Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Quantification of IP Network Reliability

There has been intense interest on the level of reliability and the techniques to improve the reliability of IP networks. As more mission-critical applications and networkbased computing move to the Internet, the importance of IP network reliability will only increase. However, so far the discussions on the level of reliability of IP networks are mainly informal or on relative scales. The estimation of IP network reliability varies over a wide range, from 99% to 99.99%. How this reliability level is calculated is unclear. Furthermore, although there is much research on traffic engineering (TE), the focus tends to be on network specific metrics such as maximum link utilization (MLU). The effectiveness of the proposed techniques on the reliability perceived by customers is unknown. In this paper, we conduct an initial quantification on the level of IP network reliability. We ask two simple but important questions: 1) what is the reliability level of IP networks? In particular, how close are they to be five nines (i.e., achieving 99.999% reliability)? 2) how effective are the IP layer techniques in improving the reliability of IP networks? In particular, do they make a difference in improving IP network reliability? The evaluations presented in this paper are our initial step in answering the preceding two questions.
Applying Bayesian Ordinal Regression to ICAP Maladaptive Behavior Subscales

APPLYING BAYESIAN ORDINAL REGRESSION TO ICAP MALADAPTIVE BEHAVIOR SUBSCALES Edward Paul Johnson Department of Statistics Master of Science This paper develops a Bayesian ordinal regression model for the maladaptive subscales of the Inventory for Client and Agency Planning (ICAP). Because the maladaptive behavior section of the ICAP contains ordinal data, current analysis strategies combine all the subscales into three indices, making the data more interval in nature. Regular MANOVA tools are subsequently used to create a regression model for these indices. This paper uses ordinal regression to analyze each original scale separately. The sample consists of applicants for aid from Utah’s Division of Services for Persons with Disabilities. Each applicant fills out the Scales of Independent Behavior—Revised (SIB-R) portion of the ICAP that measures eight different maladaptive behaviors. This project models the frequency and severity of each of these eight problem behaviors with separate ordinal regression models. Gender, ethnicity, primary disability, and mental retardation are used as explanatory variables to calculate the odds ratios for a higher maladaptive behavior score in each model. This type of analysis provides a useful tool to any researcher using the ICAP to measure maladaptive behavior.
Chemotherapy for myeloid malignancy in children with Fanconi anemia

Children with Fanconi anemia (FA) have a markedly increased risk of developing myeloid malignancies. Historically, patients with FA and myeloid malignancy have extremely poor outcomes. There are currently no clinical trials or case series addressing the use of chemotherapy for children with FA, except in the context of preparative regimens for stem cell transplantation (SCT). In this report we describe the toxicity of a chemotherapy approach for patients with FA and myeloid malignancy to achieve cytoreduction prior to SCT.
A PCB inductive sensor for compact ionic polymer metal composite (IPMC) displacement sensing used in biomedical pumps

This paper proposed the implementation of an inductive sensor which is able to accurately control the diaphragm's deflection of portable biomedical pumps. Ionic polymer metal composite (IPMC), whose low actuating voltage (< 5V), high power efficiency and biocompatibility makes it a proven candidate for biomedical applications. However, due to its inherent nonlinear and time-variant attributes, relevant control methods with reliable and space-saving deflection sensors are required for IPMC driving applications. A practical, low cost and importantly, compact inductive sensor fabricated on a printed circuit board (PCB) is proposed and experimentally evaluated here. System identification and proportional-integral-derivative (PID) control results showed comparable performances as that of laser sensors and thus prove it to be a feasible alternative.
Dendritic spine abnormalities in APP transgenic mice demonstrated by gene transfer and intravital multiphoton microscopy

Accumulation of amyloid-beta (Aβ) into senile plaques in Alzheimer’s disease (AD) is a hallmark neuropathological feature of the disorder, which likely contributes to alterations in neuronal structure and function. Recent work has revealed changes in neurite architecture associated with plaques and functional changes in cortical signaling in amyloid precursor protein (APP) expressing mouse models of AD. Here we developed a method using gene transfer techniques to introduce GFP into neurons allowing the investigation of neuronal processes in the vicinity of plaques. Multiphoton imaging of GFP-labeled neurons in living Tg2576 APP mice revealed disrupted neurite trajectories and reductions in dendritic spine density compared to age-matched control mice. A profound deficit in spine density (∼50%) extends approximately 20 μm from plaque edges. Importantly, a robust decrement (∼25%) also occurs on dendrites not associated with plaques, suggesting widespread loss of postsynaptic apparatus. Plaques and dendrites remained stable over the course of weeks of imaging. Post-mortem analysis of axonal immunostaining and co-localization of synaptophysin and postsynaptic density 95 (PSD-95) protein staining around plaques indicate a parallel loss of preand postsynaptic partners. These results show considerable changes in dendrites and dendritic spines in APP transgenic mice, demonstrating a dramatic synaptotoxic effect of dense core plaques. Decreased spine density will likely contribute to altered neural system function and behavioral impairments observed in Tg2576 mice.
Old can be new again: HAPPY whole genome sequencing, mapping and assembly

