Miracle-dz/UniFAR-FTUs huggingface dataset:
SemGraphQA@QALD5: LIMSI participation at QALD5@CLEF

For our participation to QALD-5, we developed a system for answering questions on a knowledge base. We proposed an unsupervised method for the semantic analysis of questions, that generates queries, based on graph transformations, in two steps. First step is independent of the knowledge base schema and makes use of very general constraints on the query structure that allows us to maintain semantic ambiguities in different graphs. Ambiguities are then solved globally at the final step when querying the knowledge base.
A stochastic game for privacy preserving context sensing on mobile phone

The proliferation of sensor-equipped smartphones has enabled an increasing number of context-aware applications that provide personalized services based on users' contexts. However, most of these applications aggressively collect users sensing data without providing clear statements on the usage and disclosure strategies of such sensitive information, which raises severe privacy concerns and leads to some initial investigation on privacy preservation mechanisms design. While most prior studies have assumed static adversary models, we investigate the context dynamics and call attention to the existence of intelligent adversaries. In this paper, we first identify the context privacy problem with consideration of the context dynamics and malicious adversaries with capabilities of adjusting their attacking strategies, and then formulate the interactive competition between users and adversaries as a zero-sum stochastic game. In addition, we propose an efficient minimax learning algorithm to obtain the optimal defense strategy. Our evaluations on real smartphone context traces of 94 users validate the proposed algorithm.
Generic mesh refinement on GPU

Many recent publications have shown that a large variety of computation involved in computer graphics can be moved from the CPU to the GPU, by a clever use of vertex or fragment shaders. Nonetheless there is still one kind of algorithms that is hard to translate from CPU to GPU: mesh refinement techniques. The main reason for this, is that vertex shaders available on current graphics hardware do not allow the generation of additional vertices on a mesh stored in graphics hardware. In this paper, we propose a general solution to generate mesh refinement on GPU. The main idea is to define a generic refinement pattern that will be used to virtually create additional inner vertices for a given polygon. These vertices are then translated according to some procedural displacement map defining the underlying geometry (similarly, the normal vectors may be transformed according to some procedural normal map). For illustration purpose, we use a tesselated triangular pattern, but many other refinement patterns may be employed. To show its flexibility, the technique has been applied on a large variety of refinement techniques: procedural displacement mapping, as well as more complex techniques such as curved PN-triangles or ST-meshes.
A MIMO antenna: Study on reducing mutual coupling and improving isolation

MIMO antenna has gained lot of attention for research today due to various advantages such as increase in capacity, low signal loss and less multipath fading. Although it has many benefits, the main challenge lies in the design of MIMO Antenna i.e to reduce the mutual coupling between the antenna element and to provide high isolation. To reduce the mutual coupling and for improving the isolation, various techniques are employed such as Defected Ground Structure such as slits, slot, spiral square structure, EBG structure, U shaped parasitic element, stubs, neutralization line, parasitic elements, etc. The mutual coupling of more than 20 dB is obtained by employing all these technique. The main focus of this paper is to present and compare the different technique to reduce the mutual coupling and increase the isolation for various frequency bands.
Critical Design Decisions for Cognitive Networks

The idea of the cognitive network, drawn from previous research in cognitive radios and cross-layer design, is an attempt to address issues of complexity and scalability by allowing the network to self-adapt and reach end-to-end goals. This paper identifies three critical design decisions that must be considered when creating a cognitive network: the behavior of the decision-making elements, amount of computational state available to these elements, and the degree of control they have. To measure the effect of these design tradeoffs, we develop and define a metric called the "price of a feature. " This metric is used to quantify the effect of these properties on a cognitive network with the objective of maximizing the lifetime of a multicast flow in a wireless environment. For this cognitive network, we show that the price of selfish behavior and partial control are more significant than the price of ignorance.
Detection and Classification of Changes in Buildings from Airborne Laser Scanning Data

Abstract. Building change detection serves to investigate illegal buildings. Illegal built or removed structures, especially those concealed among gable roofs such as dormers, are difficult to track among potentially millions of buildings. Nevertheless, they can be efficiently located in changed areas. An approach is proposed to automatically detect and classify changes in buildings from two epochs of Airborne Laser Scanning Data. Both datasets are classified into water, ground, building, vegetation and undefined objects in advance. After generalization of a 3D surface separation map, we verify changes by making rules on the separation map. Changes belonging to buildings are then classified into roof, wall, dormers, vehicles, construction above roof and undefined objects. As the ALS data has accuracy in strip difference of lower than 5 cm within the same epoch and from different epochs, changes that are larger than 10 cm were detected. Building changes, which areas are larger than 4 m2, are identified as change. By inspection, nearly all changes are detected and approximately 80% changes are correctly classified.
Automatic dense visual semantic mapping from street-level imagery

This paper describes a method for producing a semantic map from multi-view street-level imagery. We define a semantic map as an overhead, or bird's eye view of a region with associated semantic object labels, such as car, road and pavement. We formulate the problem using two conditional random fields. The first is used to model the semantic image segmentation of the street view imagery treating each image independently. The outputs of this stage are then aggregated over many images to form the input for our semantic map that is a second random field defined over a ground plane. Each image is related by a simple, yet effective, geometrical function that back projects a region from the street view image into the overhead ground plane map. We introduce, and make publicly available, a new dataset created from real world data. Our qualitative evaluation is performed on this data consisting of a 14.8 km track, and we also quantify our results on a representative subset.
Lesion Detectability in Diagnostic Ultrasound with Short-Lag Spatial Coherence Imaging

We demonstrate a novel imaging technique, named short-lag spatial coherence (SLSC) imaging, which uses short distance (or lag) values of the coherence function of backscattered ultrasound to create images. Simulations using Field II are used to demonstrate the detection of lesions of varying sizes and contrasts with and without acoustical clutter in the backscattered data. B-mode and SLSC imaging are shown to be nearly equivalent in lesion detection, based on the contrast-to-noise ratio (CNR) of the lesion, in noise-free conditions. The CNR of the SLSC image, however, can be adjusted to achieve an optimal value at the expense of image smoothness and resolution. In the presence of acoustic clutter, SLSC imaging yields significantly higher CNR than B-mode imaging and maintains higher image quality than B-mode with increasing noise. Compression of SLSC images is shown to be required under high-noise conditions but is unnecessary under no- and low-noise conditions. SLSC imaging is applied to in vivo imaging of the carotid sheath and demonstrates significant gains in CNR as well as visualization of arterioles in the carotid sheath. SLSC imaging has a potential application to clutter rejection in ultrasonic imaging.
Integrated Processing Produces Robust Understanding

Natural language interfaces to computers must deal with wide variation in real-world input. This paper proposes that, in order to handle real-world input robustly, a natural language interface should be constructed in accord with principles of integrated processing: processing syntax and semantics at the same time, processing syntax and semantics using the same mechanisms, and processing language and memory using the same mechanisms. This paper describes an experimental natural language interface constructed according to these principles which displays the desired robustness. The success of this interface suggests that future real-world interfaces could achieve robustness by performing integrated processing.
Parameterized MMSE spectral magnitude estimation for the enhancement of noisy speech

The enhancement of short-term spectra of noisy speech can be achieved by statistical estimation of the clean speech spectral components. We present a minimum mean-square error estimator of the clean speech spectral magnitude that uses both a parametric compression function in the estimation error criterion and a parametric prior distribution for the statistical model of the clean speech magnitude. The novel parametric estimator has many known magnitude estimators as a special solution and, additionally, affords estimators that combine the beneficial properties of different known solutions. The new estimator is evaluated in terms of segmental SNR, speech distortion, and noise suppression.
An all-in-one solution to geometric and photometric calibration

We propose a fully automated approach to calibrating multiple cameras whose fields of view may not all overlap. Our technique only requires waving an arbitrary textured planar pattern in front of the cameras, which is the only manual intervention that is required. The pattern is then automatically detected in the frames where it is visible and used to simultaneously recover geometric and photometric camera calibration parameters. In other words, even a novice user can use our system to extract all the information required to add virtual 3D objects into the scene and light them convincingly. This makes it ideal for Augmented Reality applications and we distribute the code under a GPL license.
Fast Evaluation of Power Consumption of Embedded Systems Using DIPLODOCUS

DIPLODOCUS is a UML profile targeting the design space exploration of Systems-on-Chip at a high level of abstraction. It is supported by an open source toolkit named TTool, which enables the designer to develop, simulate and formally verify SoC models. Up to now, performance criteria such as CPU and bus loads were the main metrics used for the exploration. The main objective of our research works is to provide methods and tools to quickly estimate the power consumption at the first steps of a system design. This work enhances DIPLODOCUS with power consumption modelling and analysis. Both software and hardware power management techniques can now be modelled in an abstract way. So enhanced, DIPLODOCUS offers the possibility to evaluate different power management policies, and to estimate the system power consumption.
Using WorldView-2 imagery to track flooding in Thailand in a multi-asset sensorweb

For the flooding seasons of 2011-2012 multiple space assets were used in a "sensorweb" to track major flooding in Thailand. Worldview-2 multispectral data was used in this effort and provided extremely high spatial resolution (2m / pixel) multispectral (8 bands at 0.45-1.05 μ m spectra) data from which mostly automated workflows derived surface water extent and volumetric water information for use by a range of NGO and national authorities. We first describe how Worldview-2 and its data was integrated into the overall flood tracking sensorweb. We next describe the use of Support Vector Machine learning techniques that were used to derive surface water extent classifiers. Then we describe the fusion of surface water extent and digital elevation map (DEM) data to derive volumetric water calculations. Finally we discuss key future work such as speeding up the workflows and automating the data registration process (the only portion of the workflow requiring human input).
Maternal Language and Child Vocabulary Mediate Relations Between Socioeconomic Status and Executive Function During Early Childhood.

