Miracle-dz/UniFAR-FTUs huggingface dataset:
Medical Image Registration by Normalized Mapping

A new non-rigid registration method is proposed for the bladder magnetic resonance (MR) images. The key point is normalized mapping, which transforms any image into an intermediate space. Under the uniform space, those anatomical feature points of different images are corresponded by rotating and scaling. In addition, the non-rigid registration is utilized under the application of groupwise registration. By registering a set of images, an unbiased template can be obtained. Based on this template, the analysis towards the group of images can be easily conducted. Experimental results demonstrate that the proposed method can register accurately the target image to the reference image.
TIME-VARYING AND AVALANCHINE MEMORY EFFECT IN ANOMALOUS DIFFUSIONS

We extend the simple non-Markovian walk model by memory enhanced with time which describes underlying mechanism of anomalous diffusions. In the extended models, we consider the competitions between positive and negative memory by controlling a parameter α and avalanche memory effect for various avalanche size. The models show well the anomalous behavior of diffusion including both superdiffusion and subdiffusion like the memory enhanced model, while for small α the steps are much more correlated than for the memory enhanced model. The avalanche memory does not affect the temporal correlation between steps with the same Hurst exponent as the case without avalanches in long time region, while it induces oscillatory temporal correlation in short time region.
Investigations of calorimeter clustering in ATLAS using machine learning

The Large Hadron Collider (LHC) at CERN is designed to search for new physics by colliding protons with a center-of-mass energy of 13 TeV. The ATLAS detector is a multipurpose particle detector built to record these proton-proton collisions. In order to improve sensitivity to new physics at the LHC, luminosity increases are planned for 2018 and beyond. With this greater luminosity comes an increase in the number of simultaneous proton-proton collisions per bunch crossing (pile-up). This extra pileup has adverse effects on algorithms for clustering the ATLAS detector’s calorimeter cells. These adverse effects stem from overlapping energy deposits originating from distinct particles and could lead to difficulties in accurately reconstructing events. Machine learning algorithms provide a new tool that has potential to improve clustering performance. Recent developments in computer science have given rise to new set of machine learning algorithms that, in many circumstances, out-perform more conventional algorithms. One of these algorithms, convolutional neural networks, has been shown to have impressive performance when identifying objects in 2d or 3d arrays. This thesis will develop a convolutional neural network model for calorimeter cell clustering and compare it to the standard ATLAS clustering algorithm.
Altered Primary Sulfides in CR and CM Carbonaceous Chondrites: Formation by Dissolution and Pseudomorphic Replacement

Introduction: One of the major goals of the study of primitive meteorites involves distinguishing primary solar nebular features from those resulting from secondary processes on asteroidal parent bodies. Iron sulfides are postulated to have formed in both nebular and asteroidal environments [2-6] and therefore may provide constraints on processes both before and after accretion of asteroidal parent bodies. Previous studies of iron sulfides in carbonaceous chondrites argued for a secondary origin for pyrrhotite (Fe1-xS) and pentlandite ((Fe,Ni)9S8), postulating that they formed from the aqueous alteration of troilite (FeS) and/or Fe,Ni-metal in a parent body setting [1-4]. Recent work has shown, however, that pyrrhotite and pentlandite may also be primary phases formed from sulfidization [5] or crystallization [6-8]. Our previous work [9,10] involved reevaluating the sulfide mineralogy of the CR and CM carbonaceous chondrites and determined that primary sulfides, formed by both processes, are present in both groups. This study expands on our investigations [11] of the effects of aqueous alteration on primary iron sulfides in the more heavily altered CR and CM carbonaceous chondrites. Methods: The meteorites studied for this work include: CR2s Queen Alexandra Range 99177, Elephant Moraine 92042, Meteorite Hills 00426, and Renazzo and CM2s Queen Alexandra Range 97990, Murchison, Murray, and Mighei. For microscopic-scale imaging of the textures and preparation of FIB sections for analysis by TEM, we used a FEI Quanta 3D Dualbeam FEGSEM/FIB at UNM. The major and minor element compositions of the sulfides were obtained using a JEOL 8200 EPMA at the Institute of Meteoritics, UNM. For obtaining submicron-scale images of textures, EDS analyses, and SAED patterns, we used a JEOL 2010F STEM at UNM. Results: We have identified three distinct groups of altered primary sulfide grains (Fig. 1). In all cases, the proportion of these grains increases with an increase in the degree of aqueous alteration for the bulk meteorite. Additionally, the distribution of these grains is heterogeneous in any given sample, and the different grain types can coexist within the same sample. 1) porous-pitted pyrrhotite-pentlandite (4P) grains (CM chondrites, type IIA chondrules and matrix). 2) altered pyrrhotite-pentlandite composite (COMP alt) grains (CR and CM chondrites, type IIA chondrules and matrix). 3) altered sulfide-rimmed metal (SRM alt) grains (CR chondrites, type IA and IIA chondrules).
CS 365 : Discovering semantic relation in unlabeled text

The availability of large quantity of data due to the openess to the web text has led to the presence of a huge number of unidentified concepts and semantic relation. Most of the data that was used by advanced machine learning techniques to identify relations use human supervision for labeling them, which is not possible for huge datasets. So, there are models that can identify entities and the possible relationship between entities in an unsupervised way. In this project we plan to modify and use the Infinite Relational Model to discover semantic relationships in unlabeled text. We plan to run the algorithm on the dataset obtained by crawling the web using TextRunner and show that we can identify sensible semantic relationships between entities even with unlabeled text. We plan to compare our results with Wordnet which is a human labeled semantic lexical database for english language.
Information Processing Differences Between Archaea and Eukaraya — Implications for Homologs and the Myth of Eukaryogenesis

In the grand schema of evolution, a mythical prokaryote to eukaryote cellular transition allegedly gave rise to the diversity of eukaryotic life (eukaryogenesis). One of the key problems with this idea is the fact that the prokaryotic world itself is divided into two apparent domains (bacteria and archaea) and eukarya share similarities to both domains of prokaryotes while also exhibiting many major innovative features found in neither. In this article, we briefly review the current landscape of the controversy and show how the key molecular features surrounding DNA replication, transcription, and translation are fundamentally distinct in eukarya despite superficial similarities to prokaryotes, particularly archaea. These selected discontinuous molecular chasms highlight the impossibility for eukarya having evolved from archaea. In a separate paper, we will address alleged similarities between eukarya and bacteria.
Literature Review on Leadership Theories

The leadership research in the last two decades evolved mainly under the following philosophies : trait school, focused on leaders’ dispositions ; behavioral school, concerned with leaders’ behaviors ; contingency school, focused on leadership contingencies; relational school, considered leader-follower relations; sceptics school, questioned the existence and need of leadership; information-processing school, focused on cognition; and the neo-charismatic or transformational school. Definitions of leadership and different theories were reviewed in this paper.
Source Characterization by the Allan Variance

Until now, the main criteria for selecting geodetic sources were based on astrometric stability and structure at 8 GHz [6]. But with more observations and the increase of accuracy, the statistical tools used to determine this stability become inappropriate with regards to sudden motions of the radiocenter. In this work, we propose to replace these tools by the Allan Variance [1], first used on VLBI sources by M. FeisselVernier [3], leading to a new classification of sources into three groups according to the shape of the Allan Variance. In parallel, we combine two catalogs, the Large Quasar Astrometric Catalogue [13] and the Optical Characteristics of Astrometric Radio Sources [10], in order to gather most physical characteristics known about these VLBI targets. By doing so, we may reveal physical criteria that may be useful in the selection of new targets for future VLBI observations.
Exploiting intra Database Similarities for Selection of Place Recognition Candidates in Changing Environments

Visual place recognition is an important field in mobile robotics. A typical setup contains a given database of images (the map) and the robot consecutively tries to find matchings between this database and the actual visual input (query images). For real world applications the runtime of this setup can be significantly reduced by selecting only few matching candidates from the database for the current query image. In this paper, we propose a novel approach to reduce the computational effort in the online stage of place recognition by processing the database in advance. The method can be used with any matching scheme if the following assumption on the dataset is fulfilled: Given database and query in form of image sequences, we assume directly neighbouring places in the world to be represented by images sharing similar appearance. Based on this assumption, we propose a selection of matching candidates for the next query image based on the current matchings and intra database similarities. We provide an algorithmic approach that can deal with different camera trajectories between database and query, repeatedly revisited places and kidnapped robot situations. Further, we provide preliminary results on the Nordland validation dataset and show that the proposed approach can save in many configurations about 90 % comparisons at the online stage without considerable place recognition performance loss. Finally, we discuss the intuitive parameters of the approach and evaluate them experimentally.
Argument based Online Deceptive Review Spam Detection

Deceptive reviews, which are deliberately made to deceive the readers, sometimes can be very similar to genuine reviews, rendering the deceptive reviews detection a challenging task. Existing approaches mainly focus on designing linguistic features and few work studies this task from the argumentation perspective. We observe that the evidence-conclusion discourse relations, also known as arguments, often appear in online reviews, and our hypothesis is that some argument based features, e.g. the percentage of argumentative sentences, the conclusions-evidence ratios, are good indicators between deceptive and truthful reviews. We manually annotate arguments in 80 hotel reviews, and use the argument based features proposed by [24] to investigate our hypothesis. Empirical experiments show that, using the argument based features alone outperforms the state-of-the-art baseline features; in addition, when be used together with the argument based features, the state-of-the-art baseline features can also have a performance improvement.
Towards Data Visualisation based on Conceptual Modelling and Schema Transformations AutoMed Technical Report Number 39

