Miracle-dz/UniFAR-FTUs huggingface dataset:
An Interactive, Graphical Tool for Retrospectively Assessing Symptom Frequency and Severity: An Illustration With Eating Disorder Behaviors, Body Weight, and Stress

There are few assessments that gather valid, highly detailed data on short-term (i.e., weekly) symptom frequency/severity retrospectively. In particular, methodologies that provide valid data for research investigating symptom changes are typically prospective, expensive, and burdensome. The purpose of this study was to evaluate a new interactive and graphical assessment tool for gathering detailed information about eating-related symptom frequency/severity retrospectively over a 3-month period. A mixed eating disorder sample (N = 113) recruited from the community provided symptom data once weekly for 12 weeks and completed the Interactive, Graphical Assessment Tool (IGAT) assessing eating disorder symptoms on three occasions to determine the test–retest and concurrent validity of the IGAT. The IGAT performed marginally better than other measures for retrospective symptom frequency assessment in the eating disorders and did so at a greater level of detail than other available tools. Future research should evaluate the IGAT with other behaviors of interest.
PARAMETER MAPPING SONIC ARTICULATION AND THE PERCEIVING BODY

Indatasonificationresearch,thereisawell-knownperceptual problemthatariseswhenabstractmultivariatedatasetsofa certainsizeandcomplexityareparametricallymappedinto sound.Inlisteningtosuchsonifications,whenafeature appears,itissometimesdifficulttoascertainwhetherthat featureisactuallyafeatureofthedatasetorjustaresultantof thepsychoacousticinteractionbetweenco-dependent parametricdimensions.Asimilareffectoccursinvisualisation, suchaswhenparallellinescanappearmoreorlesscurvedon differentbackgrounds.Couchedinpsycho-philosophicalterms, wecanaskwhetherthisfailureisrelatedtoclassical phenomenology'sinabilitytoproduceaneideticscienceof essentialinvariantformsthatinvolvenoassertionofactual materialexistence,ortotherenotyethavingbeenfoundsome generalisablyacceptablelimitsfromheuristicallytested mappings.Thispaperdiscussesthenatureofthisproblemand introducesasonificationresearchprojectbasedonembodied, non-representational phenomenal models of perception.
Using annotations in the naked objects framework to explore data requirements

The creation of conceptual data design that appropriately represents specific application domain is one of the main challenges in requirements engineering. An initiative to help designers is the Naked Objects framework, where it is possible to interact with conceptual model in a limited way. The interactions are restricted to entity creations and single object-relations. We created an extension of the Naked Objects framework using annotations to allow manipulation of higher level abstractions as specialization and object-relationship. These abstractions allow better interactions between the domain specialist and designers. The use of our approach to explore and validate data requirements has several benefits: 1) It reduces conceptual specification problems (like poorly data requirements identification); 2) It narrows the distance among domain and design specialists; 3) It allows the simultaneous exploration of the conceptual data design and the system requirements.
Nonlocal-Lee filter for SAR image despeckling based on hybrid patch similarity

This paper presents a novel nonlocal Lee (NL-Lee) filter based on both structure similarity and homogeneity similarity, namely hybrid patch similarity, which can effectively enhance the patch regularity assumption. The proposed NL-Lee filter shares the framework of the NLM filter but combines the traditional Lee filter in a distributive way. Structure similarity from the NLM filter and homogeneity similarity from the Lee filter are well balanced in the new filter. As a result, the new NL-Lee filter obtains good trade-off between speckle smoothing and detail preservation and leads to state-of-the-art results.
Sparse low-rank fusion based deep features for missing modality face recognition

