Miracle-dz/UniFAR-FTUs huggingface dataset:
The many weak instruments problem and Mendelian randomization

Instrumental variable estimates of causal effects can be biased when using many instruments that are only weakly associated with the exposure. We describe several techniques to reduce this bias and estimate corrected standard errors. We present our findings using a simulation study and an empirical application. For the latter, we estimate the effect of height on lung function, using genetic variants as instruments for height. Our simulation study demonstrates that, using many weak individual variants, two‐stage least squares (2SLS) is biased, whereas the limited information maximum likelihood (LIML) and the continuously updating estimator (CUE) are unbiased and have accurate rejection frequencies when standard errors are corrected for the presence of many weak instruments. Our illustrative empirical example uses data on 3631 children from England. We used 180 genetic variants as instruments and compared conventional ordinary least squares estimates with results for the 2SLS, LIML, and CUE instrumental variable estimators using the individual height variants. We further compare these with instrumental variable estimates using an unweighted or weighted allele score as single instruments. In conclusion, the allele scores and CUE gave consistent estimates of the causal effect. In our empirical example, estimates using the allele score were more efficient. CUE with corrected standard errors, however, provides a useful additional statistical tool in applications with many weak instruments. The CUE may be preferred over an allele score if the population weights for the allele score are unknown or when the causal effects of multiple risk factors are estimated jointly. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
Effects of Cytochrome P450 Inhibitors on Itraconazole and Fluconazole Induced Cytotoxicity in Hepatocytes

Itraconazole and fluconazole have been reported to induce hepatotoxicity in patients. The present study was designed to investigate the role of cytochrome P450 inhibitors, SKF 525A, and curcumin pretreatment on the cytotoxicity of antifungal drugs fluconazole and itraconazole. For 3 consecutive days, female rats were administered daily SKF 525A or curcumin (5 and 25 mg/kg). Control rats received an equivalent amount of dosed vehicle. The animals were anaesthetized 24 hours after receiving the last dose for liver perfusion. Hepatocytes were then exposed to various concentrations of antifungal drugs. In vitro incubation of hepatocytes with itraconazole revealed significantly lower viability when compared to fluconazole as assessed by lactate dehydrogenase, aspartate aminotransferase and alanine aminotransferase activities. The cytotoxicity of itraconazole was enhanced when incubated with hepatocytes pretreated with SKF 525A. SKF 525A had no effects on the cytotoxicity of fluconazole. Curcumin failed to either increase or decrease the cytotoxicity of both antifungal drugs. ATP levels also showed significant decrease in both itraconazole and fluconazole incubated hepatocytes. However, SKF 525A pretreated hepatocytes had significantly lower ATP levels after itraconazole incubations. Collectively, these results confirm the involvement of cytochrome P450 in the cytoprotection in itraconazole induced hepatocyte toxicity. Differences of the effects of SKF 525A on the cytotoxicity induced by itraconazole and fluconazole may be due to the differences on the metabolism of each antifungal drug in vivo.
Density Estimation Trees

Density estimation is one of the fundamental tasks in statistics and machine learning, as it often appears as a subroutine in tasks such as regression, classification, and clustering. However, density estimation in high dimensions remains a challenging problem due to the curse of dimensionality, which affects the convergence rates of many popular density estimation techniques. In addition, common density estimation techniques often lack in interpretability, adaptability, speed, accuracy, and reasonableness of assumptions. For instance, mixtures of Gaussians (MoGs) require parametric assumptions, fixed bandwidth kernel density estimators (KDEs) can’t adapt well to heterogeneous smoothness (even within even a single dimension), and adaptive bandwidth kernel density estimators (which seek to be more adaptable) are complex and expensive computationally.
Thermomechanical reliability of low-temperature sintered silver die-attachment

In this paper, we present a thermomechanical reliability study on the low-temperature sintered silver die-attachment for packaging semiconductor devices. The die-attachment was formed by sintering nanoscale silver paste at 300degC for 40 minutes to develop full adhesion strength between silver-coated direct-bond-copper (DBC) or copper substrates and silver-metallized SiC devices. A strong bond was formed between the die and substrate with the die-shear strength reaching 40 MPa. Using a 50% drop in the die-shear strength as the failure criterion, accelerated temperature-cycling test between 50degC and 250degC showed that the sintered silver die-attachment on DBC substrates can survive beyond 4000 cycles, indicating high reliability at the temperature cycling range. Scanning electron microscopy (SEM) observations suggest that the die-attachment failure is due to ductile fracture in the silver attachment as micro-cavities were nucleated at grain boundaries during temperature cycling. Results obtained in this study demonstrate that the low-temperature sintered silver is a promising lead-free and reliable die-attach solution for semiconductor devices. Additionally, the silver die-attachment is a potential solution for high-temperature electronics packaging because its high-temperature reliability exhibited during the temperature cycling
The molecular biology of arteriviruses.

