Miracle-dz/UniFAR-FTUs huggingface dataset:
Methods to Incorporate Foundation Elasticities in Rotordynamic Calculations

Currently the dynamic effects of foundation are usually neglected for standard design of rotor trains for steam turbines for power generation. Foundation and rotordynamic analyses are conducted nearly independently of each other. However, due to the demand for more precise rotordynamic calculations it is reasonable to take the elasticity of foundation and bearing housing into account in the calculations. Including the dynamics of the foundation in the rotordynamic calculations can be achieved by using the substructure/transfer function method. The aim of this method, which has been known for decades, is to separate the system into a main structure and a substructure. The elastic substructure, in this case the foundation or foundation and bearing/bearing housing respectively, is harmonically excited with a unity load in degrees of freedom and frequency range of interest at the coupling nodes to the rotor train. From the response at the coupling nodes, a complex flexibility matrix depending on excitation frequency can be derived. After inversion of the matrix, the real part represents the stiffness, which can be added to the stiffness matrix of the rotor system. The complex part of the matrix can be added analogously to the damping matrix. Since these matrices depend on the excitation frequency, only harmonic analyses can be carried out with this procedure. This approach allows to decentralise an overall project with external partners. Each partner can work independently of each other and the calculation tools used can vary. The foundation analysis can be carried out with any FEM-program, also measured transfer function can be included used for the rotordynamic analysis allowing for a comparison of calculation and measurement results. The rotordynamic calculations can be carried out with typical dynamic calculation tools. Having different models for rotor and foundation allows to design of rotor and foundation much easier as dynamic effects can clearly be related to the analysed structure. Another method to include the dynamic properties of the foundation is to create a combined rotor-foundationmodel. On the one hand, this can be done by a truncated modal reduction of the supporting system. Thus, eigenmodes of interest can be included via a reduced model. On the other hand, a model of the foundation using beam elements is appropriate. With these approaches the differential equations cover the total rotor foundation system, and besides the harmonic analysis, eigenfrequencies and eigenmodes can also be calculated. Combining rotor and foundation in one model results in a multiplicity of eigenfrequencies. The aim of this analysis is to evaluate these coupled rotor-foundation-eigenfrequencies and to identify critical resonances. It will be shown that the methods shown here are efficient analyses tools for cost-effective design of rotor trains.
OPTIMIZATION OF ROBOT ARM PATH PLANNING WITHOUT COLLISION IN ITS WORKSPACE USING GENETIC ALGORITHM

: In the last decade genetic algorithms have been applied in a plethora of fields such as in control, parameter and system identification, robotics, planning and scheduling, image processing, pattern recognition and speech recognition. This paper addresses the generation of a robotic manipulator structure and the planning of trajectories, namely in finding a continuous motion that takes the hand from a given starting configuration, without collision with any obstacle, up to a desired end position in the workspace
Object Filtering and Uniform Representation for Autonomous Decision Making

In autonomous systems, such as autonomous robots and vehicles, one of the most critical parts is decision making, such as collision avoidance, motion planning, etc [3]. This task heavily relies on surrounding objects detected by sensors [2]. However, there could be too many objects detected, while only a small portion of them are actually critical to the decision making. Furthermore, advanced machine learning methods have become more and more important in such tasks, such as DeepDriving [1]. Many of such advanced machine learning algorithms prefer fixed-size input, such as a fixed number of objects or a different form. However, the number of objects detected by sensors could vary from time to time, even after certain object filtering process. To bridge these gaps, we propose a framework that consisting of two parts: object filtering (Figure 1C) and uniform representations (Figure 1D). In this short paper, instead of proposing specific algorithms, we focus on the overall framework, as well as a set of novel ideas that play key roles in this framework. By putting things together in a systematical way, we hope to inspire new algorithm development and system design for autonomous systems.
Machine Learning Approach to Annotate Segmentation in MR Images of Patients with Glioblastoma Multiforme

