Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Image Forgeries using Geometric Cues

This chapter presents a framework for detecting fake regions by using various methods including watermarking technique and blind approaches. In particular, we describe current categories on blind approaches which can be divided into five: pixel-based techniques, format-based techniques, camera-based techniques, physically-based techniques and geometric-based techniques. Then we take a second look on the geometric-based techniques and further categorize them in detail. In the following section, the state-of-the-art methods involved in the geometric technique are elaborated.
Bayesian model order determination and basis selectionfor seemingly unrelated regressionsC

Bayesian methods are developed for the seemingly unrelated regressions (SUR) model where the model order or structure is presumed random. In particular we consider the class of models that are linear in some basis space. This class includes standard linear regression as a special case, as well as those models that involve non-linear transformations of the explanatory variables through a set of basis functions. Applications are given for vector autoregression (VAR) models of unknown order and multivariate splines with unknown knot points.
Fiscal Equalization in Spain

This book analyzes political decentralization and fiscal federalism in Canada and Germany, both traditional federal countries, and in Spain, a unitarian country engaged in the last two decades in a process of decentralization. Three key issues required for a well designed financing system are analyzed in depth, namely: tax assignment, equalization grants – i.e. redistribution of money from the wealthy regions or the national government to poorer regions, and the role of local governments in the administration of taxes.
Population Genetics of Identity By Descent

Recent improvements in high-throughput genotyping and sequencing technologies have afforded the collection of massive, genome-wide datasets of DNA information from hundreds of thousands of individuals. These datasets, in turn, provide unprecedented opportunities to reconstruct the history of human populations and detect genotype-phenotype association. Recently developed computational methods can identify long-range chromosomal segments that are identical across samples, and have been transmitted from common ancestors that lived tens to hundreds of generations in the past. These segments reveal genealogical relationships that are typically unknown to the carrying individuals. In this work, we demonstrate that such identical-by-descent (IBD) segments are informative about a number of relevant population genetics features: they enable the inference of details about past population size fluctuations, migration events, and they carry the genomic signature of natural selection. We derive a mathematical model, based on coalescent theory, that allows for a quantitative description of IBD sharing across purportedly unrelated individuals, and develop inference procedures for the reconstruction of recent demographic events, where classical methodologies are statistically underpowered. We analyze IBD sharing in several contemporary human populations, including representative communities of the Jewish Diaspora, Kenyan Maasai samples, and individuals from several Dutch provinces, in all cases retrieving evidence of fine-scale demographic events from recent history. Finally, we expand the presented model to describe distributions for those sites in IBD shared segments that harbor mutation events, showing how these may be used for the inference of mutation rates in humans and other species.
Epidermal Growth Factor Receptor Plays an Anabolic Role in Bone Metabolism In Vivo

While the epidermal growth factor receptor (EGFR)–mediated signaling pathway has been shown to have vital roles in many developmental and pathologic processes, its functions in the development and homeostasis of the skeletal system has been poorly defined. To address its in vivo role, we constructed transgenic and pharmacologic mouse models and used peripheral quantitative computed tomography (pQCT), micro–computed tomography (µCT) and histomorphometry to analyze their trabecular and cortical bone phenotypes. We initially deleted the EGFR in preosteoblasts/osteoblasts using a Cre/loxP system (Col‐Cre Egfrf/f), but no bone phenotype was observed because of incomplete deletion of the Egfr genomic locus. To further reduce the remaining osteoblastic EGFR activity, we introduced an EGFR dominant‐negative allele, Wa5, and generated Col‐Cre EgfrWa5/f mice. At 3 and 7 months of age, both male and female mice exhibited a remarkable decrease in tibial trabecular bone mass with abnormalities in trabecular number and thickness. Histologic analyses revealed decreases in osteoblast number and mineralization activity and an increase in osteoclast number. Significant increases in trabecular pattern factor and structural model index indicate that trabecular microarchitecture was altered. The femurs of these mice were shorter and smaller with reduced cortical area and periosteal perimeter. Moreover, colony‐forming unit–fibroblast (CFU‐F) assay indicates that these mice had fewer bone marrow mesenchymal stem cells and committed progenitors. Similarly, administration of an EGFR inhibitor into wild‐type mice caused a significant reduction in trabecular bone volume. In contrast, EgfrDsk5/+ mice with a constitutively active EGFR allele displayed increases in trabecular and cortical bone content. Taken together, these data demonstrate that the EGFR signaling pathway is an important bone regulator and that it primarily plays an anabolic role in bone metabolism. © 2011 American Society for Bone and Mineral Research.
Sentiment Analysis : Introduction and the State of the Art overview

In the following paper we present an overview of the state of the art in the area of sentiment analysis. Although this domain is quite new it already has a considerable amount of contributions. Therefore, we present the general trends and within we only focus on some of the most important topics. Additionally, we present some interesting emerging projects in the contemporary research and point out the key appliances of sentiment analysis. It has to be noted that this paper is meant to be introduction to the topic rather then a comprehensive and full overview.
Distributed Cooperative Reasoning in Ambient Environment

Cooperation plays an important role in distributed reasoning systems in Ambient Environment. In such systems, ambient entities with diverse perception and capabilities have to cooperate during the reasoning process by sharing parts of their local knowledge to achieve a set of common goals. Nevertheless, previous work in distributed reasoning assumed that ambient entities keep their local knowledge private and reason in a top-down manner to reach these goals. Such approaches do not fit with the features of Ambient environment. This paper presents a more tailored distributed peer-to-peer approach for modeling and reasoning with context information using cooperation in Ambient Environment. Ambient entities cooperate by sharing parts of their local context information and reason in a bottom-up manner. We propose an operational model and present a prototype implementation for the proposed approach.
The Impact of Life-Cycle Bias on Intergenerational Income Mobility : Estimates for the US and Germany applying a Covariance Structure Model

In this study we suggest a covariance structure model to estimate intergenerational income correlations. Within this framework we adjust for lifecycle bias which arises when lifetime earnings are approximated by current income. Furthermore, the suggested empirical strategy is immune to the attenuation bias problem of linear models. This is a particularly desirable feature when only small survey data sets are available. Our estimates for the US of about 0.45 are at the high end of what is reported in the literature. The German estimate of 0.25 is similar in magnitude to the scarce evidence for this country. JEL-Classification: J62, C51.
Rank-based dimension reduction for many-criteria populations

Interpreting individuals described by a set of criteria can be a difficult task when the number of criteria is large. Such individuals can be ranked, for instance in terms of their average rank across criteria as well as by each distinct criterion. We therefore investigate criteria selection methods which aim to preserve the average rank of individuals but with fewer criteria. Our experiments show that these methods perform effectively, identifying and removing redundancies within the data, and that they are best incorporated into a multi-objective algorithm.
A semantic-based flexible framework for automatic behavior composition

The behavior composition problem consists in the synthesis of a controller that coordinates a set of available behaviors with the aim of realizing a desired target behavior. This problem suffers from a serious drawback because, for many instances, the target behavior cannot be achieved. In that case, the user is responsible for bringing changes until the problem becomes solvable. This inherent limitation is due to the fact that an exact match is required between the actions initiated by the user and those offered by the available behaviors. The main question is, how can a controller select a suitable behavior such that the functionality of one of its available actions is close to that currently requested by the desired target behavior? This paper provides an answer to this question by associating semantics to actions based on expectations and exploiting a semantic similarity function between actions. Similar actions that have different names, but comparable tasks, are considered equivalent. This renders the behavior composition framework more flexible so that it can fit with semantic environments.
Improved LUT-Based Image Warping for Video Cameras

Image warping is usually used to perform real-time geometry transformation for video cameras. Several existing look-up table (LUT)-based algorithms achieve real-time performance, but the size of LUT is large and it has to be stored into off-chip memory. To reduce latency and bandwidth requirements due to using off-chip memory, this paper proposes an improved LUT (ILUT) scheme which compresses the LUT small enough that it can be stored in on-chip memory. Firstly, a one-stop transformation is adopted instead of several stages of on-line calculation. Then, the memory size of LUT is reduced by utilizing the similarity of neighbor coordinates and the symmetric characteristic of some image warping. Moreover, an elaborate pipeline hardware structure, cooperating with a novel 25-point interpolation algorithm, is proposed to accelerate the system and save further usage of memory. The proposed system is implemented by a field-programmable gate array-based platform. Two different examples show that the proposed ILUT achieves a real-time performance with small memory usage and low system requirements.
Multi-Task Inverse Dynamcis based on LS-Optimization

This paper presents the latest advances we made in static and dynamic locomotion with our compliant quadrupedal robot StarlETH. We outline a hierarchical task-space inverse dynamics framework based on least square optimization that allows for simple generation of complex robot behaviors and simultaneous optimization of joint torque and contact force objectives. Strong focus is put on experimental validation.
Determinants of Investor’s Subscription Level of IPOs: Evidence from Indian Capital Market in Post Mandatory IPO Grading Regime

