Miracle-dz/UniFAR-FTUs huggingface dataset:
Evidence that Localized Variation in Primate Sequence Divergence Arises from an Influence of Nucleosome Placement on DNA Repair

Understanding the origins of localized substitution rate heterogeneity has important implications for identifying functional genomic sequences. Outside of gene regions, the origins of rate heterogeneity remain unclear. Experimental studies establish that chromatin compaction affects rates of both DNA lesion formation and repair. A functional association between chromatin status and 5-methyl-cytosine also exists. These suggest that both the total rate and the type of substitution will be affected by chromatin status. Regular positioning of nucleosomes, the building block of chromatin, further predicts that substitution rate and type should vary spatially in an oscillating manner. We addressed chromatin's influence on substitution rate and type in primates. Matched numbers of sites were sampled from Dnase I hypersensitive (DHS) and closed chromatin control flank (Flank). Likelihood ratio tests revealed significant excesses of total and of transition substitutions in Flank compared with matched DHS for both intergenic and intronic samples. An additional excess of CpG transitions was evident for the intergenic, but not intronic, regions. Fluctuation in substitution rate along ∼1,800 primate promoters was measured using phylogenetic footprinting. Significant positive correlations were evident between the substitution rate and a nucleosome score from resting human T-cells, with up to ∼50% of the variance in substitution rate accounted for. Using signal processing techniques, a dominant oscillation at ∼200 bp was evident in both the substitution rate and the nucleosome score. Our results support a role for differential DNA repair rates between open and closed chromatin in the spatial distribution of rate heterogeneity.
Chikungunya Outbreaks Caused by African Genotype, India

Chikungunya fever is reported in India after 32 years. Immunoglobulin M antibodies and virus isolation confirmed the cause. Phylogenic analysis based on partial sequences of NS4 and E1 genes showed that all earlier isolates (1963–1973) were Asian genotype, whereas the current and Yawat (2000) isolates were African genotype.
The historic Christmas 2004 south Texas snow event: Diagnosis of the heavy snow band

On 24 and 25 December 2004, much of south Texas received a record snowfall. Measurable snow was reported from the Galveston/Houston region, southward to Corpus Christi, Brownsville and into Northeastern Mexico (Fig. 1). Embedded within the larger snow field was a narrow, heavier snow band. This band had maximum snow depths of 33 cm (13 inches), a half width (distance between maximum snowfall and half that amount) of approximately 48-64 km (30-40 miles), and a total length of more than 320 km (200 miles). Although Figure 1 reveals only one heavy snow band, a composite of the radar data from Weather Surveillance Radar-1988 (WSR-88D) sites across south Texas indicated that there were at least two distinct bands that formed during this event. The entire snow event occurred between the hours of 0000 and 1200 UTC 25 December, with the heaviest snow falling between 0200 to 0800 UTC.
Superior Factors to Distinguish Students to Be Cared in Introductory Programming Education

Every student has its own motivation and learning strategies, which conform a learning status of the student. Appropriate supervision according to the learning status contributes to improvement of the learning of each student. Many of existing works try to figure out learning status directly from observable learning behavior. This paper proposes to utilize internal factors consisting of learning motivation and strategies, to distinguish learning status of students. It presents a way to derive the internal factors from records collected from their usual learning behavior, using the similarity of students over successive years. The experiment results indicates the strong possibility of the distinction from learning behavior. It implies the feasibility of immediate distinction of learning status of students, which enables efficient allocation of teaching power on the spot.
Communication In General Decentralised Filters And The Coordinated Search Strategy

This paper addresses the problem of coordinating multiple heterogeneous sensing platforms performing a search mission for a single target in a dynamic environment. In this approach, the decision makers build an equivalent representation of the Probability Density Function (PDF) of the target state by communicating with their neighbors in a decentralized Bayesian sensor network enabling them to coordinate their actions without exchanging any information about their plans. This paper focuses on the communication aspect of the network. A channel filter that handles general PDF’s is developed. The channel filter assures that complete global information is recovered at each node provided that the network connectivity is acyclic. A channel manager is also developed that uses the Hellinger divergence measure between the node and channel estimates adaptively determines when to communicate on any particular channel. It is proven to significantly reduce the communication loads. Simulation results are used to evaluate the accuracy of the filtering algorithm and demonstrate the efficiency of the resulting coordinated search trajectories.
Intelligibility of 4-year-old children with and without cerebral palsy.