Glioblastoma Multiforme (GBM) is a malignant Grade IV tumor that classifies tumor in the central nervous system. This study has recruited 23 subjects with GBM to perform an AI-based model for automatically annotating the GBM regions. Semi-auto annotation for radiomics features utilizes T1-weighted imaging post-contrast enhancement (T1-WI Contrast), T2-weighted imaging (T2-WI), T2-fluid attenuated inversion recovery (T2-FLAIR), apparent diffusion coefficient (ADC) MR images in each whole tumor individually. The radiomics features have been then inserted into different machine learning algorithms to analyze the features as well as classify the GBM regions. Among of them, Random Forest achieves the best accuracy (greater than 95%) in necrosis, solid part, peri-tumoral, and edema regions in four types of MR pulse sequences imaging. Therefore, the machine learning could develop features for quantitative trait analysis that provide novel clinical insights for personalized precision medicine.
Recurrent Chunking Mechanisms for Conversational Machine Reading Comprehension

In this paper, we focus on the conversational machine reading comprehension (MRC) problem, where the input to a model could be a lengthy document and a series of interconnected questions. To deal with long inputs, previous approaches usually chunk them into equally-spaced segments and predict answers based on each chunk independently without considering the information from other chunks. As a result, they may form chunks that fail to cover complete answers or have insufﬁcient contexts around the correct answer required for question answering. Moreover, they are less capable of answering questions that need cross-chunk information. We propose to let a model learn to chunk in a more ﬂexible way via reinforcement learning: a model can decide the next chunk that it wants to process in either direction. We also apply recurrent mechanisms to allow information to be transferred between chunks. Experiments on two conversational MRC tasks – CoQA and QuAC – demonstrate the effectiveness of our recurrent chunking mechanisms: we can obtain chunks that are more likely to contain complete answers and at the same time provide sufﬁcient contexts around the ground truth answers for better predictions.
Load-balancing in P2P based RDF stores

RDF stores that use structured P2P networks are often based on the idea to store three copies of each triple in a distributed hash table using subject, predicate, and object as keys of the hash function. A common scalability issue of this strategy is the load-imbalance of nodes due to vast differences in the number of occurrences and requests of keys. This paper presents and compares load-balancing strategies for RDF stores in structured P2P networks leading to better performance in networks of many nodes. These strategies are based on the ideas of replicating and relocating data on an overlay tree.
Distributed Storage of Replicated Data

We describe a fully decentralized algorithm, called “dating service” which produces uniformly chosen random matchings of linear size connecting resources of nodes in the system. Interest-ingly enough, this property holds true even if a node is not able to choose another node uniformly at random. In particular, the dating service can be implemented over existing DHT-based systems. In order to illustrate the expressiveness and the usefulness of proposed service, we propose an algorithm for distributed storage of replicated data that randomly distributes replicas over the network in logarithmic number of steps, that ensures that node storage capacities cannot be exceded.
Sintering of Aluminium Nitride Substrates for the Electronic Industry

AlN substrates of high quality can be sintered in different furnace types with firing programs adapted to the specific product requirements. Optically homogeneous and metallizable substrates with thermal conductivities of 180 W/(mK), bending strengths >360 MPa and deflections <0,3 % are produced in industry. The sintering of the AlN ceramics is investigated by means of in situ measuring methods. Phase changes are detected by High Temperature X-ray Diffraction and sintering shrinkage as well as thermal conductivity by a Thermooptical Measuring Method. Optimized sintering cycles are determined from the in situ measuring data and computer simulations. Segregations of the melt phase can be prevented by these methods.
RO bot Manipulation Learning by Demonstration using G enerative A dversarial N etworks (ROGAN)

