently. Our main goal is to create efficient static renditions and coherent animations of line drawings in a setting where lines can be added, deleted and arbitrarily transformed on‐the‐fly. Such a dynamic setting enables us to handle interactively sketched 2D line data, as well as arbitrarily transformed 3D line data in a unified manner. We evaluate the proximity of screen projected strokes to simplify them while preserving their continuity. We achieve this by using a special data structure that facilitates efficient proximity calculations in a dynamic setting. This on‐the‐fly proximity evaluation also facilitates generation of appropriate visibility cues to mitigate depth ambiguities and visual clutter for 3D line data. As we perform all these operations using only line data, we can create line drawings from 3D models without any surface information. We demonstrate the effectiveness and applicability of our approach by showing several examples with initial line representations obtained from a variety of sources: 2D and 3D hand‐drawn sketches and 3D salient geometry lines obtained from 3D surface representations.
Tax Me, But Spend Wisely Public Finance and Government Accountability

This paper shows that local governments are more accountable when a larger fraction of their resources comes from local taxes. I construct a principal-agent model of public finance in which public revenues come from taxes and inter-governmental transfers. An increase in taxes changes the equilibrium allocation of public revenues towards more public goods and less political rents because citizens have better information on taxes than on transfers. I then compare how local governments in Brazil spend increases in tax and transfer revenues. Variations in tax revenues are created by a program that seeks to increase local tax capacity. Using a difference-in-differences methodology and quasi-exogenous variations in the timing of program take-up I find that the program increases tax collection of local governments by 11% after four years. I use several discontinuities in the rule allocating federal transfers to local governments to consider the impact of an exogenous increase in transfers. Results show, in line with the model’s predictions, that an increase in local tax revenues leads to a bigger increase in local public services (health and education) than an increase in transfers of the same amount. Moreover extra transfer revenues lead to more corruption, extra tax revenues do not. JEL classifications: D72, D73, H20, H71, H77 ∗Email : l.gadenne@ucl.ac. uk. I would like to thank Luc Behaghel, Tim Besley, Francois Bourguignon, Clement de Chaisemartin, Raj Chetty, Denis Cogneau, Claudio Ferraz, Walker Hanlon, Karen Macours, Guy Michaels, Tommaso Nannicini, Thomas Piketty, Monica Singhal, Ernesto Stein, Eric Verhoogen and Ekaterina Zhuravskaya for their helpful comments as well as Stephan Litschig and Yves Zamboni for sharing their data, Keisuke Hirano for help with inference using propensity score weighted regressions, Marcelo Fernandes and Rogerio Goulart for their help in understanding the PMAT program and seminar participants at the Paris School of Economics, the London School of Economics, Oxford University, University College London, Sciences-Po Paris, Bocconi University, Columbia University, the BNDES, PUC-Rio, FGV-Rio, the LACEA Political Economy Workshop 2011, the IV Fiscal Federalism Workshop, and the NEUDC 2011 Conference. I acknowledge financial support from the AXA Research Fund and the CEPREMAP. Part of the research was done while the author was visiting the London School of Economics as an AMID fellow.
Horizontaland vertical spillovers from FDI: Evidence from panel data for the Italian manufacturing sector

Foreign direct investment (FDI) from Multinational Enterprises (MNEs) can be beneficial to host countries, since it may generate positive externalities to domestic firms, contributing to the increase of their productivity. These positive effects can take place both within an industry ("horizontal” spillovers) and across industries ("vertical” spillovers) as in the case of technology transfers to domestic suppliers or customers in the production chain. Using a firm‐level panel data, in this paper we analyse productivity spillovers from FDI in the Italian manufacturing firms both within and across industries. Our results suggest the existence of “vertical” spillovers and no evidence of “horizontal” ones.
Superconducting low-inductance undulatory galvanometer microwave amplifier: Theory

