Miracle-dz/UniFAR-FTUs huggingface dataset:
Studies on Pd(1,4-bis(2-hydroxyethyl)piperazine)-dicarboxylic acid complexes as models for carboplatin with structural features enhancing the interaction with DNA

Abstract [Pd(BHEP)Cl2] and [Pd(BHEP)(CBDA)] complexes, where BHEP = 1,4-bis(2-hydroxyethyl)piperazine and CBDA = cyclobutanedicarboxylate, were synthesized and characterized. [Pd(BHEP) (CBDA)] crystallizes in space group P21, a = 9.5740(8) Å, b = 9.4700(7) Å, c = 9.7890(10) Å, α = 90.00°, β = 113.360(5)°, γ = 90.00°, V = 814.78(13) Å3, Z = 2. The palladium center has a typical square planar geometry with tetrahedral distortion. DFT calculations show good agreement between theoretical and X-ray data. The stability constants and stoichiometry of the complexes formed between various dicarboxylic acids and [Pd(BHEP)(H2O)2]2+ were studied. Dicarboxylic acids form 1:1 and protonated complexes. Inosine forms 1:1, 1:2 and the protonated form of 1:1 complexes. The temperature effect on the formation constant of the cyclobutanedicarboxylate complex was investigated. The hydrolysis of [Pd(BHEP)(H2O)2]2+ and complex formation with cyclobutanedicarboxylic acid are exothermic processes.
Autonomous RC Car Platform

Control and Intelligent Robotics Lab Major Qualifying Project Autonomous RC Car Platform by Jason ASHTON, Sean HUNT, Myles SPENCER This project explores building an autonomous research robot on a 1/10 scale RC car platform. The goals of the project were to build an easy to use system that allowed for the exploration of techniques such as localization, object detection, mapping, and more. The completed robot consists of a self-contained RC car, running on battery power, that uses a camera, lidar, inertial measurement unit, and other sensors to observe the environment. Completed research explored pose estimation based on combining dead reckoning, inertial measurement unit readings, and visual odometry in an Extended Kalman Filter. The result of this project included the RC car and a build guide on replicating the process for future students.
Decision-level Fusion of Infrared and Visible Images for Face Recognition

The nature of the imaging environment,illumination plays an important role in the efficiency of face recognition on visible images.Infrared image is independent of the ambient illumination,but it is sensitive to temperatures.Face recognition algorithms applied to the fusion of IR and visible images consistently demonstrated better performance than when applied to either visible or IR imagery alone.An approach based on decision-level fusion of infrared and visible images for robust face recognition is presented,combinatory of linear weighted sum and biggest match score.The combination of PCA and linear discriminant analysis method was used to extract and recognize face feature.In order to achieve the final recognition result,the decision-level fusion was implemented by previous outcome of infrared and visible images recognition and their confidence measure.The experiments have shown it improves the performance and adaptability of face recognition in lots of actual application environments.
Incorporating prior knowledge of protein localization in a neural network for protein location prediction

Determining protein subcellular location is important for understanding cellular functions and biological processes of underlying diseases. High throughput fluorescence images can be used in combination with convolutional neural networks to predict this location. In this work we propose a hierarchical model which uses prior knowledge of proteins to divide the samples in general groups before predicting the subcellular location. Results show mixed results with significant improvements for some labels and a decline in results for others.
The Electronic PSC Testing System

This paper develops an automatic PSC testing system aiming at efficiently evaluating the spoken Chinese.On the basis of 100 hours' standard Chinese database,this paper uses the characteristic of Chinese and linguist's expert knowledge to optimize the traditional speech evaluation algorithm.At the same time,a corpus-adaptive method is propose to enhance the robustness and performance of the algorithm.Experiments on 500 persons' PSC testing database prove that the new algorithm is much better than the original algorithm.After linear mapping,the error between the machine score and the human score is almost equal to the error between humans',that is 2.44. The result indicates that the automatic PSC testing system can replace the human to evaluating spoken Chinese under text-dependent condition.
Ontologies in Industrial Enterprise Content Management Systems: the EC2M Project

—Enterprise Content Management (ECM) systems represent a crucial aspect in the efﬁcient and effective management of large-scale enterprises, in particular for those made up of several sites distributed all over the world. Considering the increasing number of documents that large enterprises need to store and manage, and the need of classifying and retrieving them in real time, ECM systems need to be strengthened to represent not only standard “syntactic” information associated with documents but also more complex and structured information to represent the documents’ semantic. In this paper, we present a joint project of the Department of Informatics, Bioengineering, Robotics and System Engineering of the University of Genoa, Italy, and two companies, Nacon (part of Sempla Group) and Nis, to create an improved ECM system (named EC 2 M) exploiting ontologies to better classify, retrieve and share documentation among different sites of the involved companies. The overall architecture of the EC 2 M system is presented with a detailed description of the ontology that has been created for the ﬁrst EC 2 M prototype, modeling the Sempla’s business offers, to let users classify their documents in a semantically-driven way and search them using semantic tags. EC 2 M is “parametric” in the used ontology: the system is currently used by Sempla, with the ontology presented in this paper.
Metagraph-Based Learning on Heterogeneous Graphs

Data in the form of graphs are prevalent, ranging from biological and social networks to citation graphs and the Web. In particular, most real-world graphs are heterogeneous, containing objects of multiple types, which present new opportunities for many problems on graphs. Consider a typical proximity search problem on graphs, which boils down to measuring the proximity between two given nodes. Most earlier studies on homogeneous or bipartite graphs only measure a generic form of proximity, without accounting for different “semantic classes”—for instance, on a social network two users can be close for different reasons, such as being classmates or family members, which represent two distinct semantic classes. Learning these semantic classes are made possible on heterogeneous graphs through the concept of metagraphs. In this study, we identify metagraphs as a novel and effective means to characterize the common structures for a desired class of proximity. Subsequently, we propose a family of metagraph-based proximity, and employ a learning-to-rank technique that automatically learns the right parameters to suit the desired semantic class. In terms of efficiency, we develop a symmetry-based matching algorithm to speed up the computation of metagraph instances. Empirically, extensive experiments reveal that our metagraph-based proximity substantially outperforms the best competitor by more than 10 percent, and our matching algorithm can reduce matching time by more than half. As a further generalization, we aim to derive a general node and edge representation for heterogeneous graphs, in order to support arbitrary machine learning tasks beyond proximity search. In particular, we propose the finer-grained anchored metagraph, which is capable of discriminating the roles of nodes within the same metagraph. Finally, further experiments on the general representation show that we can outperform the state of the art significantly and consistently across various machine learning tasks.
Visual literacy for libraries: a practical, standards-based guide

As the title suggests, this volume by the Association of Library Collections and Technical Services (ALCTS) serves as a guide to linked data concepts, practice and implementation in libraries, archives and museum collections. It is edited by Ed Jones, associate director for library assessment and technical services at the National University in San Diego, and Michele Seikel, a professor from Oklahoma State University, both of whom have extensive experience working with linked data and on library standards committees. Book chapters consist of contributions from a range of experts in North America, who describe the topic from a variety of perspectives such as case studies from practitioners and involvement in the development of standards. An introduction describes linked data – what it is, how it works and a summary of associated standards for those new to the topic. The remaining six chapters then examine aspects of linked data beginning with chapter one’s survey of linked data projects from around the world, such as Linked Open Data Libraries Archives and Museums (LODLAM), Europeana and the Digital Public Library of America (DPLA) amongst others. This chapter also contains a detailed case study of the Linked Jazz project, including project phases and lessons learned. The following chapter contains a case study of the implementation linked data with the English Short Title Catalogue to make into a 21st century tool overcoming the limitations of MARC. Other chapters consider linked data and best authority control practices, with a focus on controlled vocabularies for scientific and technical works and for the web. There is a discussion of OCLC’s use of schema.org as a foundation for a linked data model for library data. Finally, chapter six looks at future directions from the perspective of the Library of Congress, and replacing MARC with the development of BIBFRAME. The text is easy to follow, well presented and very relevant. Complex topics are explained in ways that are easy to understand. There are black and white illustrations of data display and other examples throughout the book. There is also a detailed index at the end. What is missing is a glossary of terms as the text is rich in use of terminology and acronyms, and readers would benefit from a glossary, explaining the terms used throughout the work. The book is useful for those seeking an introduction to linked data and those with an interest in implementing a linked data model. It is especially useful to students new to the topic or just seeking a better understanding of linked data.
Adaptive algorithm for routing in all-optical networks

