Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Kinematic Models for Kinematically Feasible Vehicle Trajectory Predictions

Self-driving vehicles (SDVs) hold great potential for improving traffic safety and are poised to positively affect the quality of life of millions of people. To unlock this potential one of the critical aspects of the autonomous technology is understanding and predicting future movement of vehicles surrounding the SDV. This work presents a deep-learning- based method for kinematically feasible motion prediction of such traffic actors. Previous work did not explicitly encode vehicle kinematics and instead relied on the models to learn the constraints directly from the data, potentially resulting in kinematically infeasible, suboptimal trajectory predictions. To address this issue we propose a method that seamlessly combines ideas from the AI with physically grounded vehicle motion models. In this way we employ best of the both worlds, coupling powerful learning models with strong feasibility guar- antees for their outputs. The proposed approach is general, being applicable to any type of learning method. Extensive experiments using deep convnets on real-world data strongly indicate its benefits, outperforming the existing state-of-the-art.
Use of biological control and bio-fertilization against Fusarium wilt disease and its effect on growth characteristics and tomato productivity

In this research had tried control by application of biological control and biofertilizer of the pathogenic fungus causing Fusarium wilt of tomato under greenhouse and field conditions. In this, results indicated to treatment with Trichoderma harzianum, alone or biofertilizer dual inoculation with Azospirillum brasilense, proved more powerful in decreasing disease severity % of foliar yellowing and wilt or vascular browning by about (16.25 and 16.67%) and (8.75 and 10.71%), respectively of tomato cv Super-strain B, plants than other treatments and compared with control. On the other hand, previous treatment gave the highest, observed in case of inoculation with A. brasilense with T. harzianum, records of growth characters, i.e. plant height (cm), number of branches per plant, plant fresh weight and plant dry weight g/plant, by about (77.6 cm, 5.75/plant, 552.9 and 87.13g.) respectively, yield and yield components of tomato plants, i.e. Number of fruits/plant, weight of one fruit (g), fruits yield/plant (kg) and fruits yield/plant (ton/fed) by about (14.8/plant, 113.4g, 2.07kg and 25.13 ton/fed) respectively compared with the other treatments and control. Also, in previous treatment gave the highest values observed in case of inoculation with A. brasilense with T. harzianum, of dehydrogenase activity (μg TPF/g dry soil/day), N2-ase activity (nmoles C6H2/g dry soil/hr) at flowering stage and increased phenols mg/g fresh weight and total soluble solids (TSS) of tomato c.v Super-strain B, plants compared with the other treatments and control.
New Approach to the Dissemination of Bivalve Consumption Safety Information

Background: Intoxication rates due to bivalve poisoning in Europe have prompted the European Commission to pass a regulation requiring fishery companies to include specific information on their commercial product labels. This allows consumers to evaluate bivalve safety for consumption. The use of a mobile application and OCR functionalities allows and easy access to this information. This study describes an algorithm for the for automated label-reading (LRA) of bivalves, evaluates whether labels include all the necessary information, and validates the algorithm’s efficiency, concerning the correction of produced data. Methods: Eighty-six labels of bivalves were randomly selected from multiple sale points in the Lisbon metropolitan area and photographed using regular smartphones. Each label was evaluated by a human analysis to determine its completeness regarding mandatory information. An automatic data extraction algorithm was developed, used on the collected labels and validated against the data extracted by the human analysis. All the implemented algorithms were included on a mobile application which was submitted to usability tests. Results: The dataset analysis shows that 23.25% of the labels did not include all the information mandated by the European Commission, these labels lacked the harvesting zone. About 30% of the collected labels were originated in a foreign country. The LRA performs with an accuracy of 76.74%. Conclusions: High accuracy of the developed LRA shows potential for providing instant confirmation of the bivalve consumption safety to consumers. Its integration into a mobile application will suffer from the incompleteness of included information, the variability of label structure and multiple countries of the products. Label readability would increase with the standardization of the label structure, and enforcement of European Commission regulations. Despite the fact that the LRA not being accurate enough for production phase, a form-based query mobile application has potential to improve the safety conditions of bivalve consumption.
A stochastic approach to molecular replacement

# 2000 International Union of Crystallography Printed in Denmark ± all rights reserved The classical approach to the problem of placing n copies of a search model in the asymmetric unit of a target crystal structure is to divide this 6n-dimensional optimization problem into a succession of three-dimensional searches (rotation-function followed by translation-function searches for each of the models). Here, it is shown that a structuredetermination method based on a reverse Monte Carlo minimization of a suitably chosen statistic in the 6ndimensional space de®ned by the rotational and translational parameters of the n molecules is both feasible and practical, at least for small n. Because all parameters of all molecules are determined simultaneously, this algorithm is expected to improve the signal-to-noise ratio in dif®cult cases involving high crystallographic/non-crystallographic symmetry in tightly packed crystal forms. Preliminary results from the application of this method (obtained with a space-group general computer program which has been developed for this purpose) are presented. Received 6 July 1999 Accepted 25 November 1999
Evaluation and Control of a Microgeneration System in his Connection to the Power Grid

