Miracle-dz/UniFAR-FTUs huggingface dataset:
Developmental Risk Triplets Across the First 5 Years : The Discordant Infant at Birth

OBJECTIVES: To examine whether the risk posed to infant development by triplet birth persists into childhood and whether growth-discordant triplets are at a particularly high developmental risk. METHODS: Twenty-one sets of triplets were matched with 21 sets of twins and 21 singletons (N 126) for medical and demographic conditions and were followed from birth to 5 years. At 6, 12, and 24 months, cognitive development was assessed andmother-infant interactions were coded for maternal sensitivity and child social engagement. At 5 years, the children’s cognitive development and neuropsychological skills were tested, social engagement was coded from mother-child interactions, and behavior problems were examined. Maternal adjustment was assessed during interviews at 1 and 5 years. RESULTS: Although triplets showed lower cognitive performance at 6, 12, and 24 months as compared with singletons and twins, differences were attenuated by 5 years in both global IQ and executive functions. Similarly, the lower social engagement observed across infancy and the higher internalizing symptoms reported at 2 years for those in the triplet group were no longer found at 5 years. Difficulties inmaternal adjustment among mothers of triplets decreased from 1 to 5 years. However, in 65.2% of the initial sample there was a weight discordance of 15% at birth, and the discordant triplets showed poorer cognitive and social development as compared with their siblings across infancy. At 5 years, the discordant children demonstrated lower cognitive and executive functions performance, decreased social engagement, and higher internalizing symptoms as compared with both siblings and peers. CONCLUSIONS: Whereas most triplets catch up after an early developmental delay, the risk for discordant triplets seems to persist into childhood. Such infants, who are at both biological and environmental risk, should receive special and consistent professional care. Pediatrics 2009; 124:316–323 CONTRIBUTORS: Ruth Feldman, PhDa and Arthur Isaac Eidelman, MDb,c aDepartment of Psychology and the Gonda Brain Sciences Center, Bar-Ilan University, Ramat Gan, Israel; bDepartment of Neonatology, Shaare Zedek Medical Center, Jerusalem, Israel; and cDepartment of Pediatrics, Hebrew University School of Medicine, Jerusalem, Israel
Live Streaming Switch System for Wide-area , Low-cost , and High-quality Internet Broadcasting Special Feature : Broadband Stream

Although there are high hopes for broadband stream broadcasting, there are many problems to be solved, such as how to increase the capacity of the delivery server and how to reduce the operation cost, before high-quality broadcasting can be provided on a large scale and at a low cost. This article reviews stream broadcasting technologies and introduces the Live Streaming Switch being developed in NTT Laboratories, which enables a low-cost, stable-delivery, automated streaming service. Live Streaming Switch System for Wide-area, Low-cost, and High-quality Internet Broadcasting Special Feature: Broadband Stream Distribution and † NTT Information Sharing Platform Laboratories Musashino-shi, 180-8585 Japan E-mail: tanikawa.masaki@lab.ntt. co.jp 28.8 k 56 k 300 k 1.5 M 6 M Narrowband Broadband Circuit rate (bit/s)
Novel Approach for Heart Disease using Data Mining Techniques

— Data mining is the process of analysing large sets of data and then extracting the meaning of the data. It helps in predicting future trends and patterns, allowing business in decision making. Presently various algorithms are available for clustering the proposed data, in the existing work they used K mean clustering, C4.5 algorithm and MAFIA i.e. Maximal Frequent Item set algorithm for Heart disease prediction system and achieved the accuracy of 89%. As we can see that there is vast scope of improvement in our proposed system, in this paper we will implement various other algorithms for clustering and classifying data and will achieved the accuracy more than the present algorithm. Several Parameters has been proposed for heart disease prediction system but there have been always a need for better parameters or algorithms to improve the performance of heart disease prediction system.
Early Evaluation of the Cray XC 40 Xeon Phi System ‘ Theta ’ at Argonne

The Argonne Leadership Computing Facility (ALCF) has recently deployed a nearly 10 PF Cray XC40 system named Theta. Theta is nearly identical in performance capacity to the ALCF’s current IBM Blue Gene/Q system Mira and represents the first step in a path from Mira to a much larger next generation Intel-Cray system named Aurora to be deployed in 2018. Theta serves as both a production resource for scientific computation and a platform for transitioning applications to run efficiently on the Intel Xeon Phi architecture and the Dragonfly topology based network. This paper presents an initial evaluation of Theta. The Theta system architecture will be described along with the results of benchmarks characterizing the performance at the processor core, memory, and network component levels. In addition, benchmarks are used to evaluate the performance of important runtime components such as MPI and OpenMP. Finally, performance results for the scientific applications are described.
Deep Web Search in Data Mining for Data Extraction

WWW stands for World Wide Web, and it is an advanced information retrieval system. As years passed World Wide Web became weighed down with information and it became hard to retrieve data according to the need. The Web mining extracts useful information from the web pages. Web mining techniques seek to extract knowledge from Web data, including web documents, hyperlinks between documents, and usage logs of web sites. Web usage mining mines knowledge from diverse websites. Extracting appropriate data from deep web pages is an exigent dilemma due to the overflow of data in to the web. Web servers generates a huge amount of information on web users browsing activities. These are called click stream or web access log data. The click stream data can be enriched with information about the content of visited pages. The aim of this paper is to obtain all the data behind a form by multiple submissions of the form filled out in all possible ways by using agent, but efficiency concerns lead us to consider alternatives. We can estimate the amount of remaining data after a small number of submissions maximize the coverage of the data.
CATSWoTS: Context Aware Trustworthy Social Web of Things System

The inevitable revolution of the Internet of Things (IoT) and its benefits can be witnessed everywhere. Two major issues related to IoT are the interoperability and the identification of trustworthy things. The proposed Context-Aware Trustworthy Social Web of Things System (CATSWoTS) addresses the interoperability issue by incorporating web technologies including Service Oriented Architecture where each thing plays the role of a service provider as well as a role of service consumer. The aspect of social web helps in getting recommendations from social relations. It was identified that the context dependency of trust along with Quality of Service (QoS) criteria, for identifying and recommending trustworthy Web of Things (WoT), require more attention. For this purpose, the parameters of context awareness and the constraints of QoS are considered. The research focuses on the idea of a user-centric system where the profiles of each thing (level of trustworthiness) are being maintained at a centralized level and at a distributed level as well. The CATSWoTS evaluates service providers based on the mentioned parameters and the constraints and then identifies a suitable service provider. For this, a rule-based collaborative filtering approach is used. The efficacy of CATSWoTS is evaluated with a specifically designed environment using a real QoS data set. The results showed that the proposed novel technique fills the gap present in the state of the art. It performed well by dynamically identifying and recommending trustworthy services as per the requirements of a service seeker.
An Extended SURF Descriptor and Its Application of Remote Sensing Images Registration

To solve the classical methods' problems of long executing time or low accuracy,an extended SURF descriptor is proposed.On the method using SURF,the proposed method uses local normalization information and second-order gradient values of neighborhood regions to build a new one.Not only does this method perform as fast as SURF algorithm,but it also fully employs the image grayscale information and details.Considering the executing time and rate,experimental results presented in this paper show that the proposed method is more robust than classical methods.
Features in visual media analysis

Today, film analysis is still a tedious process performed mostly manually by film experts. Existing computer vision approaches aim at improved retrieval and summarization methods rather than at film understanding. While current research is predominantly focused on the question what can we learn and extract from a film as the final product, this thesis aims at the study of the filmmaking process as a source for high-level content information. The central question of this thesis is what can computer vision methods provide to support film analysis as performed by film expert? We discuss a possible mapping between factors that influence the production, presentation, and perception of movies, their application by means of well-established film techniques, and existing feature extraction methods in computer vision. This novel view on film analysis allows for the exploration and identification of three areas in the domain of automated film analysis and understanding. The first area comprises research tasks that have been subject to active research in the recent past. The second area covers research topics that are not immediately solvable for a fully automated computer vision approach without any prior knowledge. The last area identifies research tasks that are still open in the context of automated film analysis and understanding. Finally, we introduce three novel research questions and possible solutions: camera take reconstruction, film comparison, and recurring element detection. Performed experiments reveal two significant potentials. First, they can assist film experts by providing support for tasks that are currently performed manually. Second, proposed algorithms blaze the trail for advanced application scenarios such as the analysis of different montage patterns, the identification of missing shots, the reconstruction of the original film cut, or the detection of recurring elements.
EasyChair Preprint No 329 FastPath : Towards Wire-speed NVMe SSDs

The constant growth of data and its importance to drive Machine Learning and Big Data is pushing storage systems towards ever increasing I/O bandwidth and lower latency requirements. In recent years, the Non Volatile Memory Express (NVMe) standard has enabled SSD drives to deliver high I/O rates by allowing the storage to be connected directly via the fastest available interconnect to the processing chip. In parallel, the adoption of FPGAs in data centers is creating opportunities to accelerate various applications and/or Operating System (OS) operations. While, FPGAs in data centers have been connected via PCIe to mostly x86 servers, we have now also available heterogeneous SoCs with multi-cores and FPGAs integrated on the same die and connected by an onchip interconnect. In this paper, we present how to rethink and accelerate NVMe performance on heterogeneous SoC with integrated FPGAs providing a first research insight on the performance benefits of such an approach. We provide an analysis of the Linux block I/O layer and showcase the relationship between the system’s performance and its I/O implementation. Consequently, we introduce an FPGA-based fast path which accelerates the access to the NVMe drive. Our comparative evaluation demonstrates that our FPGA-based FastPath achieves up to 71% lower latency and up to 5x higher I/O performance against the baseline system on a Zynq development board. Keywords-NVMe; SSDs; FPGA; Linux Block I/O; Heterogeneous Systems
Involuntary processing of timbre: a mismatch negativity study

Abstract. Timbre is a key and complex attribute of sound that might be pre-attentively coded in sensory memory, and the mismatch negativity (MMN) component of event-related potential (ERP) might be adopted to detect this process. The present study investigated the timbre MMN in an oddball paradigm, with piano tone as standard, and sine wave or trumpet tone as deviant separately. Both deviants evoked a MMN component in a streaming of piano tones. Statistical analysis indicated that MMN for pure tone was significantly larger in magnitude than that for trumpet tone. The results evinced that the involuntary processing of timbre variations was relate to not to the spectral complexity but to the discrepancy between the standard and deviant tone.
Interpreting fMRI Decoding Weights : Additional Considerations

Ideally, decoding models could be used for the dual purpose of prediction and neuroscientific knowledge gain. However, interpreting even linear decoding models beyond classification accuracies is difficult. Haufe and colleagues suggested projecting feature weights onto activation maps may enhance interpretability. Here we show that redundancy and noise, typically found in fMRI data, can further complicate interpretation – even after projection onto activation maps. In the presence of non-Gaussian noise seen with artifacts such as scanner drift, motion, and periodic biorhythms, projecting feature weights onto activation maps yields spurious patterns; for example, noise typically associated with scanner drift can cause the activation weights of noise sources to be greater than those of task-associated signals. These analyses suggest that certain decoding or backward models are inherently limited by their approaches to successfully model the structure of incoming noise, and that improper assumptions of this noise structure yields misleading interpretive maps.
Platelet-Derived Activated Protein not Apoptosis , in Growth Factor BB-Induced p 38 Mitogen-Kinase Activation Causes Cell Growth , but Vascular Smooth Muscle Cells

The aim of this experiment was to examine the regulation of p38 mitogen-activated protein (MAP) kinase by platelet-derived growth factor (PDGF)-BB and its biological effects on rat cultured vascular smooth muscle cells (VSMCs). VSMCs were obtained from aortae of male Wistar rats by the media explant technique. After being stimulated by PDGF-BB with or without the p38 MAP kinase-specific inhibitor, SB-203580, the cells were solubilized, and the levels of phosphorylated p38 MAP kinase were examined by immunoblot analysis. The amounts of DNA synthesis and content were measured by using [3H]-thymidine and Hoechst-33258 dye, respectively. The detection of apoptotic cells was evaluated by the TUNEL method. PDGF-BB could phosphorylate p38 MAP kinase dosedependently, and the phosphorylation was specifically inhibited by SB-203580 in a dose-dependent manner. However, PDGF-BB did not affect the protein level of p38 MAP kinase. Both [3H]-thymidine incorporation and total cellular DNA content were increased by PDGF-BB, and these elevations were prevented by SB-203580. In contrast, PDGF-BB-stimulated VSMCs did not show apoptotic change in spite of the presence or absence of SB-203580. These results established that PDGF-BB activated p38 MAP kinase and subsequently regulated cell growth in VSMCs, providing a molecular mechanism by which p38 MAP kinase can cause the development of cardiovascular diseases, including atherosclerosis.
Number : WUCSE-2008-18 2008-0101 Software and Hardware Acceleration of the Genomic Motif Finding Tool

OF THE THESIS Software and Hardware Acceleration of the Genomic Motif Finding Tool PhyloNet by Justin Tyler Brown Master of Science in Computer Science Washington University in St. Louis, 2008 Research Advisor: Professor Jeremy Buhler A major area of research in molecular and computational biology is deciphering the cis-regulatory network that governs transcriptional regulation. This task has proven to be a challenge because regulatory elements are usually short, degenerate, and hidden in very long sequences. A recently developed algorithm known as PhyloNet attempts to computationally identify conserved regulatory motifs of an organism by using alignments to related species as evidence of conservation. In this thesis, we address the problem of scaling PhyloNet to handle large genomes. We first work to improve PhyloNet in software alone, improving its speed and sensitivity. Our improved version of PhyloNet on the budding yeast genome yields 1.6x more known yeast motifs and a speedup of 20x over the originial. We then develop a streaming architecture design of PhyloNet and show how the seed matching and extension stages can be implemented using Field Programmable Gate arrays. We estimate that our FPGA design yields an additional order of magnitude speedup over our best software version of PhyloNet.
Investigating the Preference to Invest in Real Estate Investment Trusts

Recently, studies on behavioral finance have gained interest among academicians. The study of investors’ behavioral perspective is deemed important to understand the decision-making process pertaining to investment. Decision making processes involve factors that influence investor’s behavior to invest. Various topics and areas have been discussed to further understand the behavior of investors. However, only a few researches have actually been conducted in the area of behavioral finance resulting in a lack of conclusive findings. Findings gained thus far are insufficient to specifically identify investors’ intention to invest in certain investment portfolios. Therefore, the objective of this paper was to identify the factors that influence investor’s behavior to invest in Real Estate Investment Trusts (REITs). This paper reviews literatures on factors that influence investors’ decision-making process and proposes a model of influencing factors. The findings from this paper would aid and facilitate future research on investors’ behavior to invest in REITs. This paper is expected to add to the current literature on behavioral finance of REITs.
Multigrid Efficiency for Leading Edge Stagnation

A multigrid solver is defined as having textbook multigrid efficiency (TME) if the solutions to the governing system of equations are attained in a computational work which is a small (less than 10) multiple of the operation count in evaluating the discrete residuals. TME in solving the incompressible inviscid fluid equations is demonstrated for leadingedge stagnation flows. The contributions of this paper include (1) a special formulation of the boundary conditions near stagnation allowing convergence of the Newton iterations on coarse grids, (2) the boundary relaxation technique to facilitate relaxation and residual restriction near the boundaries, (3) a modified relaxation scheme to prevent initial error amplification, and (4) new general analysis techniques for multigrid solvers. Convergence of algebraic errors below the level of discretization errors is attained by a full multigrid (FMG) solver with one full approximation scheme (FAS) cycle per grid. Asymptotic convergence rates of the FAS cycles for the full system of flow equations are very fast, approaching those for scalar elliptic equations.
Generative Adversarial Networks with Inverse Transformation Unit

In this paper we introduce a new structure to Generative Adversarial Networks by adding an inverse transformation unit behind the generator. We present two theorems to claim the convergence of the model, and two conjectures to nonideal situations when the transformation is not bijection. A general survey on models with different transformations was done on the MNIST dataset and the Fashion-MNIST dataset, which shows the transformation does not necessarily need to be bijection. Also, with certain transformations that blurs an image, our model successfully learned to sharpen the images and recover blurred images, which was additionally verified by our measurement of sharpness.
CT reconstruction from sparse projections based on extrapolation in Fourier domain

The sparse scanning imaging methods for x-ray CT is a promising approach to speed up scanning or reduce radiation dose to patients. The major problem for sparse parallel projections is hard to reconstruct high quality image. It suffers severe streak artifacts in reconstruction if the popular filtered back projection (FBP) method is employed. Although several total variation (TV) regularization based algorithms have been developed for sparse-view CT imaging, they still face challenges in both time consumption and computational complexity when the objective image is large. In this paper, a CT reconstruction algorithm, which is named INNG-TV (iterative next-neighbor regridding-total variation), based on extrapolation in frequency is proposed to improve the performance. We first convert data, which is sampled from parallel beam CT, into frequency domain by Fourier transform and linear interpolation. In the following process of iteration, the known data of projection in Fourier space keep constant, whereas the unknown data are estimated by INNG extrapolation. At the same time, prior knowledge and constrained optimization, such as non-negativity constraint and total variation regularization, are introduced to image reconstruction in image space. The numerical simulation results show that the proposed method has better performance in reconstruction quality than ART-TV (algebraic reconstruction technique-total variation). The proposed method not only demonstrates its superiority in time consumption, but also offers outstanding reconstruction quality for sparse-view scan, which makes it significant to sparse-view CT imaging.
Investigating multimodal affect sensing in an Affective Tutoring System using unobtrusive sensors

Affect inextricably plays a critical role in the learning process. In this study, we investigate the multimodal fusion of facial, keystrokes, mouse clicks, head posture and contextual features for the detection of student’s frustration in an Affective Tutoring System. The results (AUC=0.64) demonstrated empirically that a multimodal approach offers higher accuracy and better robustness as compared to a unimodal approach. In addition, the inclusion of keystrokes and mouse clicks makes up for the detection gap where video based sensing modes (facial and head postures) are not available. The findings in this paper will dovetail to our end research objective of optimizing the learning of students by adapting empathetically or tailoring to their affective states.
Optimal Spline Generators for Derivative Sampling

The goal of derivative sampling is to reconstruct a signal from the samples of the function and of its first-order derivative. In this paper, we consider this problem over a shift-invariant reconstruction subspace generated by two compact-support functions. We assume that the reconstruction subspace reproduces polynomials up to a certain degree. We then derive a lower bound on the sum of supports of its generators. Finally, we illustrate the tightness of our bound with some examples.