Miracle-dz/UniFAR-FTUs huggingface dataset:
Cerenkov Luminescence Imaging of Medical Isotopes

The development of novel multimodality imaging agents and techniques represents the current frontier of research in the field of medical imaging science. However, the combination of nuclear tomography with optical techniques has yet to be established. Here, we report the use of the inherent optical emissions from the decay of radiopharmaceuticals for Cerenkov luminescence imaging (CLI) of tumors in vivo and correlate the results with those obtained from concordant immuno-PET studies. Methods: In vitro phantom studies were used to validate the visible light emission observed from a range of radionuclides including the positron emitters 18F, 64Cu, 89Zr, and 124I; β-emitter 131I; and α-particle emitter 225Ac for potential use in CLI. The novel radiolabeled monoclonal antibody 89Zr-desferrioxamine B [DFO]-J591 for immuno-PET of prostate-specific membrane antigen (PSMA) expression was used to coregister and correlate the CLI signal observed with the immuno-PET images and biodistribution studies. Results: Phantom studies confirmed that Cerenkov radiation can be observed from a range of positron-, β-, and α-emitting radionuclides using standard optical imaging devices. The change in light emission intensity versus time was concordant with radionuclide decay and was also found to correlate linearly with both the activity concentration and the measured PET signal (percentage injected dose per gram). In vivo studies conducted in male severe combined immune deficient mice bearing PSMA-positive, subcutaneous LNCaP tumors demonstrated that tumor-specific uptake of 89Zr-DFO-J591 could be visualized by both immuno-PET and CLI. Optical and immuno-PET signal intensities were found to increase over time from 24 to 96 h, and biodistribution studies were found to correlate well with both imaging modalities. Conclusion: These studies represent the first, to our knowledge, quantitative assessment of CLI for measuring radiotracer uptake in vivo. Many radionuclides common to both nuclear tomographic imaging and radiotherapy have the potential to be used in CLI. The value of CLI lies in its ability to image radionuclides that do not emit either positrons or γ-rays and are, thus, unsuitable for use with current nuclear imaging modalities. Optical imaging of Cerenkov radiation emission shows excellent promise as a potential new imaging modality for the rapid, high-throughput screening of radiopharmaceuticals.
Face Recognition using Wavelet, PCA, and Neural Networks

This work presents a method to increased the face recognition accuracy using a combination of Wavelet, PCA, and Neural Networks. Preprocessing, feature extraction and classification rules are three crucial issues for face recognition. This paper presents a hybrid approach to employ these issues. For preprocessing and feature extraction steps, we apply a combination of wavelet transform and PCA. During the classification stage, the Neural Network (MLP) is explored to achieve a robust decision in presence of wide facial variations, also we have used RBF Neural Network but results show that MLP Neural Network outperforms RBF. The computational load of the proposed method is greatly reduced as comparing with the original PCA based method. Moreover, the accuracy of the proposed method is improved.
Improvement in camera calibration based on RAC

The two-stage technique proposed by Tsai has advantage in terms of accuracy, speed, and versatility over existing state of the art. But it does not calibrate the image center so that the errors in the translation and rotation are induces by choosing a wrong image center. The proposed technique only requires the camera to observe a planar pattern shown at a few (at least two) different orientations and computes the image center using Z.Zhang method. The remaining parameters are calibrated with the accurate center estimate. Finally, we can refine the above solution through maximum likelihood criterion. Real data has been used to test the proposed technique and very good results have been obtained. Compared with Tsai's method, the proposed method is more accurate and simple.
Walking balance control for biped robot based on multi-sensor integration

This paper deals with walking balance control of biped robot inspired by biological concept of sensory integration. The control system is proposed with a trajectory generator and a walking balance controller. The inverted pendulum model is used to generate a more natural and energy efficient biped gait. The walking balance controller, which includes Kalman filter and fuzzy controller, is designed based on sensory fusion to keep the body balance and make the robot walking following the desired reference trajectory. A retrofitted biped platform Bioloid is used to validate the proposed balance control method. The experimental results confirm the effectiveness of the proposed control strategy.
Association Rules Mining Based on Simulated Annealing Immune Programming Algorithm

