Miracle-dz/UniFAR-FTUs huggingface dataset:
Batmen - Hybrid collaborative object manipulation using mobile devices

We present an interactive and collaborative 3D object manipulation system using off the shelf mobile devices coupled with Augmented Reality (AR) technology that allows multiple users to collaborate concurrently on a scene. Each user interested in participating in this collaboration uses both a mobile device running android and a desktop (or laptop) working in tandem. The 3D scene is visualized by the user in the desktop system. The changes in the scene viewpoint and the object manipulation are performed using a mobile device through object tracking. Multiple users can collaborate on object manipulation by using a laptop and a mobile device each. The system leverages user's knowledge of common tasks performed on current mobile devices using gestures. We built a prototype system that allows users to complete the requested tasks and performed an informal user study with experienced VR researchers to validate the system.
Common Polymorphisms Influencing Serum Uric Acid Levels Contribute to Susceptibility to Gout, but Not to Coronary Artery Disease

Background Recently, a large meta-analysis including over 28,000 participants identified nine different loci with association to serum uric acid (UA) levels. Since elevated serum UA levels potentially cause gout and are a possible risk factor for coronary artery disease (CAD) and myocardial infarction (MI), we performed two large case-control association analyses with participants from the German MI Family Study. In the first study, we assessed the association of the qualitative trait gout and ten single nucleotide polymorphisms (SNP) markers that showed association to UA serum levels. In the second study, the same genetic polymorphisms were analyzed for association with CAD. Methods and Findings A total of 683 patients suffering from gout and 1,563 healthy controls from the German MI Family Study were genotyped. Nine SNPs were identified from a recently performed genome-wide meta-analysis on serum UA levels (rs12129861, rs780094, rs734553, rs2231142, rs742132, rs1183201, rs12356193, rs17300741 and rs505802). Additionally, the marker rs6855911 was included which has been associated with gout in our cohort in a previous study. SNPs rs734553 and rs6855911, located in SLC2A9, and SNP rs2231142, known to be a missense polymorphism in ABCG2, were associated with gout (p = 5.6*10−7, p = 1.1*10−7, and p = 1.3*10−3, respectively). Other SNPs in the genes PDZK1, GCKR, LRRC16A, SLC17A1-SLC17A3, SLC16A9, SLC22A11 and SLC22A12 failed the significance level. None of the ten markers were associated with risk to CAD in our study sample of 1,473 CAD cases and 1,241 CAD-free controls. Conclusion SNP markers in SLC2A9 and ABCG2 genes were found to be strongly associated with the phenotype gout. However, not all SNP markers influencing serum UA levels were also directly associated with the clinical manifestation of gout in our study sample. In addition, none of these SNPs showed association with the risk to CAD in the German MI Family Study.
Composite Concept Discovery for Zero-Shot Video Event Detection

We consider automated detection of events in video without the use of any visual training examples. A common approach is to represent videos as classification scores obtained from a vocabulary of pre-trained concept classifiers. Where others construct the vocabulary by training individual concept classifiers, we propose to train classifiers for combination of concepts composed by Boolean logic operators. We call these concept combinations composite concepts and contribute an algorithm that automatically discovers them from existing video-level concept annotations. We discover composite concepts by jointly optimizing the accuracy of concept classifiers and their effectiveness for detecting events. We demonstrate that by combining concepts into composite concepts, we can train more accurate classifiers for the concept vocabulary, which leads to improved zero-shot event detection. Moreover, we demonstrate that by using different logic operators, namely "AND", "OR", we discover different types of composite concepts, which are complementary for zero-shot event detection. We perform a search for 20 events in 41K web videos from two test sets of the challenging TRECVID Multimedia Event Detection 2013 corpus. The experiments demonstrate the superior performance of the discovered composite concepts, compared to present-day alternatives, for zero-shot event detection.
Applying Distributed Source Coding Principles to Remote Sensing Image Compression