Multi-modality data recently attract more and more research attention. In this paper, we concentrate on a very interesting problem - image classification with missing modality. Specifically, only images in one modality as well as a relevant auxiliary database are accessible during the training phase, which is significantly different from general image classification under the same modality. To this end, we propose a novel framework integrating multiple deep autoencoders with bagging strategy. For each autoencoder, we generate its input by randomly sampling data from other modality and the auxiliary database, and enforce its output to lie in a common feature space through Robust PCA. Finally, a novel sparse low-rank feature fusion approach is proposed in the test phase to integrate multiple features learned from different autoencoders, followed by a decision voting. Extensive experiments on two databases, i.e., BUAA-NIRVIS, Oulu-CASIA NIRVIS databases demonstrate the effectiveness of the proposed framework when there is only one modality available for training.
BubbleType : Enabling Text Entry within a Walk-Up Tabletop Installation

We address the issue of enabling text entry for walk-up-and-use interactive tabletop displays located in public spaces. Public tabletop installations are characterized by a diverse target user group, multiperson interaction, and the need for high approachability and intuitiveness. We first define the design constraints of text-entry methods for public tabletop installations such as clear affordances, audience expertise, support of direct-touch interaction, visual appearance, space requirements, multi-user support, and technical simplicity. We then describe an iterative design process that was informed by these constraints and led to the development of two stylus keyboard prototypes—BubbleQWERTY and BubbleCIRCLE—for use in interactive public tabletop installations.
O ct 2 01 7 Manifold regularization based on Nyström type subsampling

In this paper, we study the Nyström type subsampling for large scale kernel methods to reduce the computational complexities of big data. We discuss the multi-penalty regularization scheme based on Nyström type subsampling which is motivated from well-studied manifold regularization schemes. We develop a theoretical analysis of multi-penalty least-square regularization scheme under the general source condition in vector-valued function setting, therefore the results can also be applied to multi-task learning problems. We achieve the optimal minimax convergence rates of multi-penalty regularization using the concept of effective dimension for the appropriate subsampling size. We discuss an aggregation approach based on linear function strategy to combine various Nyström approximants. Finally, we demonstrate the performance of multi-penalty regularization based on Nyström type subsampling on Caltech-101 data set for multi-class image classification and NSL-KDD benchmark data set for intrusion detection problem.
Padé methods for reconstruction and feature extraction in magnetic resonance imaging

Methods utilizing Padé approximants are investigated for implementation with magnetic resonance imaging data and are presented both for direct image reconstruction and for feature extraction. Padé approximants are a numerical tool that can be used to accelerate the convergence of a slowly converging sequence by estimating the fully converged sequence values from early data points. Padé approximants can be calculated directly from k‐space data by solving a set of linear matrix equations to produce signal values for any desired location in the image domain. This gives an estimate of the fully converged signal intensity at each pixel location in the image, raising the possibility of reconstructing a better estimate of the object from a reduced data set. These methods have been tested on phantom and human data both for image reconstruction and for feature extraction. In image reconstruction, considerable convergence acceleration can be achieved, with steep intensity boundaries reproduced in keeping with higher resolution reconstructions and oscillatory truncation artifact characteristic of Fourier reconstruction removed. The convergence acceleration is variable and there is the possibility of fine detail suppression when insufficient data are included. The use of Padé methods as a tool for feature extraction has shown good agreement with extraction from high‐resolution reference data. In this approach the edge information comes intrinsically from Padé reconstruction. Magn Reson Med, 2005. © 2005 Wiley‐Liss, Inc.
Calibration-free fusion of step counter and wireless fingerprints for indoor localization

In order to improve the accuracy of fingerprint-based localization, one may fuse step counter measurement with location estimation. Previous works on this often require a pre-calibrating the step counter with training sequence or explicit user input, which is inconvenient for practical deployment. Some assume conditional independence on successive sensor readings, which achieves unsatisfactory accuracy in complex and noisy environment. Some other works need a calibration process for RSSI measurement consistency if different devices are used for offline fingerprint collection and online location query. We propose SLAC, a fingerprint positioning framework which simultaneously localizes the target and calibrates the system. SLAC is calibration-free, and works transparently for heterogeneous devices and users. It is based on a novel formulation embedded with a specialized particle filter, where location estimations, wireless signals and user motion are jointly optimized with resultant consistent and correct model parameters. Extensive experimental trials at HKUST campus and Hong Kong International Airport further confirm that SLAC accommodates device heterogeneity, and achieves significantly lower errors compared with other state-of-the-art algorithms.
Rethinking Undergraduate Business Education

