Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable interactive volume rendering using off-the-shelf components

Describes an application of a second generation implementation of the Sepia architecture (Sepia-2) to interactive volumetric visualization of large rectilinear scalar fields. By employing pipelined associative blending operators in a sort-last configuration a demonstration system with 8 rendering computers sustains 24 to 28 frames per second while interactively rendering large data volumes (1024/spl times/256/spl times/256 voxels, and 512/spl times/512/spl times/512 voxels). We believe interactive performance at these frame rates and data sizes is unprecedented. We also believe these results can be extended to other types of structured and unstructured grids and a variety of GL rendering techniques including surface rendering and shadow mapping. We show how to extend our single-stage crossbar demonstration system to multi-stage networks in order to support much larger data sizes and higher image resolutions. This requires solving a dynamic mapping problem for a class of blending operators that includes Porter-Duff compositing operators.
Shuffles and Circuits: (On Lower Bounds for Modern Parallel Computation)

The goal of this paper is to identify fundamental limitations on how efficiently algorithms implemented on platforms such as MapReduce and Hadoop can compute the central problems in the motivating application domains, such as graph connectivity problems. We introduce an abstract model of massively parallel computation, where essentially the only restrictions are that the "fan-in" of each machine is limited to s bits, where s is smaller than the input size n, and that computation proceeds in synchronized rounds, with no communication between different machines within a round. Lower bounds on the round complexity of a problem in this model apply to every computing platform that shares the most basic design principles of MapReduce-type systems. We prove that computations in our model that use few rounds can be represented as low-degree polynomials over the reals. This connection allows us to translate a lower bound on the (approximate) polynomial degree of a Boolean function to a lower bound on the round complexity of every (randomized) massively parallel computation of that function. These lower bounds apply even in the "unbounded width" version of our model, where the number of machines can be arbitrarily large. As one example of our general results, computing any non-trivial monotone graph property --- such as connectivity --- requires a super-constant number of rounds when every machine can accept only a sub-polynomial (in n) number of input bits s. Finally, we prove that, in two senses, our lower bounds are the best one could hope for. For the unbounded-width model, we prove a matching upper bound. Restricting to a polynomial number of machines, we show that asymptotically better lower bounds require proving that P ≠ NC1.
A Globally Convergent Linearly Constrained Lagrangian Method for Nonlinear Optimization

For optimization problems with nonlinear constraints, linearly constrained Lagrangian (LCL) methods solve a sequence of subproblems of the form "minimize an augmented Lagrangian function subject to linearized constraints. " Such methods converge rapidly near a solution but may not be reliable from arbitrary starting points. Nevertheless, the well-known software package MINOS has proved effective on many large problems. Its success motivates us to derive a related LCL algorithm that possesses three important properties: it is globally convergent, the subproblem constraints are always feasible, and the subproblems may be solved inexactly. The new algorithm has been implemented in MATLAB, with an option to use either MINOS or SNOPT (Fortran codes) to solve the linearly constrained subproblems. Only first derivatives are required. We present numerical results on a subset of the COPS, HS, and CUTE test problems, which include many large examples. The results demonstrate the robustness and efficiency of the stabilized LCL procedure.
Joint Channel Coding based on LDPC Codes with Gaussian Kernel Reflecton and CS Redundancy

This paper proposes a new joint decoding algorithm frame based on compressed sensing CS and LDPC (Low-Density Parity-Check) codes. Redundant information can be effectively extracted and amplified by CS reconstruction as a compensation to correct decoding of LDPC codes. We adopt Gaussian kernel function of image segmentation as a reflection. Simulation results indicate, compared with LDPC algorithm, the algorithm presented in this paper can obviously make BER (bit error ratio) lower and improve system decoding performance, and different variance of Gaussian kernel function can obtain different results.
Minimizing search errors due to delayed bigrams in real-time speech recognition systems

When building applications from large vocabulary speech recognition systems, a certain amount of search errors due to pruning often has to be accepted in order to obtain the required speed. We tackle the problems resulting from aggressive pruning strategies as typically applied in large vocabulary systems to achieve close to real-time performance. We consider a typical scenario of a two pass Viterbi search with the first pass being organized as a phoneme (allophone) tree. For such a tree organized lexicon, there are two possibilities to use a bigram language model: either by building tree copies or by using so-called delayed bigrams. Since copying trees turns out to be too expensive for real time applications we basically refer to delayed bigrams, discuss their drastic influence on the word accuracy and show how to alleviate the disastrous effect of delayed bigrams under aggressive pruning.
Research Statement past and Current Research

