Miracle-dz/UniFAR-FTUs huggingface dataset:
NONLINEAR BAYESIAN FILTERING FOR STATE AND PARAMETER ESTIMATION

This paper provides efficient filtering algorithms for nonlinear estimation. Adaptive nonlinear filtering for recursive estimation of the states and parameters with unknown noise statistics and a novel method called the sigma particle filter for nonlinear/non-Gaussian estimation are illustrated. The adaptive nonlinear filters combine adaptive estimation techniques for system noise statistics with nonlinear filters that include the unscented Kalman filter and divided difference filter. The purpose of the new nonlinear filters is to not only compensate for the nonlinearity effects neglected from linearization by utilizing nonlinear filters, but also to take into account the system modeling errors by adaptively estimating the noise statistics and unknown parameters. Simulation results indicate that the advantages of the nonlinear filters make these attractive alternatives to the standard nonlinear filters for the state and unknown parameter estimation in not only satellite orbit determination but also other application areas.
Short-run Total Cost Change and Productivity of US Class I Railroads

This paper examines how productivity changes contribute to short-run cost variations over time and across railroads. Using an unbalanced panel of US Class I railroad data for the period 1996-2003, decomposition models were used to attribute intertemporal and multilateral cost variations to their causal factors. We find that (i) railroads were able to reduce their physical capitals in recent years; (ii) railroads experienced technical progress over time, but they were technically and allocatively inefficient in some time periods; and (iii) the benchmarking railroads could learn from the low-cost and cost-efficient benchmark when it comes to cost savings. © 2008 LSE and the University of Bath
Network-Adaptive Image and Video Transmission in Camera-Based Wireless Sensor Networks

We investigate image and video transmission in camera-based wireless sensor networks (WSNs). Our objective is to design a mechanism that finds the transmission strategies that maximize the expected reconstruction quality of the transmitted data at the base station while making efficient use of the limited resources of the WSN. Our approach is based on quality-scalable coding and multipath transmission. Unlike conventional approaches where data is sent over the paths regardless of its importance at the destination, we propose a network-adaptive transmission mechanism that decomposes the source bitstream into segments of unequal importance and reserves the most reliable paths to transmit the segments with the highest importance. Moreover, we use unequal segment loss protection with erasure codes of different strengths to maximize the expected quality at the destination and propose a fast algorithm that finds nearly optimal transmission strategies. We also present simulation results using a test video sequence that show that the proposed transmission mechanism provides adaptability to the time and space varying conditions of camera-based wireless sensor networks.
A Novel Method of Visualization for SAR Images

Visual interpretation of SAR images plays a critical role in remote sensing applications. For obtaining the image suitable for human observation, a new method of visualization for SAR images is proposed in this paper. The proposed algorithm takes a negative exponential transformation on the original SAR image. The parameter of the transformation can be computed rapidly based on the characteristic statistics of the SAR images. Using several areas of a NASA/JPL AIRSAR images, the effectiveness of the proposed method is demonstrated by comparing with two commonly used algorithms.
Mimicking Metastases Including Tumor Stroma: A New Technique to Generate a Three-Dimensional Colorectal Cancer Model Based on a Biological Decellularized Intestinal Scaffold

Tumor models based on cancer cell lines cultured two-dimensionally (2D) on plastic lack histological complexity and functionality compared to the native microenvironment. Xenogenic mouse tumor models display higher complexity but often do not predict human drug responses accurately due to species-specific differences. We present here a three-dimensional (3D) in vitro colon cancer model based on a biological scaffold derived from decellularized porcine jejunum (small intestine submucosa+mucosa, SISmuc). Two different cell lines were used in monoculture or in coculture with primary fibroblasts. After 14 days of culture, we demonstrated a close contact of human Caco2 colon cancer cells with the preserved basement membrane on an ultrastructural level as well as morphological characteristics of a well-differentiated epithelium. To generate a tissue-engineered tumor model, we chose human SW480 colon cancer cells, a reportedly malignant cell line. Malignant characteristics were confirmed in 2D cell culture: SW480 cells showed higher vimentin and lower E-cadherin expression than Caco2 cells. In contrast to Caco2, SW480 cells displayed cancerous characteristics such as delocalized E-cadherin and nuclear location of β-catenin in a subset of cells. One central drawback of 2D cultures—especially in consideration of drug testing—is their artificially high proliferation. In our 3D tissue-engineered tumor model, both cell lines showed decreased numbers of proliferating cells, thus correlating more precisely with observations of primary colon cancer in all stages (UICC I-IV). Moreover, vimentin decreased in SW480 colon cancer cells, indicating a mesenchymal to epithelial transition process, attributed to metastasis formation. Only SW480 cells cocultured with fibroblasts induced the formation of tumor-like aggregates surrounded by fibroblasts, whereas in Caco2 cocultures, a separate Caco2 cell layer was formed separated from the fibroblast compartment beneath. To foster tissue generation, a bioreactor was constructed for dynamic culture approaches. This induced a close tissue-like association of cultured tumor cells with fibroblasts reflecting tumor biopsies. Therapy with 5-fluorouracil (5-FU) was effective only in 3D coculture. In conclusion, our 3D tumor model reflects human tissue-related tumor characteristics, including lower tumor cell proliferation. It is now available for drug testing in metastatic context—especially for substances targeting tumor–stroma interactions.
A continuous-time recurrent neural network for real-time support vector regression