Much of the literature on management education focuses on the postgraduate level: this paper reviews the existing undergraduate business studies curriculum. Despite superficial indicators of health it is argued that there is a need for fundamental debate on the shape and purpose of this curriculum. Such debate, it is argued, opens up the space for the development of more critical perspectives. The paper seeks to suggest ways in which the curriculum might develop and how critical perspectives might be addressed.
Polynomial uniform convergence and polynomial-sample learnability

In this work we study the relationship between PAC learning and theproperty of uniform convergence. We define the concept of<? Pub Fmt italic>polynomial uniform convergence<? Pub Fmt /italic> ofrelative frequencies to probabilities in thedistribution–dependent context. Let<? Pub Fmt italic>X<subscrpt>n</subscrpt> =(0,1)<subscrpt>n</subscrpt><? Pub Fmt /italic>, let<? Pub Fmt italic>P<subscrpt>n</subscrpt><? Pub Fmt /italic> be aprobability distribution on<? Pub Fmt italic>X<subscrpt>n</subscrpt><? Pub Fmt /italic> and let<inline-equation><f>F<inf>n</inf>⊂2<sup>x</sup><inf>n</inf></f></inline-equation> be a class of events. The family{(<? Pub Fmt italic>X<subscrpt>n</subscrpt>, P<subscrpt>n</subscrpt>,F<subscrpt>n</subscrpt><? Pub Fmt /italic>)}n≥1 is said to bepolynomially uniformly convergent if, for all<? Pub Fmt italic>n<? Pub Fmt /italic>, the probability that the maximum difference (over<? Pub Fmt italic>F<subscrpt>n</subscrpt><? Pub Fmt /italic>) between therelative frequency and probability of an event exceed a given positiveε is at most δ (0 < δ < 1), when the sample on whichthe frequency is evaluated has size polynomial in<? Pub Fmt italic>n<? Pub Fmt /italic>, 1/ε, 1/δ. Givena<? Pub Fmt italic>t<? Pub Fmt /italic>-sample(<? Pub Fmt italic>x<subscrpt>1</subscrpt>,…,x<subscrpt>t</subscrpt><? Pub Fmt /italic>),let<? Pub Fmt italic>C<subscrpt>n</subscrpt><supscrpt>(t)</supscrpt>(x<subscrpt>1</subscrpt>,…,x<subscrpt>t</subscrpt><? Pub Fmt /italic>)be the Vapnik-Chervonenkis dimension (VCdim) of the set(x<subscrpt>1</subscrpt>,…x<subscrpt>t</subscrpt><inline-equation><f>∩</f></inline-equation> f | f <inline-equation><f><g>e</g></f></inline-equation> F<subscrpt>n</subscrpt> and<? Pub Fmt italic>M(n,t)<? Pub Fmt /italic> the expectation<? Pub Fmt italic>E(C<subscrpt>n</subscrpt><supscrpt>(t)</supscrpt>/t)<? Pub Fmt /italic>. The results we obtain are: <list><item>1. (X<subscrpt>n</subscrpt>,P<subscrpt>n</subscrpt>,F<subscrpt>n</subscrpt>)n≥1 is polynomially uniformly convergent iffthere exists β > 0 such that<? Pub Fmt italic>M(n,t)=O(n/t<supscrpt>β</supscrpt>)<? Pub Fmt /italic>. </item><item>2. The family {(X<subscrpt>n</subscrpt>,F<subscrpt>n</subscrpt>)}n≥1 is polynomially uniformly convergent forall probability distributions<? Pub Fmt italic>P<subscrpt>n</subscrpt><? Pub Fmt /italic> on<? Pub Fmt italic>X<subscrpt>n</subscrpt><? Pub Fmt /italic> iff VCdim(<? Pub Fmt italic>F<subscrpt>n</subscrpt><? Pub Fmt /italic>) isbounded by a polynomial <? Pub Fmt italic>p(n)<? Pub Fmt /italic> iff (X<subscrpt>n</subscrpt>, F<subscrpt>n</subscrpt>)≥1 ispolynomial–sample learnable. </item><item>3. If (X<subscrpt>n</subscrpt>, P<subscrpt>n</subscrpt>,F<subscrpt>n</subscrpt>) is polynomially uniformly convergent then(X<subscrpt>n</subscrpt>, P<subscrpt>n</subscrpt>, F<subscrpt>n</subscrpt>)≥1 is polynomial–sample learnable, butthere exist polynomial–sample learnable families(X<subscrpt>n</subscrpt>, P<subscrpt>n</subscrpt>, F<subscrpt>n</subscrpt>)≥1 which do not satisfy the property ofpolynomial uniform convergence. </item></list>
Asymptotic regularity of solutions to Hartree–Fock equations with Coulomb potential

We study the asymptotic regularity of solutions to Hartree–Fock (HF) equations for Coulomb systems. To deal with singular Coulomb potentials, Fock operators are discussed within the calculus of pseudo‐differential operators on conical manifolds. First, the non‐self‐consistent‐field case is considered, which means that the functions that enter into the nonlinear terms are not the eigenfunctions of the Fock operator itself. We introduce asymptotic regularity conditions on the functions that build up the Fock operator, which guarantee ellipticity for the local part of the Fock operator on the open stretched cone ℝ+ × S2. This proves the existence of a parametrix with a corresponding smoothing remainder from which it follows, via a bootstrap argument, that the eigenfunctions of the Fock operator again satisfy asymptotic regularity conditions. Using a fixed‐point approach based on Cancès and Le Bris analysis of the level‐shifting algorithm, we show via another bootstrap argument that the corresponding self‐consistent‐field solutions to the HF equation have the same type of asymptotic regularity. Copyright © 2008 John Wiley & Sons, Ltd.
'One Immense Gold Field!' British imaginings of the Australian gold rushes, 1851-59

The gold rushes of the 19th century opened up a range of new vistas and landscapes to the British public, widening popular awareness and knowledge of distant places. Between 1851 and 1859, the gold rushes in south-eastern Australia, in New South Wales and Victoria, stimulated the publication, in Britain, of written accounts of life on the gold fields. Here it is argued that these accounts were important in shaping popular perceptions of colonial Australia in imperial Britain, and creating an image of the 'ideal' emigrant. The literature stimulated by gold discoveries in Australia thus prompted yet further imperial exploitation and emigration, and was a form of popular geographical knowledge which today reveals much about colonial and metropolitan imaginings of Australia in the mid 19th century.
Flat Lorentz 3-Manifolds and Cocompact Fuchsian Groups

This paper gives a new proof of a result of Geoff Mess that the linear holonomy group of a complete flat Lorentz 3-manifold cannot be cocompact in SO(2,1). The proof uses a signed marked Lorentzian length-spectrum invariant developed by G.Margulis, reinterpreted in terms of deformations of hyperbolic surfaces.
Functional Brain Mapping in Freely Moving Rats during Treadmill Walking

A dilemma in functional neuroimaging is that immobilization of the subject, necessary to avoid movement artifact, extinguishes all but the simplest behaviors. Recently, we developed an implantable microbolus infusion pump (MIP) that allows bolus injection of radiotracers by remote activation in freely moving, nontethered animals. The MIP is examined as a tool for brain mapping in rats during a locomotor task. Cerebral blood flow–related tissue radioactivity (CBF-TR) was measured using [14C]-iodoantipyrine with an indicator-fractionation method, followed by autoradiography. Rats exposed to walking on a treadmill, compared to quiescent controls, showed increases in CBF-TR in motor circuits (primary motor cortex, dorsolateral striatum, ventrolateral thalamus, midline cerebellum, copula pyramis, paramedian lobule), in primary somatosensory cortex mapping the forelimbs, hindlimbs and trunk, as well as in secondary visual cortex. These results support the use of implantable pumps as adjunct tools for functional neuroimaging of behaviors that cannot be elicited in restrained or tethered animals.
Automatic Tuning of Inlining Heuristics