In this paper, we study an adaptive algorithm for routing in an all-optical wavelength routed network where a lightpath is dynamically created in response to a request for communication and the WDM channels constituting the lightpath are reclaimed when the communication is over. We have proposed three possible distributed strategies to determine, if possible, a lightpath when there is a request for communication. Each of these strategies have their advantages and disadvantages in terms of the expected blocking probability and the set-up time. We consider the Kautz digraph as the network topology and study the performance of our distributed strategies through simulation.
Combination of saliency histogram equalisation and seam carving for image resizing

This study presents a novel approach to content-aware image sizing by combining the continuous approach using saliency histogram equalisation (SHE) and the discrete approach using seam carving (SC). It constructs a non-uniform quad mesh to represent an image by equalising its saliency histogram (SH), and changes the mesh size to represent the resized image by carving seams of insignificant quads in the SH domain. As SHE is simple to implement, and SC can be obtained via dynamic programming, the proposed SHE combined with SC (SHE-SC) algorithm is computationally efficient. Experimental results show that SHE-SC is preferable to other existing algorithms.
The moisture content effect on coal cleaning performance of dry separator in different feed rates

ABSTRACT This research is concerned with the effects of moisture content in different fractions on the coal cleaning performance of dry separator. The lignite samples, coming from Çorum, Muğla and Soma districts, were used in the tests. Whereas the all samples were tested in three different moisture contents and three different feed rates, the other variables, riffle height, table frequency and separator surface slope, have been kept constant at their best values after were tested in three steps. Results were analysed with tromp curves to indicate the parameters’ effects in detail. The relationships, between coal mass recoveries, combustion recoveries and moisture contents in different fractions, were presented. Consequently, the results revealed that the moisture content had a remarkable effect on separation efficiency. The separation efficiency was, however, inversely affected as the moisture content increase, and the best results were obtained in minimum moisture content for all samples tested. The best-obtained Ep values were 0.15 for the Soma, 0.165 for Mugla while it was 0.145 for the Corum sample. The Soma sample was the most affected sample by increasing moisture content.
SEMANTIC SEGMENTATION OF MICROSCOPIC BLOOD IMAGE DATA USING SELF-TRAINING TO AUGMENT SMALL TRAINING SETS AND ITS APPLICATION FOR COUNTING CELLS

viii Acknowledgements . ix Chapter
Downlink Radio Resource Management for QoS Provisioning in OFDMA Systems: with emphasis on Admission Control and Packet Scheduling

Orthogonal Frequency Division Multiple Access (OFDMA) is t he preferred technology for future downlink mobile broadband access systems as the 3rd Generat io Partnership Project Long Term Evolution (LTE) in downlink, where the diversification of th e proposed services (Voice Over Internet Protocol, video streaming, gaming or simple web brow sing.) and higher throughputs are key targets. Therefore, Quality of Service in OFDMA is a key i ssue for the success of next generation mobile systems. The present thesis aims at proposin g a concrete Quality of Service aware packet scheduling and Radio Admission Control solution for a realistic OFDMA based system in downlink where LTE is taken as a case study. In the frame of the thesis work, a detailed system model of LTE dedicated to Radio Resource Management study has been developed and implemented in a sem i-static system level simulator. In a first phase, the study focuses on the dual time / frequency domain packet scheduling concept which allows a linear complexity. Fundamental desi gn principles for throughput control are highlighted. Using those principles, the design of a com plete Quality of Service aware packet scheduling algorithm based on the Required Activity Detect ion principle is proposed. The time domain Required Activity Detection algorithm shows to be am ong the best performing algorithms in terms of Quality of Service outage. However, the study sho ws that it can be greatly improved by introducing delay awareness. In the frequency domain, Re quir d Activity Detection applies weights to the frequency domain schedulers and when used, ca n e rease the outage of up to 50%. Packet scheduling also is studied in fractional load condit ion defined by a partial use of the frequency domain resource. The study shows that, fractiona l l ad cannot be used as a coverage enhancement technique with the packet scheduling algorith ms of the thesis, furthermore, when fractional load occurs due to a lack of traffic, the block erro r rate can increase severely thus affecting the experienced Quality of Service. The thesis prop oses various solutions to overcome that problem, among which the simplest consists in using Wid eband Interference Reporting in the Channel Quality Information reporting scheme. Finally, the thesis focuses on Radio Admission Control. New algorithms, namely the adaptive throughput and Required Activity Detection-based Rad io Admission Control are tested and compared to a simple fixed throughput algorithm. It is shown t hat he Required Activity Detection based Radio Admission Control can track the channel and ther efor keep the system in feasibility region.
An innovative approach to automatically identify control point set for model deformation rectification

Rectifying 3D model deformation based on control point set is one of the most important problems frequently applied in virtual reality fields, in which control point set is the key for implementing deformed manipulation. This paper presents a technique to automatically identify control point set by analyzing the changes of each point in a 3D model through its variations, then gathering clusters and selecting important points to become a control point set. Our proposed approach was tested and proved to be effective in our practical experiments with a deforming technique based on the interpolation of the radial basis function.
3D Shape Reconstruction From Images in the Frequency Domain

Reconstructing the high-resolution volumetric 3D shape from images is challenging due to the cubic growth of computational cost. In this paper, we propose a Fourier-based method that reconstructs a 3D shape from images in a 2D space by predicting slices in the frequency domain. According to the Fourier slice projection theorem, we introduce a thickness map to bridge the domain gap between images in the spatial domain and slices in the frequency domain. The thickness map is the 2D spatial projection of the 3D shape, which is easily predicted from the input image by a general convolutional neural network. Each slice in the frequency domain is the Fourier transform of the corresponding thickness map. All slices constitute a 3D descriptor and the 3D shape is the inverse Fourier transform of the descriptor. Using slices in the frequency domain, our method can transfer the 3D shape reconstruction from the 3D space into the 2D space, which significantly reduces the computational cost. The experiment results on the ShapeNet dataset demonstrate that our method achieves competitive reconstruction accuracy and computational efficiency compared with the state-of-the-art reconstruction methods.
Design and Implementation of DDA Interpolator Algorithm Based on FPGA

A method of DDA interpolation algorithm based on field programmable gate array(FPGA) is presented.The algorithm described by Verilog hardware describe language(HDL),compiled,synthesized and downloaded with QuartusⅡ,which is implemented in a FPGA chip in series of Cyclone from Altera company.The state-machine,program flow chart and the simulation results are also presented.The DDA algorithm running on FPGA chip has the advantages of high speed and excellent anti-disturb capability,and the DDA algorithm designed by Verilog HDL based on IP core can be used repeatedly so as to improve the design efficiency.
Traffic, a Toolbox for Processing and Analysing Air Traffic Data