Selecting data, transformations and visual encodings in current data visualisation tools is undertaken at a relatively low level of abstraction namely, on tables of data and ignores the conceptual model of the data. Domain experts, who are likely to be familiar with the conceptual model of their data, may find it hard to understand tabular data representations, and hence hard to select appropriate data transformations and visualisations to meet their exploration or question-answering needs. We propose an approach that addresses these problems by defining a set of visualisation schema patterns that each characterise a group of commonly-used data visualisations, and by using knowledge of the conceptual schema of the underlying data source to create mappings between it and the visualisation schema patterns. The benefits of the approach are that we use the full knowledge of the conceptual model of the underlying data to identify feasible visualisations for that data; moreover, once this mapping is in place, the implementation of actual visual charts can utilise the mapping to extract data, drill-down, roll-up, pivot, switch visualisation, etc. To our knowledge, this is the first work to propose a conceptual modelling approach to matching data and visualisations.
Title Compact MIMO antenna for portable devices in UWBapplications

A compact multiple-input-multiple-output (MIMO) antenna with a small size of mm is proposed for portable ultrawideband (UWB) applications. The antenna consists of two planar-monopole (PM) antenna elements with microstrip-fed printed on one side of the substrate and placed perpendicularly to each other to achieve good isolation. To enhance isolation and increase impedance bandwidth, two long protruding ground stubs are added to the ground plane on the other side and a short ground strip is used to connect the ground planes of the two PMs together to form a common ground. Simulation and measurement are used to study the antenna performance in terms of reflection coefficients at the two input ports, coupling between the two input ports, radiation pattern, realized peak gain, efficiency and envelope correlation coefficient for pattern diversity. Results show that the MIMO antenna has an impedance bandwidth of larger than 3.1–10.6 GHz, low mutual coupling of less than 15 dB, and a low envelope correlation coefficient of less than 0.2 across the frequency band, making it a good candidate for portable UWB applications.
Know-Who Based Innovation Networks : For Cross-Level Breakthroughs

The overall purpose of this paper is to enhance our understanding of how innovation can be managed across different networks and at different levels of analysis based on know who, i.e., better knowledge of who knows what in strategically relevant areas of knowledge. We address recent innovation literature and link this to a more extensive review of leading networking theories for new combinations across these disciplines. An abductive research method is applied based on iteration between an indepth case study of a sports car manufacturer, and the development of a theoretical framework, to help us better analyze and understand how breakthrough innovation depends on human interaction across different types and levels of networks. Our research findings are that the stage of profiting from networked innovation is mainly a result of having openness and building human bridges across the entire process of innovation, but to manage the openness through different network structures depending on whether the focus is on exploration, or on exploitation. By managing these different network structures, a company does not only have an open policy to the exploration phase, but can be partially open in the exploitation phase to allow for other companies to profit from its own innovation efforts, while getting licensing income in return. As a main contribution of our paper, the human bridges and links are integrated into a new management approach that supports the transformation of science into sales – using know-who based networks to manage the transition from knowledge creation to business implementation. This approach can enhance a company’s ability to capture synergies between external research and corporate technology innovation and thereby balance exploration and exploitation for faster commercialization of breakthrough innovation.
Integrated optical time division reflectometer

An optical time domain reflectometer (OTDR) is a device commonly used for testing and monitoring of fiber-optic networks of every kind [1,2]. Despite significant progress in the field, it should be noted that contemporary devices are typically bulky, comprising discrete optoelectronic components and designed for monitoring of a single optical fiber link only. In this work we propose two application specific photonic integrated circuits (ASPICs) designed and developed for a novel OTDR system, which combines the advantages of compactness and multi-channel operation.
FROG: A Fast and Reliable Crowdsourcing Framework