Association rules mining is an important problem of data mining, in this paper we propose a association rules mining algorithm based on the simulated annealing immune programming algorithm which combines each character of the simulated annealing algorithm and immune programming algorithm. Through the theoretical analysis and the experiment we can find that this algorithm has better robustness and the global and local ability of search, thus it can quickly, effectively excavate more association rules which meet the conditions.
Shape Representation: Comparison between the Morphological Skeleton and Morphological Shape Decomposition

The morphological skeleton and morphological shape decomposition (MSD) are two popular approaches for morphological shape representation. Each method represents an object as an algebraic combination of a number of components, where each component is given by a locus of points dilated by a specified structuring-element homothetic. This correspondence develops a theoretical comparison between the two methods. Combining the theoretical results with several representation cost measures, we make a concrete comparison of the efficiency of the two methods. The results indicate that for complex objects-i.e., objects requiring a full range of homothetic sizes in the morphological skeleton representation-the MSD represents objects more efficiently than the morphological skeleton for three of four suggested cost measures.
On Matching ANN Structure to Problem Domain Structure

To achieve reduced training time and improved generalization with artificial neural networks (ANN, or NN), it is important to use a reduced complexity NN structure. A "problem" is defined by constraints among the variables describing it. If knowledge about these constraints could be obtained a priori, this could be used to reduce the complexity of the ANN before training it. Systems theory literature contains methods for determining and representing structural aspects of constrained data (these methods are herein called GSM, general systems method). The suggestion here is to use the GSM model of the given data as a pattern for modularizing a NN prior to training it. The present work assumes the GSM model for the given problem context has been determined (represented here in the form of Boolean functions of known decompositions). This means that certain information is available about constraint among the system variables, and is used to develop a modularized NN. The modularized NN and an equivalent general NN (full interconnect, feed-forward NN) are both trained on the same data. Various predictions are offered: 1) The general NN and the modularized NN will both learn the task, but the modularized NN will learn it faster. 2) If trained on an (appropriate) subset of possible inputs, the modularized NN will perform better generalization than the general NN. 3) If trained on a non-decomposable function of the same variables, the general NN will learn the task, but the modularized NN will not. All of these predictions are verified experimentally. Future work will explore more decomposition types and more general data types.
An Unfixed-elasticity Mass Spring Model based simulation for soft tissue deformation

Mass Spring Model (MSM) and Finite Element Method(FEM) are the two basic models for soft tissue deformation simulation. MSM has high computation efficiency and low physical accuracy, while the case for FEM is just the reverse. How to simulate the deformation of the tissue in real time and accurately is still a challenging. This paper proposed an improved MSM by determining the parameters of MSM using FEM The improved MSM is called Unfixed-elasticity Mass Spring Model (UMSM), meaning the parameters of each spring is different from those of others, and will change gradually during deformation. The experimental results show a fairly realistic simulation can be generated by UMSM The results indicate that the UMSM has a relatively high reasonability and practicability.
A Fault Tolerant Control strategy for an unmanned aerial vehicle based on a Sequential Quadratic Programming algorithm

In this paper a fault tolerant control strategy for the nonlinear model of an unmanned aerial vehicle (UAV) equipped with numerous redundant controls is proposed. Asymmetric actuator failures are considered and, in order to accommodate them, a sequential quadratic programming (SQP) algorithm which takes into account nonlinearities, aerodynamic and gyroscopic couplings, state and control limitations is implemented. This algorithm computes new trims such that around the new operating point, the faulty linearized model remains nearby from the fault free model. For the faulty linearized models, linear state feedback controllers based on an eigenstructure assignment method are designed to obtain soft transients during accommodation. Real time implementation of the SQP algorithm is also discussed.
OUTPUT GAP ESTIMATION, ESTIMATION UNCERTAINTY AND ITS EFFECT ON POLICY RULES

The authors propose a short run model for the monetary transmission mechanism in which the output gap is model as an unobserved variable. By estimating this model using maximum likelihood on a Kalman Filter, the authors find an estimate of the unobserved output gap as well as its estimation uncertainty. The performance of monetary rules is studied both with certainty on the output gap values as well as with estimation uncertainty.
Global statistical analysis of the protein homology network

The similarity between protein sequences is a directly and easly computed quantity from which to deduce information about their evolutionary distance and to detect homologous proteins. The SIMAP database -- Similarity Matrix of Proteins -- provides a pre-computed similarity matrix covering the similarity space formed by about all publicly available amino acid sequences from public databases and completely sequenced genomes. From SIMAP we construct the protein homology network, where the proteins are the nodes and the links represent homology relationships. With more than 5 million nodes and about 70 10^9 edges it is the greatest protein homology network ever been builded. We describe the basic features and we perform a global statistical analysis of the network. Starting from the Smith-Waterman similarity score, we define for each edge a weight w to measure the similarity distance between two nodes. Keeping only edges with a weigth greater than a minimal w_min, and by varying w_min we build a family of networks with different degree of similarity. We investigate the distribution of connected components (clusters) of the networks at different w_min and in particular we find a behaviour similar to a phase transition guided by the formation of a giant component. Moreover we study selected sequence features and protein domains of protein pairs that connect different clusters in the networks at different level of similarity. We observed specific, non-random distributions of the protein features and domains for proteins connecting clusters at certain weight intervals.
Exploring Indirect Inference : An application to LIBOR data

For this study, we have chosen a model with γ = 1, identified as BrennanSchwartz in CKLS. We also impose the restrictions α > 0 and β < 0 in order to ensure reversion to the mean [7]. Ideally, we would like to estimate the value of γ as well, but the choice of this model was for simplicity: The maximum likelihood estimates (MLEs) for α, β, and σ can be found explicitly if we assume normally distributed errors. Alternatively, we could have used a model with a fixed value for γ, such as γ = 0.5 (Cox–Ingersoll–Ross) or γ = 1.5 (Variable Rate model). Values of γ greater than 1 were favoured by CKLS [2], whereas the Cox–Ingersall–Ross model with γ = 0.5 was preferred by a robust GMM [4]. There is no broad consensus for the optimal value of γ; therefore, we decided to use the simplest one. Following the standard approach (seen in [2, 7, 4, 3] and others), we discretize the SDE to estimate the parameters:
Arithmetic reasoning in DPLL-based SAT solving Technical Report EIS-1103-1

Abstract We propose a new arithmetic reasoning calculus to speed up a SAT solver based on the Davis Putnam Longman Loveland (DPLL) procedure. It is based on an arithmetic bit level description of the arithmetic circuit parts and the property. This description can easily be provided by the front-end of an RTL property checker. The calculus yields significant speedup and more robustness on hard SAT instances derived from the formal verification of arithmetic circuits.
Analyzing Movements over Time in Employment Status and Welfare Participation while Controlling for Seam Bias using SIPP 1

Economists and policymakers have long been interested in the determinants of employment and welfare dynamics among less-educated women. Estimating duration models on longitudinal data allows for time changing factors that may differentially impact entries and exits from various labor market states to be identified. However, in using such longitudinal data, researchers must confront particular data-quality issues. In this paper we develop a parametric approach to address seam bias, a common source of reporting errors in longitudinal surveys, in a duration model setting. “Seam bias” refers to the tendency for a much larger fraction of transitions to be reported as occurring at the end of the reference period than would be expected to occur by chance. We apply this approach to the analysis of transitions between employment and nonemployment, and transitions between participation and non-participation in welfare, among lesseducated single mothers using the Survey of Income and Program Participation (SIPP). We discuss identification of the model, and show that the model is identified without restricting the duration dependence. We find substantial evidence of misreporting. We compare results from our approach to those obtained following the standard approach in applied work of using only the observations from the last month of the reference period, and find some differences in the two sets of estimates.
International Military Alliances, 1648-2008

The inaugural title in the "Correlates of War" series from CQ Press, this 2-volume set catalogs every official interstate alliance signed from the Peace of Westphalia in 1648 through the early twenty-first century, ranking it among the most thorough and accessible reviews of formal military treaties ever published. Maps and introductions showcase the effects of alliances on the region or international system in century-specific chapters, while individual narratives and summaries of alliances simultaneously provide basic information, such as dates and member states, as well as essential insights on the conditions that prompted the agreement. Additionally, separate and/or secret articles are highlighted for additional context and interest. Supplementary features of this two-volume set include: a timeline cataloging major events in political and military history; guides listing allegiances by region and by century; and, an alphabetical treaty index; maps illustrating political boundaries across the centuries. "International Military Alliances" is an indispensable resource for any library serving students of law, politics, history, and military science.
An HMM/MRF-based stochastic framework for robust vehicle tracking