This article examined longitudinal relations among socioeconomic risk, maternal language input, child vocabulary, and child executive function (EF) in a large sample (N = 1,009) recruited for a prospective longitudinal study. Two measures of maternal language input derived from a parent-child picture book task, vocabulary diversity (VOCD), and language complexity, showed variation by socioeconomic risk at child ages 15, 24, and 36 months. Maternal VOCD at child age 24 months and maternal language complexity at child age 36 months mediated the relation between socioeconomic risk and 48-month child EF, independent of parenting sensitivity. Moreover, 36-month child vocabulary mediated the relation between maternal language input and child EF. These findings provide novel evidence about mechanisms linking socioeconomic risk and child executive function.
Circulating tumor cells in breast cancer: detection systems, molecular characterization, and future challenges.

BACKGROUND Circulating tumor cell (CTC) analysis is a promising new diagnostic field for estimating the risk for metastatic relapse and metastatic progression in patients with cancer. CONTENT Different analytical systems for CTC isolation and detection have been developed as immunocytochemical and molecular assays, most including separation steps by size or biological characteristics, such as expression of epithelial- or cancer-specific markers. Recent technical advancements in CTC detection and characterization include methods based on multiplex reverse-transcription quantitative PCR and approaches based on imaging and microfilter and microchip devices. New areas of research are directed toward developing novel assays for CTC molecular characterization. QC is an important issue for CTC analysis, and standardization of micrometastatic cell detection and characterization methodologies is important for the incorporation of CTCs into prospective clinical trials to test their clinical utility. The molecular characterization of CTCs can provide important information on the molecular and biological nature of these cells, such as the status of hormone receptors and epidermal and other growth factor receptor family members, and indications of stem-cell characteristics. This information is important for the identification of therapeutic targets and resistance mechanisms in CTCs as well as for the stratification of patients and real-time monitoring of systemic therapies. SUMMARY CTC analysis can be used as a liquid biopsy approach for prognostic and predictive purposes in breast and other cancers. In this review we focus on state-of-the-art technology platforms for CTC isolation, imaging, and detection; QC of CTC analysis; and ongoing challenges for the molecular characterization of CTCs.
An efficient MapReduce algorighm for parallelizing large-scale graph clustering

www.PosterPresentations. com Identifying close-knit communities (or “clusters”) in graphs is an advanced operation with a broad range of scientific applications. While theoretical formulations of this operation are either intractable or computationally prohibitive, practical algorithmic heuristics exist to efficiently tackle the problem. However, implementing these heuristics to work for large real world graphs still remains a significant challenge, owing to a combination of factors that include magnitude of the data, irregular data access patterns and compute-intensive operations to better the approximation. In this paper, we propose i) a novel MapReducebased [2] algorithm for a well known serial graph clustering heuristic called Shingling [3]; and ii) a novel application of the method to cluster biological graphs built out of proteins and domains. Operating on an input graph that is simply represented as a list of edges, our algorithm uses a combination of shuffling and sorting operations, and pipelined MapReduce stages to implement the various phases of the algorithm. Preliminary results show linear scaling of the time-dominant phase up to 64 cores on a relatively small real world graph containing 8.41M vertices (8,407,839 proteins and 11,823 domains) and 11M edges (protein to domain connections). More importantly, MapReduce parallelization has allowed us to enhance the problem size reach by about two to three orders of magnitude (from 20K to 8M vertices) relative to our previous serial implementation, in roughly the same amount of time.
Multiprimer PCR system for differential identification of mycobacteria in clinical samples

A novel multiprimer PCR method with the potential to identify mycobacteria in clinical samples is presented. The assay relies on the simultaneous amplification of three bacterial DNA genomic fragments by using different sets of oligonucleotide primers. The first set of primers amplifies a 506-bp fragment from the gene for the 32-kDa antigen of Mycobacterium tuberculosis, which is present in most of the species belonging to the genus Mycobacterium. The second set of primers amplifies a 984-bp fragment from the IS6110 insertion sequence of the bacteria belonging to the M. tuberculosis complex. The third set of primers, derived from an M. tuberculosis species-specific sequence named MTP40, amplifies a 396-bp genomic fragment. Thus, while the multiprimer system would render three amplification fragments from the M. tuberculosis genome and two fragments from the Mycobacterium bovis genome, a unique amplification fragment would be obtained from nontuberculous mycobacteria. The results obtained, using reference mycobacterial strains and typed clinical isolates, show that the multiprimer PCR method may be a rapid, sensitive, and specific tool for the differential identification of various mycobacterial strains in a single-step assay.
Quasidynamic layout optimizations for improving data locality