During the last three decades, both genome mapping and sequencing methods have advanced significantly to provide a foundation for scientists to understand genome structures and functions in many species. Generally speaking, genome mapping relies on genome sequencing to provide basic materials, such as DNA probes and markers for their localizations, thus constructing the maps. On the other hand, genome sequencing often requires a high-resolution map as a skeleton for whole genome assembly. However, both genome mapping and sequencing have never come together in one pipeline. After reviewing mapping and next-generation sequencing methods, we would like to share our thoughts with the genome community on how to combine the HAPPY mapping technique with the new-generation sequencing, thus integrating two systems into one pipeline, called HAPPY pipeline. The pipeline starts with preparation of a HAPPY panel, followed by multiple displacement amplification for producing a relatively large quantity of DNA. Instead of conventional marker genotyping, the amplified panel DNA samples are subject to new-generation sequencing with barcode method, which allows us to determine the presence/absence of a sequence contig as a traditional marker in the HAPPY panel. Statistical analysis will then be performed to infer how close or how far away from each other these contigs are within a genome and order the whole genome sequence assembly as well. We believe that such a universal approach will play an important role in genome sequencing, mapping, and assembly of many species; thus advancing genome science and its applications in biomedicine and agriculture.
TCP Extensions for High Performance

This memo presents a set of TCP extensions to improve performance over large bandwidth*delay product paths and to provide reliable operation over very high-speed paths. It defines new TCP options for scaled windows and timestamps, which are designed to provide compatible interworking with TCP's that do not implement the extensions. The timestamps are used for two distinct mechanisms: RTTM (Round Trip Time Measurement) and PAWS (Protect Against Wrapped Sequences). Selective acknowledgments are not included in this memo.
The Individualized Component Face Lift: Developing a Systematic Approach to Facial Rejuvenation

Background: Accurate preoperative planning combined with facial fat compartment augmentation can improve precision and balance in facial rejuvenation techniques. Understanding the concept of “facial shaping” with respect to symmetry and soft-tissue (fat) distribution preoperatively is critical to optimizing aesthetic outcomes in various face lift techniques. Methods: A review of 822 consecutive face lifts performed from January of 1994 to June of 2007 by a single surgeon (R.J.R.) was conducted. From this database, randomly selected cohorts of 50 preoperative and postoperative photographs were critically analyzed by three plastic surgeons exclusive of the senior surgeon (R.J.R.). Three facial parameters were compared on each facial side: facial height, degree of malar deflation, and orbit size. Long-term improvement was evaluated to delineate factors contributing to success in creating an aesthetically balanced facial shape. Results: Asymmetry between the two facial sides was noted in every patient preoperatively with respect to the three study parameters and was improved postoperatively. There was no statistically significant interobserver bias in the evaluations (p < 0.005). Facial asymmetry dictated differential treatment of the superficial musculoaponeurotic system (SMAS) tissue between facial sides to achieve the desired youthful facial shape. The angle (vector) and extent of SMAS-stacking varied depending on the preoperative analysis. Similarly, the selection of SMAS-ectomy versus SMAS-stacking depended on the degree of malar deflation and resultant cheek fullness. Conclusions: Proper preoperative analysis for evaluating facial shape should address (1) facial height, (2) facial width, and (3) overall distribution/location of facial fullness. This method of evaluating facial shape and symmetry is simple and reproducible, and can aid in formulating a comprehensive treatment plan.
Kullback Leibler property of kernel mixture priors in Bayesian density estimation

Positivity of the prior probability of Kullback-Leibler neighborhood around the true density, commonly known as the Kullback-Leibler property, plays a fundamental role in posterior consistency. A popular prior for Bayesian estimation is given by a Dirichlet mixture, where the kernels are chosen depending on the sample space and the class of densities to be estimated. The Kullback-Leibler property of the Dirichlet mixture prior has been shown for some special kernels like the normal density or Bernstein polynomial, under appropriate conditions. In this paper, we obtain easily verifiable sufficient conditions, under which a prior obtained by mixing a general kernel possesses the Kullback-Leibler property. We study a wide variety of kernel used in practice, including the normal, $t$, histogram, gamma, Weibull densities and so on, and show that the Kullback-Leibler property holds if some easily verifiable conditions are satisfied at the true density. This gives a catalog of conditions required for the Kullback-Leibler property, which can be readily used in applications.
Human Visual Perception of Materials in Realistic Computer Graphics

Materials are an essential component of realistic three-dimensional (3D) com- puter graphics. Each material model offers a set of parameters to control every aspect of its appearance. Choosing the right material parameters to obtain a desired look requires some experience. The appearance of a material is also affected by the shape of the object. We present a broad exploratory study of the influence of shape on material perception, using a same/different pair comparison experiment. The influence was found to be significant enough to cause a noticeable difference in the perceived material. While most traditional material editing tools visualize materials on a sphere, analysis of the psychometric function showed that this shape is the least discriminating. Suggested improvements include the use of one of the more discriminating objects examined, or ideally a visualization on the target shape directly. An alternative solution is to compensate for the influence of shape on material perception. The influence was measured using cue combination in a 2-alternative forced choice (2AFC) experiment. The influence was found to cause a constant shift in perception, dependent only on the shape. By applying the opposite shift to the input material, the accurate perception of the material can be restored. As a proof of concept, this correction was built into an existing material editing tool. In the final part of this dissertation we consider how a scene can be observed indirectly through reflections in glossy materials. The concept of visual equivalence was applied to assess the fidelity of dynamic scenes with distorted reflections. Appearance properties such as shape, motion, and lighting were judged using same/different and 2AFC experimental designs. Rotating objects were found to yield equivalence for small distortions. Equivalence for translating objects depends on the plausible motion of highlights and reflections produced by the illumination interpolation method. Throughout this dissertation, we have combined well-thought-out experimental designs and thorough statistical analyses. By using high-quality photorealistic rendering techniques, such as differential rendering, global illumination, and image-based lighting, we ensure that our results are directly generalizable to a wide range of real and virtual applications.
A high-performance tracking system based on camera and IMU

We consider an indoor tracking system consisting of an inertial measurement unit (IMU) and a camera that detects markers in the environment. There are many camera based tracking systems described in literature and available commercially, and a few of them also has support from IMU. These are based on the best-effort principle, where the performance varies depending on the situation. In contrast to this, we start with a specification of the system performance, and the design is based on an information theoretic approach, where specific user scenarios are defined. Precise models for the camera and IMU are derived for a fusion filter, and the theoretical Cramér-Rao lower bound and the Kalman filter performance are evaluated. In this study, we focus on examining the camera quality versus the marker density needed to get at least a one mm and one degree accuracy in tracking performance.
Scalable parallel minimum spanning forest computation

The proliferation of data in graph form calls for the development of scalable graph algorithms that exploit parallel processing environments. One such problem is the computation of a graph's minimum spanning forest (MSF). Past research has proposed several parallel algorithms for this problem, yet none of them scales to large, high-density graphs. In this paper we propose a novel, scalable, parallel MSF algorithm for undirected weighted graphs. Our algorithm leverages Prim's algorithm in a parallel fashion, concurrently expanding several subsets of the computed MSF. Our effort focuses on minimizing the communication among different processors without constraining the local growth of a processor's computed subtree. In effect, we achieve a scalability that previous approaches lacked. We implement our algorithm in CUDA, running on a GPU and study its performance using real and synthetic, sparse as well as dense, structured and unstructured graph data. Our experimental study demonstrates that our algorithm outperforms the previous state-of-the-art GPU-based MSF algorithm, while being several orders of magnitude faster than sequential CPU-based algorithms.
A Linear Max K-min classifier

The mathematical modeling of classifier has been intensively investigated in pattern recognition for decades. Maximin classifier, which conducts optimization based on the perpendicularly closest data point(s) to the decision boundary, has been widely used. However, such method may lead to inferior performance when the boundary data point(s) is significantly influenced by noise. This paper presents a new Linear Max K-min (LMKM) classifier for 2-class classification problems, which offers a general classification solution by considering the K closest data points (K ≥ 1). In other words, given any dataset, the algorithm offers the flexibility to the classification process using the most appropriate number of K boundary points, instead of the the most closet one(s). To tackle the high computational complexity when K or N is relatively large, we propose a new method, which transforms the original objective function into a linear programming problem with 2N constraints which can be solved with high efficiency (where N indicates the number of training samples and K ≤ N). Experimental study shows that the proposed algorithm consistently offers high quality classification results across 18 publicly available 2-class classification datasets, and meanwhile, outperforms Linear Support Vector Machine (SVM) and Logistic Regression (LR) methods.
Direct extraction of boundaries from computed tomography scans

Presents a method, based on the filtered backprojection technique (FBP), to extract directly the boundaries of X-ray images, without previous image reconstruction. The author preprocess the raw data in order to compute directly the reconstructed values of the gradient or of the Laplacian at any location in the plane (defined with real coordinates). The reconstructed value of the gradient and of the Laplacian correspond to the exact mathematical definition of the differentials of the image. For noisy data, the author proposes also to use an extension of existing FBP techniques, adapted to the computation of the gradient and of the Laplacian. Finally, the author shows how to use the corresponding operators to perform the segmentation of a slice, without image reconstruction. Images of the reconstructed gradient, Laplacian, and segmented objects are presented.
Vascular endothelial growth factor and basic fibroblast growth factor expression positively correlates with angiogenesis and peritumoural brain oedema in astrocytoma.

BACKGROUND Astrocytoma is the most malignant intracranial neoplasm and is characterized by high neovascularization and peritumoural brain oedema. Angiogenesis is a complicated process in oncogenesis regulated by the balance between angiogenic and antiangiogenic factors. METHODS The expression of two angiogenic growth factors, vascular endothelial growth factor and basic fibroblast growth factor were investigated using immunohistochemistry for astrocytoma from 82 patients and 11 normal human tissues. RESULTS The expression of vascular endothelial growth factor and basic fibroblast growth factor positively correlate with the pathological grade of astrocytoma, microvessel density numbers and brain oedema, which may be responsible for the increased tumour neovascularization and peritumoural brain oedema. CONCLUSION The results support the idea that inhibiting vascular endothelial growth factor and basic fibroblast growth factor are useful for the treatment of human astrocytoma and to improve patient's clinical outcomes and prognosis.
A Web-based Analysis for Dengue Tracking and Prediction using Artificial Neural Network

Providing an accurate analysis of dengue epidemic seasons will allow sufficient time in taking necessary decisions and actions to safeguard the situation for local authorities. This research aims to develop a web-based dengue tracking system (DTS) that makes use of environmental input factors in predicting the future behavior of dengue cases using Artificial Neural Network. The system can serve a valuable purpose for the health sectors as it informs them to take action on recorded cases in areas which are prone to dengue. Through mapping, the system can also generate an analysis of the area where outbreaks of dengue commonly occurs using a graphical representation of case patterns.
Hemorrhage risks and obliteration rates of arteriovenous malformations after gamma knife radiosurgery.

OBJECT The purpose of this study was to analyze the risk of hemorrhage and the obliteration rate after treatment of patients with arteriovenous malformations (AVMs). METHODS Between 1991 and 1995, 115 patients were treated using gamma knife radiosurgery (GKS). Surgical planning was based on angiograms and three-dimensional images. The angiographic features of the AVMs and the risk factors for hemorrhage were then evaluated. Hemorrhages occurred in eight patients (7%) 7 to 42 months after GKS. Based on AVM morphology, the rates of hemorrhage were five (7.6%) of 66 for AVMs with a single draining vein, seven (14%) of 66 for AVMs with deep drainage, four (26.7%) of 15 for AVMs with a varix, four (28.6%) of 14 for AVMs with venous obstruction, eight (17.0%) of 47 for high-flow (shunt- and mixed-type) AVMs, and five (35.7%) of 14 for large AVMs with a volume of more than 10 cm3. No hemorrhages were observed in association with low-flow (moyamoya-type) AVMs in this series. Total AVM obliteration was achieved in 81.3% of 80 patients who underwent angiography. The obliteration rate was 91.3% for moyamoyatype AVMs and 67.6% for shunt- and mixed-type AVMs. Early obliteration within 12 months was achieved in 63% of the moyamoya-type AVMs. CONCLUSIONS Moyamoya-type AVMs seem to be at risk for post-GKS hemorrhage. Intravascular embolization should be considered prior to GKS for mixed- and shunt-type AVMs in an attempt to reduce the hemodynamic stress and thereby decrease the risk of hemorrhage.
Sparse Bilinear Logistic Regression

In this paper, we introduce the concept of sparse bilinear logistic regression for decision problems involving explanatory variables that are two-dimensional matrices. Such problems are common in computer vision, brain-computer interfaces, style/content factorization, and parallel factor analysis. The underlying optimization problem is biconvex; we study its solution and develop an ecient algorithm based on block coordinate descent. We provide a theoretical guarantee for global convergence and estimate the asymptotical convergence rate using the Kurdyka- Lojasiewicz inequality. A range of experiments with simulated and real data demonstrate that sparse bilinear logistic regression outperforms current techniques in several important applications.
R&D (Re)Location and Offshore Outsourcing: A Management Perspective

The paper reviews and systematizes the management literature directly and indirectly related to (re)location of research and development (R&D), in the form of both insourcing and outsourcing. The focus in particular is on R&D offshore outsourcing and its main drivers, including locational constraints and opportunities, motives, strategic decisions and managerial challenges. The analysis synthesizes and links the main propositions and findings of the theoretical and empirical work to build an original management and strategy centred framework which looks at R&D (re)location from a client–vendor perspective. In this case, the client is the offshoring company, and the vendors are the firms located in the host country. The characteristics of the firms and locations considered vis‐a‐vis the offshoring firm's goals are examined, the strategies implemented, the management and organizational challenges that R&D offshore outsourcing entails, and the ways in which offshoring impacts on both home and host locations are investigated.