Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint signal detection and parameter estimation in multiuser communications

The problem of simultaneously detecting the information bits and estimating signal amplitudes and phases in a K-user asynchronous direct-sequence spread-spectrum multiple-access communication system is addressed. The joint maximum-likelihood (ML) estimator has a computational complexity that is exponential in the total number of bits transmitted and thus does not represent a practical solution to the problem. An estimator that combines a suboptimum tree-search algorithm with a recursive least-squares estimator of complex signal amplitude is considered. The complexity of this estimator is O(K/sup 2/) computations per decoded bit, and its performance is very close to that of the joint ML receiver. This receiver has the advantage that the transmitted signal powers and phases are extracted from the received signal in an adaptive fashion without using a test sequence. >
Computerized biological brain phantom for evaluation of PET and SPECT reconstruction

A digital brain phantom was created from available primate autoradiographic (AR) data for use in emission computed tomography studies. The tissue was radio-labelled with a functional analogue of the PET agent [/sup 18/F]-fluoro-deoxyglucose (FDG). Following sacrifice of the animal, film records from serial 20 /spl mu/m thickness sections were digitized and calibrated to obtain ground truth 2D spatial distributions of relative radionuclide density. A 3D version was constructed by using a video subtraction method to align consecutive slices. In order to assess the effects of accurate modelling of activity, the AR data, containing cortical and basal ganglia structures, was used as a phantom in the context of a partial-volume correction method for obtaining accurate regional quantitation. A second phantom, less realistic in terms of activity assignment, was constructed and also tested. The results indicate that quantitation errors due to effects of nonuniform activity in the AR phantom are significant and comparable in magnitude to errors due to non-phantom effects.
Age and energy intake interact to modify cell stress pathways and stroke outcome

Age and excessive energy intake/obesity are risk factors for cerebrovascular disease, but it is not known if and how these factors affect the extent of brain damage and outcome in ischemic stroke. We therefore determined the interactions of age and energy intake on the outcome of ischemic brain injury, and elucidated the underlying mechanisms.
Lower Bounds for Constant Query Affine-Invariant LCCs and LTCs

Affine-invariant codes are codes whose coordinates form a vector space over a finite field and which are invariant under affine transformations of the coordinate space. They form a natural, well-studied class of codes; they include popular codes such as Reed-Muller and Reed-Solomon. A particularly appealing feature of affine-invariant codes is that they seem well suited to admit local correctors and testers. In this work, we give lower bounds on the length of locally correctable and locally testable affine-invariant codes with constant query complexity. We show that if a code C ⊂ ΣKn is an r-query affine invariant locally correctable code (LCC), where K is a finite field and Σ is a finite alphabet, then the number of codewords in C is at most exp(OK,r,|Σ|(nr−1)). Also, we show that if C ⊂ ΣKn is an r-query affine invariant locally testable code (LTC), then the number of codewords in C is at most exp(OK,r,|Σ|(nr−2)). The dependence on n in these bounds is tight for constant-query LCCs/LTCs, since Guo, Kopparty, and Sudan (ITCS’13) constructed affine-invariant codes via lifting that have the same asymptotic tradeoffs. Note that our result holds for non-linear codes, whereas previously, Ben-Sasson and Sudan (RANDOM’11) assumed linearity to derive similar results. Our analysis uses higher-order Fourier analysis. In particular, we show that the codewords corresponding to an affine-invariant LCC/LTC must be far from each other with respect to Gowers norm of an appropriate order. This then allows us to bound the number of codewords, using known decomposition theorems, which approximate any bounded function in terms of a finite number of low-degree non-classical polynomials, up to a small error in the Gowers norm.
A Language Independent Method for Question Classification

Previous works on question classification are based on complex natural language processing techniques: named entity extractors, parsers, chunkers, etc. While these approaches have proven to be effective they have the disadvantage of being targeted to a particular language. We present here a simple approach that exploits lexical features and the Internet to train a classifier, namely a Support Vector Machine. The main feature of this method is that it can be applied to different languages without requiring major modifications. Experimental results of this method on English, Italian and Spanish show that this approach can be a practical tool for question answering systems, reaching a classification accuracy as high as 88.92%.
Threats, Abuses, Flirting, and Blackmail: Gender Inequity in Social Media Voice Forums