Compiler-directed locality optimization techniques are effective in reducing the number of cycles spent in off-chip memory accesses. Recently, methods have been developed that transform memory layouts of data structures at compile-time to improve spatial locality of nested loops beyond current control-centric (loop nest-based) optimizations. Most of these data-centric transformations use a single static (program-wide) memory layout for each array. A disadvantage of these static layout-based locality enhancement strategies is that they might fail to optimize codes that manipulate arrays, which demand different layouts in different parts of the code. We introduce a new approach, which extends current static layout optimization techniques by associating different memory layouts with the same array in different parts of the code. We call this strategy "quasidynamic layout optimization. " In this strategy, the compiler determines memory layouts (for different parts of the code) at compile time, but layout conversions occur at runtime. We show that the possibility of dynamically changing memory layouts during the course of execution adds a new dimension to the data locality optimization problem. Our strategy employs a static layout optimizer module as a building block and, by repeatedly invoking it for different parts of the code, it checks whether runtime layout modifications bring additional benefits beyond static optimization. Our experiments indicate significant improvements in execution time over static layout-based locality enhancing techniques.
A Collector Overlay Architecture for Fault Diagnosis in Access Networks

To prevent subscriber churn, network service providers of VoD, SDV and IPTV have a pressing need to pro-actively detect, isolate and fix outages within an access network. Network induced degradations prove to be detrimental for streaming applications. This typically leads to a poor quality of experience (QoE) for subscribers. By monitoring key functional points of the access network for traces of degradation, service providers can devise mechanisms to mitigate the problem. In this work we propose a hierarchy of exporters, collectors and ANCON (ANalysis and CONtrol) nodes that can semiautonomously monitor, detect and isolate impairments within an access network. Exporters on the data plane gather and disseminate statistics for individual subnets, which are streamed onto "collector" nodes on an orthogonal plane. Collector nodes aggregate traffic from various exporters, and stream them onto the root of the tree (ANCON). With an even placement of exporters, root cause analysis can now take the granularity of loss rates or delay rates in individual segments or subnets of an access network. As an extension to our architecture, we show that the overlay can support instrumentations of quality evaluation for streaming video. As an example, we use a simple MOS model that is in part an extension of the ITU-T Erlang model to predict the quality of a video stream much before it reaches the end user. Extensive simulations are presented to justify the design choices made in the process.
ADL™: A Topic Model for Discovery of Activities of Daily Living in a Smart Home

We present an unsupervised approach for discovery of Activities of Daily Living (ADL) in a smart home. Activity discovery is an important enabling technology, for example to tackle the healthcare requirements of elderly people in their homes. The technique applied most often is supervised learning, which relies on expensive labelled data and lacks the flexibility to discover unseen activities. Building on ideas from text mining, we present a powerful topic model and a segmentation algorithm that can learn from unlabelled sensor data. The model has been evaluated extensively on datasets collected from real smart homes. The results demonstrate that this approach can successfully discover the activities of residents, and can be effectively used in a range of applications such as detection of abnormal activities and monitoring of sleep quality, among many others.
Effect of Advanced Glycation End Products on Oxidative Stress and Senescence of Trabecular Meshwork Cells

Purpose To investigate the effect of advanced glycation end products (AGE) on oxidative stress and cellular senescence in cultured human trabecular meshwork cells (HTMC). Methods Primarily cultured HTMC were exposed to 0, 10, 50, 100, 200 µg/mL of glycated bovine serum albumin (G-BSA) for 5 days. Also co-exposed were L-arginine, sepiapterin, and antioxidant N-acetylcysteine (NAC). Cellular survival and production of nitric oxide (NO), superoxide, and reactive oxygen species were assessed by 3-[4, 5-dimethylthiazol-2-yl]-2, 5-diphenyltetrazolium bromide assay, Griess assay, cytochrome c assay, and dichlorofluorescin diacetate assay, respectively. Senescence-associated β-galactosidase staining was performed to quantify the degree of cellular senescence. Results G-BSA decreased cellular survival, NO production, and increased superoxide production significantly in a dose-dependent manner. The effects of G-BSA were abolished with co-exposure of L-arginine, sepiapterin, and NAC. G-BSA enhanced cellular senescence accompanied by increased production of reactive oxygen species. G-BSA-induced cellular senescence was suppressed by application of L-arginine, sepiapterin, and NAC. Conclusions AGE enhances cellular senescence of HTMC accompanied with increased oxidative stress. AGE-induced oxidative stress and cellular senescence could be delayed by application of anti-oxidants.