Trajectories are mathematical objects used to describe the evolution of a moving object with a finite list of parameters. The most common features in ATM include latitude, longitude, altitude, all indexed by time, with derived ones such as ground speed, track angle and vertical rate. Aircraft dynamic models may expect pitch, roll and yaw angles, together with indicated, computed or true airspeed, Mach number, and more.
Radar Based Traffic Incident Detection using Support Vector Classification for Road Safety

This research presents the usage of Support Vector Classification (SVC) to classify incident of vehicles driving in the opposite traffic lane by using data collected from traffic radar. First, we develop a data collection program for gathering traffic data from a traffic radar sensor remotely located in Prachinburi province, Thailand. The gathered data are used in training and verifying our proposed SVC model. Secondly, we develop an Automatic Incident Detection (AID) program with SVC algorithm to detect desired incidents such as driving in an opposite lane. Our research is tested in 6 different environments such as the time of day and the average speed. In the results, we found that it can classify thepattern of driving in the opposite traffic lane, TTD is 1.616 seconds, DR is 99.85 percent and FAR is 1.74 percent. From the result, our proposed work with SVC algorithm can be efficiently applied to classify road incidentsfrom the data collected from traffic radar.
Transforming Earth Science Education Through Immersive Experiences: Delivering on a Long Held Promise

The value of field trips is undisputed across disciplines. Field-site visits whether in social or physical sciences provide grounding for place- and discovery-based learning. Yet field trips have limitations that can now be overcome by the promise of immersive technologies that can improve quality and accessibility. This promise is twofold: First, we can harness advancements made in sensing technologies to create immersive experiences of places across the earth efficiently; second, we can provide detailed empirical evaluations on immersive learning and quantify educational value. We report on a study that splits an introductory geosciences course into two groups with one group experiencing a traditional field trip, while a second group visits the same site virtually, immersing the students in the site using a head-mounted device. Results show the advantages of virtual field trips (VFTs) concerning enjoyment, learning experience, and actual lab scores. We embed the discussion of these results into a more general assessment of the advantages of VFTs and a taxonomy of VFTs as a basis for future studies.
Wide-bandgap, low-bandgap, and tandem perovskite solar cells

Organic-inorganic metal halide perovskite single-junction solar cells have attracted great attention in the past few years due to a high record power conversion efficiency (PCE) of 23.7% and low-cost fabrication processes. Beyond single-junction devices, low-temperature solution processability, and bandgap tunability make the metal halide perovskites ideal candidates for fabricating tandem solar cells. Tandem solar cells combining a wide-bandgap perovskite top cell and a low-bandgap bottom cell based on mixed tin (Sn)-lead (Pb) perovskite or a dissimilar material such as silicon (Si) or copper indium gallium selenide (CIGS) offer an extraordinary opportunity to achieve PCEs higher than Shockley-Queisser (SQ) radiative efficiency limits (∼33%) for single-junction cells. In this review, we will summarize recent research progress on the fabrication of wide- (1.7 to 1.9 eV) and low-bandgap (1.1 to 1.3 eV) perovskite single-junction cells and their applications in tandem cells. Key challenges and issues in wide- and low-bandgap single-junction cells will be discussed. We will survey current state-of-the-art perovskite tandem cells and discuss the limitations and challenges for perovskite tandem cells. Lastly, we conclude with an outlook for the future development of perovskite tandem solar cells.
OMT: A DYNAMIC AUTHENTICATED DATA STRUCTURE FOR SECURITY KERNELS

We introduce a family of authenticated data structures — Ordered Merkle Trees (OMT) — and illustrate their utility in security kernels for a wide variety of sub-systems. Specifically, the utility of two types of OMTs: a) the index ordered merkle tree (IOMT) and b) the range ordered merkle tree (ROMT), are investigated for their suitability in security kernels for various sub-systems of Border Gateway Protocol (BGP), the Internet’s inter-autonomous system routing infrastructure. We outline simple generic security kernel functions to maintain OMTs, and sub-system specific security kernel functionality for BGP subsystems (like registries, autonomous system owners, and BGP speakers/routers), that take advantage of OMTs.