HCI4D researchers and practitioners have leveraged voice forums to enable people with literacy, socioeconomic, and connectivity barriers to access, report, and share information. Although voice forums have received impassioned usage from low-income, low-literate, rural, tribal, and disabled communities in diverse HCI4D contexts, the participation of women in these services is almost non-existent. In this paper, we investigate the reasons for the low participation of women in social media voice forums by examining the use of Sangeet Swara in India and Baang in Pakistan by marginalized women and men. Our mixed-methods approach spanning content analysis of audio posts, quantitative analysis of interactions between users, and qualitative interviews with users indicate gender inequity due to deep-rooted patriarchal values. We found that women on these forums faced systemic discrimination and encountered abusive content, flirts, threats, and harassment. We discuss design recommendations to create social media voice forums that foster gender equity in use of these services.
Iterative Edge-Preserving Adaptive Wiener Filter for Image Denoising

In this paper, an image denoising method for noisy image corrupted by additive white noise is proposed. It is well known that the Adaptive Wiener Filter (AWF) is suitable for such denoising. However, some noises remain in the image processed by AWF. In order to improve the performance of the AWF, an iterative algorithm is derived. To prevent original image signal loss, a weighting parameter is used for the noise variance estimate and a technique adjusting the filter kernel is employed. Compared to the conventional AWF, the proposed filter provides better edge performance.
Brain Size and Life History Interact to Predict Urban Tolerance in Birds

Urbanization is a major driver of local biodiversity losses, but the traits that determine whether species are able to tolerate urban environments remain poorly understood. Theory suggests that a larger brain should provide higher tolerance to urbanization by enhancing behavioral flexibility to cope with novel challenges. However, assembling empirical evidence for a link between brain size and tolerance to urbanization has proven to be difficult, perhaps because the effect of the brain interacts with life history to influence persistence in urban environments. Here, we provide a global-scale assessment of the role of brain size on urban tolerance, combining quantitative estimations of urban tolerance with detailed information on brain size, life history and ecology for 629 avian species across 27 cities. Our analysis confirms the expected positive association between brain size and urban tolerance, but shows that the relationship is more complex than previously shown. While a large relative brain size generally increases urban tolerance, species with small brains can still attain high success in urban environments if they spread the risk of reproduction across multiple events (i.e., have a low brood value). These alternative strategies, although uncommon in natural conditions, seem to be favored in urban environments, fundamentally restructuring the composition of urban communities. Thus, our results support the notion that brain size mediates tolerance to urbanization, but also shows that there are alternative ways of exploiting urban environments. Our findings reconcile previous conflicting results regarding the effect of brain size on urban tolerance, and provide the basis for improved predictions of the responses of organisms to increasing urbanization over the coming decades.
Concept assignment as a debugging technique for code generators

Code generators are notoriously difficult to debug, yet their correctness is crucial. This paper demonstrates that concept assignment can be performed in an entirely syntax-directed manner for code generators and other highly structured program modules. Anomalies in this concept assignment indicate the possible existence of bugs. These insights enable the formulation of a new debugging technique for code generators. This paper describes the procedure, a practical implementation, and results from the application of this debugging technique to an experimental code generator.
Random walks with the minimum degree local rule have $O(n^2)$ cover time

For a simple (unbiased) random walk on a connected graph with $n$ vertices, the cover time (the expected number of steps it takes to visit all vertices) is at most $O(n^3)$. We consider locally biased random walks, in which the probability of traversing an edge depends on the degrees of its endpoints. We confirm a conjecture of Abdullah, Cooper and Draief [2015] that the min-degree local bias rule ensures a cover time of $O(n^2)$. For this we formulate and prove the following lemma about spanning trees. Let $R(e)$ denote for edge $e$ the minimum degree among its two endpoints. We say that a weight function $W$ for the edges is feasible if it is nonnegative, dominated by $R$ (for every edge $W(e) \le R(e)$) and the sum over all edges of the ratios $W(e)/R(e)$ equals $n-1$. For example, in trees $W(e) = R(e)$, and in regular graphs the sum of edge weights is $d(n-1)$. {\bf Lemma:} for every feasible $W$, the minimum weight spanning tree has total weight $O(n)$. For regular graphs, a similar lemma was proved by Kahn, Linial, Nisan and Saks [1989].
Comparison of methodological data measurement limits in CD4+ T lymphocyte flow cytometric enumeration and their clinical impact on HIV management

UK NEQAS for Leucocyte Immunophenotyping, an ILAC G13:2000 accredited External Quality Assessment (EQA) organization, with over 3000 international laboratories participating in 14 programmes, issues 2 proficiency testing samples of stabilized whole blood to 824 participants in the Immune Monitoring (lymphocyte subset) programme every two months. We have undertaken a study of 58,626 flow cytometric absolute CD4+ T lymphocyte count data sets from these laboratories over a 12‐year‐period (2001–2012) to determine counting method variation in data measurement limits and how this could influence the clinical management of HIV patients.
Managing CALIPSOneo Project: Learning from Trenches