This paper presents a continuous-time recurrent neural network described by differential equations for realtime support vector regression (SVR). The SVR is first formulated as a convex quadratic programming problem, and then a continuous-time recurrent neural network with one-layer structure is designed for training the support vector machine. Furthermore, simulation results on an illustrative example are given to demonstrate the effectiveness and performance of the proposed neural network.
Cross-lingual dependency parsing for closely related languages - Helsinki’s submission to VarDial 2017

This paper describes the submission from the University of Helsinki to the shared task on cross-lingual dependency parsing at VarDial 2017. We present work on annotation projection and treebank translation that gave good results for all three target languages in the test set. In particular, Slovak seems to work well with information coming from the Czech treebank, which is in line with related work. The attachment scores for cross-lingual models even surpass the fully supervised models trained on the target language treebank. Croatian is the most difficult language in the test set and the improvements over the baseline are rather modest. Norwegian works best with information coming from Swedish whereas Danish contributes surprisingly little.
JCLext: A Java Tool for Compiling Finite-State Transducers from Full-Form Lexicons

JCLexT is a compiler of finite-state transducers from full-form lexicons, this tool seems to be the first Java implementation of such functionality. A comparison between JCLexT and Foma was performed based on extensive data from Portuguese. The main disadvantage of JCLexT is the slower compilation time, in comparison to Foma. However, this is negated by the fact that a large transducer compiled with JCLexT was shown to be 8.6% smaller than the Foma created counterpart.
Design and Simulation of Two Robotic Systems for Automatic Artichoke Harvesting

The target of this research project was a feasibility study for the development of a robot for automatic or semi-automatic artichoke harvesting. During this project, different solutions for the mechanical parts of the machine, its control system and the harvesting tools were investigated. Moreover, in cooperation with the department DISPA of University of Catania, different field structures with different kinds of artichoke cultivars were studied and tested. The results of this research could improve artichoke production for preserves industries. As a first step, an investigation on existing machines has been done. From this research, it has been shown that very few machines exist for this purpose. Based also on previous experiences, some proposals for different robotic systems have been done, while the mobile platform itself was developed within another research project. At the current stage, several different configurations of machines and harvesting end-effectors have been designed and simulated using a 3D CAD environment interfaced with Matlab®. Moreover, as support for one of the proposed machines, an artificial vision algorithm has been developed in order to locate the artichokes on the plant, with respect to the robot, using images taken with a standard webcam.
An Integrative Approach to the Determinants of Private Equity Fundraising

This paper builds on previous papers about private equity fundraising in two ways. Firstly, a micro and macroeconomic theoretical base is developed to support the empirical model proposed. Secondly, new explanatory variables that are related to the private equity process itself are added, thus completing the set of variables, which were basically macroeconomic and environment-related variables, introduced in previous papers. Evidence is found of the positive and significant effect of lagged aggregated investments and divestments on new funds raised, together with gross domestic product growth and the evolution of gross domestic savings.
Adaptation of Language Models for SMT Using Neural Networks with Topic Information

Neural network language models (LMs) are shown to be effective in improving the performance of statistical machine translation (SMT) systems. However, state-of-the-art neural network LMs usually use words before the current position as context and neglect global topic information, which can help machine translation (MT) systems to select better translation candidates from a higher perspective. In this work, we propose improvement of the state-of-the-art feedforward neural language model with topic information. Two main issues need to be tackled when adding topics into neural network LMs for SMT: one is how to incorporate topics to the neural network; the other is how to get target-side topic distribution before translation. We incorporate topics by appending topic distribution to the input layer of a feedforward LM. We adopt a multinomial logistic-regression (MLR) model to predict the target-side topic distribution based on source side information. Moreover, we propose a feedforward neural network model to learn joint representations on the source side for topic prediction. LM experiments demonstrate that the perplexity on validation set can be greatly reduced by the topic-enhanced feedforward LM, and the prediction of target-side topics can be improved dramatically with the MLR model equipped with the joint source representations. A final MT experiment, conducted on a large-scale Chinese--English dataset, shows that our feedforward LM with predicted topics improves the translation performance against a strong baseline.
Effects of Ceftiofur and Chlortetracycline Treatment Strategies on Antimicrobial Susceptibility and on tet(A), tet(B), and bla CMY-2 Resistance Genes among E. coli Isolated from the Feces of Feedlot Cattle

A randomized controlled field trial was conducted to evaluate the effects of two sets of treatment strategies on ceftiofur and tetracycline resistance in feedlot cattle. The strategies consisted of ceftiofur crystalline-free acid (CCFA) administered to either one or all of the steers within a pen, followed by feeding or not feeding a therapeutic dose of chlortetracycline (CTC). Eighty-eight steers were randomly allocated to eight pens of 11 steers each. Both treatment regimens were randomly assigned to the pens in a two-way full factorial design. Non-type-specific (NTS) E. coli (n = 1,050) were isolated from fecal samples gathered on Days 0, 4, 12, and 26. Antimicrobial susceptibility profiles were determined using a microbroth dilution technique. PCR was used to detect tet(A), tet(B), and bla CMY-2 genes within each isolate. Chlortetracycline administration greatly exacerbated the already increased levels of both phenotypic and genotypic ceftiofur resistance conferred by prior CCFA treatment (P<0.05). The four treatment regimens also influenced the phenotypic multidrug resistance count of NTS E. coli populations. Chlortetracycline treatment alone was associated with an increased probability of selecting isolates that harbored tet(B) versus tet(A) (P<0.05); meanwhile, there was an inverse association between finding tet(A) versus tet(B) genes for any given regimen (P<0.05). The presence of a tet(A) gene was associated with an isolate exhibiting reduced phenotypic susceptibility to a higher median number of antimicrobials (n = 289, median = 6; 95% CI = 4–8) compared with the tet(B) gene (n = 208, median = 3; 95% CI = 3–4). Results indicate that CTC can exacerbate ceftiofur resistance following CCFA therapy and therefore should be avoided, especially when considering their use in sequence. Further studies are required to establish the animal-level effects of co-housing antimicrobial-treated and non-treated animals together.
On the Origin of Services Using RIDDL for Description, Evolution and Composition of RESTful Services

WSDL as a description language serves as the foundation for a host of technologies ranging from semantic annotation to composition and evolution. Although WSDL is well understood and in widespread use, it has its shortcomings which are partly imposed by the way how the SOAP protocol works and is used. Cloud computing fostered the rise of Representational State Transfer (REST), a return to arguably simpler but more flexible ways to expose services solely through the HTTP protocol. For RESTful services many achievements that have been acquired have to be rethought and reapplied. We perceive that one of the biggest hurdles is the lack of a dedicated and simple yet powerful language to describe RESTful services. In this paper we want to introduce RIDDL, a flexible and extensible XML based language that not only allows to describe services but also covers the basic requirements of service composition and evolution to provide a clean foundation for further developments.
Reducing semantic drift in bootstrapping for entity relation extraction

This paper presents a novel bootstrapping algorithm for entity relation extraction. Shortest dependency patterns connecting entity pairs in sentences are captured initially and in turn applied to extract new binary relationships. The patterns are evaluated through correlation detection. In addition, we effectively prevent semantic drift by co-training with trigger words. Experiments for slot filling on the Knowledge Base Population (KBP) newspaper corpora show that our enhanced bootstrapping system achieves an 11% F1-score improvement over traditional bootstrapping algorithm.
Representation and mapping of dexterous manipulation through task primitives

The goal of this work is to teach a robot to regrasp an object using knowledge obtained from human demonstration. This paper presents a task model that represents a human regrasping movement. The task model is based on the topological information and comprised of four task primitives. Human regrasping movement is recognised and represented as a sequence of these task primitives by the proposed recognition algorithm. The proposed method then maps each task primitive to the target robot hand using knowledge obtained from human demonstration. The experimental result verified the proposed task model by executing the regrasping movement on the real robot hand.
Subcategory-Aware Object Detection

