Miracle-dz/UniFAR-FTUs huggingface dataset:
ECG markers associated with ischemic stroke at young age – a case-control study

Abstract Introduction: Certain electrocardiographic (ECG) abnormalities are associated with ischemic stroke (IS), especially cardioembolic subtype. Besides atrial fibrillation, markers of left ventricular hypertrophy (LVH) or atrial pathology also reflect elevated risk. We studied the association of ECG markers with IS in young adults. Methods: We performed a case-control study including 567 consecutive IS patients aged 15–49 years (inclusion period: 1994–2007) and one or two age- and sex-matched control subjects enrolled during 1978–1980 (n = 1033), and investigated also the stroke aetiologic subgroups. We studied ECGs of all participants for markers of atrial abnormality, i.e. P-terminal force (PTF) on lead V1, interatrial blocks (IAB; P-wave duration ≥110 ms), and LVH. Conditional logistic regression analyses were used. Results: IAB (hazard ratio [HR]: 1.57, 95% confidence interval [CI]: 1.16–2.13) and PTF combined with LVH (HR: 6.83, 95% CI: 1.65–28.31), were independently associated with IS. LVH, abnormal P-wave (HR: 6.87, 95% CI: 1.97–135.29), PTF, IAB, and combinations of these P-wave abnormalities with LVH – were associated with cardioembolic subtype. Abnormal P-wave and IAB were associated with cryptogenic stroke subtype. In unadjusted analysis, LVH was associated with small-vessel disease subtype. Conclusion: P-wave abnormalities on ECG were associated with cardioembolic but also with a cryptogenic subtype of IS. Key messages ECG patterns associated with atrial pathology are markers of increased risk of ischemic stroke in young adults. The ECG markers reflecting atrial pathology were seen in patients with cardioembolic and cryptogenic subtypes of ischemic stroke.
RELATING PHYSICOCHEMMICAL PROPERTIES OF AMINO ACIDS TO VARIABLE NUCLEOTIDE SUBSTITUTION PATTERNS AMONG SITES

Markov-process models of codon substitution were implemented that account for features of DNA sequence evolution (such as transition/transversion bias and codon usage bias) as well as heterogeneity of amino acid substitution pattern over sites. The codon (amino acid) sites are assumed to come from several classes (such as secondary structure categories), among which the rate of amino acid substitution and the effect of amino acid chemical properties vary. Parameters are estimated by the maximum likelihood method, which accounts for the phylogenetic relationship among species and corrects for multiple hits at the same site. The likelihood ratio test is used to compare models. Mitochondrial cytochrome b genes of 28 primate species are analyzed. The site-heterogeneity models provide much better fit to previous homogeneous models.
Inconsistency Management in Generalized Knowledge and Action Bases

The combination of static and dynamic aspects in modeling complex organizational domains is a challenging task that has led to study the combination of formalisms from knowledge representation, database theory, and process management [18,23,11]. Specifically, Knowledge and Action Bases (KABs) [3] have been put forward recently to provide a semantically rich representation of a domain. In KABs, static aspects are modeled using a Description Logic (DL) [1] knowledge base (KB), while actions are used to evolve its extensional part over time, possibly introducing fresh individuals. An important aspect that has received little attention so far in such systems is the management of inconsistency with respect to domain knowledge that may arise when the extensional information is evolved over time. In fact, inconsistency, both in KABs and in related approaches, is typically handled naively by just rejecting updates in actions when they would lead to inconsistency, see e.g., [16,4,9,2]. To overcome this limitation, KABs have been extended lately with mechanisms to handle inconsistency [12]. However, this has been done by defining ad-hoc execution semantics and corresponding ad-hoc verification techniques geared towards specific semantics for inconsistency management. It has also been left open whether adding inconsistency management to the rich setting of KABs, actually increases expressive power. This work attacks these issues by: (i) Proposing (standard) GKABs, which enrich KABs with a compact action language inspired by Golog [20] that can be conveniently used to specify processes at a high-level of abstraction. As in KABs, standard GKABs still manage inconsistency naively. (ii) Defining a parametric execution semantic for GKABs that is able to elegantly accomodate a plethora of inconsistency-aware semantics based on the well-known notion of repair [17,5,19,13]. (iii) Providing several reductions showing that verification of sophisticated first-order temporal properties over inconsistency-aware GKABs can be recast as a corresponding verification problem over standard GKABs. (iv) Showing that verification of standard and inconsistency-aware GKABs can be addressed using known techniques, developed for standard KABs.
Draft Genome Sequence of the Microbispora sp. Strain ATCC-PTA-5024, Producing the Lantibiotic NAI-107