The model-driven engineering paradigm is a paradigm that combines the power of concepts and its relations, using abstracts models in order to offer suitable mechanisms for software development. In fact, software engineering community has applied MDE in different contexts of software engineering, getting suitable results. Despite these mechanisms, both global coordination and effective management of big software projects are not always easy task. Lack in communication lines, configuration plan execution and risky management are activities that have been executed by development teams in order to assure the quality of results, an essential point for a project to succeed. The paper’s aim is to present an approach based on the NDT methodology in order to solve these problems pulsar and how it has been applied in a real project called CALIPSOneo.
Automatic Craniofacial Structure Detection on Cephalometric Images

Anatomical structure tracing on cephalograms is a significant way to obtain cephalometric analysis. Cephalometric analysis is divided in two categories, manual and automatic approaches. The manual approach is limited in accuracy and repeatability due to differences in inter- and intra-personal marking. In this paper, we have attempted to develop and test a novel method for automatic localization of craniofacial structures based on the detected edges in the region of interest. Before edge detection of the particular region, the region was filtered by adaptive non local filter for noise removal by keeping the edge information undisturbed. According to the gray-scale feature at the different regions of the cephalograms, modified Canny edge detection algorithm for obtaining tissue contour was proposed. With the application of morphological opening and edge linking approaches, an improved bidirectional contour tracing methodology was proposed by an interactive selection of the starting edge pixels, the tracking process searches repetitively for an edge pixel at the neighborhood of previously searched edge pixel to segment images, and then craniofacial structures are obtained. The effectiveness of the algorithm is demonstrated by the preliminary experimental results obtained with the proposed method.
DMove: Directional Motion-based Interaction for Augmented Reality Head-Mounted Displays

We present DMove, directional motion-based interaction for Augmented Reality (AR) Head-Mounted Displays (HMDs) that is both hands- and device-free. It uses directional walk-ing as a way to interact with virtual objects. To use DMove, a user needs to perform directional motions such as mov-ing one foot forward or backward. In this research, we first investigate the recognition accuracy of the motion direc-tions of our method and the social acceptance of this type of interactions together with users' comfort rating for each direction. We then optimize its design and conduct a sec-ond study to compare DMove in task performance and user preferences (workload, motion sickness, user experience), with two approaches-Hand interaction (Meta 2-like) and Head+Hand interaction (HoloLens-like) for menu selection tasks. Based on the results of these two studies, we provide a set of guidelines for DMove and further demonstrate two applications that utilize directional motions.
Cry-based detection of developmental disorders in infants

Developmental disorders are a group of neurological conditions originating in early neural development. These disorders involve serious impairments in language, learning, and motor skills. An early detection of developmental disorders is crucial, as it enables early intervention (e.g., speech-language and occupational therapy) that may reduce neurological and functional deficits. In this work, we propose a k-nearest neighbours (k-nn) classifier for an early identification of developmental disorders in infants based on their cry. The classifier is based on temporal and spectral features extracted from the cry signal, where the contribution of each feature is estimated in an optimization process. Performance is evaluated against a database of diagnosed infants, with 89% accuracy in cross-validation testing.
Fine-Grained Recognition with Automatic and Efficient Part Attention

Fine-grained recognition is challenging due to the subtle local inter-class differences versus the large intra-class variations such as poses. A key to address this problem is to localize discriminative parts to extract pose-invariant features. However, ground-truth part annotations can be expensive to acquire. Moreover, it is hard to deﬁne parts for many ﬁne-grained classes. This work introduces Fully Convolutional Attention Networks (FCANs) , a reinforcement learning framework to optimally glimpse local discriminative regions adaptive to different ﬁne-grained domains. Compared to previous methods, our approach enjoys four advantages: 1) the three components including feature extraction, visual attention and ﬁne-grained classi-ﬁcation are uniﬁed in an end-to-end system; 2) the weakly-supervised reinforcement learning procedure requires no expensive part annotations; 3) the fully-convolutional architecture speeds up both training and testing; 4) the greedy reward strategy accelerates the convergence of the learning. We demonstrate the effectiveness of our method with extensive experiments on four challenging ﬁne-grained benchmark datasets, including Stanford Dogs, Stanford Cars, CUB-200-2011 and Food-101.
Simulation environment of X-ray rotational angiography using 3D+t coronary tree model

