Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of correlation between genotypes and environmental variables. A fast computational approach for genomewide studies

Genomic regions (or loci) displaying outstanding correlation with some environmental variables are likely to be under selection and this is the rationale of recent methods of identifying selected loci and retrieving functional information about them. To be efficient, such methods need to be able to disentangle the potential effect of environmental variables from the confounding effect of population history. For the routine analysis of genome-wide datasets, one also needs fast inference and model selection algorithms. We propose a method based on an explicit spatial model which is an instance of spatial generalized linear mixed model (SGLMM). For inference, we make use of the INLA-SPDE theoretical and computational framework developed by Rue et al. (2009) and Lindgren et al (2011). The method we propose allows one to quantify the correlation between genotypes and environmental variables. It works for the most common types of genetic markers, obtained either at the individual or at the population level. Analyzing simulated data produced under a geostatistical model then under an explicit model of selection, we show that the method is efficient. We also re-analyze a dataset relative to nineteen pine weevils (Hylobius abietis}) populations across Europe. The method proposed appears also as a statistically sound alternative to the Mantel tests for testing the association between genetic and environmental variables.
Efficient Modeling Techniques for Dynamic Voltage Drop Analysis

Since the IC technology scales down the effect of voltage drop/ground bounce becomes increasingly significant. Voltage drop and ground bounce can compromise the gate driving capability and degrade the IC performance, and even can cause IC functional failures. Hence, it is crucial to capture this effect efficiently and accurately in order to improve circuit reliability. In this paper, we propose efficient modeling techniques for analyzing power distribution in deep sub micron (DSM) ASIC designs. Current source-based model (CSM) and voltage controlled resistor (VCR) are the key concepts in our approach. A basic prerequisite for the new approach are CMOS standard libraries that are pre-characterized with respect to the corresponding modeling requirements. This paper also presents an approach for this characterization step. The proposed techniques can efficiently handle multiple-input-switching (MIS), including single-input-switching events (SIS) and provide good analysis results compared to the reference with two orders of magnitude speedup, although the cell library pre-characterization is based on SPICE simulations. Our model is independent of the power network context, which implies that different power distribution networks may be analyzed based on the same model and the same cell characterizations. The run-time, memory and accuracy efficiency of the proposed method are demonstrated on an industrial design.
Minimum measurement deterministic compressed sensing based on complex reed solomon decoding

Compressed Sensing (CS) is an emerging field in communications and mathematics that is used to measure few measurements of long sparse vectors with the ability of lossless reconstruction. In this paper we use results from channel coding to design a recovery algorithm for CS with a deterministic measurement matrix by exploiting error correction schemes. In particular, we show that a generalized Reed Solomon encoding-decoding structure can be used to measure sparsely representable vectors, that are sparse in some fitting basis, down to the theoretical minimum number of measurements with the ability of guaranteed lossless reconstruction, even in the low dimensional case.
Haplotype pattern mining & classification for detecting disease associated site

Finding the causative genes for common diseases using SNP (single nucleotide polymorphism) markers is now becoming a real challenge. Although traditional statistical SNP association tests exist, these tests could not explain the effects of SNP combinations or probable recombination histories from ancestral chromosomes. Haplotype analysis of disease associated site provides more powerful markers than individual SNP analysis, and can help identify probable causative mutations. In this paper, we introduce a new method for effective haplotype pattern mining to detect disease associated mutations. Using this procedure, we can discover some of the new disease associated SNPs, which can not be detected by traditional methods. We will introduce a powerful tool for implementing this procedure with some worked examples.
Crowdsourcing a News Query Classification Dataset

Web search engines are well known for aggregating news vertical content into their result rankings in response to queries classified as news-related. However, no dataset currently exists upon which approaches to news query classification can be evaluated and compared. This paper studies the generation and validation of a news query classification dataset comprised of labels crowdsourced from Amazon’s Mechanical Turk and details insights gained. Notably, our study focuses around two challenges when crowdsourcing news query classification labels: 1) how to overcome our workers’ lack of information about the news stories from the time of each query and 2) how to ensure the resulting labels are of high enough quality to make the dataset useful. We empirically show that a worker’s lack of information about news stories can be addressed through the integration of news-related content into the labelling interface and that this improves the quality of the resulting labels. Overall, we find that crowdsourcing is suitable for building a news query classification dataset.
Low-grade glioma surgery in eloquent areas: volumetric analysis of extent of resection and its impact on overall survival. A single-institution experience in 190 patients: clinical article.