ABSTRACT We report the draft genome sequence of Microbispora sp. strain ATCC-PTA-5024, a soil isolate that produces NAI-107, a new lantibiotic with the potential to treat life-threatening infections caused by multidrug-resistant Gram-positive pathogens. The draft genome of strain Microbispora sp. ATCC-PTA-5024 consists of 8,543,819 bp, with a 71.2% G+C content and 7,860 protein-coding genes.
Lightweight Robust Device-Free Localization in Wireless Networks

Due to its ability of realizing localization without the need of equipping the target with a wireless device, the device-free wireless localization technique has become a crucial technique for many security and military applications. However, there still lacks an efficient scheme which could achieve robust location estimation performance with low computational cost. To solve this problem, we propose a lightweight robust Bayesian grid approach (BGA) in this paper. The BGA utilizes not only the observation information of the shadowed links, but also the prior information involved in the previous estimations and the constraint information involved in the non-shadowed links, which ensure its robust performance. Meanwhile, the BGA can be carried out with a series of lightweight grid multiplication and addition operations, which eliminates the complex matrix inversion computation involved in the traditional algorithm. The experimental results demonstrate that BGA could achieve a mean tracking error of 0.155 m with a running time of only 1.5 ms.
Modelling Visual Objects Invariant to Depictive Style

Representing visual objects is an interesting open question of relevance to many important problems in Computer Vision such as classification and location. State of the art allows thousands of visual objects to be learned and recognised, under a wide range of variations including lighting changes, occlusion, point of view, and different object instances. Only a small fraction of the literature addresses the problem of variation in depictive style (photographs, drawings, paintings etc.), yet considering photographs and artwork on equal footing is philosophically appealing and of true practical significance. This paper describes a model for visual object classes that is learnable and which is able to classify over a broad range of depictive styles. The model is a graph in which simple shapes label region nodes. We use our model to classify twenty classes in CalTech 256, each class augmented by additional images to increase the variance in style. When compared to a Bag of Words classifier and to a structure only based classifier, our results show a significant increase in robustness to variance in depictive style.
The MSR system for IWSLT 2011 evaluation

This paper describes the Microsoft Research (MSR) system for the evaluation campaign of the 2011 international workshop on spoken language translation. The evaluation task is to translate TED talks (www.ted.com). This task presents two unique challenges: First, the underlying topic switches sharply from talk to talk. Therefore, the translation system needs to adapt to the current topic quickly and dynamically. Second, only a very small amount of relevant parallel data (transcripts of TED talks) is available. Therefore, it is necessary to perform accurate translation model estimation with limited data. In the preparation for the evaluation, we developed two new methods to attack these problems. Specifically, we developed an unsupervised topic modeling based adaption method for machine translation models. We also developed a discriminative training method to estimate parameters in the generative components of the translation models with limited data. Experimental results show that both methods improve the translation quality. Among all the submissions, ours achieves the best BLEU score in the machine translation Chinese-to-English track (MT_CE) of the IWSLT 2011 evaluation that we participated.
Joint Optimization of Run-Length Coding, Huffman Coding, and Quantization Table With Complete Baseline JPEG Decoder Compatibility

To maximize rate distortion performance while remaining faithful to the JPEG syntax, the joint optimization of the Huffman tables, quantization step sizes, and DCT indices of a JPEG encoder is investigated. Given Huffman tables and quantization step sizes, an efficient graph-based algorithm is first proposed to find the optimal DCT indices in the form of run-size pairs. Based on this graph-based algorithm, an iterative algorithm is then presented to jointly optimize run-length coding, Huffman coding, and quantization table selection. The proposed iterative algorithm not only results in a compressed bitstream completely compatible with existing JPEG and MPEG decoders, but is also computationally efficient. Furthermore, when tested over standard test images, it achieves the best JPEG compression results, to the extent that its own JPEG compression performance even exceeds the quoted PSNR results of some state-of-the-art wavelet-based image coders such as Shapiro's embedded zerotree wavelet algorithm at the common bit rates under comparison. Both the graph-based algorithm and the iterative algorithm can be applied to application areas such as web image acceleration, digital camera image compression, MPEG frame optimization, and transcoding, etc.
Enhanced email spam filtering through combining similarity graphs