The newly introduced cardiac rotational angiography (RA) can provide a large amount of projections from different angles which greatly improve the 3D coronary tree reconstruction. However, the reconstruction methods are difficult to be objectively evaluated due to the complicated topology of coronary tree and non-linear cardiac motion. In this paper, we present a simulation environment of rotational angiography acquisition system to facilitate the improvements and the evaluations of reconstruction algorithms. A 3D+t coronary tree model reconstructed from MSCT sequence is employed to enhance the reality of simulation. A simulation environment of X-ray coronary angiography is developed based on distance-driven projection algorithm. The static angiography is firstly simulated to verify the dynamic model by comparing the displacements of landmarks with the real static angiography of the same patient. Rotational simulation results are then obtained with real system parameters to provide a complete and true-to-life RA sequence representing the morphology of moving coronary tree.
Engineering change using spare cells with constant insertion

In the VLSI design process, a design implementation often needs to be corrected because of new specifications or design constraint violations. This correction process is referred to as engineering change (EC). Usually, an EC problem is resolved by using spare cells, which have been inserted into the unused spaces of a chip. In this paper, we propose an iterative method to generate feasible mapping solutions for an EC problem considering spare cells whose inputs may be tied to Vdd or Gnd, called constant insertion. Applying constant insertion can increase a cell's flexibility in aspect of functionalities, so far-away spare cells need not be used just for some specific functionality. Our experimental results show that the area in which there are enough spare cells for a mapping solution with constant insertion is only 82% of the area without constant insertion.
NRC Publications

- Range scanning of building interiors generates very large, partially spurious and unstructured point cloud data. Accurate information extraction from such data sets is a complex task due to the presence of multiple objects, diversity of their shapes, large disparity in the feature sizes, and the spatial uncertainty due to occluded regions. A fast segmentation of such data is necessary for quick understanding of the scanned scene. Unfortunately, traditional range segmentation methodologies are computationally expensive because they rely almost exclusively on shape parameters (normal, curvature) and are highly sensitive to small geometric distortions in the captured data. This paper introduces a quick and effective segmentation technique for large volumes of colorized range scans from unknown building interiors and labelling clusters of points that represent distinct surfaces and objects in the scene. Rather than computing geometric parameters, the proposed technique uses a robust Hue, Saturation and Value (HSV) color model as an effective means of identifying rough clusters (objects) that are further refined by eliminating spurious and outlier points through region growth and a fixed distance neighbors (FDNs) analysis. The results demonstrate that the proposed method is effective in identifying continuous clusters and can extract meaningful object clusters, even from geometrically similar regions.
The Name-Letter-Effect in Groups: Sharing Initials with Group Members Increases the Quality of Group Work

Although the name-letter-effect has been demonstrated reliably in choice contexts, recent research has called into question the existence of the name-letter-effect–the tendency among people to make choices that bear remarkable similarity with the letters in their own name. In this paper, we propose a connection between the name-letter-effect and interpersonal, group-level behavior that has not been previously captured in the literature. Specifically, we suggest that sharing initials with other group members promotes positive feelings toward those group members that in turn affect group outcomes. Using both field and laboratory studies, we found that sharing initials with group members cause groups to perform better by demonstrating greater performance, collective efficacy, adaptive conflict, and accuracy (on a hidden-profile task). Although many studies have investigated the effects of member similarity on various outcomes, our research demonstrates how minimal a degree of similarity among members is sufficient to influence quality of group outcomes.
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Genetic Programming (GP) has recently been applied to solve problems in several areas. It has the goal of inducing programs from test cases by using the concepts of Darwin's evolution theory. On the other hand, software testing, that is a fundamental and expensive activity for software quality assurance, has the objective of generating test cases from the program being tested. In this sense, a symmetry between induction of programs based on GP and testing is noticed. Based on such symmetry, this work presents GPTesT, a testing tool based on GP. Faultbased testing criteria generally derive test data using a set of mutant operators to produce alternatives that di er from the program under testing by a simple modi cation. GPtesT uses a set of alternatives genetically derived, which allow the test of interactions between faults. GPTesT implements two test procedures respectively for guiding the selection and evaluation of test data sets. Examples with these procedures show that the approach can be used as a testing criterion.
Classification of Illumination Methods for Mixed Reality

A mixed reality (MR) represents an environment composed both by real and virtual objects. MR applications are used more and more, for instance in surgery, architecture, cultural heritage, entertainment, etc. For some of these applications it is important to merge the real and virtual elements using consistent illumination. This paper proposes a classification of illumination methods for MR applications that aim at generating a merged environment in which illumination and shadows are consistent. Three different illumination methods can be identified: common illumination, relighting and methods based on inverse illumination. In this paper a classification of the illumination methods for MR is given based on their input requirements: the amount of geometry and radiance known of the real environment. This led us to define four categories of methods that vary depending on the type of geometric model used for representing the real scene, and the sdifferent radiance information available for each point of the real scene. Various methods are described within their category.