OBJECT A growing number of published studies have recently demonstrated the role of resection in overall survival (OS) for patients with gliomas. In this retrospective study, the authors objectively investigated the role of the extent of resection (EOR) in OS in patients with low-grade gliomas (LGGs). METHODS Between 1998 and 2011, 190 patients underwent surgery for LGGs. All surgical procedures were conducted under corticosubcortical stimulation. The EOR was established by analyzing the pre- and postoperative volumes of the gliomas on T2-weighted MRI studies. The difference between the preoperative tumor volumes was also investigated by measuring the volumetric difference between the T2- and T1-weighted MRI images (ΔVT2T1) to evaluate how the diffusive tumor-growing pattern affected the EOR achieved. RESULTS The median preoperative tumor volume was 55 cm(3), and in almost half of the patients the EOR was greater than 90%. In this study, patients with an EOR of 90% or greater had an estimated 5-year OS rate of 93%, those with EOR between 70% and 89% had a 5-year OS rate of 84%, and those with EOR less than 70% had a 5-year OS rate of 41% (p < 0.001). New postoperative deficits were noted in 43.7% of cases, while permanent deficits occurred in 3.16% of cases. There were 41 deaths (21.6%), and the median follow-up was 4.7 years. A further volumetric analysis was also conducted to compare 2 different intraoperative protocols (Series 1 [intraoperative electrical stimulation alone] vs Series 2 [intraoperative stimulation plus overlap of functional MRI/fiber tracking diffusion tensor imaging data on a neuronavigation system]). Patients in Series 1 had a median EOR of 77%, while those in Series 2 had a median EOR of 90% (p = 0.0001). Multivariate analysis showed that OS is influenced not only by EOR (p = 0.001) but also by age (p = 0.003), histological subtype (p = 0.005), and the ΔVT2T1 value (p < 0.0001). Progression-free survival is similarly influenced by histological subtype (fibrillary astrocytoma, p = 0.003), EOR (p < 0.0001), and ΔVT2T1 value (p < 0.0001), as is malignant progression-free survival (p = 0.003, p < 0.0001, and p < 0.0001, respectively). Finally, the study shows that the higher the ΔVT2T1 value, the less extensive the currently possible resection, highlighting an apparent correlation between the ΔVT2T1 value itself and EOR (p < 0.0001). CONCLUSIONS The EOR and the ΔVT2T1 values are the strongest independent predictors in improving OS as well as in delaying tumor progression and malignant transformation. Furthermore, the ΔVT2T1 value may be useful as a predictive index for EOR. Finally, due to intraoperative corticosubcortical mapping and the overlap of functional data on the neuronavigation system, major resection is possible with an acceptable risk and a significant increase in expected OS.
Robust and real-time multi-cue map verification for the road ahead

Digital maps are a key information for road course estimation in large distances, especially for automated driving with higher speeds or during nighttime. However, digital maps may contain errors caused by road changes or inaccurate recordings. As these errors may lead to severe application level failures, early and reliable detection of these errors, generally termed map verification, is a must. This work presents an online approach to verification of digital maps for automotive use based on neural multi-sensor fusion. Unlike previous works, the system is able to verify the digital map integrity ahead of the vehicle, before the error position is reached. Thus, appropriate countermeasures to mitigate possible effects can be initiated upfront. On the basis of a large database of map geometry and sensor information, a neural network is trained to classify the digital map integrity by optimally fusing sensor information at different abstraction levels. Using this technique, our system is able to verify the map integrity 100 meters ahead of the vehicle with an error rate of less than 3.4 %, while being real-time capable at the same time. Experiments validate the performance and robustness of this approach.
Microfabrication through Electrostatic Self-Assembly

We use electrostatic interactions to direct the patterning of gold disks having ∼10-μm diameters on functionalized surfaces. Planar and curved substrates with patterned surface charge were generated either by microcontact printing or by photolithography. Small charged gold disks were generated by electroplating gold into photoresist molds and derivatizing these disks with charged self-assembled monolayers. When agitated as a suspension in contact with the patterned surfaces, the charged gold disks deposited specifically but as disordered aggregates on regions presenting the opposite charge. Positively-charged disks deposited on phosphonate-, carboxylate-, and SiOH-terminated surfaces but not on trimethylammonium- and dimethylammonium-terminated surfaces, and vice versa for negatively-charged disks. Methyl- and CF3-terminated surfaces resisted deposition of disks of either charge. Selective and dense assembly was achieved in methanol, ethanol, 2-propanol, and dioxane; in water, deposition was nonspecific.
Robust spectro-temporal speech features with model-based distribution equalization

Previously, we applied a distribution equalization on our HIerarchical Spectro-Temporal (HIST) features using distributions estimated from histogram of one or several utterances. Although a performance increase could be observed in both cases, we noticed low performance improvement when estimating the distribution only from one utterance. The aim here is to determine a parametric distribution from few data samples which gives the highest probability of producing the observed data considering different models. Afterwards, we perform a distribution equalization based on the estimated model after each feature extraction step of our HIST feature extraction framework. We compare the performance of the HIST features with those of conventional spectral features (RASTA-PLP), when a corresponding distribution equalization has been carried out using the TIDigits database and different noise types. By combining the HIST and RASTA-PLP features we achieved on the one hand better performance than spectral features (RASTA-PLP), on the other hand better performance than the same feature combination with distribution equalization obtained directly from the histogram without imposing a parametric distribution model.
Global aspects of T-duality, gauged sigma models and T-folds

The gauged sigma-model argument that string backgrounds related by T-duality give equivalent quantum theories is revisited, taking careful account of global considerations. The topological obstructions to gauging sigma-models give rise to obstructions to T-duality, but these are milder than those for gauging: it is possible to T-dualise a large class of sigma-models that cannot be gauged. For backgrounds that are torus fibrations, it is expected that T-duality can be applied fibrewise in the general case in which there are no globally-defined Killing vector fields, so that there is no isometry symmetry that can be gauged; the derivation of T-duality is extended to this case. The T-duality transformations are presented in terms of globally-defined quantities. The generalisation to non-geometric string backgrounds is discussed, the conditions for the T-dual background to be geometric found and the topology of T-folds analysed.
A GPS navigation system with QR code decoding and friend positioning in smart phones

Global Positioning System (GPS) navigation is a popular assistant during a trip. By using a GPS navigation system, the travelers can easily and quickly arrive to the destination in an unfamiliar area. However, there is no free and open-source GPS navigation system which integrates many useful applications. Thus, this paper proposes a GPS navigation system on the Android platform, called Android Mobile Navigation System (AMNS). AMNS not only provides users the GPS navigation function, but also supports Quick Response (QR) code decoding and friend positioning. Furthermore, AMNS is free and open-source software, so service providers or developers can easily extend their own services on this system.
Evaluating Entity Models on the TREC Question Answering Task

We propose entity models, a representation of the language used to describe a named entity (person, organization, or location). The model is purely statistical and constructed from snippets of text surrounding mentions of an entity. We evaluate the effectiveness of entity models for fact-based question answering. The results obtained on question answering are promising indicating that entity models contain useful information which would aid textual data mining and other related tasks.
Compatibility, standards, and software production

■ Compatibility is a key strategic decision in software production. Proposals exist for standards in several fields of software production, such as networking (ISO and IEEE), operating systems (Posix), and object management (OMG). However, a formal treatment of standards in software is still missing. This article tries to overcome this lack, presenting a model of the effects of compatibility in software production. It overviews existing work on compatibility; details a model on the effects of compatibility decisions in software development; and describes the application of this model to new products being introduced and to well-established incumbents. ompatibility is a key strategic decision in software production, in particular when the software development process is based on domain analysis and reuse. For some years software firms have been facing process improvement and product quality issues. However, few are conscious of the role of market structure in their operations. Often “experts,” following rules of thumb, perform the analysis of the application. They aim at identifying feasibility and potential return on investment, but usually do not consider in depth aspects related to market structure, such as compatibility and consumer network effects. Proposals exist for standards in several fields of software production, such as networking (ISO and IEEE), operating systems (Posix), and object management (OMG). However, a formal treatment of standards in software is still missing. This article describes a mathematical model of the effects of compatibility on software production and discusses the application of this model in different scenarios. Papers by Matutes and Regibeau [1988; 1992] are the base; references are also made to the proposals of Farrell and Saloner [1985; 1986] and to Katz and Shapiro [1985]. This paper is organized as follows. A review of the existing approaches is presented in Section 1. Section 2 proposes a framework to determine the effects of standards in software production. Section 3 analyses the conclusions coming from the application of the framework. Section 4 draws some conclusions and outlines future directions of this research.
Perturbative analysis on infrared and ultraviolet aspects of noncommutative QED on R^4

Here we discuss the ultraviolet and infrared aspects of the noncommutative counterpart of QED, which is called as noncommutative QED, as well as some infrared dynamics of noncommutative Yang-Mills (NCYM) theory. First we demonstrate that the divergence in the theory can be subtracted by the similar counterterms as in ordinary theory at one loop level. Then the anomalous magnetic moment is calculated to see the infrared aspect of the theory which reflects the violation of Lorentz symmetry. The evaluation of the finite part of the photon vacuum polarization shows that the logarithmically singular term in the infrared limit appears with the same weight as UV logarithmic divergence, showing the correlation between the UV and infrared dynamics in NCYM theory. NC-QED theory does not show such a property. We also consider the extension to chiral gauge theory in the present context, but the requirement of anomaly cancellation allows only noncommutative QED.
Optimal multibit Digital to Analog Conversion

When employing multibit data converters the necessity arises to compensate for digital to analog converter (DAC) element mismatch. The most widespread compensation techniques are based on digital element matching (DEM) and, if properly designed, these can achieve almost arbitrary DAC mismatch noise shaping. This paper gives a closed form expression for the optimal DEM noise shaping profile. It depends upon the spectrum of the analog signal to be quantized and may also include a frequency weighting filter which reflects perceptual criteria.
Personalized Adaptive Interfaces

INTRODUCTION Mass-produced user interfaces are inadequate for today’s computing needs: interfaces shipped with today’s complex applications are designed in a “one size fits all” manner and by aiming to address the needs of the average user they miss essential needs of most individual users. In particular, I note that: • users own diverse display-equipped devices, which support different interaction methods and have displays varying in size over several orders of magnitude; • different users use the same software to accomplish different tasks; • people’s work habits change over time; • users differ in their perceptual, physical and cognitive abilities.
Diffusion Maps for Multimodal Registration

Multimodal image registration is a difficult task, due to the significant intensity variations between the images. A common approach is to use sophisticated similarity measures, such as mutual information, that are robust to those intensity variations. However, these similarity measures are computationally expensive and, moreover, often fail to capture the geometry and the associated dynamics linked with the images. Another approach is the transformation of the images into a common space where modalities can be directly compared. Within this approach, we propose to register multimodal images by using diffusion maps to describe the geometric and spectral properties of the data. Through diffusion maps, the multimodal data is transformed into a new set of canonical coordinates that reflect its geometry uniformly across modalities, so that meaningful correspondences can be established between them. Images in this new representation can then be registered using a simple Euclidean distance as a similarity measure. Registration accuracy was evaluated on both real and simulated brain images with known ground-truth for both rigid and non-rigid registration. Results showed that the proposed approach achieved higher accuracy than the conventional approach using mutual information.
Maude: A Wide Spectrum Language for Secure Active Networks

Abstract : Modeling and formally analyzing active network systems and protocols is quite challenging, due to their highly dynamic nature and the need, for new network models. In this report, we propose a wide-spectrum methodology using executable rewriting logic specifications to address this challenge. We also show how, using the Maude rewriting logic language and tools, active network systems, languages, and protocols can be formally specified and analyzed using a wide range of formal methods. Benefits include: precise documentation of designs; early discovery of many bugs and omissions; and higher assurance of correct behavior. In this paper we illustrate these methods and their practical usefulness through two case studies: the AER/NCA protocol suite, and the PLAN active network language.
Stability radii of linear discrete‐time systems and symplectic pencils

In this paper, we introduce and analyse robustness measures for the stability of discrete-time system x(t + 1) = Ax(t) under parameter perturbations of the form AA + BDC where B,C are given matrices. In particular we characterize the stability radius of the uncertain system x(t + 1) = (A + BDC)x(t), D an unknown complex perturbation matrix, via an associated symplectic pencil and present an algorithm for the computation of that radius.
Throughput Scaling in Decentralized Single-Hop Wireless Networks with Fading Channels

A network of n communication links operating over a shared wireless channel is considered. Fading is assumed to be the dominant factor affecting the strength of the channels between nodes. The objective is to maximize the throughput of the network. A scheme that can be simply implemented in a distributed and single-hop fashion is proposed and analyzed. It is shown that under Rayleigh and log-normal fading conditions, the proposed scheme achieves sum-rates that scale as log n ande √ 2S √ log , respectively, whereS is a channel parameter and a constant. This is the same as what is obtained by a centralized and multihop method in the work of Gowaikar et al. Nevertheless, an analysis in the shadow fading model points out that when the network is not well-connected, single-hop methods are suboptimum and invoking multihop communication is inevitable to achieve the maximum throughput. An interesting aspect of the proposed method is the tradeoff it introduces between the scaling factor and the rate per link; it is shown that the proposed method allows rate-per-links of order Θ(1) while keeping the This work is financially supported by Communications and Information Technology Ontario (CITO), Nortel Networks, and Natural Sciences and Engineering Research Council of Canada (NSERC). The authors are affiliated with the Coding and Signal Transmission Laboratory, Electrical and Computer Engineering Department, University of Waterloo, Waterloo, ON, N2L 3G1, Canada, Tel: 519-884-8552, Fax: 519-888-4338, Emails: {masoud, mohammad, khandani }@cst.uwaterloo. ca. This material was presented in part at the 40th Conference on Information Sciences and Systems (CISS), Princeton University, Princeton, NJ, March 2006.