Over the last decade Email Spam has evolved from being just an irritant to users to being truly dangerous. This has led web-mail providers and academic researchers to dedicate considerable resources towards tackling this problem [9, 21, 22, 24, 26]. However, we argue that some aspects of the spam filtering problem are not handled appropriately in existing work. Principal among these are adversarial spammer efforts -- spammers routinely tune their spam emails to bypass spam-filters, and contaminate ground truth via fake HAM/SPAM votes -- and the scale and sparsity of the problem, which essentially precludes learning with a very large set of parameters. In this paper we propose an approach that learns to filter spam by striking a balance between generalizing HAM/SPAM votes across users and emails (to alleviate sparsity) and learning local models for each user (to limit effect of adversarial votes); votes are shared only amongst users and emails that are "similar" to one another. Moreover, we define user-user and email-email similarities using spam-resilient features that are extremely difficult for spammers to fake. We give a methodology that learns to combine multiple features into similarity values while directly optimizing the objective of better spam filtering. A useful side effect of this methodology is that the number of parameters that need to be estimated is very small: this helps us use off-the-shelf learning algorithms to achieve good accuracy while preventing over-training to the adversarial noise in the data. Finally, our approach gives a systematic way to incorporate existing spam-fighting technologies such as IP blacklists, keyword based classifiers, etc into one framework. Experiments on a real-world email dataset show that our approach leads to significant improvements compared to two state-of-the-art baselines.
On the Linear Elastic Properties of Regular and Random Open-Cell Foam Models

Foams can be created from coagulation of gas bubbles in liquid. After removal of cell faces, an open-cell foam remains consisting of a strut framework. In the past, mechanical properties were estimated by a small unit cell consisting of only a few struts. However, the random geometry of the foam can be of importance for the linear elastic properties. Here, large foam unit cells are created using Voronoi techniques. A smooth transition from regular to random geometries is made, showing the strong sensitivity of the mechanical properties from the geometry of the microstructure. Uniaxial global loads are transmitted through chains of highly loaded struts. The deformation of the struts in the foam is a mixture of bending and normal deformation, the ratio of which shown here to be dependent on the magnitude of the density.
Discrimination of Stochastic Frequency Modulation by Cochlear Implant Users.

BACKGROUND Past work has shown that low-rate frequency modulation (FM) may help preserve signal coherence, aid segmentation at word and syllable boundaries, and benefit speech intelligibility in the presence of a masker. PURPOSE This study evaluated whether difficulties in speech perception by cochlear implant (CI) users relate to a deficit in the ability to discriminate among stochastic low-rate patterns of FM. RESEARCH DESIGN RESEARCH DESIGN This is a correlational study assessing the association between the ability to discriminate stochastic patterns of low-rate FM and the intelligibility of speech in noise. STUDY SAMPLE Thirteen postlingually deafened adult CI users participated in this study. DATA COLLECTION AND ANALYSIS Using modulators derived from 5-Hz lowpass noise applied to a 1-kHz carrier, thresholds were measured in terms of frequency excursion both in quiet and with a speech-babble masker present, stimulus duration, and signal-to-noise ratio in the presence of a speech-babble masker. Speech perception ability was assessed in the presence of the same speech-babble masker. Relationships were evaluated with Pearson product-moment correlation analysis with correction for family-wise error, and commonality analysis to determine the unique and common contributions across psychoacoustic variables to the association with speech ability. RESULTS Significant correlations were obtained between masked speech intelligibility and three metrics of FM discrimination involving either signal-to-noise ratio or stimulus duration, with shared variance among the three measures accounting for much of the effect. Compared to past results from young normal-hearing adults and older adults with either normal hearing or a mild-to-moderate hearing loss, mean FM discrimination thresholds obtained from CI users were higher in all conditions. CONCLUSIONS The ability to process the pattern of frequency excursions of stochastic FM may, in part, have a common basis with speech perception in noise. Discrimination of differences in the temporally distributed place coding of the stimulus could serve as this common basis for CI users.
An analytical model for the energy hole problem in many-to-one sensor networks

In a many-to-one sensor network, all sensor nodes generate CBR data and send them to a single sink via multihop transmissions. Sensor nodes sitting around the sink need to relay more traffic and suffer much faster energy consumption rates (ECR), and thus have much shorter expected lifetime. This may result in severe consequences such as early dysfunction of the entire network. While this phenomenon was reported previously in the existing literature, there is a lack of an analytical model on the characteristics of this issue. In this paper we present a mathematical model and characterize the energy hole problem. Using our model, we investigate the effectiveness of some existing approaches towards mitigating this problem in a formal manner. We have used simulation results to validate our analysis.
Automatic Recognition of Colloquial Australian Sign Language

This paper presents an automatic Australian sign language (Auslan) recognition system, which tracks multiple target objects (the face and hands) throughout an image sequence and extracts features for the recognition of sign phrases. Tracking is performed using correspondences of simple geometrical features between the target objects within the current and the previous frames. In signing, the face and a hand of a signer often overlap, thus the system needs to segment these for the purpose of feature extraction. Our system deals with the occlusion of the face and a hand by detecting the contour of the foreground moving object using a combination of motion cues and the snake algorithm. To represent signs, features that are invariant to scaling, 2D rotations, and signing speed are used for recognition. The features represent the relative geometrical positioning and shapes of the target objects, as well as their directions of motion. These are used to recognise Auslan phrases using Hidden Markov Models. Experiments were conducted using 163 test sign phrases with varying grammatical formations. Using a known grammar, the system achieved over 97% recognition rate on a sentence level and 99% success rate at a word level.
Characterization of (4,4)- and (5,3)-Type Stacking-Faults in 4deg.-Off 4H-SiC Epitaxial Wafers by Synchrotron X-Ray Topography and by Photo-Luminescence Spectroscopy