With the growing ease of implementation of microgeneration systems, there is also an increase in the generation of electricity from Decentralized Production based on renewable energy sources. Although this increase has recognized advantages, there are certain operating conditions that can result in grid overvoltages and consequent disconnection of the microgeneration system. To address this issue, a theoretical solution is presented to control the microgeneration system, based on a decentralized regulation that, by modifying the values of Active Power and Reactive Power, can mitigate the overvoltage at the point of connection of the microgeneration system to the grid. This adjustment is carried out in the real inverter control software, based on the results obtained from the theoretical Matlab / Simulink simulations. The obtained results show that the proposed strategy guarantees the control of the grid voltage. Index Terms — Microgeneration, Active Power, Reactive Power, Voltage Regulation, Overvoltage, Power Quality.
From Global Value Chains (GVC) to Innovation Systems for Local Value Chains and Knowledge

This paper puts forth a hypothesis that while at the initial stage of growth by a latecomer, more participation at the global value chains (GVC) is desired to learn foreign knowledge and production skills, functional upgrading requires effort to seek separation and independence from existing foreigndominated GVC, and that latecomer firms and economies might have to re-seek an integration back into GVC after building up their own local value chains. This paper attempted to verify this “In-OutIn Again” hypothesis by looking into cases of “upgrading and independence” in Korea and Brazil, and by checking the national level data of the share of foreign value-added (FVA). Trends of FVA in successful catching-up economies, including China in recent years, are consistent with this In-Out-In Again pattern. Regression results also confirm correlations between the degree of local creation and diffusion of knowledge and FVA values. This finding can be viewed as an important contribution because the relationship illustrates the linkage between innovation system variables (knowledge localization) to the GVC variable of the FVA. This finding further implies that building local innovation systems is the key in making possible upgrading and local value creation while integrating in the GVC.
MB-OFDM metropolitan networks with concatenation of optical add-drop multiplexers

Multi-band (MB) orthogonal frequency division multiplexing (OFDM) signals have recently been proposed to be used in optical fibre telecommunications systems. The major reason for this is the increased bandwidth allocation flexibility, high spectral efficiency, higher capacity provisioning granularity and high tolerance to linear fibre distortion effects. In transparent metropolitan (metro) networks, the filtering concatenation effect due to the use of optical add-drop multiplexers (OADM) in consecutive nodes has been identified as the main limiting factor of the number of transparent nodes that can be traversed with acceptable performance. In this work, the transmission performance of 42.8 Gb/s virtual carrier-assisted direct detection MB-OFDM signals is evaluated numerically along a set of concatenated metro network nodes. An extension of a physical model of the wavelengthselective switch (WSS), which is the enabling technology of the reconfigurable OADM (ROADM), is proposed to take into account the group delay introduced by the WSS. The bit error ratio (BER) along a set of concatenated MB-OFDM metro network nodes is evaluated. It is shown that, for a 3-band MB-OFDM system, up to 26 ROADMs can be traversed and, for a 4-band MB-OFDM system, up to 18 ROADMs can be traversed, with BER<10−3.
SoC Implementation of OpenMSP430 Microcontroller in UMC 130nm

—The increasing need for decentralised, dedicated, sensoring and processing units on the scope of Internet of Things (IoT) networking creates demand for embedded low-power microcontrollers. The openMSP430 is an open-source 16-bit microcontroller core written in Verilog, that is compatible with the Texas Instruments’ MSP430 microcontroller family. Due to its characteristics, the openMSP430 was selected to integrate the System on Chip (SOC) of the PROTEUS project. This open-core, that will be implemented as an Application Speciﬁc Integrated Circuit (ASIC), was previously synthesised, for a UMC CMOS 130nm target technology process. In this work, dedicated test structures were added to the structural circuit description that allow the post-manufacture veriﬁcation of the circuit. Four scan chains were resynthesised into the circuit, a Design For Testability (DFT) technique, to increase fault coverage and provide an efﬁcient way to input test vectors that will verify the correct operation of the circuits internal logic. An Automatic Test Pattern Generation (ATPG) was conﬁgured to generate the test vector for each scan chain. Through a back-end process presented, the ﬁnal layout of the circuit was obtained using EDA tools that execute Place&Route steps, required to achieve a fully functional layout. The ﬁnal microcontroller circuit, the openMSP430 with Program and Data memories and scan chains occupies and area of 1050 x 2800 µm , with a fault coverage of 94.9% and a power consumption of 1.99mW when run at 16 MHz. Two versions of the openMSP430 were implemented, under the PROTEUS project, and sent to fabrication prior to this work.
A Topic-Based Approach to Multiple Corpus Comparison