PURPOSE The authors examined speech intelligibility in typically developing (TD) children and 3 groups of children with cerebral palsy (CP) who were classified into speech/language profile groups following Hustad, Gorton, and Lee (2010). Questions addressed differences in transcription intelligibility scores among groups, the effects of utterance length on intelligibility, the relationship between ordinal ratings of intelligibility and orthographic transcription intelligibility scores, and the difference between parent and naïve listener ordinal ratings. METHOD Speech samples varying in length from 1 to 7 words were elicited from 23 children with CP ( M (age) = 54.3 months) and 20 TD children ( M (age) = 55.1 months). Two hundred fifteen naïve listeners made orthographic transcriptions and ordinal ratings of intelligibility. Parent ordinal ratings of intelligibility were obtained from a previous study (Hustad et al., 2010). RESULTS Intelligibility varied with speech/language profile group and utterance length, with different patterns observed by profile group. Ratings of intelligibility by parents and naïve listeners did not differ, and both were highly correlated with transcription intelligibility scores. CONCLUSION Intelligibility was reduced for all groups of children with CP relative to TD children, suggesting the importance of speech-language intervention and the need for research investigating variables associated with changes in intelligibility in children.
Static Power Estimation of CMOS Logic Blocks in a Library Free Design Environment

This paper introduces a new approach to pattern dependent static power estimation in logic blocks, which are realized ‘on-the-fly’ in a library-free design environment. A static current model is first developed at the transistor level and then extended to the logic gate level and finally the logic block level. For varying transistors widths and input stimuli, the transistor level model has performed with good accuracy compared to SPICE for technologies ranging from 65nm down to 32nm. The gate level model is pattern dependent and deals with basic gates and complex gates. A transistor collapsing scheme was developed to achieve simpler structure leading to analytical models with high computational efficiency and good accuracy ranging from 0.1-5.4% for basic logic gates and 3.7-6.2 % for complex gates. Using these static current estimation models, a methodology has been introduced to estimate static power dissipation of logic blocks in a library-free design environment, in which the cells are generated and sized ‘on-the-fly’ driven by specification and targeted technology. Across several MCNC benchmarks, the estimation methodology proposed exhibits a worst case mean percentage error of 1.1% compared to SPICE. It also exhibits runtime that is on average 43 times faster than SPICE.
Design and Evaluation of a Peripheral Robotic Conversation Companion

We present the design, implementation, and evaluation of a peripheral empathy-evoking robotic conversation companion, Kip1. The robot’s function is to increase people’s awareness to the effect of their behavior towards others, potentially leading to behavior change. Specifically, Kip1 is designed to promote nonaggressive conversation between people. It monitors theconversation’s nonverbal aspects and maintains an emotional model of its reaction to the conversation. If the conversation seems calm, Kip1 responds by a gesture designed to communicate curious interest. If the conversation seems aggressive, Kip1 responds by a gesture designed to communicate fear. We describe the design process of Kip1, guided by the principles of peripheral and evocative. We detail its hardware and software systems, and a study evaluating the effects of the robot’s autonomous behavior on couples’ conversations. We find support for our design goals. A conversation companion reacting to the conversation led to more gaze attention, but not more verbal distraction, compared to a robot that moves but does not react to the conversation. This suggests that robotic devices could be designed as companions tohuman-human interaction without compromising the natural communication flow between people. Participants also rated the reacting robot as having significantly more social human charactertraits and as being significantly more similar to them. This points to the robot’s potential to elicit people’s empathy.Categories and Subject Descriptors H.1. 2 [Models and Principles]: User/Machine Systems; J.4 [Computer Applications]: Social and Behavioral Sciences— psychology. General Terms Experimentation, Human Factors.
Why School Resilience Should Be Critical for the Post-Earthquake Recovery of Communities in Divided Societies

From pre-disaster to recovery, education is a fundamental key in reducing the impacts of disasters on children, people, and communities. This article aims to highlight the key components that are necessary for building school resilience to earthquakes and to demonstrate that promotion of the seismic resilience of schools is not only critical for children’s safety and the continuity of their education but also for the effective post-earthquake recovery of communities. It is shown that promoting social cohesion through the improvement of the seismic structural safety of school buildings and the capabilities of school administrators with regard to shifting to an online teaching and learning mode during school dysfunctions or closures are key to the post-earthquake recovery of vulnerable communities in divided societies. This article can be of practical significance to educational administrators interested in school protection and in the continuity of educational services in the aftermath of disasters.
End point tracking for a moving object with several attention regions by composite vision system

This paper describes an approach of multi-target tracking for gaze control to know the motions of end points on a moving object. In order to track several moving parts from image streams, three different types of tracker to observe temporal, spatial and appearance changes are combined. Also, we developed a composite vision system on which two wide-angle cameras and two zoom-enabled cameras are mounted. We tested the gaze control system and the head system by observing a human working in daily environment. This results showed the effectiveness of our approach.
Voxel selection algorithms for fMRI