Shadows of moving objects often obstruct robust visual tracking. In this paper, we present a car tracker based on a hidden Markov model/Markov random field (HMM/MRF)-based segmentation method that is capable of classifying each small region of an image into three different categories: vehicles, shadows of vehicles, and background from a traffic-monitoring movie. The temporal continuity of the different categories for one small region location is modeled as a single HMM along the time axis, independently of the neighboring regions. In order to incorporate spatial-dependent information among neighboring regions into the tracking process, at the state-estimation stage, the output from the HMMs is regarded as an MRF and the maximum a posteriori criterion is employed in conjunction with the MRF for optimization. At each time step, the state estimation for the image is equivalent to the optimal configuration of the MRF generated through a stochastic relaxation process. Experimental results show that, using this method, foreground (vehicles) and nonforeground regions including the shadows of moving vehicles can be discriminated with high accuracy.
Molecular Epidemiology and Genetic Variation of Pathogenic Vibrio parahaemolyticus in Peru

Vibrio parahaemolyticus is a foodborne pathogen that has become a public health concern at the global scale. The epidemiological significance of V. parahaemolyticus infections in Latin America received little attention until the winter of 1997 when cases related to the pandemic clone were detected in the region, changing the epidemic dynamics of this pathogen in Peru. With the aim to assess the impact of the arrival of the pandemic clone on local populations of pathogenic V. parahaemolyticus in Peru, we investigated the population genetics and genomic variation in a complete collection of non-pandemic strains recovered from clinical sources in Peru during the pre- and post-emergence periods of the pandemic clone. A total of 56 clinical strains isolated in Peru during the period 1994 to 2007, 13 strains from Chile and 20 strains from Asia were characterized by Multilocus Sequence Typing (MLST) and checked for the presence of Variable Genomic Regions (VGRs). The emergence of O3:K6 cases in Peru implied a drastic disruption of the seasonal dynamics of infections and a shift in the serotype dominance of pathogenic V. parahaemolyticus. After the arrival of the pandemic clone, a great diversity of serovars not previously reported was detected in the country, which supports the introduction of additional populations cohabitating with the pandemic group. Moreover, the presence of genomic regions characteristic of the pandemic clone in other non-pandemic strains may represent early evidence of genetic transfer from the introduced population to the local communities. Finally, the results of this study stress the importance of population admixture, horizontal genetic transfer and homologous recombination as major events shaping the structure and diversity of pathogenic V. parahaemolyticus.
An Evaluation of the Teacher Advancement Program (TAP) in Chicago: Year Two Impact Report

The Teacher Advancement Program (TAP), a whole-school intervention that aims to improve schools by raising teacher quality, provides teachers with opportunities for professional growth, promotion to school leadership roles without leaving the classroom, structured feedback, and performance-based compensation. This report focuses on the Chicago Public Schools, which began implementing TAP in 2007. Early findings from Mathematicaâ€™s study, which focused on the districtâ€™s K-8 schools, note that teachers in TAP schools reported significantly more mentoring and support than their peers in similar schools. Although TAP led to changes inside schools, these changes did not produce measurable impacts on student test scores through March of the start-up year. In addition, the program had a significant impact on teacher retention. TAP teachers were five percentage points more likely to return to their schools than were non-TAP teachers.
Real-time tracking algorithm with locking on a given object for VLSI CNN-UM implementations

An algorithm to lock on and track objects is presented in this paper. The algorithm is designed to take advantages of the highly parallel architecture and speed of the VLSI implementations of the Cellular Neural Network Universal Machine (CNN-UM). Given a dynamical image containing different objects and initial information about which object to track, this algorithm is able to lock on the object and calculate its instantaneous coordinates within the image. CNN chip-specific robust templates were generated in order to implement the method on a real CNNUM chip. The experiments and the results of analysis show that a CNN-UM chip running this algorithm can be used in real-time tracking applications being able to achieve good performance.
Implicit Contracts and the Explanatory Power of Top Executive Compensation for Future Performance

Recent research suggests that implicit incentive contracts may be based on performance measures that are observable only to the contracting parties. We derive and test implications of this insight for the relationship between executive compensation and firm performance. If corporate boards optimally use both observable and unobservable (to outsiders) measures of executive performance and the unobservable measures are correlated with future firm performance, then unexplained variation in current compensation should predict future variation in firm performance. Further, compensation should be more positively associated with future performance when observable measures are less useful for contracting. Our results are consistent with these hypotheses.