My main research interests lie in theoretical computer science with a primary focus on randomness in computation. I prefer to study basic algorithmic problems, which are in the same time well-motivated by applications in practice. Among others I have been focusing on applications in physics. The examples include finding matchings in graphs, randomized approximation schemes for counting perfect matchings, transitive closure and shortest distances in graphs. I have been also studying the possibility of solving these problem in dynamic setup. I am also pursuing research in solid state theory. I am working on modeling basic spintronics devices. Spintronics is concerned with building electronics that takes advantage of the spin of electron.
Performance of I-vector speaker verification and the detection of synthetic speech

In this paper, we present new research results on the vulnerability of speaker verification (SV) systems to synthetic speech. Using a state-of-the-art i-vector SV system and evaluating with the Wall-Street Journal (WSJ) corpus, our SV system has a 0.00% false rejection rate (FRR) and 1.74 × 10-5 false acceptance rate (FAR). When the i-vector system is tested with state-of-the-art speaker-adaptive, hidden Markov model (HMM)-based synthetic speech generated from speaker models derived from the WSJ journal corpus, 22.9% of the matched claims are accepted highlighting the vulnerability of SV systems to synthetic speech. We propose a new synthetic speech detector (SSD) which uses previously-proposed features derived from image analysis of pitch patterns but extracted on phoneme-level segments and which leverages the available enrollment speech from the SV system. When the SSD is applied to human and synthetic speech accepted by the SV system, the overall system has a FRR of 7.35% and a FAR of 2.34 × 10-4 which is lower than previously-reported systems and thus significantly reduces the vulnerability.
On the approximability of budget feasible mechanisms

Budget feasible mechanisms, recently initiated by Singer (FOCS 2010), extend algorithmic mechanism design problems to a realistic setting with a budget constraint. We consider the problem of designing truthful budget feasible mechanisms for monotone submodular functions: We give a randomized mechanism with an approximation ratio of 7.91 (improving on the previous best-known result 233.83), and a deterministic mechanism with an approximation ratio of 8.34. We also study the knapsack problem, which is a special submodular function, give a 2 + √2 approximation deterministic mechanism (improving on the previous best-known result 5), and a 3 approximation randomized mechanism. We provide similar results for an extended knapsack problem with heterogeneous items, where items are divided into groups and one can pick at most one item from each group. Finally we show a lower bound of 1 + √2 for the approximation ratio of deterministic mechanisms and 2 for randomized mechanisms for knapsack, as well as the general monotone submodular functions. Our lower bounds are unconditional, and do not rely on any computational or complexity assumptions.
A Dark Incubation Period Is Important for Agrobacterium-Mediated Transformation of Mature Internode Explants of Sweet Orange, Grapefruit, Citron, and a Citrange Rootstock

Background Citrus has an extended juvenile phase and trees can take 2–20 years to transition to the adult reproductive phase and produce fruit. For citrus variety development this substantially prolongs the time before adult traits, such as fruit yield and quality, can be evaluated. Methods to transform tissue from mature citrus trees would shorten the evaluation period via the direct production of adult phase transgenic citrus trees. Methodology/Principal Findings Factors important for promoting shoot regeneration from internode explants from adult phase citrus trees were identified and included a dark incubation period and the use of the cytokinin zeatin riboside. Transgenic trees were produced from four citrus types including sweet orange, citron, grapefruit, and a trifoliate hybrid using the identified factors and factor settings. Significance The critical importance of a dark incubation period for shoot regeneration was established. These results confirm previous reports on the feasibility of transforming mature tissue from sweet orange and are the first to document the transformation of mature tissue from grapefruit, citron, and a trifoliate hybrid.
Interactive, multi-modality image registrations for combined MRI/MRSI-planned HDR prostate brachytherapy

Purpose This study presents the steps and criteria involved in the series of image registrations used clinically during the planning and dose delivery of focal high dose-rate (HDR) brachytherapy of the prostate. Material and methods Three imaging modalities – Magnetic Resonance Imaging (MRI), Magnetic Resonance Spectroscopic Imaging (MRSI), and Computed Tomography (CT) – were used at different steps during the process. MRSI is used for identification of dominant intraprosatic lesions (DIL). A series of rigid and nonrigid transformations were applied to the data to correct for endorectal-coil-induced deformations and for alignment with the planning CT. Mutual information was calculated as a morphing metric. An inverse planning optimization algorithm was applied to boost dose to the DIL while providing protection to the urethra, penile bulb, rectum, and bladder. Six prostate cancer patients were treated using this protocol. Results The morphing algorithm successfully modeled the probe-induced prostatic distortion. Mutual information calculated between the morphed images and images acquired without the endorectal probe showed a significant (p = 0.0071) increase to that calculated between the unmorphed images and images acquired without the endorectal probe. Both mutual information and visual inspection serve as effective diagnostics of image morphing. The entire procedure adds less than thirty minutes to the treatment planning. Conclusion This work demonstrates the utility of image transformations and registrations to HDR brachytherapy of prostate cancer.
LD

Data race detection has become an important problem in GPU programming. Previous designs of CPU race-checking tools are mainly task parallel and incur high overhead on GPUs due to access instrumentation, especially when monitoring many thousands of threads routinely used by GPU programs. This article presents a novel data-parallel solution designed and optimized for the GPU architecture. It includes compiler support and a set of runtime techniques. It uses value-based checking, which detects the races reported in previous work, finds new races, and supports race-free deterministic GPU execution. More important, race checking is massively data parallel and does not introduce divergent branching or atomic synchronization. Its slowdown is less than 5 × for over half of the tests and 10 × on average, which is orders of magnitude more efficient than the cuda-memcheck tool by Nvidia and the methods that use fine-grained access instrumentation.
Securing big data in the Cloud by protected mapping over multiple providers

The Cloud is increasingly being used to store and process big data, and conventional security mechanisms using encryption are either not sufficiently efficient or not suited to the task of protecting big data in the Cloud. In this paper we present an alternative approach which divides big data among multiple Cloud providers, and instead of protecting the data itself, protects the mapping of the various data elements to each provider using a trapdoor function. Our initial analysis indicates that this is an efficient and secure approach for securing big data.
Protein Intake as a Risk Factor of Overweight/Obesity in 8– to 12–Year-Old Children

AbstractSeveral studies investigating the relationship between body mass index (BMI), waist circumference (WC), and/or body fat (BF) with macronutrient composition of the diet have suggested that dietary composition may play an important role to overweight/obesity in childhood, but its relation remains inconclusive. The aim was to assess the association between energy intake (EI) and macronutrient diet composition with overweight/obesity among children.Nonrandomized cohort study including 396 Italian children and preadolescents (9–13 years old), 200 overweight/obese and 196 normal-weight. The children's weight, height, WC, and food intake were measured.Reported EI was higher in overweight/obese than in nonoverweight children; however, after body weight was considered, the overweight/obese children had less EI than their leaner counterparts. Percentages of EI from proteins, SFA, MUFA and PUFA (in males), and dietary fiber (g/1000 kcal) were higher in the overweight/obese children than in the leaner ones. EI from carbohydrates and fats was lower in overweight/obese males and females, respectively. Positive correlations between BMI and waist-to-height ratio with EI from proteins were found in males (r = 0.296, P < 0.01 and r = 0.326, P < 0.01; respectively) and females (r = 0.374, P < 0.01 and r = 0.405, P < 0.01; respectively), but negative correlations with fats were found in females (r = −0.240, P < 0.01 and r = −0.188, P < 0.05; respectively). Using binary logistic regression, the highest EI from proteins were associated with higher odds ratio for overweight/obesity, while the lowest EI from carbohydrates was associated with higher odds ratio for overweight/obesity in males.Reported EI of overweight/obese children was higher than nonoverweight peers. Overweight/obese children had higher intakes of proteins compared with nonoverweight ones. Overweight/obese males and females showed lower EI from carbohydrates and fats, respectively, than their leaner counterparts.
Robust tracking of a mobile receiver using unsynchronized time differences of arrival

Localization based on the time difference of arrival (TDoA) has turned out to be a promising approach for indoor environments, especially in combination with innovative self-calibrating TDoA algorithms that eliminate the need to measure the positions of reference receivers. We consider the previously unsolved problem of locating a moving target receiver by discrete signals from stationary beacons at unknown locations. We assume that the beacons are small and inexpensive and they require no further communication, i.e. they are unsynchronized. They only emit short discrete signals at regular intervals, of which we assume that they can be distinguished. The moving target travels on an unknown trajectory, receiving signals from the beacons and calculating the TDoA of the signals. First, we discuss adaptions of two TDoA algorithms by which the senders can be located from unknown signals. Second, we propose two novel approaches based on probabilistic state estimation to enable robust localization of the mobile receiver using the discrete arrival times, once the senders have been located. The probabilistic algorithms use the particle filter and the unscented Kalman filter to estimate the position and velocity of the target, as well as the unknown synchronization offsets of the senders. We provide a motion model and a sensor model for which we take into account that the signals of the beacons are received as singles, each at a different time. We verify the feasibility and robustness of our approach in extensive simulations, where we analyze the reliability of localization and compare both algorithms.
Religiousness in First-Episode Psychosis

The aim of the present study is to explore the significance of religiousness for patients suffering from first-episode psychosis. Our study is a thematic analysis. The study illustrates how the patients understood their hallucinations as mystical experiences. Even so, many of the patients describe their religiousness to be helpful in coping with their disorder, giving meaning to life as well as a relationship to a sacred figure. However, their religiousness often contained religious omnipotent delusions, and built on hallucinations, displayed an unsecure relationship to the sacred figure. From a psychiatric point of view, the misinterpretation of hallucinations as mystical experiences may reinforce their delusional system and cause an obstacle to recovery. This misinterpretation may also cause problems for patients’ religious coping. Our findings underline the importance of taking patients’ religiousness into account in psychotherapy.
Fast Valuation and Calibration of Credit Default Swaps Under Levy Dynamics

In this paper we address the issue of finding an efficient and flexible numerical approach for calculating survival/default probabilities and pricing Credit Default Swaps under advanced jump dynamics. We have chosen to use the firm's value approach, modeling the firm's value by an exponential\levy\model. For this approach the default event is defined as a first passage of a barrier and it is therefore possible to exploit a numerical technique developed to price barrier options under\levy\models to calculate the default probabilities. The method presented is based on the Fourier-cosine series expansion of the underlying model's density function.With this method we calibrate two well known\levy\models, the Normal Inverse Gaussian and the CGMY, to the market quotes of the iTraxx Series 7 and 8 constituents. In the calibration study we look at the root mean square error of the fit and find that both models give a very good fit to market data. Furthermore, we investigate how the out of calibration given parameters evolve over the year covered by the two series. Finally, we look at the changes in the default probability term structure over the year.
helicases ofreveal structural similarities between SF 1 and SF 2 superfamilies Comparisons between the structures of HCV and Rep helicases

Three helicase structures have been determined recently: that of the DNA helicase PcrA, that of the hepatitis C virus RNA helicase, and that of the Escherichia coli DNA helicase Rep. PcrA and Rep belong to the same super-family of helicases (SFI) and are structurally very similar. In contrast, the HCV helicase belongs to a different super-family of helicases, SF2, and shows little sequence homology with the PcrA/Rep helicases. Yet, the HCV helicase is structurally similar to Rep/PcrA, suggesting preservation of structural scaffolds and relationships between helicase motifs across these two super-families. The comparison study presented here also reveals the existence of a new helicase motif in the SF1 family of helicases.
An Application-Controlled Framework for Reliable Multicast Transport

IP multicast has enabled a number of large-scale collaborative applications, several of which require reliability. However, no single reliable multicast (RM) transport protocol fits the requirements of all classes of applications and functions efficiently over all types of heterogeneous networks. In this paper, we propose a novel framework approach to RM protocol design that involves a generic core protocol skeleton that can be customized in different ways resulting in protocols that are optimized for specific applications. We identify three key issues that must be tackled before such a flexible architecture can be realized. First, to accomodate diverse applications, the semantics of message delivery (i.e., reliability and ordering) must not be fixed, but rather must be flexibly controllable. Second, to scale to large and dynamic groups, our multicast transport protocol framework must be robust against host failures and group membership changes. Third, in combination with a “safe” congestion control scheme, our transport framework must allow applications to manage available bandwidth optimally. In this paper, we discuss our solutions to these problems. To demonstrate the power of our flexible framework approach, we have designed and implemented a prototype of our transport protocol library, called libsrm.
SyQL : Querying Software Process Data Through an Object-Oriented Metamodel

The effective usage of the automatically collected software process data may be challenging. By using general purpose query languages like SQL, retrieval of software process information introduces a cost and competence barrier, which limits the adoption of Automated In-process Software Engineering Measurement and Analysis (AISEMA) systems, since it requires a considerable effort for query writing as well as a deep understanding of process data collection and representation. In this paper, we describe the implementation of a query language, SyQL, mainly but not exclusively designed for software process data. SyQL significantly reduces the competence barrier by providing a query interface aimed to software process data.
SECURING INTERPRETABILITY OF FUZZY MODELS FOR MODELING NONLINEAR MIMO SYSTEMS USING A HYBRID OF EVOLUTIONARY ALGORITHMS

In this study, a Multi-Objective Genetic Algorithm (MOGA) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear Multi-input Multi-output (MIMO) systems. In the process of non- linear system identication, structure selection, parameter estimation, model performance and model validation are important objectives. Furthermore, se- curing low-level and high-level interpretability requirements of fuzzy models is especially a complicated task in case of modeling nonlinear MIMO systems. Due to these multiple and conicting objectives, MOGA is applied to yield a set of candidates as compact, transparent and valid fuzzy models. Also, MOGA is combined with a powerful search algorithm namely Dierential Evolution (DE). In the proposed algorithm, MOGA performs the task of membership function tuning as well as rule base identication simultaneously while DE is utilized only for linear parameter identication. Practical applicability of the proposed algorithm is examined by two nonlinear system modeling prob- lems used in the literature. The results obtained show the eectiveness of the proposed method.