We describe a novel scheme for low-noise phase-insensitive linear amplification at microwave frequencies based on the superconducting low-inductance undulatory galvanometer (SLUG). Direct integration of the junction equations of motion provides access to the full scattering matrix of the SLUG. We discuss the optimization of SLUG amplifiers and calculate amplifier gain and noise temperature in both the thermal and quantum regimes. Loading of the SLUG element by the finite input admittance is taken into account, and strategies for decoupling the SLUG from the higher-order modes of the input circuit are discussed. The microwave SLUG amplifier is expected to achieve noise performance approaching the standard quantum limit in the frequency range from 5–10 GHz, with gain around 15 dB for a single-stage device and instantaneous bandwidths of order 1 GHz.
0 A ug 2 00 5 Forest matrices around the Laplacian matrix

We study the matrices Qk of in-forests of a weighted digraph Γ and their connections with the Laplacian matrix L of Γ. The (i, j) entry of Qk is the total weight of spanning converging forests (in-forests) with k arcs such that i belongs to a tree rooted at j. The forest matrices, Qk, can be calculated recursively and expressed by polynomials in the Laplacian matrix; they provide representations for the generalized inverses, the powers, and some eigenvectors of L. The normalized in-forest matrices are row stochastic; the normalized matrix of maximum in-forests is the eigenprojection of the Laplacian matrix, which provides an immediate proof of the Markov chain tree theorem. A source of these results is the fact that matrices Qk are the matrix coefficients in the polynomial expansion of adj(λI + L). Thereby they are precisely Faddeev’s matrices for −L. AMS classification: 05C50; 15A48
An Authentication Scheme in Peer-to-Peer Based Live Stream Sharing Management Systems

Peer-to-peer based live stream sharing platform is a novel technique for distributing visual data. Because of developments in computer equipment and network service, viewers can watch shows via the Internet rather than through the conventional broadcasted manner. Although data access via the Internet is more convenient, it is more difficult to manage the data transmission, and issues such as copyrighting become more of a problem. In this paper, we will propose an authentication scheme which accounts for this peer-to-peer based structure and tolerates a large amount of participants.
Hide and New in the Pi-Calculus

In this paper, we enrich the pi-calculus with an operator for confidentiality (hide), whose main effect is to restrict the access to the object of the communication, thus representing confidentiality in a natural way. The hide operator is meant for local communication, and it differs from new in that it forbids the extrusion of the name and hence has a static scope. Consequently, a communication channel in the scope of a hide can be implemented as a dedicated channel, and it is more secure than one in the scope of a new. To emphasize the difference, we introduce a spy context that represents a side-channel attack and breaks some of the standard security equations for new. To formally reason on the security guarantees provided by the hide construct, we introduce an observational theory and establish stronger equivalences by relying on a proof technique based on bisimulation semantics.
Proactive neighbor pushing for enhancing provider mobility support in content-centric networking

CCN (Content-Centric Networking) enables users to retrieve content using the content's name. Researchers face critical challenges in terms of mobility. Since the routing information is part of the content name, when the provider moves, it is necessary to update all the routers' routing information. However, this requires significant costs. In this paper, we propose PNPCCN (Proactive Neighbor Pushing CCN), considering the popularity and rarity of mobility support, for providers in CCN environments. Via simulation studies, we demonstrate that our solutions are effective in terms of shorter numbers of retransmitted Interest packets, and average download times and higher delivery ratios during mobility.
Classification of Human Whole-Body Motion using Hidden Markov Models

Human motion plays an important role in many fields. Large databases exist that store and make available recordings of human motions. However, annotating each motion with multiple labels is a cumbersome and error-prone process. This bachelor's thesis presents different approaches to solve the multi-label classification problem using Hidden Markov Models (HMMs). First, different features that can be directly obtained from the raw data are introduced. Next, additional features are derived to improve classification performance. These features are then used to perform the multi-label classification using two different approaches. The first approach simply transforms the multi-label problem into a multi-class problem. The second, novel approach solves the same problem without the need to construct a transformation by predicting the labels directly from the likelihood scores. The second approach scales linearly with the number of labels whereas the first approach is subject to combinatorial explosion. All aspects of the classification process are evaluated on a data set that consists of 454 motions. System 1 achieves an accuracy of 98.02% and system 2 an accuracy of 93.39% on the test set.
Using MUSIC and CC(CO)NH for Backbone Assignment of Two Medium-Sized Proteins Not Fully Accessible to Standard 3D NMR