This paper deals with the application of distributed source coding (DSC) theory to remote sensing image processing systems. We first provide a brief overview of DSC of correlated information sources. We discuss several possible applications of DSC in a remote sensing system, aimed at improving the performance of on-board, on-the-ground and hybrid lossless image compression systems. We then focus on one specific application, namely on-board lossless image compression, and apply DSC techniques in order to reduce the complexity of the on-board encoder, at the expense of the decoder’s, by exploiting the correlation of different bands of a hyperspectral dataset. Specifically, we propose two different compression schemes, one based on powerful binary error-correcting codes employed as source codes, and one based on simpler multilevel coset codes. The performance of both schemes is evaluated on a few AVIRIS scenes, and compared with other state-of-the-art 2-D and 3-D compression schemes. Both schemes turn out to achieve very competitive compression performance, and one of them also has reduced complexity. Based on these results, we highlight the main technical issues that are still to be solved to further improve the performance of DSC-based remote sensing systems.
A Multi-layered Context Modelling Approach for End Users, Expert Users and Programmers

Context awareness plays an important role in smart environments and embedded interactions. In order to increase user satisfaction and acceptance, context-aware solutions should be controllable by end users. Over the last few years we have therefore witnessed an emerging trend of visual programming tools for context-aware applications based on simple “if this then that” rules. Unfortunately, existing solutions do not support the easy reuse of the “this” part in other rules. Further, the desired level of control varies among individuals. In order to let users choose the right level of automation and control, we propose a multi-layered context modelling approach distinguishing between end users, expert users and programmers. We report on our on-going development of the Context Modelling Toolkit (CMT) consisting of the necessary context modelling concepts as well as a rule-based context processing engine. We further discuss an initial design of the graphical user interface for the presented multi-layered context modelling approach.
Quality Evaluation of the Sequence Iris Image

Iris recognition technology is one of the most promising biological recognition technologies now. In order to ensure the efficiency and accuracy of recognition algorithm, we need to evaluate the quality of iris images. Five indexes are used to evaluate the iris images. These indexes are eyelid occlusion, living, definition, eyelashes occlusion and cosmetic contact lenses respectively. In this way, the poor quality of images will be removed, and the rest of images will be located and segmented. Experiment results show that the proposed method can accurately eliminate the poor quality of images. This result is consistent with the subjective evaluation. After image quality evaluation, the iris images will be located and segmented accurately. It is almost 100%.
Current Scenario of Powerful Clustering Technique

This paper explains clustering, a data mining technique which mainly focuses on the grouping of similar objects, in this technique many researchers have done the work in different areas like web document clustering, text clustering and different models of clustering like connectivity models(hierarchical clustering), centroid model (k-means),distribution models, density models, subspace model, group model, graph based model, this paper provides the view of different techniques and models.
Please Don't Try this at Home

Abstract The British chef Heston Blumenthal is at the forefront of a well-publicized shift within high-end restaurant cuisine towards avant-garde innovation and experimentation. At the same time, Blumenthal has increasingly pursued a parallel career as a television chef. This article considers how Blumenthal's TV shows rework the conventions of British cookery television through their foregrounding of cooking as an intellectual and aesthetic practice. It argues that this marks a break with the dominant form of TV cookery in the UK, in which cuisine is an achievable lifestyle practice, offering the promise of self-transformation and improvement. Using the work of Pierre Bourdieu and Stephen Mennell, the article discusses the ways in which such a televised demonstration of artistic integrity and autonomy has been used as a means to brand the celebrity chef as “a culinary alchemist” and to convert the symbolic profits accruing from culinary distinction into economic profits.
Rank-based characterization of pollen assemblages collected by honey bees using a multi-locus metabarcoding approach1

