Miracle-dz/UniFAR-FTUs huggingface dataset:
Deciphering the Code for Retroviral Integration Target Site Selection

Upon cell invasion, retroviruses generate a DNA copy of their RNA genome and integrate retroviral cDNA within host chromosomal DNA. Integration occurs throughout the host cell genome, but target site selection is not random. Each subgroup of retrovirus is distinguished from the others by attraction to particular features on chromosomes. Despite extensive efforts to identify host factors that interact with retrovirion components or chromosome features predictive of integration, little is known about how integration sites are selected. We attempted to identify markers predictive of retroviral integration by exploiting Precision-Recall methods for extracting information from highly skewed datasets to derive robust and discriminating measures of association. ChIPSeq datasets for more than 60 factors were compared with 14 retroviral integration datasets. When compared with MLV, PERV or XMRV integration sites, strong association was observed with STAT1, acetylation of H3 and H4 at several positions, and methylation of H2AZ, H3K4, and K9. By combining peaks from ChIPSeq datasets, a supermarker was identified that localized within 2 kB of 75% of MLV proviruses and detected differences in integration preferences among different cell types. The supermarker predicted the likelihood of integration within specific chromosomal regions in a cell-type specific manner, yielding probabilities for integration into proto-oncogene LMO2 identical to experimentally determined values. The supermarker thus identifies chromosomal features highly favored for retroviral integration, provides clues to the mechanism by which retrovirus integration sites are selected, and offers a tool for predicting cell-type specific proto-oncogene activation by retroviruses.
Comparison of oral surgery task performance in a virtual reality surgical simulator and an animal model using objective measures

The use of virtual reality (VR) simulation for surgical training has gathered much interest in recent years. Despite increasing popularity and usage, limited work has been carried out in the use of automated objective measures to quantify the extent to which performance in a simulator resembles performance in the operating theatre, and the effects of simulator training on real world performance. To this end, we present a study exploring the effects of VR training on the performance of dentistry students learning a novel oral surgery task. We compare the performance of trainees in a VR simulator and in a physical setting involving ovine jaws, using a range of automated metrics derived by motion analysis. Our results suggest that simulator training improved the motion economy of trainees without adverse effects on task outcome. Comparison of surgical technique on the simulator with the ovine setting indicates that simulator technique is similar, but not identical to real world technique.
SpIES: THE SPITZER IRAC EQUATORIAL SURVEY

We describe the first data release from the Spitzer-IRAC Equatorial Survey (SpIES); a large-area survey of ∼115 deg2 in the Equatorial SDSS Stripe 82 field using Spitzer during its “warm” mission phase. SpIES was designed to probe sufficient volume to perform measurements of quasar clustering and the luminosity function at 3 to test various models for “feedback” from active galactic nuclei (AGNs). Additionally, the wide range of available multi-wavelength, multi-epoch ancillary data enables SpIES to identify both high-redshift ( 5) quasars as well as obscured quasars missed by optical surveys. SpIES achieves 5σ depths of 6.13 μJy (21.93 AB magnitude) and 5.75 μJy (22.0 AB magnitude) at 3.6 and 4.5 μm, respectively—depths significantly fainter than the Wide-field Infrared Survey Explorer (WISE). We show that the SpIES survey recovers a much larger fraction of spectroscopically confirmed quasars (∼98%) in Stripe 82 than are recovered by WISE (∼55%). This depth is especially powerful at high-redshift ( ), where SpIES recovers 94% of confirmed quasars, whereas WISE only recovers 25%. Here we define the SpIES survey parameters and describe the image processing, source extraction, and catalog production methods used to analyze the SpIES data. In addition to this survey paper, we release 234 images created by the SpIES team and three detection catalogs: a 3.6 μm only detection catalog containing ∼6.1 million sources, a 4.5 μm only detection catalog containing ∼6.5 million sources, and a dual-band detection catalog containing ∼5.4 million sources.
An efficient coverage algorithm for use in macrocell-small cell systems

The deployment of small cell base stations within the coverage area of a macrocell base station serves as an important means of increasing energy efficiency. Furthermore, such a deployment also constitutes a key technology for future communication network markets. There is a shortage of research, however, on how to efficiently solve the problem of coverage holes (that is, specific locations or areas in which a user is not able to get an adequate signal from the wireless network) when such a network architecture is used. In this study, we propose an efficient coverage algorithm for use in such macrocell-small cell network systems. The proposed algorithm is able to provide users with comprehensive wireless signal coverage in various scenarios, while also avoiding unnecessary power consumption. Simulation results demonstrate that the proposed algorithm can provide much better overall system performance than conventional ones.
Color restoration for objects of interest using robust image features

Illumination distortion due to uncontrolled lighting can severely degrade the color appearance of a photo. Frequently, the desired colors for objects in a newly taken query image are found in a previously stored database image. Then, the goal is to change the colors in the query image to match the colors in the database image. This paper presents a color restoration system that automatically retrieves a database image which matches the query image, even if the two images are taken from different viewpoints and under different illuminations. Robust features enable both accurate retrieval from the database and efficient sampling of the color differences between the query and database images. A spatially varying color mismatch model is generated, and the colors of the query image are effectively restored.
The fallacy of the Good Samaritan: Volunteering as a weird way of making money

This paper explores individual motives for volunteering: The analysis is based on the interpretation of volunteering as a consumption good (consumption model) or as a mean to increase individual’s own human capital (investment model). We present an econometric framework taking into account self selection into volunteering and simultaneity between the volunteering decision and the determination of income in order to test these two models and to identify the underlying motives. We find strong statistical evidence for the investment model with a highly robust and significant impact of volunteering on the wage rate. On average the wage premium as the difference in the wage rate between one person volunteering and not volunteering amounts up to 15.7 percent. Within the framework of the investment model it turns out that the number of volunteering hours plays a major role in explaining this wage premium. This supports the idea of volunteering as a mean to accumulate human capital and signalling willingness to perform. As far as the consumption model is concerned we find no convincing statistical evidence for its validity. The strong evidence for the investment model requires the consideration of voluntary activities in the estimation of wage equations. Moreover, we conclude that the existence of the wage premium is a decisive factor for people to volunteer or not and therefore reflects an important argument in the recruitment process of volunteers for several organisations.
Ventricular Arrhythmias Assessment

An integrated framework for ventricular arrhythmias (VA) assessment, composed of two levels, is proposed in this work. The first level consists of four independent neural networks (NN), designed for specific detection tasks: signal quality, premature ventricular contractions (PVC), ventricular tachycardia (VT) and ventricular fibrillation (VF). Time and frequency domain features, obtained from the electrocardiogram (ECG) and selected through a correlation analysis procedure, form the inputs to the neural modules. The outputs feed the second layer, which consists of a global classifier (ANFIS structure), returns the global result for the VA assessment scheme. Sensitivity and specificity values, evaluated from public MIT- BIH databases, show the effectiveness of the proposed strategy.
Estimating the 3D Layout of Indoor Scenes and Its Clutter from Depth Sensors

In this paper we propose an approach to jointly estimate the layout of rooms as well as the clutter present in the scene using RGB-D data. Towards this goal, we propose an effective model that is able to exploit both depth and appearance features, which are complementary. Furthermore, our approach is efficient as we exploit the inherent decomposition of additive potentials. We demonstrate the effectiveness of our approach on the challenging NYU v2 dataset and show that employing depth reduces the layout error by 6% and the clutter estimation by 13%.
Robust Visual Tracking Using an Adaptive Coupled-Layer Visual Model

This paper addresses the problem of tracking objects which undergo rapid and significant appearance changes. We propose a novel coupled-layer visual model that combines the target's global and local appearance by interlacing two layers. The local layer in this model is a set of local patches that geometrically constrain the changes in the target's appearance. This layer probabilistically adapts to the target's geometric deformation, while its structure is updated by removing and adding the local patches. The addition of these patches is constrained by the global layer that probabilistically models the target's global visual properties, such as color, shape, and apparent local motion. The global visual properties are updated during tracking using the stable patches from the local layer. By this coupled constraint paradigm between the adaptation of the global and the local layer, we achieve a more robust tracking through significant appearance changes. We experimentally compare our tracker to 11 state-of-the-art trackers. The experimental results on challenging sequences confirm that our tracker outperforms the related trackers in many cases by having a smaller failure rate as well as better accuracy. Furthermore, the parameter analysis shows that our tracker is stable over a range of parameter values.
Real-time scale-adaptive compressive tracking using two classification stages

In this paper, we describe a method for Scale-Adaptive visual tracking using compressive sensing. Instead of using scale-invariant-features to estimate the object size every few frames, we use the compressed features at different scale then perform a second stage of classification to detect the best-fit scale. We describe the proposed mechanism of how we implement the Bayesian Classifier used in the algorithm and how to tune the classifier to address the scaling problem and the method of selecting the positive training samples and negative training samples of different scales. The obtained results demonstrate enhanced tracking accuracy when compared to the original compressive tracking algorithm.
Liver Steatosis and Increased ChREBP Expression in Mice Carrying a Liver Specific SIRT1 Null Mutation under a Normal Feeding Condition

SIRT1, a homolog of yeast Sir2, is a type III NAD+ dependent histone and protein deacetylase. Previous studies of mice carrying liver specific deletion of exon 4 of the Sirt1 gene revealed opposite responses of mutant mice to a high-fat diet in terms of fatty liver formation, which obscures the function of SRIT1 in liver development and lipid metabolism. To investigate this, we deleted exons 5 and 6 of Sirt1 in the liver by using a Cre-loxP approach. Western blot using an antibody to N-terminal SIRT1 does not detect a truncated protein in the liver of the mutant mice (Sirt1flox5-6/flox5-6;Alb-Cre), suggesting a null mutation for SIRT1 is generated in the liver. Unlike the previously reported phenotypes, the Sirt1flox5-6/flox5-6;Alb-Cre mice develop fatty liver under a normal feeding condition. The disease starts at two months of age and incidence increases as the animals become older, affecting 78% of them when they are over one year of age. We showed that the steatosis is accompanied by altered expression of a number of genes, including increased expression of ChREBP, which acts as one of the central determinants of lipid synthesis in the liver. This data uncovers an important role of SIRT1 in regulating lipid metabolism in the liver, and the SIRT1 mutant mice may serve as an animal model for studying human fatty liver disease and facilitate the development of effective therapeutic approach for the disease.
Application of Higher-order IRT models and Hierarchical IRT models to Computerized Adaptive Testing

Author(s): Lee, Moonsoo | Advisor(s): Cai, Li | Abstract: In recent years, the importance of formative assessments has been emphasized within educational measurement. This type of assessment often includes multiple correlated sub-domains and a hierarchical structure among the proficiencies. In this dissertation, several multidimensional CAT procedures are investigated to improve the measurement aspects of diagnostic testing and to better match the psychometric models to the test structure. Five factors are manipulated with higher-order IRT models and hierarchical IRT models: (1) the different correlation conditions between two primary factors (low, medium, and high), (2) the number of group factors per primary factor (two and four), (3) the number of items (40, 80 and 160), (4) the item selection method (MFI and Bayesian), and (5) the proficiency score estimation method (MLE and EAP). Three outcome measures, including correlations between true and estimated proficiency scores, Root Mean Square Error (RMSE) of estimated proficiency scores, and Standard Errors (SE) are computed totaling 192 different conditions. As expected, the correlation between true and estimated proficiency scores increase while RMSE and SE decrease when the test length correlation between two primary factors increase under different correlations among the factors, different item selection methods and different scoring methods. In overall, the higher-order IRT model CAT has an advantage over the hierarchical IRT model CAT when we need scores for the primary factors. On the other hand, if test designers are interested in more specific group factors, hierarchical IRT models outperformed the higher-order IRT models. This study undertakes a comprehensive comparison of item selection methods and proficiency scores estimation in several multidimensional IRT models in conjunction with a CAT. The item selection and proficiency score estimation methods are negligible across the four multidimensional IRT CAT algorithms. However, the Bayesian item selection method has smaller RMSEs and SEs than the MFI method in specific cases and the EAP scoring method outperforms the MLE method, especially for short test length in this study.
Dynamic population mapping using mobile phone data

Significance Knowing where people are is critical for accurate impact assessments and intervention planning, particularly those focused on population health, food security, climate change, conflicts, and natural disasters. This study demonstrates how data collected by mobile phone network operators can cost-effectively provide accurate and detailed maps of population distribution over national scales and any time period while guaranteeing phone users’ privacy. The methods outlined may be applied to estimate human population densities in low-income countries where data on population distributions may be scarce, outdated, and unreliable, or to estimate temporal variations in population density. The work highlights how facilitating access to anonymized mobile phone data might enable fast and cheap production of population maps in emergency and data-scarce situations. During the past few decades, technologies such as remote sensing, geographical information systems, and global positioning systems have transformed the way the distribution of human population is studied and modeled in space and time. However, the mapping of populations remains constrained by the logistics of censuses and surveys. Consequently, spatially detailed changes across scales of days, weeks, or months, or even year to year, are difficult to assess and limit the application of human population maps in situations in which timely information is required, such as disasters, conflicts, or epidemics. Mobile phones (MPs) now have an extremely high penetration rate across the globe, and analyzing the spatiotemporal distribution of MP calls geolocated to the tower level may overcome many limitations of census-based approaches, provided that the use of MP data is properly assessed and calibrated. Using datasets of more than 1 billion MP call records from Portugal and France, we show how spatially and temporarily explicit estimations of population densities can be produced at national scales, and how these estimates compare with outputs produced using alternative human population mapping methods. We also demonstrate how maps of human population changes can be produced over multiple timescales while preserving the anonymity of MP users. With similar data being collected every day by MP network providers across the world, the prospect of being able to map contemporary and changing human population distributions over relatively short intervals exists, paving the way for new applications and a near real-time understanding of patterns and processes in human geography.
Joint detection and decoding for non-binary LDPC coded MIMO systems

Due to its outstanding spectral efficiency, multiple-input and multiple-output (MIMO) technology has been developed and drawn increasing attentions for recent decades. Well balancing complexity and performance, K-best algorithm has become one of the most favorable MIMO detections. In this paper, a joint detection and decoding (JDD) for non-binary LDPC (NB-LDPC) coded MIMO systems has been proposed with better performance compared to the existing K-best detection, which prunes the tree-search with NB-LDPC coding constraint. Numerical results have demonstrated that the JDD system with 8×8 MIMO and GF(4) (8, 2) NB-LDPC code can achieve up to 4 dB gain at BER = 2 × 10−2 compared to the state-of-the-art (SOA) approach. Hardware architecture and schedule analysis have shown the advantages and feasibility of proposed JDD.
Cocreation as Moderator between the Experience Value and Satisfaction Relationship

The article explores the concept of cocreation of value, defined as the tourist’s interest in mental and physical participation in an activity and its role in tourist experiences. Based on the theoretical perspective of “the new service-dominant logic,” customer participation in tourist experiences is explored and tested as a moderating variable on the perceived value – satisfaction relationship. In essence, the customer partakes mentally and physically in an experience, which moderates the role that experience value has on overall satisfaction. The study thus hypothesizes that the higher the level of participation, the stronger the experience value–satisfaction link becomes and vice versa. Using a sample drawn from tourists in Norway, the results confirm that experience value is an effective predictor of tourist satisfaction. The study reveals that the level of cocreation moderates the effect between the experience value of winter tourism activities and satisfaction.
Adaptive Fuzzy Robust Control for Nonlinear System with Dynamic Uncertainties Based on Backstepping

In this paper, a fuzzy adaptive backstepping design procedure is proposed for a class of nonlinear systems with three types of uncertainties: (i) unknown nonlinear functions; (ii) uncertain nonlinearities; (iii) unmodeled dynamics. The fuzzy logic systems are used to approximate the unknown nonlinear functions, nonlinear damping terms are used to counteract the uncertain nonlinearities. The derived fuzzy adaptive control approach guarantees the global bounded property for all the signals and the states and at the same time, steers the output to a small neighborhood of the origin.
DTM: Degraded Test Mode for Fault-Aware Probabilistic Timing Analysis

Existing timing analysis techniques to derive Worst-Case Execution Time (WCET) estimates assume that hardware in the target platform (e.g., the CPU) is fault-free. Given the performance requirements increase in current Critical Real-Time Embedded Systems (CRTES), the use of high-performance features and smaller transistors in current and future hardware becomes a must. The use of smaller transistors helps providing more performance while maintaining low energy budgets, however, hardware fault rates increase noticeably, affecting the temporal behaviour of the system in general, and WCET in particular. In this paper, we reconcile these two emergent needs of CRTES, namely, tight (and trustworthy) WCET estimates and the use of hardware implemented with smaller transistors. To that end we propose the Degraded Test Mode (DTM) that, in combination with fault-tolerant hardware designs and probabilistic timing analysis techniques, (i) enables the computation of tight and trustworthy WCET estimates in the presence of faults, (ii) provides graceful average and worst-case performance degradation due to faults, and (iii) requires modifications neither in WCET analysis tools nor in applications. Our results show that DTM allows accounting for the effect of faults at analysis time with low impact in WCET estimates and negligible hardware modifications.
Tinkering Under The Hood : Interactive Zero-Shot Learning with Pictorial Classifiers

We consider the task of visual zero-shot learning, in which a system must learn to recognize concepts omitted from the training set. While most prior work make use of linguistic cues to do this, we do so by using a pictorial language representation of the training set, implicitly learned by a CNN, to generalize to new classes. We first demonstrate the robustness of pictorial language classifiers (PLCs) by applying them in a weakly supervised manner: labeling unlabeled concepts for visual classes present in the training data. Specifically we show that a PLC built on top of a CNN trained for ImageNet classification can localize humans in Graz-02 and determine the pose of birds in PASCAL-VOC without extra labeled data or additional training. We then apply PLCs in an interactive zero-shot manner, demonstrating that pictorial languages are expressive enough to detect a set of visual classes in MSCOCO that never appear in the ImageNet training set.