Experimentally,the grazing-incident X-ray topography at different diffraction conditions, and room temperature photo-luminescence spectroscopy, various different types of stacking-faults in epitaxial films on 4-degrees-off 4H-SiC wafers were identified precisely without wafer cutting. Their types and the numbers were investigated statistically. It became clear that (4,4) type stacking-faults were the most common ones and two different types were identified. Still 34% of the stacking-faults were unknown types in the present investigation.Several different kinds of stacking-faults formed on the surface of 4-degrees-off 4H-SiC epitaxial wafers were investigated. Their types could be identified and type distribution in a wafer could be obtained using X-ray topography and room temperature Photo-Luminescence without wafer cutting. Type determination of 8H(4,4)- stacking fault ; with or without strain field, could also be decideddemonstrated using this method.
Active slow light in silicon photonic crystals : tunable delay and Raman gain

In the past decade, great research effort was inspired by the need to realise active optical functionalities in silicon, in order to develop the full potential of silicon as a photonic platform. In this thesis we explore the possibility of achieving tunable delay and optical gain in silicon, taking advantage of the unique dispersion capabilities of photonic crystals. To achieve tunable optical delay, we adopt a wavelength conversion and group velocity dispersion approach in a miniaturised engineered slow light photonic crystal waveguide. Our scheme is equivalent to a two-step indirect photonic transition, involving an alteration of both the frequency and momentum of an optical pulse, where the former is modified by the adiabatic tuning possibilities enabled by slow light. We apply this concept in a demonstration of continuous tunability of the delay of pulses, and exploit the ultrafast nature of the tuning process to demonstrate manipulation of a single pulse in a train of two pulses. In order to address the propagation loss intrinsic to slow light structures, with a prospect for improving the performance of the tunable delay device, we also investigate the nonlinear effect of stimulated Raman scattering as a means of introducing optical gain in silicon. We study the influence of slowdown factors and pump-induced losses on the evolution of a signal beam along the waveguide, as well as the role of linear propagation loss and mode profile changes typical of realistic photonic crystal structures. We then describe the work conducted for the experimental demonstration of such effect and its enhancement due to slow light. Finally, as the Raman nonlinearity may become useful also in photonic crystal nanocavities, which confine light in very small volumes, we discuss the design and realisation of structures which satisfy the basic requirements on the resonant modes needed for improving Raman scattering.
Blind Multiband Signal Reconstruction: Compressed Sensing for Analog Signals

We address the problem of reconstructing a multiband signal from its sub-Nyquist pointwise samples, when the band locations are unknown. Our approach assumes an existing multi-coset sampling. To date, recovery methods for this sampling strategy ensure perfect reconstruction either when the band locations are known, or under strict restrictions on the possible spectral supports. In this paper, only the number of bands and their widths are assumed without any other limitations on the support. We describe how to choose the parameters of the multi-coset sampling so that a unique multiband signal matches the given samples. To recover the signal, the continuous reconstruction is replaced by a single finite-dimensional problem without the need for discretization. The resulting problem is studied within the framework of compressed sensing, and thus can be solved efficiently using known tractable algorithms from this emerging area. We also develop a theoretical lower bound on the average sampling rate required for blind signal reconstruction, which is twice the minimal rate of known-spectrum recovery. Our method ensures perfect reconstruction for a wide class of signals sampled at the minimal rate, and provides a first systematic study of compressed sensing in a truly analog setting. Numerical experiments are presented demonstrating blind sampling and reconstruction with minimal sampling rate.
A Comparison of Retention and Complication Rates of Large-Bore (Paparella II) and Small-Bore Middle Ear Ventilating Tubes

Large-bore myringotomy tubes are usually reserved for the treatment of refractory middle ear effusion. Theoretically, they have an extended intubation time and a higher complication rate. There is, however, scant support of this in the literature. The duration of intubation, efficacy, and complication rates of the large-bore Paparella type II tube were compared with Paparella type I, Shepard, and Armstrong tubes. The study included 242 patients with 600 intubations. In addition, a subpopulation of patients receiving their initial intubation during this study was reviewed. Findings were similar for both groups. Paparella type II tubes had a prolonged period of intubation and a decreased reintubation rate when compared with the smaller bore tubes. Larger bore tubes had an increased complication rate when compared with the smaller bore tubes. Complications included occasional or frequent otorrhea and an increased rate of permanent perforation of the tympanic membrane. There was no instance of cholesteatoma formation secondary to intubation. Guidelines are presented for the use of the Paparella type II tube.