Premise of the study: Difficulties inherent in microscopic pollen identification have resulted in limited implementation for large-scale studies. Metabarcoding, a relatively novel approach, could make pollen analysis less onerous; however, improved understanding of the quantitative capacity of various plant metabarcode regions and primer sets is needed to ensure that such applications are accurate and precise. Methods and Results: We applied metabarcoding, targeting the ITS2, matK, and rbcL loci, to characterize six samples of pollen collected by honey bees, Apis mellifera. Additionally, samples were analyzed by light microscopy. We found significant rank-based associations between the relative abundance of pollen types within our samples as inferred by the two methods. Conclusions: Our findings suggest metabarcoding data from plastid loci, as opposed to the ribosomal locus, are more reliable for quantitative characterization of pollen assemblages. Furthermore, multilocus metabarcoding of pollen may be more reliable than single-locus analyses, underscoring the need for discovering novel barcodes and barcode combinations optimized for molecular palynology.
Comprehensible Visualization for Augmented Reality

This article presents interactive visualizations to support the comprehension of spatial relationships between virtual and real world objects for augmented reality (AR) applications. To enhance the clarity of such relationships we discuss visualization techniques and their suitability for AR. We apply them on different AR applications with different goals, e.g. in X-Ray vision or in applications which draw a user's attention to an object of interest. We demonstrate how Focus and Context (F+C) visualizations are used to affect the user's perception of hidden or nearby objects by presenting contextual information in the area of augmentation. We discuss the organization and the possible sources of data for visualizations in augmented reality and present cascaded and multi level F+C visualizations to address complex, cluttered scenes that are inevitable in real environments. This article also shows filters and tools to interactively control the amount of augmentation. It compares the impact of real world context preserving to a pure virtual and uniform enhancement of these structures for augmentations of real world imagery. Finally this paper discusses the stylization of sparse object representations for AR to improve x-ray vision.
CRLB and low-complex algorithm for joint estimation of carrier frequency/phase and IQ imbalance in direct-conversion receivers

Due to aggressive goals for future communication systems, direct-conversion receivers are an attractive option in terms of compactness and power dissipation. However, the price to pay is an additional impairment introduced by IQ imbalance, which might be compensated by appropriately designed DSP algorithms. In this context, joint recovery of carrier phase and IQ gain/phase imbalance has been presented in some recent publications. Since correlation of the noise components has been neglected, this issue was explored by the authors in a follow-on paper. In the current contribution, the carrier frequency offset is taken into account as well. Through several simplification steps, an approximate closed-form solution is developed for joint estimation of carrier frequency/phase and IQ gain/phase imbalance, whose jitter performance is close to the Cramer-Rao lower bound (CRLB) as the theoretical limit.
Guest Editors' Introduction: What Kinds of Nails Need a Domain-Specific Hammer?

Domain-specific techniques, languages, tools, and models, such as Fortran and Cobol can easily be viewed as domain-specific languages for scientific and business computing, respectively. Their domain is just very wide. What has changed is the technology for creating domain-specific languages (DSLs). Now it is easier to define languages and get tool support for narrower domains. Such focus offers increased abstraction, making development faster and easier. In domain-specific approaches, developers construct solutions from concepts representing things in the problem domain, not concepts of a given general-purpose programming language. Ideally, a DSL follows the domain abstractions and semantics as closely as possible, letting developers perceive themselves as working directly with domain concepts. The created specifications might then represent simultaneously the design, implementation, and documentation of the system, which can be generated directly from them. The mapping from the high-level domain concepts to implementation is possible because of the domain specificity: the language and code generators fit the requirements of a narrowly defined domain.
Automatic Performance Tuning of SpMV on GPGPU