The establishment of an arterivirus family acknowledgesthemanyuniquebiologicalandmolecularpropertiesofitsfourpresent members. On the other hand, the aﬃliation with thecoronavirusesrecognizesanintriguingancestralrelationshipatthelevelofgenomeorganizationandreplication.ThegenomesequencesofEAV,PRRSV,LDVandSHFVwereinstrumentalin uniting and reclassifying the arteriviruses (den Boon
Can generic software be assured?

There are many definitions as to what COTS software is. The author uses a very simple definition: COTS software is software functionality obtained from a third party and that is used on an "as is" basis. Examples here include operating system utilities, class libraries, databases, word processing applications, and browser plug-ins. Software assurance problems become magnified for systems that are heavily comprised from COTS software because most of the prevalent quality improvement techniques cannot be applied by the user of the acquired software. These techniques are of course available to the software publisher, but whether the publisher opts to use them cannot be independently verified by the user. That is alarming and could be highly problematic for organizations that opt to buy versus build software systems. In summary, the lack of software quality and publisher responsibility are the greatest concerns that the author sees facing the software industry. The upside, however, is that certification and IV&V can bring assurance back to generic software.
Three‐dimensional mantle convection beneath a segmented spreading center: Implications for along‐axis variations in crustal thickness and gravity

Segmentation and along-axis variations within individual segments indicate the inherently three-dimensional nature of mantle up welling and melting beneath oceanic spreading centers. Numerical convection experiments are used to explore the effects of local buoyancy forces on upwelling and melt production beneath a segmented spreading center. The experiments are conducted in a region consisting of a thermally defined rigid lithosphere and a uniform viscosity asthenosphere overlying a higher-viscosity mantle half-space. A periodic plate boundary geometry is imposed consisting of spreading segments and transform offsets. Buoyancy forces are caused by thermal expansion and the compositional density reduction due to the extraction of partial melt. The relative magnitudes of the buoyant and plate-driven components of mantle flow are controlled by the spreading rate and mantle viscosity, with buoyant flow more important at lower spreading rates and viscosities. Buoyant flow beneath the spreading axis amplifies along-axis variations in upwelling near a ridge-transform intersection, and distributes the variations along the entire spreading axis. Buoyant flow may thus be responsible for the more three-dimensional character of slow spreading centers. Away from the spreading axis, thermal buoyancy drives convective rolls that align with the direction of plate motion and which have an along-axis wavelength controlled by the prescribed thickness of the asthenosphere. However, the position and stability of rolls are influenced by the segmentation geometry. In cases where the spreading center geometry does not allow a stable configuration of rolls, the flow is time-dependent. Along-axis variations in upwelling cause variations in melt production, which imply large variations in crustal thickness that dominate the surface gravity signal. The crustal thickness distributions implied by these numerical experiments produce bulls-eye-shaped negative mantle Bouguer anomalies centered over spreading segments, as observed at several spreading centers. The amplitude of the anomaly increases with decreasing spreading rate.
A Bandit Approach for Intelligent IoT Service Composition across Heterogeneous Smart Spaces

The number of connected devices and services available across the Internet of Things (IoT) is rapidly expanding. In this paper, we present a novel mechanism that improves the ability of mobile clients to dynamically discover potentially unknown IoT services across the IoT's increasingly fragmented protocol landscape. Our approach leverages the detected usage patterns of crowds of mobile users interacting with various IoT services in-situ. We model these usage patterns as a contextual bandit problem that incorporates arbitrarily complex contextual cues, such as device settings, users, environment, activities, etc. We present a modified LinUCB-hybrid algorithm that regulates the exploitation of known services based on contextual cues and makes use of exploration at appropriate times to discover contextually relevant IoT services for mobile users who may not have sufficient prior knowledge. These recommended services can then be used by other middleware platforms, plug-ins or apps to compose new services. We present the overall approach, an operational prototype system and a validation of the system, obtained using two experimental scenarios, which demonstrates promising initial results.
A Modulation Type Recognition Method Using Wavelet Support Vector Machines

Soft defined radio is again the research issue because of cognitive radio ， Modulation type recognition (MTR) is the key issue of the soft defined radio, in this paper, a new MTR method based on the wavelet support vector machine (WSVM) has been proposed, we derive the WSVM kernel function and utilize it to classify the modulation type, the results show when the SNR threshold for the modulation scheme classifier is approximately 10 dB. The correct recognizing rate exceeds 91%. It is shows that this method is better than the tradition method.
Deep Learning Framework with Confused Sub-Set Resolution Architecture for Automatic Arabic Diacritization