In this letter, we introduce a subcategory-aware object detection framework to detect generic object classes with high intra-class variance. Motivated by the observation that the object appearance demonstrates some clustering property, we split the training data into subcategories and train a detector for each subcategory. Since the proposed ensemble of detectors relies heavily on subcategory clustering, we propose an effective subcategories generation method that is tuned for the detection task. More specifically, we first initialize subcategories by constrained spectral clustering based on mid-level image features used in object recognition. Then we jointly learn the ensemble detectors and the latent subcategories in an alternative manner. Our performance on the PASCAL VOC 2007 detection challenges and INRIA Person dataset is comparable with state-of-the-art, even with much less computational cost.
Palm oil and the crisis : a macro view

Ce chapitre 3 traite des effets des fluctuations des taux de change et des taux d'interet en Indonesie, en 1997-98 (periode de la crise economique), sur le marche mondial et sur le marche domestique indonesien de l'huile de palme
Novel Wearable and Wireless Ring-Type Pulse Oximeter with Multi-Detectors

The pulse oximeter is a popular instrument to monitor the arterial oxygen saturation (SPO2). Although a fingertip-type pulse oximeter is the mainstream one on the market at present, it is still inconvenient for long-term monitoring, in particular, with respect to motion. Therefore, the development of a wearable pulse oximeter, such as a finger base-type pulse oximeter, can effectively solve the above issue. However, the tissue structure of the finger base is complex, and there is lack of detailed information on the effect of the light source and detector placement on measuring SPO2. In this study, the practicability of a ring-type pulse oximeter with a multi-detector was investigated by optical human tissue simulation. The optimal design of a ring-type pulse oximeter that can provide the best efficiency of measuring SPO2 was discussed. The efficiency of ring-type pulse oximeters with a single detector and a multi-detector was also discussed. Finally, a wearable and wireless ring-type pulse oximeter was also implemented to validate the simulation results and was compared with the commercial fingertip-type pulse oximeter.
A systematic approach to experimental modeling and assessment of elastic actuators by component-wise parameter identification

This paper presents a systematic approach for experimental identification and assessment of mechanical effects on the dynamics of elastic actuators. The variable torsion stiffness (VTS) actuator is used as an example. As a basis for parameter identification, a flexible joint robot model considering friction and damping is used. To identify and assess occurring effects, a component-wise experimental investigation of the VTS drive train is performed. In this, influences of friction and inertia are examined using numerical least-squares regression to identify link inertia, friction in bearings, stiffness and damping in the elastic element, and the friction of gear box and actuator based on measured data. Comparing simulations to measured data, allows an evaluation of the obtained particular parameters and an assessment of their impact on overall system dynamics. Hence, the component-wise procedure enables to decide if those should be considered. This approach can be generalized to other elastic actuators by adapting model structure and/or performed experiments.
A negative conductance voltage gain enhancement technique for low voltage high speed CMOS op amp design

A new circuit technique for voltage gain enhancement in CMOS op amp design suitable for low voltage and high speed operation is presented in this paper. A negative conductance is used to cancel the positive output conductance of an amplifier thereby reducing the total equivalent output conductance and increasing the voltage gain of the amplifier. The negative conductance is derived from the output conductance of a transistor, as opposed to a transconductance or some other parameters, to enhance tracking over process and environment variations. A single stage CMOS op amp was designed using this technique that achieved a simulated DC gain of 83 dB.
Transferring Human Impedance Behavior to Heterogeneous Variable Impedance Actuators

This paper presents a comparative study of approaches to control robots with variable impedance actuators (VIAs) in ways that imitate the behavior of humans. We focus on problems where impedance modulation strategies are recorded from human demonstrators for transfer to robotic systems with differing levels of heterogeneity, both in terms of the dynamics and actuation. We categorize three classes of approach that may be applied to this problem, namely, 1) direct, 2) feature-based, and 3) inverse optimal approaches to transfer. While the first is restricted to highly biomorphic plants, the latter two are shown to be sufficiently general to be applied to various VIAs in a way that is independent of the mechanical design. As instantiations of such transfer schemes, 1) a constraint-based method and 2) an apprenticeship learning framework are proposed, and their suitability to different problems in robotic imitation, in terms of efficiency, ease of use, and task performance, is characterized. The approaches are compared in simulation on systems of varying complexity, and robotic experiments are reported for transfer of behavior from human electromyographic data to two different variable passive compliance robotic devices.