Functional Magnetic Resonance Imaging (fMRI) is a technique to measure and image the BloodOxygen Level Dependent (BOLD) signal in the human brain. The BOLD signal is strongly correlated with the brain activity. Consequently fMRI makes it possible to image activity patterns in a brain of a living organism and thus observe and record its responses to a variety of stimuli. For this reason fMRI techniques have become very popular in psychology and cognitive sciences. A typical fMRI experiment consists in stimulating subjects in some way (vision, hearing, touch etc.) and recording the corresponding activity pattern for a large number of small brain volumes called voxels. Activities within voxels are used as features for machine learning algorithms to relate brain areas with certain types of stimuli. Unfortunately the fMRI data has many pitfalls. In most cases it is characterized with a very large number of features formed by brain voxel activations (up to several thousands) and a low number of training examples, typically of the order of hundreds. Data pre-processing is of particular interest for two reasons. First, selecting a smaller set of features can improve stimulus prediction accuracy and reduce computational complexity. Second, given the location of voxels most relevant to the prediction task, conclusions about brain circuits can be made. Current research employs a number of voxel selection techniques. They can be as simple as manual segmentation of regions of interest from the entire brain [1] or more elaborate involving statistical significance tests. Recently more advanced algorithms such as ridge regression, lasso, sparse logistic regression or graph Laplacian based methods have started to be more frequently used [2]. Many of these methods promote sparse solutions, i.e. such where many of the voxels are assigned a zero score and therefore considered uninformative. This notion agrees with neurological understanding of brain organization into different regions responsible for different tasks. For example speech recognition will cause increased activity of certain areas of the brain, and will have little influence on other. So far none of the above methods explicitly encodes correlations between features using a covariance matrix. One of the problems with using empirical covariance is the fact that just a few data points are available making this matrix non-invertible. Therefore it cannot be directly used in optimization algorithms. This project investigates an approximate inverse covariance matrix estimation technique and its applicability to imposing a Gaussian prior distribution on feature scoring weights.
Software reflexion models: bridging the gap between source and high-level models

Software engineers often use high-level models (for instance, box and arrow sketches) to reason and communicate about an existing software system. One problem with high-level models is that they are almost always inaccurate with respect to the system's source code. We have developed an approach that helps an engineer use a high-level model of the structure of an existing software system as a lens through which to see a model of that system's source code. In particular, an engineer de nes a high-level model and speci es how the model maps to the source. A tool then computes a software re exion model that shows where the engineer's high-level model agrees with and where it di ers from a model of the source. The paper provides a formal characterization of reexion models, discusses practical aspects of the approach, and relates experiences of applying the approach and tools to a number of di erent systems. The illustrative example used in the paper describes the application of re exion models to NetBSD, an implementation of Unix comprised of 250,000 lines of C code. In only a few hours, an engineer computed several re exion models that provided him with a useful, global overview of the structure of the NetBSD virtual memory subsystem. The approach has also been applied to aid in the understanding and experimental reengineering of the Microsoft Excel spreadsheet product. This research was funded in part by the NSF grant CCR-8858804 and a Canadian NSERC post-graduate scholarship. 0 Permission to make digital/hard copies of all or part of this material without fee is granted provided that the copies are not made or distributed for pro t or commercial advantage, the ACM copyright/server notice, the title of the publication and its date appear, and notice is given that copyright is by permission of the Association for Computing Machinery, Inc. (ACM). To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior speci c permission and/or a fee. SIGSOFT '95 Washington, D.C., USA c 1995 ACM 0-89791-716-2/95/0010. $3.50
Factors influencing GAP-43 gene expression in PC12 pheochromocytoma cells

We have studied factors controlling message levels for the neuronal growth- and plasticity-associated protein, GAP-43. Following exposure of PC12 cells to various effectors, cytoplasmic RNA was isolated and analyzed by Northern transfer and autoradiography using a GAP-43 cDNA probe. Induction by NGF is apparent after 3 hr exposure and reaches maximal levels at 24 hr. Beyond 24 hr, levels remain constant in the continued presence of NGF. Induction is insensitive to variations in culture conditions, such as plating density or substrate, which influence NGF-induced neurite outgrowth. Other inducers, in order of decreasing efficacy, are FGF, dBcAMP, TPA, K+, and EGF. Insulin and retinoic acid are ineffective. Dexamethasone partially inhibited basal expression as well as induction by NGF, FGF, dBcAMP, and TPA. The methyltransferase inhibitor 5′-S-(2-methyl-propyl)adenosine completely inhibited induction by NGF, FGF, and dBcAMP. Inhibition of protein synthesis by cycloheximide partially decreased induction by NGF, FGF, and TPA but slightly enhanced dBcAMP induction. Complete down- regulation of protein kinase C by chronic TPA treatment completely eliminated the TPA response but slightly enhanced induction by NGF. These findings and the results of additivity experiments in which cells were stimulated with various combinations of NGF, dBcAMP and TPA suggest that NGF induction of GAP-43 RNA (1) does not involve activation of protein kinase C but (2) may be mediated partially via activation of protein kinase A.
High-performance SAR simulation on retail video gaming consoles for education and training purposes