The Arabic language belongs to a group of languages that require diacritization over their characters. Modern Standard Arabic (MSA) transcripts omit the diacritics, which are essential for many machine learning tasks like Text-To-Speech (TTS) systems. In this work Arabic diacritics restoration is tackled under a deep learning framework that includes the Confused Sub-set Resolution (CSR) method to improve the classification accuracy, in addition to an Arabic Part-of-Speech (PoS) tagging framework using deep neural nets. Special focus is given to syntactic diacritization, which still suffers low accuracy as indicated in prior works. Evaluation is done versus state-of-the-art systems reported in literature, with quite challenging datasets collected from different domains. Standard datasets like the LDC Arabic Tree Bank are used in addition to custom ones we have made available online to allow other researchers to replicate these results. Results show significant improvement of the proposed techniques over other approaches, reducing the syntactic classification error to 9.9% and morphological classification error to 3% compared to 12.7% and 3.8% of the best reported results in literature, improving the error by 22% over the best reported systems.
FadE: whole genome methylation analysis for multiple sequencing platforms

DNA methylation plays a central role in genomic regulation and disease. Sodium bisulfite treatment (SBT) causes unmethylated cytosines to be sequenced as thymine, which allows methylation levels to reflected in the number of ‘C’-‘C’ alignments covering reference cytosines. Di-base color reads produced by lifetech’s SOLiD sequencer provide unreliable results when translated to bases because single sequencing errors effect the downstream sequence. We describe FadE, an algorithm to accurately determine genome-wide methylation rates directly in color or nucleotide space. FadE uses SBT unmethylated and untreated data to determine background error rates and incorporate them into a model which uses Newton–Raphson optimization to estimate the methylation rate and provide a credible interval describing its distribution at every reference cytosine. We sequenced two slides of human fibroblast cell-line bisulfite-converted fragment library with the SOLiD sequencer to investigate genome-wide methylation levels. FadE reported widespread differences in methylation levels across CpG islands and a large number of differentially methylated regions adjacent to genes which compares favorably to the results of an investigation on the same cell-line using nucleotide-space reads at higher coverage levels, suggesting that FadE is an accurate method to estimate genome-wide methylation with color or nucleotide reads. http://code.google. com/p/fade/.
Recognize foreign low-frequency words with similar pairs

Low-frequency words place a major challenge for automatic speech recognition (ASR). The probabilities of these words, which are often important name entities, are generally under-estimated by the language model (LM) due to their limited occurrences in the training data. Recently, we proposed a word-pair approach to deal with the problem, which borrows information of frequent words to enhance the probabilities of low-frequency words. This paper presents an extension to the word-pair method by involving multiple `predicting words' to produce better estimation for low-frequency words. We also employ this approach to deal with out-of-language words in the task of multi-lingual speech recognition.
The Validation of a Novel Surveillance System for Monitoring Bloodstream Infections in the Calgary Zone

Background. Electronic surveillance systems (ESSs) that utilize existing information in databases are more efficient than conventional infection surveillance methods. The objective was to assess an ESS for bloodstream infections (BSIs) in the Calgary Zone for its agreement with traditional medical record review. Methods. The ESS was developed by linking related data from regional laboratory and hospital administrative databases and using set definitions for excluding contaminants and duplicate isolates. Infections were classified as hospital-acquired (HA), healthcare-associated community-onset (HCA), or community-acquired (CA). A random sample of patients from the ESS was then compared with independent medical record review. Results. Among the 308 patients selected for comparative review, the ESS identified 318 episodes of BSI of which 130 (40.9%) were CA, 98 (30.8%) were HCA, and 90 (28.3%) were HA. Medical record review identified 313 episodes of which 136 (43.4%) were CA, 97 (30.9%) were HCA, and 80 (25.6%) were HA. Episodes of BSI were concordant in 304 (97%) cases. Overall, there was 85.5% agreement between ESS and medical record review for the classification of where BSIs were acquired (kappa = 0.78, 95% Confidence Interval: 0.75–0.80). Conclusion. This novel ESS identified and classified BSIs with a high degree of accuracy. This system requires additional linkages with other related databases.
The relationship between native vegetation and in-stream salinity : an Australian case study

The Glenelg-Hopkins area is a large regional watershed (2.6 million ha) in southwest Victoria that has been extensively cleared for agriculture. In-stream electrical conductivity (EC) in relation to remnant native vegetation is examined from the headwaters to the upper extent of the estuary of the Glenelg River. Five water quality gauging stations were selected. Their contributing subcatchments represent a continuum of disturbance. Proportions of native vegetation ranged from ∼100% at the headwaters of the river to ∼30% at the furthest downstream gauge station. The relationship between remnant vegetation and in-stream EC was examined using aggregated and non-aggregated land use statistics over a period of 22 years from three land use maps. Increased proportions of native vegetation were significantly negatively correlated with in-stream EC and were consistent across all scenarios investigated.
Identification of a cis-Acting Replication Element within the Poliovirus Coding Region

ABSTRACT The replication of poliovirus, a positive-stranded RNA virus, requires translation of the infecting genome followed by virus-encoded VPg and 3D polymerase-primed synthesis of a negative-stranded template. RNA sequences involved in the latter process are poorly defined. Since many sequences involved in picornavirus replication form RNA structures, we searched the genome, other than the untranslated regions, for predicted local secondary structural elements and identified a 61-nucleotide (nt) stem-loop in the region encoding the 2C protein. Covariance analysis suggested the structure was well conserved in the Enterovirus genus of the Picornaviridae. Site-directed mutagenesis, disrupting the structure without affecting the 2C product, destroyed genome viability and suggested that the structure was required in the positive sense for function. Recovery of revertant viruses suggested that integrity of the structure was critical for function, and analysis of replication demonstrated that nonviable mutants did not synthesize negative strands. Our conclusion, that this RNA secondary structure constitutes a novel polioviruscis-acting replication element (CRE), is supported by the demonstration that subgenomic replicons bearing lethal mutations in the native structure can be restored to replication competence by the addition of a second copy of the 61-nt wild-type sequence at another location within the genome. This poliovirus CRE functionally resembles an element identified in rhinovirus type 14 (K. L. McKnight and S. M. Lemon, RNA 4:1569–1584, 1998) and the cardioviruses (P. E. Lobert, N. Escriou, J. Ruelle, and T. Michiels, Proc. Natl. Acad. Sci. USA 96:11560–11565, 1999) but differs in sequence, structure, and location. The functional role and evolutionary significance of CREs in the replication of positive-sense RNA viruses is discussed.
People detection in crowded scenes using active contour models and texture analysis

A vision system designed to detect people in complex backgrounds is presented. The purpose of the proposed algorithms is to allow the identification and tracking of single persons under difficult conditions - in crowded places, under partial occlusion and in low resolution images. In order to detect people reliably, we combine different information channels from video streams. Most emphasis for the initialization of trajectories and the subsequent pedestrian recognition is placed on the detection of the head-shoulder contour. In the first step a simple and fast shape model selects promising candidates, then a local active shape model is matched against the gradients found in the image with the help of a cost function. Texture analysis in the form of co-occurrence features ensures that shape candidates form coherent trajectories over time. In order to reduce the amount of false positives and to become more robust, a pattern analysis step based on Eigenimage analysis is presented. The cues which form the basis of pedestrian detection are integrated into a tracking algorithm which uses the shape information for initial pedestrian detection and verification, propagates positions into new frames using local motion and matches pedestrians with the help of texture information.
An Investigation of Smoothness Constraints for the Estimation of Displacement Vector Fields from Image Sequences

A mapping between one frame from an image sequence and the preceding or following frame can be represented as a displacement vector field. In most situations, the mere gray value variations do not provide sufficient information in order to estimate such a displacement vector field. Supplementary constraints are necessary, for example the postulate that a displacement vector field varies smoothly as a function of the image position. Taken as a general requirement, this creates difficulties at gray value transitions which correspond to occluding contours. Nagel therefore introduced the ``oriented smoothness'' requirement which restricts variations of the displacement vector field only in directions with small or no variation of gray values. This contribution reports results of an investigation about how such an ``oriented smoothness'' constraint may be formulated and evaluated.
Learning deep structured network for weakly supervised change detection

Conventional change detection methods require a large number of images to learn background models or depend on tedious pixel-level labeling by humans. In this paper, we present a weakly supervised approach that needs only image-level labels to simultaneously detect and localize changes in a pair of images. To this end, we employ a deep neural network with DAG topology to learn patterns of change from image-level labeled training data. On top of the initial CNN activations, we define a CRF model to incorporate the local differences and context with the dense connections between individual pixels. We apply a constrained mean-field algorithm to estimate the pixel-level labels, and use the estimated labels to update the parameters of the CNN in an iterative EM framework. This enables imposing global constraints on the observed foreground probability mass function. Our evaluations on four benchmark datasets demonstrate superior detection and localization performance.