Following its introduction in industrial manufacturing, robotics is nowadays entering into new application domains through its introduction in urban environments. This provides opportunities but also poses challenges in redefining conventional practices and methods, as is the case in healthcare and well-being that can become more user-oriented, by offering services remotely and with higher degree of autonomy. Our research interest in this thesis proposal concerns the development of such services through the use of humanoid service robots, via machine learning methods capable of transferring human demonstrated skills to the robot. This topic is broadly referred as learning/programming from demonstration (LfD) and deals with task-based coupling of robot action with its sensory/perceptual data. This thesis provides the opportunity to build upon recent work in deep learning and probabilistic graphical models research [12, 6], mainly applied in computer vision applications, to vision-guided robot manipulation skills in use cases related to personal assistance of human users by a companion robot [4]. The thesis is the product of synergy between the teams HAAL of CNRS LabSTICC in IMT-A and PGM group of University of Adelaide, specialized in smart spaces and cognitive robotics on the one hand and high-end artificial intelligence based on deep neural networks and probabilistic graphical models on the other. The thesis will address challenges at a technological and methodological level. In the latter case, it will primarily pursue a task-based mapping of human kinematics to robot arm kinematics, namely, addressing the correspondence problem, via Generative Adversarial Networks (GANs) [10, 8]. This will be the case of supervised human demonstrations given to the robot within the LivingLab facility of IMT-A which emulates the real operating conditions of an assisting robot along human users and experts. Complementary to such demonstrations, the thesis will also explore weakly supervised learning from unanno-
Expert Review and Proposals for Measurement of Health Inequalities in the European Union

2 1. Measurement of Health & Health inequalities in the EU . 3 1.1. Health Data . 3 1.2. What is Health Inequality? Definitions & Measurement.5 2. Most suitable summary measures for monitoring health inequalities in the EU . 7 2.1. Indicators for assessing inequalities in mortality across the EU area, regions and time.8 2.1.1. Analysis of Trends . 13 2.2. Indicators for assessing inequalities on Self Perceived Health, Chronic illness, and Disability by Social groups.16 2.2.1. Analysis of Trends . 21 3. Conclusions.25 Bibliography . 30
Experiences of Internet Traffic Monitoring with Tstat

—Network monitoring has always played a key role in understanding telecommunication networks since the pioneering time of the Internet. Today, monitoring trafﬁc has become a key element to characterize network usage and users’ activities, to understand how complex applications work, to identify anomalous or malicious behaviors. In this paper, we present our experience in engineering and deploying Tstat, a free open source passive monitoring tool that has been developed in the past ten years. Started as a scalable tool to continuously monitor packets that ﬂow on a link, Tstat has evolved into a complex application that gives to network researchers and operators the possibility to derive extended and complex measurements via advanced trafﬁc classiﬁers. After discussing Tstat capabilities and internal design, we present some examples of measurements collected deploying Tstat at the edge of several ISP networks in the past years. We then discuss the scalability issues that software based tools have to cope with when deployed in real networks, showing the importance of properly identifying bottlenecks.
Translational Science Sarcoidosis, alveolar b-actin and pulmonary fibrosis

G. COOKE, P. GOVENDER, C.J. WATSON, M.E. ARMSTRONG, D.N. O’DWYER, M.P. KEANE, R. KING, A. TYNAN, M. DUNN* and S.C. DONNELLY* From the School of Medicine and Medical Science, Conway Institute for Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Ireland, Medicine & Therapeutics, St. Vincent’s University Hospital, Crumlin, Dublin 12, Ireland and Our Lady’s Children’s Hospital, Crumlin, Dublin 12, Ireland
Reinforcement Learning of Multi-Domain Dialog Policies Via Action Embeddings

Learning task-oriented dialog policies via reinforcement learning typically requires large amounts of interaction with users, which in practice renders such methods unusable for real-world applications. In order to reduce the data requirements, we propose to leverage data from across different dialog domains, thereby reducing the amount of data required from each given domain. In particular, we propose to learn domain-agnostic action embeddings , which capture general-purpose structure that informs the system how to act given the current dialog context, and are then specialized to a speciﬁc domain. We show how this approach is capable of learning with signiﬁcantly less interaction with users, with a reduction of 35% in the number of dialogs required to learn, and to a higher level of proﬁciency than training separate policies for each domain on a set of simulated domains.
Mechanical Properties Prediction of Thermoplastic Composites using Fuzzy Models

The ability to predict mechanical properties of thermoplastic composites in order to satisfy the performance requirements is of great importance in course of the design. In this paper, a general meth od group for data driven fuzzy modeling and its applic ation is presented. Two low complexity fuzzy models were generated for the prediction of Charpy impact stren gth and yield strength as a function of the percent amo unt of the components. The models take as input parameters he percentage of the nanotube and ABS
A simple dynamic bandit algorithm for hyper-parameter tuning

Hyper-parameter tuning is a major part of modern machine learning systems. The tuning itself can be seen as a sequential resource allocation problem. As such, methods for multi-armed bandits have been already applied. In this paper, we view hyper-parameter optimization as an instance of best-arm identiﬁcation in inﬁnitely many-armed bandits. We propose D-TTTS , a new adaptive algorithm inspired by Thompson sampling, which dynamically balances between reﬁning the estimate of the quality of hyper-parameter conﬁg-urations previously explored and adding new hyper-parameter conﬁgurations to the pool of candidates. The algorithm is easy to implement and shows competitive performance compared to state-of-the-art algorithms for hyper-parameter tuning.
FUSE: Finding File Upload Bugs via Penetration Testing

—An Unrestricted File Upload (UFU) vulnerability is a critical security threat that enables an adversary to upload her choice of a forged ﬁle to a target web server. This bug evolves into an Unrestricted Executable File Upload (UEFU) vulnerability when the adversary is able to conduct remote code execution of the uploaded ﬁle via triggering its URL. We design and implement FUSE, a penetration testing tool designed to discover UFU and UEFU vulnerabilities in server-side PHP web applications. The goal of FUSE is to generate upload requests; each request becomes an exploit payload that triggers a UFU or UEFU vulnerability. However, this approach entails two technical challenges: (1) it should generate an upload request that bypasses all content-ﬁltering checks present in a target web application; and (2) it should preserve the execution semantic of the resulting uploaded ﬁle. We address these technical challenges by mutating standard upload requests with carefully designed mutations that enable the bypassing of content-ﬁltering checks and do not tamper with the execution of uploaded ﬁles. FUSE discovered 30 previously unreported UEFU vulnerabilities, including 15 CVEs from 33 real-world web applications, thereby demonstrating its efﬁcacy in ﬁnding code execution bugs via ﬁle uploads.
UCSD MOLECULE PAGES Numb

Mammalian Numb (Numb) encodes an endocytic adaptor protein first characterized in the Drosophila nervous system as an intrinsic cell fate determinant which is asymmetrically localized and preferentially segregates into only one of the two daughter cells upon division (Rhyu et al. 1994; Knoblich et al. 1995; Spana and Doe 1995). Mammalian Numb homologues have been identified, and homozygous deletion of Numb in mice leads to embryonic lethality, suggesting that Numb plays an essential role in mammalian development (Verdi et al. 1996; Zhong et al. 1996). In addition to playing a role in asymmetric cell division(ACD), Numb has been shown to function in endocytosis, ubiquitination, cell adhesion, migration, and cancer.
Classification of Incident-related Tweets: Exploiting Word and Sentence Embeddings

—In this paper, we present our ﬁve approaches submitted to the Text REtrieval Conference (TREC) Incident Streams (IS) 2019B edition. The goal is to classify crisis-related tweets into a variable set of information classes and to provide an importance score. This multi-class, multi-label and multi-task problem turns out to be even more challenging because of extremely unbalanced training data available. We use recently proposed, publicy available word and sentence embeddings and deep neural network models for this task.
Outlier Detection and Description 2013

Personalized medicine has been hailed as one of the main directions for medical research in this century. In the first half of the talk, we give an overview on our personalized medicine projects that use gene expression, proteomics, DNA and clinical features. In the second half, we give two applications where outlier detection is valuable for the success of our work. The first one focuses on identifying mislabeled patients, and the second one deals with quality control of microarrays. Bio Dr. Raymond Ng is a professor in Computer Science at the University of British Columbia. His main research area for the past two decades is on data mining, with a specific focus on health informatics and text mining. He has published over 150 peer-reviewed publications on data clustering, outlier detection, OLAP processing, health informatics and text mining. He is the recipient of two best paper awards from 2001 ACM SIGKDD conference and the 2005 ACM SIGMOD conference. He was one of the program co-chairs of the 2009 International conference on Data Engineering, and one of the program co-chairs of the 2002 ACM SIGKDD conference. He was also one of the general cochairs of the 2008 ACM SIGMOD conference. He was an editorial board member of the Very large Database Journal and the IEEE Transactions on Knowledge and Data Engineering until 2008. For the past decade, Dr. Ng has co-led several large scale genomic projects, funded by Genome Canada, Genome BC and NSERC. The total amount of funding of those projects well exceeded $40 million Canadian dollars. He now holds the Chief Informatics Officer position of the PROOF Centre of Excellence, which focuses on biomarker development for end-stage organ failures. Copyright is held by the author. ODD’13, August 11, 2013, Chicago, IL, USA. ACM 978-1-4503-2335-2.
Dynamically Evolving, Large-scale Information Systems

In this paper we present a study on the distribution of nodes in Chord protocol. We prove that Chord is a strongly unbalanced structure – i.e. there is a signiﬁcant number of nodes that have either very small or very large load in comparison with the average. In addition, we also present a fairly practical algorithm which can be used for estimating number of nodes in set-up Chord. All presented results are provided with rigid mathematical proofs, but we also show some experimental results which support analytic considerations.
Are zombies productive or not ?

Increasing productivity at the firm level is important contributor to the overall economic activity. At the same time bank lending is a significant source of financing for firms, especially in continental European countries where non-bank financial intermediation is less developed. However, due to their self-interest, bankers sometimes opt for saving financially weak firms by extending loans or issuing new. We call firms with persistent financial difficulties zombie firms. In this paper we investigate the relationship between zombie firms’ loan arrangements and their productivity. In other words, we are interested in how productivity behaves in firms that are financially weak, but still receive loans from the bank. Results show that there is a statistically significant positive link between increasing financing and productivity for healthy firms, while this relationship is non-existent for zombie firms. Since giving loans to financially weak firms means less loans for sound firms, which could use additional funds in a more productive way, our result implies that additional vigilance is warranted for the policy makers.
Dynamically Evolving

We consider pervasive systems and identiﬁers for objects in these systems. Using unique global identiﬁers for these objects increases the size of the ID’s and requires some global coordination. However, severe privacy threats are the key issues here. On the other hand, for performing the goals of a pervasive system the identiﬁers are normally used in small local environments , and we need uniqueness limited to these environments only. This yields an opportunity to re-use the ID’s and in this way anonymize the objects. The problem is that we cannot predict assignment of the objects to local environment or set it in advance, while on the other hand in many application scenarios we cannot change an ID already assigned to an object. Random predistribution of ID’s is a technique that partially solves this problem, but has drawbacks due to the birthday paradox. We propose a solution in which each object holds k preinstalled ID’s (where k is a small parameter like k = 2 , 3 , . ). While entering a local environment, one of its ID’s not used so far in this local environment is chosen for the object. We analyze probability of a conﬂict, i.e. of the event that no identity can be chosen for this object. We show that the size of ID’s may be signiﬁcantly reduced compared to random predistribution without increasing conﬂict probability. Apart from implementation advantages it contributes to privacy protection: since globally a large number of objects holds the same ID, privacy threats are reduced.