For SAR education a combination of theoretical lectures and practical image analysis is advisable. Using podcasts for assisting the dissemination of background knowledge and retail video gaming consoles for the practical lectures in combination is proposed. SAR simulators are widely used for teaching SAR image analysis, because they can deliver pre-defined SAR images. Even more, they can provide thousands of different images of the same object. Learning by using these training databases of simulated images is normally not very effective, due to the monotonous presentation of the data. A real-time simulation can overcome these limitations by interactively presenting the results. The user interface design of the educational simulator system adapts the user interface of modern computer games. The idea is to give the impression of gaming instead of learning. Additionally, beside the visualization of scenes, educational game modes are available.
Linear Pose Estimation from Points or Lines

Estimation of camera pose from an image of n points or lines with known correspondence is a thoroughly studied problem in computer vision. Most solutions are iterative and depend on nonlinear optimization of some geometric constraint, either on the world coordinates or on the projections to the image plane. For real-time applications, we are interested in linear or closed-form solutions free of initialization. We present a general framework which allows for a novel set of linear solutions to the pose estimation problem for both n points and n lines. We then analyze the sensitivity of our solutions to image noise and show that the sensitivity analysis can be used as a conservative predictor of error for our algorithms. We present a number of simulations which compare our results to two other recent linear algorithms, as well as to iterative approaches. We conclude with tests on real imagery in an augmented reality setup.
Compact WLAN dual-band filter using independent band stop resonators combined with diplexer

In this paper, a highly miniaturized dual-band band pass filter has firstly been developed by embedding all the passive lumped elements into a LTCC substrate for 2.4–2.5 GHz (802.11b) and 5.15–5.85 GHz (802.11a/g) WLAN applications. In order to reduce a size/volume of the filter and improve its performances, the proposed filter was newly designed by using J-inverter transformation technology, independent transmission zero technology and independent band-stop resonators. The J-inverter transformation technology was highly effective to minimize electromagnetic parasitic coupling between the large inductors. The newly applied independent band-stop resonator helped to reduce the number of passive elements in the filter circuit. Finally, designed dual-band filter was combined with simple LC diplexer. The measured maximum insertion losses of the fabricated dual-band WLAN filter were better than 2.3 and 1.3 dB in the lower and upper pass-bands, respectively. Return loss was higher than 15 dB. The overall size/volume of the fabricated filter module was 4.7 × 2.3 × 0.68 mm3
Traffic capture beyond 10 Gbps: Linear scaling with multiple network interface cards on commodity servers

As the number of users on Internet is increasing day by day, Network traffic rate is going up exponentially. The traffic analysis capability also need to be scaled up correspondingly to handle this high traffic rates. The modern commodity platforms equipped with processors rich in number of cores and having hyper threading technology has enabled emergence of software based solutions rather than dedicated platforms for traffic analysis. But the general purpose network stacks provided by operating systems focus more on compatibility and hence are not able to fully exploit the hardware capabilities of these platforms. Lot of work is reported on customizing the stack for packet capture and achieving line rate (10 Gbps) performance on a single Network Interface Card (NIC). Some work is also reported on use of multiple 10GbE NICs for handling traffic beyond 10 Gbps. This paper brings out through experimental setup and observations the issues involved in linear scaling of performance with additional NICs on commodity Non Uniform Memory Access (NUMA) servers. It shows that linear scaling requires optimum configurations of compute, memory and network resources especially affinity of NICs to NUMA nodes. Using these configurations we are able to achieve linear scaling of traffic analysis capability with additional 10 GbE NICs. We report scaled performance upto 40 Gbps with the use of four NIC cards.
Research Proposal Programming Language Abstractions for Mobile Code

Languages supporting mobile code differ from other languages or middleware for distributed systems (like CORBA) because they explicitly model the concept of separate computational environments and how code and computations move among these environments. Some concepts shared with traditional languages acquire new dimensions in the case of languages supporting mobile code (e.g. scope rules and name resolution) and others are typical of those languages (e.g. dynamic binding and security). Mobility has a strong impact on programming language features: