Miracle-dz/UniFAR-FTUs huggingface dataset:
Different Epigenetic Alterations Are Associated with Abnormal IGF2/Igf2 Upregulation in Neural Tube Defects

The methylation status of DNA methylation regions (DMRs) of the imprinted gene IGF2/Igf2 is associated with neural tube defects (NTDs), which are caused by a failure of the neural tube to fold and close and are the second-most common birth defect; however, the characterization of the expression level of IGF2/Igf2 in neural tissue from human fetuses affected with NTDs remains elusive. More importantly, whether abnormal chromatin structure also influences IGF2/Igf2 expression in NTDs is unclear. Here, we investigated the transcriptional activity of IGF2/Igf2 in normal and NTD spinal cord tissues, the methylation status of different DMRs, and the chromatin structure of the promoter. Our data indicated that in NTD samples from both human fetuses and retinoic acid (RA)-treated mouse fetuses, the expression level of IGF2/Igf2 was upregulated 6.41-fold and 1.84-fold, respectively, compared to controls. H19 DMR1, but not IGF2 DMR0, was hypermethylated in human NTD samples. In NTD mice, h19 DMR1 was stable, whereas the chromatin structure around the promoter of Igf2 might be loosened, which was displayed by higher H3K4 acetylation and lower H3K27 trimethylation. Therefore, the data revealed that IGF2/Igf2 expression can be ectopically up-regulated by dual epigenetic factors in NTDs. In detail, the upregulation of IGF2/Igf2 is likely controlled by hypermethylation of H19 DMR1 in human NTDs, however, in acute external RA-induced NTD mice it is potentially determined by more open chromatin structure.
Concept Acquisition from Corpora: Using an Automatic Clustering Method Based on Chinese Measure Words

This paper presents a new concept representation based on measure words. Concepts are modeled as vectors with one component corresponding to each measure word. We propose a weighting scheme that assigns each measure word a weight in a concept. Then we conduct experiments to identify concept similarities via clustering, and the results show measure words can categorize most concept classes.
Atmospheric escape from hot Jupiters

The extra-solar planet HD209458b has been found to have an extended atmosphere of escaping atomic hydrogen (Vidal-Madjar et al. 2003), suggesting that "hot Jupiters" closer to their parent stars could evaporate. Here we estimate the atmospheric escape (so called evaporation rate) from hot Jupiters and their corresponding life time against evaporation. The calculated evaporation rate of HD209458b is in excellent agreement with the H i Lyman-� observations. We find that the tidal forces and high temperatures in the upper atmosphere must be taken into account to obtain reliable estimate of the atmospheric escape. Because of the tidal forces, we show that there is a new escape mechanism at intermediate temperatures at which the exobase reaches the Roche lobe. From an energy balance, we can estimate plausible values for the planetary exospheric temperatures, and thus obtain typical life times of planets as a function of their mass and orbital distance.
Drug-Encoded Biomarkers for Monitoring Biological Therapies

Blood tests are necessary, easy-to-perform and low-cost alternatives for monitoring of oncolytic virotherapy and other biological therapies in translational research. Here we assessed three candidate proteins with the potential to be used as biomarkers in biological fluids: two glucuronidases from E. coli (GusA) and Staphylococcus sp. RLH1 (GusPlus), and the luciferase from Gaussia princeps (GLuc). The three genes encoding these proteins were inserted individually into vaccinia virus GLV-1h68 genome under the control of an identical promoter. The three resulting recombinant viruses were used to infect tumor cells in cultures and human tumor xenografts in nude mice. In contrast to the actively secreted GLuc, the cytoplasmic glucuronidases GusA and GusPlus were released into the supernatants only as a result of virus-mediated oncolysis. GusPlus resulted in the most sensitive detection of enzyme activity under controlled assay conditions in samples containing as little as 1 pg/ml of GusPlus, followed by GusA (25 pg/ml) and GLuc (≥375 pg/ml). Unexpectedly, even though GusA had a lower specific activity compared to GusPlus, the substrate conversion in the serum of tumor-bearing mice injected with the GusA-encoding virus strains was substantially higher than that of GusPlus. This was attributed to a 3.2 fold and 16.2 fold longer half-life of GusA in the blood stream compared to GusPlus and GLuc respectively, thus a more sensitive monitor of virus replication than the other two enzymes. Due to the good correlation between enzymatic activity of expressed marker gene and virus titer, we conclude that the amount of the biomarker protein in the body fluid semiquantitatively represents the amount of virus in the infected tumors which was confirmed by low light imaging. We found GusA to be the most reliable biomarker for monitoring oncolytic virotherapy among the three tested markers.
A vector-perturbation technique for near-capacity multiantenna multiuser communication-part I: channel inversion and regularization

Recent theoretical results describing the sum capacity when using multiple antennas to communicate with multiple users in a known rich scattering environment have not yet been followed with practical transmission schemes that achieve this capacity. We introduce a simple encoding algorithm that achieves near-capacity at sum rates of tens of bits/channel use. The algorithm is a variation on channel inversion that regularizes the inverse and uses a "sphere encoder" to perturb the data to reduce the power of the transmitted signal. This work is comprised of two parts. In this first part, we show that while the sum capacity grows linearly with the minimum of the number of antennas and users, the sum rate of channel inversion does not. This poor performance is due to the large spread in the singular values of the channel matrix. We introduce regularization to improve the condition of the inverse and maximize the signal-to-interference-plus-noise ratio at the receivers. Regularization enables linear growth and works especially well at low signal-to-noise ratios (SNRs), but as we show in the second part, an additional step is needed to achieve near-capacity performance at all SNRs.
Sampled-Data Synchronization Control of Dynamical Networks With Stochastic Sampling

This technical note is concerned with the sampled-data synchronization control problem for a class of dynamical networks. The sampling period considered here is assumed to be time-varying that switches between two different values in a random way with given probability. The addressed synchronization control problem is first formulated as an exponentially mean-square stabilization problem for a new class of dynamical networks that involve both the multiple probabilistic interval delays (MPIDs) and the sector-bounded nonlinearities (SBNs). Then, a novel Lyapunov functional is constructed to obtain sufficient conditions under which the dynamical network is exponentially mean-square stable. Both Gronwall's inequality and Jenson integral inequality are utilized to substantially simplify the derivation of the main results. Subsequently, a set of sampled-data synchronization controllers is designed in terms of the solution to certain matrix inequalities that can be solved effectively by using available software. Finally, a numerical simulation example is employed to show the effectiveness of the proposed sampled-data synchronization control scheme.
PREDICTABLE SEQUENCES AND COMPETING WITH STRATEGIES

Acknowledgements It seems like yesterday that I arrived on the beautiful campus of the University of Pennsylvania, and it is hard to believe that four wonderful years have already passed. With memories everywhere across the campus, I owe thanks to many people in many ways. I would like to express my deepest appreciation to all of my wonderful professors. Thanks to Professor Charles L. Epstein for accepting me to The Graduate Group in Applied Mathematics and Computational Science (AMCS). That marked the beginning of all the memories. Thanks also to Professor Abraham J. Wyner for introducing me to the field of statistics and to the Wharton Statistics Department and many interesting projects. I would also like to extend my appreciation to Professor Alexander Rakhlin for introducing me to the field of online learning, and for his support and encouragement throughout my research. Karthik Sridharan o↵ered many brilliant and inspiring ideas, and contributed greatly to my research. Professor Dean Foster served on my thesis committee, and provided extremely valuable career advice. Many thanks, as well, to all of the great professors who instructed me during my twenty courses at Penn. I could not have completed this program without the generous financial support of several professors and departments at the University of Pennsylvania. Thanks Furthermore, I would like to express deep appreciation to all of the sta↵ members of AMCS and the Wharton Statistics Department who helped me and made my life much easier. Thank you, In addition, thanks to all my friends, who took classes, shared oces and ideas, discussed problems, played bridge, travelled and tasted delicious food together. Most especially, I would like to thank my family, my parents and my dear husband, for holding my hand and being so supportive. First, we study online learning with an extended notion of regret, which is defined with respect to a set of strategies. We develop tools for analyzing the minimax rates and deriving ecient learning algorithms in this scenario. While the standard methods for minimizing the usual notion of regret fail, through our analysis we demonstrate the existence of regret-minimization methods that compete with such sets of strategies as: autoregressive algorithms, strategies based on statistical models, regularized least squares, and follow-the-regularized-leader strategies. In several cases, we also derive ecient learning algorithms. Then we study how online linear optimization competes with strategies while benefiting from the predictable sequence. We analyze the minimax …
NVM aware MariaDB database system