This research paper determines the factors that influence investor’s appetite for Graded Initial Public Offerings (IPOs) between 2007 and 2013, in the context of Indian capital market. Company specific factors related to pre-issue financial position, corporate governance, and post issue promoter holding, firm age at the time of the issue, reputation of the lead manager of the issue, reputation of the credit rating agency, IPO Grade, and also market sentiment related factors like change in the money supply, Foreign Institutional Investor’s (FII) inflow, Price to Earnings (PE) ratio of the market, and market return, are considered. Out of these factors, FII Inflow, Market PE, Money Supply, Debt to Equity (DE) ratio, and board size of the companies have statistically significant impact on the investor’s interest in the IPOs in terms of subscription level. This is a significant addition to the existing literature.
The FIRST Bright Quasar Survey. III. The South Galactic Cap

We present the results of an extension of the FIRST Bright Quasar Survey (FBQS) to the South Galactic cap, and to a fainter optical magnitude limit. Radio source counterparts with SERC R magnitudes brighter than 18.9 which meet the other FBQS criteria are included. We supplement this list with a modest number of additional objects to test our completeness for quasars with extended radio morphologies. The survey covers 589 deg2 in two equatorial strips in the southern cap. We have obtained spectra for 86% of the 522 candidates and find 321 radio-selected quasars of which 264 are reported here for the first time. A comparison of this fainter sample with the FBQS sample shows the two to be generally similar. Fourteen new broad absorption line (BAL) quasars are included in this sample. When combined with the previously identified BAL quasars in our earlier papers, we can discern a break in the frequency of BAL quasars with radio loudness, namely that the relative number of high-ionization BAL quasars drops by a factor of 4 for quasars with a radio-loudness parameter R* > 100.
Pilot-Scale Development of a UAV-UGV Hybrid with Air-Based UGV Path Planning

Traditionally, UAVs and Mobile Robots are viewed as two separate entities. However, upon closer examination of their synergies, a more unified conception of a closely-coupled system of the two could easily justify a view where both are just seen as separable parts of the body of a unitary hybrid symbiotic system -- essentially, one robotic entity, whose body parts can separate temporarily, and get together again later. In this paper, we will describe a prototype system consisting of a small-scale indoor pilot version of a much larger outdoors full scale system, as an illustration of this concept. Such indoor pilot versions have multiple advantages, as we shall show. In our prototype, a mobile robot UGV serves as a transport as well as recharge station for a lightweight quad-rotor UAV, while the UAV serves as a separable long-range vision system for the UGV, providing top-down views of its environment, which are stitched and transformed into maps, and which are utilized towards the navigation of the robot hybrid. Multiple avenues of extension of our system and the concept are also introduced, illustrating the power of the separable-body heterogeneous symbiotic multi-robot system concept.
A local learning algorithm for dynamic feedforward and recurrent networks

Abstract Most known learning algorithms for dynamic neural networks in non-stationary environments need global computations to perform credit assignment. These algorithms either are not local in time or not local in space. Those algorithms which are local in both time and space usually cannot deal sensibly with ‘hidden units’. In contrast, as far as we can judge, learning rules in biological systems with many ‘hidden units’ are local in both space and time. In this paper we propose a parallel on-line learning algorithms which performs local computations only, yet still is designed to deal with hidden units and with units whose past activations are ‘hidden in time’. The approach is inspired by Holland's idea of the bucket brigade for classifier systems, which is transformed to run on a neural network with fixed topology. The result is a feedforward or recurrent ‘neural’ dissipative system which is consuming ‘weight-substance’ and permanently trying to distribute this substance onto its connections in an ap.
The Effect of Block Size, Training Set and K-Value in the Classification of Food Grains Using HSI Color Model

The method proposed for the classification of food grains can be divided into two phases: 1) color, global and local feature extraction 2) classification using extracted features. This paper presents the effect of block size, training set and K-value in the classification of food grains using HSI color model by combining color and texture information without pre-processing. The first phase in the proposed system is feature extraction. The features are computed locally and globally. The co-occurrence matrix helps to extract features locally. The non-uniformity of RGB color space is eliminated by Hue, Saturation and Intensity (HSI) color space. Further, minimum distance and K nearest neighbour algorithms are used for classification. Percentage of correct classification and error analysis are carried out by confusion matrix. The proposed work attains maximum average accuracy of 95.83% and 70.37% for block size 512x512 and 256x256 with K=5 and K=1 respectively.
Learning algorithm for constructing fuzzy neural networks with application to regression problems

In this paper, we present a new learning algorithm for self-constructing fuzzy neural networks (FNN). First, an initial network starts with no hidden neurons and grows neurons based on the growth criteria. After the generation process, a neuron pruning algorithm based on optimal brain surgeon (OBS) is employed to reduce the size of the FNN. After the structure design process, weight adjustment method is adopted to tune all the consequent parameters. Applications to regression problems are carried out. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.
Emerging client-server and ad-hoc approach in inter-vehicle communication platform

Integrating different communication approaches in an inter-vehicle communication platform to guarantee continuous and reliable communication between the communicating vehicles is an inherently challenging task. It also requires very good functionality of each communication approach, transition mechanisms between these approaches and adaptation between their communicating functions. We present an inter-vehicle communication platform in which the client-server and ad-hoc communication approaches have been integrated. This platform, called client-server ad-hoc (CSAH) platform, requires a host to allocate IP addresses dynamically to the communicating vehicles which are equipped with a computer controlled IEEE 802.11b radio modem, GPS, GSM modem and camera. Our platform maintains a high quality of communication and a guaranteed reliable transition from client-server to ad-hoc communication. We present the results of some simulation experiments, showing that the two communication approaches have been merged very well to work together in one communication unit.
Performance measures of CAC mechanism in OFDMA system using shadow server approximation

We study in this paper the problem of queueing theoretic of OFDMA-based WiMAX with two classes of connections, connections with High Priority (HP) and connections with Low Priority (LP). The key idea to solve the problem of the mixed priority strategy is the technique of the Shadow server approximation. Specifically, we present a single-cell WiMAX environment in which the base station allocates subchannels to the subscriber stations in its coverage area. The subchannels allocated to a subscriber station are shared by multiple connections at that subscriber station. To ensure the Quality of Service (QoS) performances, a Connection Admission Control (CAC) mechanism is considered at a subscriber station. Then, based on the queueing model, both the connection-level and the packet-level performances are studied and compared with their analogues in the case without CAC. The connection arrival is modeled by a Poisson process and the packet arrival for a connection by a Markov Modulated Poisson Process (MMPP). Several performance measures are derived.
1 Introducing the Merton problem : a jump free world

It is a well-documented fact in the option pricing and continuous time empirical finance literature that a jump-diffusion with stochastic volatility is necessary to explain observed option prices jointly with returns on the underlying security such as the Standard and Poors 500 Index. This paper examines whether these distributional features have any implications for investors’ portfolio decisions. By using the Markov Chain Approximation technique it is possible to expand on existing theoretical models of investment under jumps to examine some of the cases found in empirical studies.
Model-Based Analysis–Synthesis for Realistic Tree Reconstruction and Growth Simulation

Due to complexity, vegetation analysis and reconstruction of remote sensing data are challenging problems. Using architectural tree models combined with model inputs estimated from aerial image analysis, this paper presents an analysis-synthesis approach for urban vegetation detection, modeling, and reconstruction. Tree species, height, and crown size information are extracted by aerial image analysis. These variables serve for model inversion to retrieve plant age, climatic growth conditions, and competition with neighbors. Functional-structural individual-based tree models are used to reconstruct and visualize virtual trees and their time evolutions realistically in a 3-D viewer rendering the models with geographical coordinates in the reconstructed scene. Our main contributions are: 1) a novel approach for generating plant models in 3-D reconstructed scenes based on the analysis of the geometric properties of the data, and 2) a modeling workflow for the reconstruction and growth simulation of vegetation in urban or natural environments.
Restaurant Menu Generation from User Reviews

User online reviews contain a great amount of non-structured information about local businesses. Yet only a small portion has been well studied. In this paper, we apply a number of different techniques in solving the named entity recognition problem in user reviews. Specifically, the named entity recognizer is used to identify and extract food entities from user reviews about local restaurant. Experimental results show that bi-directional recurrent neural networks outperform feed-forward neural networks as well as traditional CRF-based baselines.
Discrete hierarchical organization of social group sizes

The ‘social brain hypothesis’ for the evolution of large brains in primates has led to evidence for the coevolution of neocortical size and social group sizes, suggesting that there is a cognitive constraint on group size that depends, in some way, on the volume of neural material available for processing and synthesizing information on social relationships. More recently, work on both human and non–human primates has suggested that social groups are often hierarchically structured. We combine data on human grouping patterns in a comprehensive and systematic study. Using fractal analysis, we identify, with high statistical confidence, a discrete hierarchy of group sizes with a preferred scaling ratio close to three: rather than a single or a continuous spectrum of group sizes, humans spontaneously form groups of preferred sizes organized in a geometrical series approximating 3–5, 9–15, 30–45, etc. Such discrete scale invariance could be related to that identified in signatures of herding behaviour in financial markets and might reflect a hierarchical processing of social nearness by human brains.