Inlining improves the performance of programs by reducing the overhead of method invocation and increasing the opportunities for compiler optimization. Incorrect inlining decisions, however, can degrade both the running and compilation time of a program. This is especially important for a dynamically compiled language such as Java. Therefore, the heuristics that control inlining must be carefully tuned to achieve a good balance between these two costs to reduce overall total execution time. This paper develops a genetic algorithms based approach to automatically tune a dynamic compiler’s internal inlining heuristic. We evaluate our technique within the Jikes RVM [1] compiler and show a 17% average reduction in total execution time on the SPECjvm98 benchmark suite on a Pentium-4. When applied to the DaCapo benchmark suite, our approach reduces total execution time by 37%% outperforming all existing techniques.
Phase mapping of optical fields in integrated optical waveguide structures

The phase evolution of optical waves in a waveguide structure has been studied with a heterodyne interferometric photon scanning tunneling microscope. Both phase and amplitude of the local optical field are measured with subwavelength resolution. Topographical maps of the waveguide surface are obtained simultaneously with the optical information. Unexpected phase patterns, with phase jumps and phase singularities, have been observed. The phase patterns can be fully understood by taking into account the total field that is the sum of the optical fields of the various modes. We show that with the unique spatial phase information, the relative field profiles and wave vectors of all the excited modes in a multimodal waveguide structure can be determined independently.
Feature-specific illumination patterns for automated visual inspection

The choice of an appropriate illumination design is one of the most important steps in creating successful machine vision systems for automated inspection tasks. In a popular technique, multiple inspection images are captured under angular-varying illumination directions over the hemisphere, which yields a set of images referred to as illumination series. However, most existing approaches are restricted in that they use rather simple and generic illumination patterns on the hemisphere. Furthermore, the spectrum of the illumination is assumed to be fixed and is not considered. In this paper, we present an illumination technique which reduces the effort of capturing a series of inspection images for individual reflectance features by using linear combinations of basis light patterns that vary in their directional and spectral radiance. The key idea is to encode linear functions for feature extraction as angular- and wavelength-dependent illumination patterns, and thereby to compute linear features from the scene's spectral reflectance field directly in the optical domain. Finally, we evaluate and verify the proposed illumination technique to the problem of optical material type classification of printed circuit boards (PCBs).
Effects of Religiosity and Spirituality on Depressive Symptoms and Prosocial Behaviors

SUMMARY Evidence demonstrates salubrious effects of religious participation on health-related outcomes. Results from studies relating its effects to psychosocial outcomes have been equivocal. However, many psychosocial outcomes have not been examined. The current study sought to address these limitations by testing the degree to which religious behaviors and subjective spirituality are associated with depressive symptoms and prosocial behaviors. Data from 68 older adults were used to test two linear regression models in which public religious behaviors, private religious behaviors, and subjective spirituality were used to predict depressive symptoms and prosocial behavior. This set of regressors accounted for significant amounts of variance in both outcomes, although a divergent pattern of prediction emerged. More public religious behaviors and fewer private religious behaviors were associated with lower levels of depressive affect, whereas higher reports of subjective spirituality were associated with increased prosocial behavior. The need to broaden the investigation of the effects of religiosity to include more specific predictors and an inclusion of psychosocial outcomes are both discussed.
A new algorithm for completing fragmented boundaries in images

In any image processing system, the process of detection and identification of individual objects within an image is often impeded by the presence of gaps in the boundaries of different objects. Various algorithms have been described in the literature for closing such gaps and hence completing fragmented boundaries making the detection of different objects within an image easier. In this paper a new algorithm, the Growing Circle Algorithm, is presented for completing broken boundaries. The algorithm was initially developed for completing the eyes' boundaries in medical images of the human face. In particular, the algorithm has been successfully implemented in a medical image processing system for diagnosis of paranasal sinuses conditions. In addition, the algorithm has also been successfully used for estimating and completing boundaries of varying geometry, complexity and degree of fragmentation.