Sparse Matrix-Vector Multiplication (SpMV) is an important computational kernel in scientific applications that tends to perform poorly on modern processors because of irregular memory accesses. GPU have evolved into a very attractive hardware platform for general purpose computations due to their high floating-point computation performance, which results in that GPGPU becomes the hot and popular topic in HPC. Therefore, we need to parallelize and optimize SpMV on GPGPU to get a better performance. In this paper, we studied the register-level blocking algorithm and the heuristic algorithm to optimize SpMV performance on GPGPU. Based on AMD Stream Computing, We propose an automatic performance tuning SpMV software package on GPGPU, with its name GOSpMV. Experimental results on AMD Radeon HD 3690 shows that, compared with the CSR format SpMV implementation on CPU, our GOSpMV can achieve an average speedup with 3.11 and the max speedup is 5.95.
Crystal structure of D‐glycero‐Β‐D‐manno‐heptose‐1‐phosphate adenylyltransferase from Burkholderia pseudomallei

The crystal structure of HldC from B. pseudomallei (BpHldC), the fourth enzyme of the heptose biosynthesis pathway, has been determined. BpHldC converts ATP and d‐glycero‐β‐d‐manno‐heptose‐1‐phosphate into ADP‐d‐glycero‐β‐d‐manno‐heptose and pyrophosphate. The crystal structure of BpHldC belongs to the nucleotidyltransferase α/β phosphodiesterase superfamily sharing a common Rossmann‐like α/β fold with a conserved T/HXGH sequence motif. The invariant catalytic key residues of BpHldC indicate that the core catalytic mechanism of BpHldC may be similar to that of other closest homologues. Intriguingly, a reorientation of the C‐terminal helix seems to guide open and close states of the active site for the catalytic reaction.
Towards a Small Set of Robust Acoustic Features for Emotion Recognition: Challenges

The search of a small acoustic feature set for emotion recognition faces three main challenges. Such a feature set must be robust to large diversity of contexts in real-life applications; model parameters must also be optimized for reduced subsets; finally, the result of feature selection must be evaluated in cross-corpus condition. The goal of the present study is to select a consensual set of acoustic features for valence recognition using classification and non-classification based feature ranking and cross-corpus experiments, and to optimize emotional models simultaneously. Five realistic corpora are used in this study: three of them were collected in the framework of the French project on robotics ROMEO, one is a game corpus (JEMO) and one is the well-known AIBO corpus. Combinations of features found with non-classification based methods (information gain and Gaussian mixture models with Bhattacharyya distance) through multi-corpora experiments are tested under cross-corpus conditions, simultaneously with SVM parameters optimization. Reducing the number of features goes in pair with optimizing model parameters. Experiments carried on randomly selected features from two acoustic feature sets show that a feature space reduction is needed to avoid over-fitting. Since a Grid search tends to find non-standard values with small feature sets, the authors propose a multi-corpus optimization method based on different corpora and acoustic feature subsets which ensures more stability. The results show that acoustic families selected with both feature ranking methods are not relevant in cross-corpus experiments. Promising results have been obtained with a small set of 24 voiced cepstral coefficients while this family was ranked in the 2nd and 5th positions with both ranking methods. The proposed optimization method is more robust than the usual Grid search for cross-corpus experiments with small feature sets.
Case-Based Reasoning: An Overview

This paper contains a brief overview of case-based reasoning (CBR) with an emphasis on European activities in the field. The main objective was to have a balance between brevity and expressiveness and providing helpful pointers to the field. It identifies major open problems of CBR associated with: retrieval/selection, memory organization, matching, adaptation/evaluation, forgetting and, finally, integration with other techniques. It is intended for readers with knowledge in the area and contains a list of almost one hundred references in the field.
Examining the importance of the family on pre-school attachment and children's social development