The backbone assignment of medium-sized proteins is rarely as straightforward as that of small proteins, and thus often requires creative solutions. Here, we describe the application of a combination of standard 3D heteronuclear methods with CC(CO)NH and a variety of MUltiplicity Selective In-phase Coherence transfer (MUSIC) experiments. Both CC(CO)NH and MUSIC are, in theory, very powerful methods for the backbone assignment of proteins. Due to low sensitivity, their use has usually been linked to small proteins only. However, we found that combining CC(CO)NH and MUSIC experiments simplified the assignment of two challenging medium-sized proteins of 13 and 19.5 kDa, respectively. These methods are to some extent complementary to each other: CC(CO)NH acquired with a long isotropic mixing time can identify amino acids with large aliphatic side chains. Whereas the most sensitive MUSIC experiments identify amino acid types that cannot be detected by CC(CO)NH, comprising the residues with acid and amide groups, and aromatic rings in their side chains. Together these methods provide a means of identifying the majority of peaks in the 2D 15N HSQC spectrum which simplifies the backbone assignment work even for proteins, e.g., small kinases, whose standard spectra resulted in little spectral resolution and low signal intensities.
Information technology and relationship marketing in an inter-firm context: implications for research

The focus in the relationship marketing literature is often on retaining customers, and the use of face-to-face relationships. Although the use of technology in business is continually increasing and consequently, limited research addresses the impact of technology on relationships. While it is accepted that trust is necessary for relationships (Morgan and Hunt, 1994) there is little evidence to suggest what impact technology has on these relationships, other than value adding (Stone and Woodcock, 1997). This paper reviews the impact of technology on relationships and concludes with a series of research propositions for an exploratory study. Due to the higher perceived risk of purchasing services, and increased importance on trust, this paper particularly focuses on Relationship Marketing in a service context.
A Deep Learning Pipeline for Image Understanding and Acoustic Modeling

Abstract : One of the biggest challenges artificial intelligence faces is making sense of the real world through sensory signals such as audio or video. Noisy inputs, varying object viewpoints, deformations and lighting conditions turn it into a high-dimensional problem which cannot be efficiently solved without learning from data. This thesis explores a general way of learning from high dimensional data (video, images, audio, text, financial data, etc.) called deep learning. It strives on the increasingly large amounts of data available to learn robust and invariant internal features in a hierarchical manner directly from the raw signals. We propose an unified pipeline for feature learning, recognition localization and detection using Convolutional Networks (ConvNets) that can obtain state-of-the-art accuracy on a number of pattern recognition tasks, including acoustic modeling for speech recognition and object recognition in computer vision. ConvNets are particularly well suited for learning from continuous signals in terms of both accuracy and efficiency. Additionally, a novel and general deep learning approach to detection is proposed and successfully demonstrated on the most challenging vision datasets. We then generalize it to other modalities such as speech data. This approach allows accurate localization and detection objects in images or phones in voice signals by learning to predict boundaries from internal representations. We extend the reach of deep learning from classification to detection tasks in an integrated fashion by learning multiple tasks using a single deep model. This work is among the first to outperform human vision and establishes a new state of the art on some computer vision and speech recognition benchmarks.
Stochastic effects in adaptive reconstruction of body damage: implied the creativity of natural selection

After an injury occurs, mechanical/biochemical loads on muscles influence the composition and structure of recovering muscles; this effect likely occurs in other tissues, cells and biological molecules as well owing to the similarity, interassociation and interaction among biochemical reactions and molecules. The ‘damage and reconstruction’ model provides an explanation for how an ideal cytoarchitecture is created by reducing components not suitable for bearing loads; in this model, adaptive changes are induced by promoting the stochasticity of biochemical reactions. Biochemical and mechanical loads can direct the stochasticity of biochemical reactions, which can in turn induce cellular changes. Thus, mechanical and biochemical loads, under natural selection pressure, modify the direction of cell‐ and tissue‐level changes and guide the formation of new structures and traits, thereby influencing microevolution. In summary, the ‘damage and reconstruction’ model accounts for the role of natural selection in the formation of new organisms, helps explain punctuated equilibrium, and illustrates how macroevolution arises from microevolution.