Corpus comparison techniques are often used to compare different types of online media, for example social media posts and news articles. Most corpus comparison algorithms operate at a word-level and results are shown as lists of individual discriminating words which makes identifying larger underlying differences between corpora challenging. Most corpus comparison techniques also work on pairs of corpora and do need easily extend to multiple corpora. To counter these issues, we introduce Multi-corpus Topic-based Corpus Comparison (MTCC) a corpus comparison approach that works at a topic level and that can compare multiple corpora at once. Experiments on multiple real-world datasets are carried demonstrate the effectiveness of MTCC and compare the usefulness of different statistical discrimination metrics - the χ 2 and Jensen-Shannon Di-vergence metrics are shown to work well. Finally we demonstrate the usefulness of reporting corpus comparison results via topics rather than individual words. Overall we show that the topic-level MTCC approach can capture the difference between multiple corpora, and show the results in a more meaningful and inter-pretable way than approaches that operate at a word-level.
Communicating healthier food choice : food composition data, front-of-pack nutrition labelling and health claims.

Background: Food composition data, front-of-pack nutrition labelling and nutrition and health claims have an important role to play in the development of appropriate policy, regulation and public health interventions ultimately aimed at reducing the burden of diet-related chronic disease. The overarching aim of this thesis is to explore whether the communication of healthier food choice through front-of-pack (FOP) nutrition labelling and health claims can be enhanced by the development of consumer derived frameworks (typologies) of these domains, a greater understanding of the degree to which the different FOP labelling schemes impact on consumer health inferences and an improved approach to the sharing of food composition data between stakeholders. Method: The potential for more effective approaches to the transfer of food composition data on processed foods, was explored via a survey conducted within the UK food industry (Study 1). To facilitate the development of a consumer derived typology of FOP nutrition labelling schemes in Europe, a free-sorting study utilising the ‘Multiple Sort Procedure’ (MSP) was performed in four countries; France, Poland, Turkey and the United Kingdom (Study 2). Building on the MSP methodology utilised in Study 2, a further study on nutrition and health claims was performed in five countries; Germany, the Netherlands, Slovenia, Spain and the United Kingdom. (Study 3). The final study in this thesis sought to quantify the extent to which consumer perceptions of healthiness are impacted by the interpretative elements of the prevalent FOP labelling schemes in four countries; Germany, Poland, Turkey and the United Kingdom (Study 4). Conclusion: The outcomes of this research propose an optimised approach to the sharing of food composition data, an optimised approach to FOP labelling and consumer derived typologies for both the FOP labelling and nutrition and health claims domains.
Measured Skill Premium and Input Trade Liberalization: Evidence from Chinese Firms

Using Chinese rm-level production data, this paper developed a Mincer (1974)-type approach to investigate the impact of input trade liberalization on rmswage inequality between skilled and unskilled workers (or skill premium). After controlling for output trade liberalization, we nd strong evidence that input trade liberalization increases rmsskill premium and the e¤ect is more pronounced for ordinary (i.e. non-processing) rms. The ordinary importers respond more forcefully to input trade liberalization in their wage schedule. The ndings are robust to di¤erent measures of wage inequality, as well as di¤erent empirical speci cations and data spans. JEL Classi cations: F10, F12, F14
Performance design of hypervelocity shock tube facilities

A new kinetic shock tube is being commissioned at Instituto Superior Técnico under funding from the European Space Agency. Its main goal is to support planetary exploration missions, by studying high-speed radiative and chemical processes kinetics relevant to planetary entries. This thesis focuses on the performance estimates made for ESTHER shock tube in both single and double stage configuration. The single stage configuration is expected to reach 8 km/s, satisfying the requirements for most Mars, Titan and LEO entries. For higher performance, using a double stage configuration, ESTHER is expected to reach up to 14.5 km/s, simulating Earth return missions and Venus entries. A drawback of the double-stage configuration is that it makes slower velocities difficult to achieve, and Mars and Titan entries have to be reproduced resorting to a few performance tweaks. More recently, there has been renewed interest in exploring Gas Giants. The atmosphere for these planets is mainly composed of molecular hydrogen with ten to twenty percent of helium and some trace elements. Although their small molecular weight means that the gas may be more easily accelerated, simulations in ground facilities are nevertheless difficult to achieve, due to high entry velocities imposed by their respective mass. Velocities up to 18.4 km/s are the fastest that can be expected to be reached by the ESTHER facility, hence, alternatives such as changing the helium and neon diluent fraction have been considered. These two changes increase the post-shock temperature, allowing important flight condition phenomena to be reproduced. Combining the high velocities obtainable with the short test times available for the experiments, an accurate trigger system is also essential for the appropriate operation of the facility. The devised ESTHER streak camera trigger system includes four sensors, an FPGA rapid development board, a FMC fast ADC converter and a signal conditioning system. These are discussed in detail in this work.
Enhancing Biometrics Security with Distributed Source Coding

1— The use of biometrics (e.g., fingerprints, irises, faces) for recognizing individuals is becoming increasingly popular and many applications are already available. Biometrics are intrinsically associated with individuals and cannot be forgotten or shared with others. However, one of the most relevant vulnerabilities of biometrics is that once a biometric template is compromised, it cannot be reissued, updated or destroyed. An attacker could then gain access to all the accounts/services/applications using that same biometric trait. This paper proposes a biometric verification system using distributed source coding principles, with enhanced security with respect to traditional biometric verification systems. The generation of different templates from the same biometric data is supported, as well as cancelable templates. Furthermore, it will not be possible to recover the original biometric data from the stored data, thus guaranteeing its privacy.
Data Streams Organization in Query Executor for Parallel DBMS 1

The paper describes mechanism of data exchange between nodes of the query tree of the Omega parallel database system designed for the multiprocessor system MBC-100. This mechanism is based on streams concept and bracket model. The stream is a representation of a relation on the level of physical algebra. The streams are arguments and results of operations of physical algebra, that is the input and the output data of the query tree nodes. Operations are synchronized on the basis of consumer/supplier model. The paper describes the synchronization principle designed for the DBMS Omega, concentrates on stream types and framework for their implementation
1 Two-Phase Object-Based Deep Learning for Multi-2 temporal SAR image change Detection

: Change detection is one of the fundamental applications of synthetic aperture radar (SAR) 14 images. However, speckle noise presented in SAR images has a negative effect on change detection, 15 leading to frequent false alarms in the mapping products. In this research, a novel two-phase object-16 based deep learning approach is proposed for multi-temporal SAR image change detection. 17 Compared with traditional methods, the proposed approach brings two main innovations. One is 18 to classify all pixels into three categories rather than two categories: unchanged pixels, changed 19 pixels caused by strong speckle (false changes), and changed pixels formed by real terrain variation 20 (real changes). The other is to group neighboring pixels into superpixel objects such as to exploit 21 local spatial context. Two phases are designed in the methodology: 1) Generate objects based on the 22 simple linear iterative clustering (SLIC) algorithm, and discriminate these objects into changed and 23 unchanged classes using fuzzy c-means (FCM) clustering and a deep PCANet. The prediction of 24 this Phase is the set of changed and unchanged superpixels. 2) Deep learning on the pixel sets over 25 the changed superpixels only, obtained in the first phase, to discriminate real changes from false 26 changes. SLIC is employed again to achieve new superpixels in the second phase. Low rank and 27 sparse decomposition are applied to these new superpixels to suppress speckle noise significantly. 28 A further clustering step is applied to these new superpixels via FCM. A new PCANet is then trained 29 to classify two kinds of changed superpixels to achieve the final change maps. Numerical 30 experiments demonstrate that, compared with benchmark methods, the proposed approach can 31 distinguish real changes from false changes effectively with significantly reduced false alarm rates, 32 and achieve up to 99.71% change detection accuracy using multi-temporal SAR imagery.
Data-adaptive Estimation in Longitudinal Data Structures with Applications in Vaccine Efficacy Trials

Data-adaptive Estimation in Longitudinal Data Structures with Applications in Vaccine Efficacy Trials David Benkeser Co-Chairs of the Supervisory Committee: Dr. Peter Gilbert University of Washington Department of Biostatistics Dr. Marco Carone University of Washington Department of Biostatistics This dissertation develops methodology for data-adaptive estimation of parameters defined on longitudinal data structures, while this abstract serves as an introduction to the material covered herein. The dissertation is organized into three related, but distinct chapters. Each chapter considers a similar data structure, wherein subjects are enrolled and followed over a period of time to obtain additional measurements, for example their failure status. During this followup period, subjects may drop out and therefore researchers are unable to observe the entire study population at all time points. Using the observed data, this dissertation develops asymptotically efficient estimators that may draw valid inferences on the original study population. Data from preventive vaccine trials serve as the motivation for much of the work in this dissertation. In such trials, subjects are randomized to receive an active vaccine or placebo vaccine and are subsequently followed over some period of time to ascertain infection status. This infection data may be augmented with pathogen genetic data. Scientific interest may lie in assessing the vaccine’s efficacy to prevent infections of a certain genotype; this problem is considered in Chapter 1. Researchers may possess additional information on the expected incidence of an infection in the population under study. For example, such information may be ascertained from previous studies in the same population. In Chapter 2, we show how this information may be included in the estimation procedure to improve performance. The third and final chapter explores the construction of estimators that enjoy the unique property of being robust to model misspecification in terms of both estimation and inference drawn from the estimator.