MariaDB is a community-developed fork of the MySQL relational database management system and originally designed and implemented to use traditional spinning disk architecture. Now that devices with Non-Volatile memory (NVM) technologies are available, MariaDB addresses this challenge by adding support for NVM devices and introduces NVM Compression method. NVM Compression is a novel hybrid technique that combines application level compression with flash awareness for optimal performance and storage efficiency. Utilizing new interface primitives exported by Flash Translation Layers (FTLs), we leverage the garbage collection available in flash devices to optimize the capacity management required by compression systems. We implement NVM Compression in the popular MariaDB database and use variants of commonly available POSIX file system interfaces to provide the extended FTL capabilities to the user space application. The experimental results show that the hybrid approach of NVM Compression can improve compression performance by 2-7x, deliver compression performance for flash devices that is within 5% of uncompressed performance, improves storage efficiency by 19% over legacy Row-Compression, reduce data writes by up to 4x when combined with other flash aware techniques such as Atomic Writes, and deliver further advantages in power efficiency and CPU utilization.
Interacting particles in two dimensions: numerical solution of the four-dimensional Schr\

We study numerically the Coulomb interacting two-particle stationary states of the Schr\"odinger equation, where the particles are confined in a two-dimensional infinite square well. Inside the domain the particles are subjected to a steeply increasing isotropic harmonic potential, resembling that in a nucleus. For these circumstances we have developed a fully discretized finite difference method of the Numerov-type that approximates the four-dimensional Laplace operator, and thus the whole Schr\"odinger equation, with a local truncation error of $\mathcal{O}(h^6)$, with $h$ being the uniform step size. The method is built on a 89-point central difference scheme in the four-dimensional grid. As expected from the general theorem by Keller [Num. \ Math. \textbf{7}, 412 (1965)], the error of eigenvalues so obtained are found to be the same order of magnitude which we have proved analytically as well.
Gesture Recognition Using Wireless Signals

FIGURE 1. Gesture sketches: WiSee can detect and classify these nine gestures in line-of-sight, non-line-of-sight, and through-the-wall scenarios with an average accuracy of 94%. A s computing moves increasingly away from the desktop, there is a growing need for new ways to interact with computer interfaces. The Xbox Kinect is an example of a commercially available input sensor that enables gesture-based interaction using depth sensing and computer vision. The commercial success of these kinds of devices has spurred interest in developing new user interfaces that remove the need for a traditional keyboard and mouse. Gestures enable a whole new set of interaction techniques for always-available computing embedded in the environment. For example, using a swipe hand motion in-air, a user could control the music volume while showering, or change the song playing on a music system installed in the living room while cooking, or turn up the thermostat while in bed. Such a capability can enable applications in diverse domains including home-automation, elderly health care, and gaming. However, the burden of installation and cost make most vision-based sensing devices hard to deploy at scale, for example, throughout an entire home or building. Given these limitations, researchers have explored in different ways to move some of the sensing onto the body and reduce the need for environmental sensors (Table 1). However, even on-body approaches are limited to what people are willing to constantly carry or wear, and may be infeasible in many scenarios (e.g., in a shower). By leveraging wireless signals (e.g. Wi-Fi), WiSee offers the first whole-home gesture recognition system that requires neither user instrumentation nor an infrastructure of cameras. Since wireless signals do not require line-of-sight and can traverse through walls, very few signal sources need to be present in the space (e.g., a Wi-Fi AP and a few mobile devices in the living room). WiSee works by looking at the minute Doppler shifts and multi-path distortions that occur with these wireless signals from human motion in the environment. Unlike previous works which show the feasibility of wireless systems on detecting coarse motions such as walking or running [4, 2], WiSee can perform fine-grained gesture classification anywhere in an environment. Our results show that WiSee can identify and classify gestures performed with a mean accuracy of 94%. We believe WiSee will open up a new viewpoint on how we leverage wireless infrastructure today – wireless as not only a …
Background extraction from video sequences via motion-assisted matrix completion

Background extraction from video sequences is a useful and important technique in video surveillance. This paper proposes a motion-assisted matrix completion model for background extraction from video sequences. A binary motion map is first calculated for each frame by optical flow. By excluding areas associated with moving objects with the binary motion maps, the background extraction is formulated into a motion-assisted matrix completion (MAMC) problem. Experimental results show that our method not only extracts promising backgrounds but also outperforms many state-of-the-art methods in distinguishing moving objects on challenging datasets.
Acoustic Model Adaptation for Indonesian Language Utterance Training System

Problem statement: In order to build an utterance training system for Indonesian language, a speech recognition system designed for Indonesian is necessary. However, the system hardly works well due to the pronunciation variants of non-nativ e utterances may lead to substitution/deletion erro r. This research investigated the pronunciation varian t and proposes acoustic model adaptation to improve performance of the system. Approach: The proposed acoustic model adaptation worked in three steps: to analyze pronunciation variant with knowledge-based and data-derived methods; to align knowledge-based and data-derived results in order t o list frequently mispronounced phones with their variants; to perform a state-clustering proce dure with the list obtained from the second step. Further, three Speaker Adaptation (SA) techniques were used in combination with the acoustic model adaptation and they are compared each other. In ord er to evaluate and tune the adaptation techniques, perceptual-based evaluation by three human raters i s performed to obtain the "true" recognition result s. Results: The proposed method achieved an average gain in Hit + Rejection (the percentage of correctly accepted and correctly rejected utterances by the s ystem as the human raters do) of 2.9 points and 2 p oints for native and non-native subjects, respectively, w hen compared with the system without adaptation. Average gains of 12.7 and 6.2 points for native and non-native students in Hit + Rejection were obtain ed by combining SA to the acoustic model adaptation. Conclusion/Recommendations: Performance evaluation of the adapted system demonstrated that the proposed acoustic model adaptation can improve Hit even though there is a slight increase of False Ala rm (FA, the percentage of incorrectly accepted utterances by the system of which the human raters reject). The performance of the proposed acoustic model adaptation depends strongly on the effectiven ess of state-clustering procedure to recover only i n- vocabulary words. For future research, a confidence measure to discriminate between in-vocabulary and out-vocabulary words will be investigated.
Various Approaches for Driver and Driving Behavior Monitoring: A Review

In recent years, driver drowsiness and distraction have been important factors in a large number of accidents because they reduce driver perception level and decision making capability, which negatively affect the ability to control the vehicle. One way to reduce these kinds of accidents would be through monitoring driver and driving behavior and alerting the driver when they are drowsy or in a distracted state. In addition, if it were possible to predict unsafe driving behavior in advance, this would also contribute to safe driving. In this paper, we will discuss various monitoring methods for driver and driving behavior as well as for predicting unsafe driving behaviors. In respect to measurement methods of driver drowsiness, we discussed visual and non-visual features of driver behavior, as well as driving performance behaviors related to vehicle-based features. Visual feature measurements such as eye related measurements, yawning detection, facial expression are discussed in detail. As for non-visual features, we explore various physiological signals and possible drowsiness detection methods that use these signals. As for vehicle-based features, we describe steering wheel movement and the standard deviation of lateral position. To detect driver distraction, we describe head pose and gaze direction methods. To predict unsafe driving behavior, we explain predicting methods based on facial expressions and car dynamics. Finally, we discuss several issues to be tackled for active driver safety systems. They are 1) hybrid measures for drowsiness detection, 2) driving context awareness for safe driving, 3) the necessity for public data sets of simulated and real driving conditions.
The e-PCA and m-PCA: dimension reduction of parameters by information geometry

We propose a method for extracting a low dimensional structure from a set of parameters of probability distributions. By an information geometrical interpretation, we show that there exist two kinds of possible flat structures for fitting (e-PCA and m-PCA). We derive alternating procedures to find the low dimensional structures. Each alternating procedure can be written in a nonlinear equation. It can be solved analytically in some special cases. Otherwise, we need to apply gradient type methods that we also derive. Since the overall algorithm may converge to a local optimum, we propose a method to find a good initial solution by using metric information.
Using system and user performance features to improve emotion detection in spoken tutoring dialogs

In this study, we incorporate automatically obtained system/user performance features into machine learning experiments to detect student emotion in computer tutoring dialogs. Our results show a relative improvement of 2.7% on classification accuracy and 8.08% on Kappa over using standard lexical, prosodic, sequential, and identification features. This level of improvement is comparable to the performance improvement shown in previous studies by applying dialog acts or lexical/prosodic-/discourse- level contextual features. Index Terms: emotional speech, emotion detection, spoken dialog systems
The effectiveness of post-16 pathways for gaining employment associated with undergraduate degree study

Abstract This article sets out to examine the effectiveness of five different post-16 study pathways and the impact that spending time away from education has on gaining employment associated with an undergraduate degree. It also explores advice and guidance for students looking at post-16 study options, higher education and employment. An initial online survey was used, followed by case studies to form a mixed-method approach. The findings suggest that a vocational pathway better prepares students for making decisions around employment and study in comparison to the more traditional A Level pathway. They also highlight that a period of one to four years after finishing post-16 study and starting undergraduate degree study is the most effective for students to gain employment associated with their undergraduate degree. Finally, it was found that most participants did not have positive experiences with advice and guidance regardless of their pathway.
Cost-Effectiveness of Bariatric Surgery for Severely Obese Adults With Diabetes

OBJECTIVE To analyze the cost-effectiveness of bariatric surgery in severely obese (BMI ≥35 kg/m2) adults who have diabetes, using a validated diabetes cost-effectiveness model. RESEARCH DESIGN AND METHODS We expanded the Centers for Disease Control and Prevention–RTI Diabetes Cost-Effectiveness Model to incorporate bariatric surgery. In this simulation model, bariatric surgery may lead to diabetes remission and reductions in other risk factors, which then lead to fewer diabetes complications and increased quality of life (QoL). Surgery is also associated with perioperative mortality and subsequent complications, and patients in remission may relapse to diabetes. We separately estimate the costs, quality-adjusted life-years (QALYs), and cost-effectiveness of gastric bypass surgery relative to usual diabetes care and of gastric banding surgery relative to usual diabetes care. We examine the cost-effectiveness of each type of surgery for severely obese individuals who are newly diagnosed with diabetes and for severely obese individuals with established diabetes. RESULTS In all analyses, bariatric surgery increased QALYs and increased costs. Bypass surgery had cost-effectiveness ratios of $7,000/QALY and $12,000/QALY for severely obese patients with newly diagnosed and established diabetes, respectively. Banding surgery had cost-effectiveness ratios of $11,000/QALY and $13,000/QALY for the respective groups. In sensitivity analyses, the cost-effectiveness ratios were most affected by assumptions about the direct gain in QoL from BMI loss following surgery. CONCLUSIONS Our analysis indicates that gastric bypass and gastric banding are cost-effective methods of reducing mortality and diabetes complications in severely obese adults with diabetes.