Contemporary accounts of attachment theory are usually provided in terms of the mother-child dyad, isolated from the family. This focus has served to ostracise the caregiving context, once viewed as central to understanding child attachment quality (Bowlby, 1969/1982). While a call for more contextual attachment research has been in place for some time, there remains a lack of ecologically valid attachment research. Observations of naturally occurring family interactions were assessed using the Attachment Q-Set, the Parental Secure Base Support Q-Set, Ainsworth’s Sensitivity scale and Bales analysis. In addition, parental and nursery reports of the Teachers Checklist for Social Behaviour and the Child Behaviour Scale were collected. In doing so, the current research expands upon knowledge related to the role of mothers, fathers, family structure, siblings and the stability of attachment in the home. Study One examined the validity of measuring attachment quality in the presence of the family by establishing links between maternal attachment and both maternal sensitivity and child-peer interactions. Study Two went on to establish similar associations between paternal attachment and both paternal sensitivity and child-peer interactions. Study Three investigated differences between maternal attachment quality, mother-child interactions and child-peer interactions across three family types (those with an absent father, uninvolved father and involved father). Study Four investigated differences in sibling attachment. Study Five explored the role of the child’s context in their stability of attachment quality over six months and Study Six extended this by exploring attachment stability in a military family case study over a year.
A Novel Ecdysone Receptor Mediates Steroid-Regulated Developmental Events during the Mid-Third Instar of Drosophila

The larval salivary gland of Drosophila melanogaster synthesizes and secretes glue glycoproteins that cement developing animals to a solid surface during metamorphosis. The steroid hormone 20-hydroxyecdysone (20E) is an essential signaling molecule that modulates most of the physiological functions of the larval gland. At the end of larval development, it is known that 20E—signaling through a nuclear receptor heterodimer consisting of EcR and USP—induces the early and late puffing cascade of the polytene chromosomes and causes the exocytosis of stored glue granules into the lumen of the gland. It has also been reported that an earlier pulse of hormone induces the temporally and spatially specific transcriptional activation of the glue genes; however, the receptor responsible for triggering this response has not been characterized. Here we show that the coordinated expression of the glue genes midway through the third instar is mediated by 20E acting to induce genes of the Broad Complex (BRC) through a receptor that is not an EcR/USP heterodimer. This result is novel because it demonstrates for the first time that at least some 20E-mediated, mid-larval, developmental responses are controlled by an uncharacterized receptor that does not contain an RXR-like component.
Approximation Algorithm for the Max k-CSP Problem

We present a ck 2 approximation algorithm for the Max k-CSP problem (where c > 0.44 is an absolute constant). This result improves the previously best known algorithm by Hast, which has an approximation guarantee of Ω( k 2 log k ). Our approximation guarantee matches the upper bound of Samorodnitsky and Trevisan up to a constant factor (their result assumes the Unique Games Conjecture).
Biologically-Inspired Computing Approaches To Cognitive Systems: a partial tour of the literature

cognitive systems, biologicallyinspired computing, artificial life, artificial intelligence, autonomous agents This paper presents a review of the academic literature on biologicallyinspired computing approaches to the science and engineering of cognitive systems. This review is intended as a rapid tour through the area (rather than a leisurely wander); and it should be readable in a few hours. The tour is partial in both senses of the word: it is only partially complete, and it is biased (i.e., it is not an impartial tour). Originally written in 1997, but revised and updated since, its primary intention is as background reading for undergraduate and postgraduate teaching in cognitive science and artificial intelligence. Its content is based on courses taught by the author at the University of Sussex (UK); at MIT (USA); and at the University of Southampton (UK).
internet Communication in Six Classrooms: Conversations Across Time, Space, and Culture

Contents: C. Bruce, Foreword. Preface. List of Contributors. Part I:Introduction. Introduction to Internet Conversations As Intensely Social Activity. Part II:Internet Communication in Six Classrooms. Ruth Coleman's Fifth-Grade Classroom: Involvement Strategies. Chris Meier's Fifth- and Sixth-Grade Classroom: Village Stories That Entertain and Teach. Hugh Dyment's High School Classroom: Talk Between Equals. Kathy Plamondon's Seventh-Grade Classroom: Social Activity on an E-Mail List. Daniel Wilcox's High School Classroom: A Collegial and Public Kind of Teaching. Kathy Nell's Fourth-Grade Classroom: Literature and Social Studies Activities On the Web. Part III:Patterns. Continuity Across Classrooms.