For decades, the crowdsourcing has gained much attention from both academia and industry, which outsources a number of tasks to human workers. Typically, existing crowdsourcing platforms include CrowdFlower, Amazon Mechanical Turk (AMT), and so on, in which workers can autonomously select tasks to do. However, due to the unreliability of workers or the difficulties of tasks, workers may sometimes finish doing tasks either with incorrect/incomplete answers or with significant time delays. Existing studies considered improving the task accuracy through voting or learning methods, they usually did not fully take into account reducing the latency of the task completion. This is especially critical, when a task requester posts a group of tasks (e.g., sentiment analysis), and one can only obtain answers of all tasks after the last task is accomplished. As a consequence, the time delay of even one task in this group could delay the next step of the task requester’s work from minutes to days, which is quite undesirable for the task requester. Inspired by the importance of the task accuracy and latency, in this paper, we will propose a novel crowdsourcing framework, namely Fast and Reliable crOwdsourcinG framework (FROG), which intelligently assigns tasks to workers, such that the latencies of tasks are reduced and the expected accuracies of tasks are met. Specifically, our FROG framework consists of two important components, task scheduler and notification modules. For the task scheduler module, we formalize a FROG task scheduling (FROG-TS) problem, in which the server actively assigns workers to tasks to achieve high task reliability and low task latency. We prove that the FROG-TS problem is NP-hard. Thus, we design two heuristic approaches, request-based and batch-based scheduling. For the notification module, we define an efficient worker notifying (EWN) problem, which only sends task invitations to those workers with high probabilities of accepting the tasks. To tackle the EWN problem, we propose a smooth kernel density estimation approach to estimate the probability that a worker accepts the task invitation. Through extensive experiments, we demonstrate the effectiveness and efficiency of our proposed FROG platform on both real and synthetic data sets.
Sensors Layer Data Retrieval Layer Context Processing Layer Applications Layer Context Management Layer

In the scope of ubiquitous computing, one of the key issues is the awareness of context, which includes diverse aspects of the user’s situation including his activities, physical surroundings, location, emotions and social relations, device and network characteristics and their interaction with each other. This contextual knowledge is typically acquired from physical, virtual or logical sensors. To overcome problems of heterogeneity and hide complexity, a significant number of middleware approaches have been proposed for systematic and coherent access to manifold context parameters. These frameworks deal particularly with context representation, context management and reasoning, i.e. deriving abstract knowledge from raw sensor data. This article surveys not only related work in these three categories but also the required evaluation principles.
Bl 235 IMPROVED IMPLEMENTATION OF AN APPROXIMATION ALGORITHM VVITH FACTOR TWO FOR A CYCLIC ROUTING PROBLEM OF GRASP-AND-DELIVERY ROBOTS

In this paper, we consider a cyclic routing problem of a single grasp-and-delivery rebot with unit capacity, which is working at an assembly line of printed circuit beards. The grasp-and-delivery robot arranges n identical pins from their current configuration to the next required configuration by transfening the pins one by one. The n pins support a printed circuit board from underneath to prevent it from overbending, while an automated manipulator embeds a number of electronic parts onto the printed circuit board from above. Given an initial configuration and a sequence of m required cenfigurations, the routing problem asks to find a transfer route of the grasp-and-delivery robot which minimizes the route length over all m cycles. In this paper, we design a dynamic programming procedure to improye the empirical perfbrrnance of a known polynomial time approximation algorithm with factor two, and also conduct numerical experiments to test the proposed procedure.
THE INTEGRATION OF TEXT AND IMAGE, ITS COGNITIVE IMPACTS FOR LEARNING WITH

This paper outlines a recent quasi-experimental study in fulfillment of the Ph.D. in Design. The study stands as an example of new research in the formation of a discipline of design (from a practice or field), according to a statistical model most consistent with psychological research. (The Ph.D. in Design can also extend into historical and argumentative models of research.) The study outlined (Peterson, 2011) addressed the design of instructional media both holistically and authentically by focusing on text–image relationships at the level of design strategy. Three text–image integration strategies are proposed and illustrated herein: prose primary (PP), with a central prose column and marginal imagery; prose subsumed (PS), with shorter prose segmented by imagery; and fully integrated (FI), where smaller textual chunks populate imagery. Over 150 middle school students participated in this study. Text–image integration strategies were tested for comprehension of abstract concepts, sense of difficulty, and interest level. Subjects completed comprehension tests using the supplied media. They rated their sense of difficulty and their interest in science textbook pages designed according to the outlined strategies. Their selections of interest strongly favored higher levels of text–image integration, such that FI was rated more interesting than PS, which was in turn more interesting than PP. Interest-level results were rated reliable and significant at a 95% confidence level. Comprehension results were less conclusive, with one treatment of FI proving more effective than PP, while two other treatments were found statistically insignificant. There was no suggestion that text–image integration strategy impacts sense of task difficulty.
First Class Overloading via Insersection Type Parameters ?

The Hindley-Milner type system imposes the restriction that function parameters must have monomorphic types. Lifting this restriction and providing system F “first class” polymorphism is clearly desirable, but comes with the difficulty that inference of types for system F is undecidable. More practical type systems incorporating types of higherrank have recently been proposed, that rely on system F but require type annotations for the definition of functions with polymorphic type parameters. However, these type annotations inevitably disallow some possible uses of higher-rank functions. To avoid this problem and to promote code reuse, we explore using intersection types for specifying the types of function parameters that are used polymorphically inside the function body, allowing a flexible use of such functions, on applications to both polymorphic or overloaded arguments.