Miracle-dz/UniFAR-FTUs huggingface dataset:
Parallel Algorithms for Constructing Data Structures for Fast Multipole Methods

We present efficient algorithms to build data structures and the lists needed for fast multipole methods. The algorithms are capable of being efficiently implemented on both serial, data parallel GPU and on distributed architectures. With these algorithms it is possible to map the FMM efficiently on to the GPU or distributed heterogeneous CPU-GPU systems. Further, in dynamic problems, as the distribution of the particles change, the reduced cost of building the data structures improves performance. Using these algorithms, we demonstrate example high fidelity simulations with large problem sizes by using FMM on both single and multiple heterogeneous computing facilities equipped with multi-core CPU and many-core GPUs.
Learning queries for relational, semi-structured, and graph databases

Web applications store their data within various database models, such as relational, semi-structured, and graph data models to name a few. We study learning algorithms for queries for the above mentioned models. As a further goal, we aim to apply the results to learning cross-model database mappings, which can also be seen as queries across different schemas.
DETECTION OF X-RAY ELONGATED EMISSION FROM AN ULTRALUMINOUS X-RAY SOURCE IN THE INTERACTING PAIR OF GALAXIES NGC 5953/5954

We present radio through X-ray observations of a bright (>1040 erg s−1 in the 0.5–8.0 keV band) ultraluminous X-ray source (ULX), CXOU J153434.9+151149, in the starburst, interacting pair of galaxies NGC 5953/5954. A Chandra image of this ULX shows that it is elongated. From HST/WFPC2/F606W data we have detected a counterpart of the ULX system with MF606W ∼ −7.1 ± 0.7 mag. This optical counterpart may be either an O-type supergiant star or a young star cluster. From our Fabry–Perot interferometric observations, we have detected Hα and [N ii] (6584 Å) diffuse emission, with velocity gradients up to 60 km s−1 at the astrometric corrected Chandra position of the ULX. Different scenarios have been invoked as to explain the possible nature of CXOU J153434.9+151149. Based on the observed X-ray morphology of the ULX, we estimate that the inclination between the line of sight to the observer and the direction of a possibly beamed outflow is ∼53°. Beaming with this geometry from a stellar-mass black hole system will be inadequate to explain the observed X-ray luminosity of this ULX. Finally, we suggest that mild-beaming from a binary black hole with mass more than 50 M☉ associated with a young star cluster, is the most favorable scenario that describes the multiwavelength properties of this ULX. Future observations are definitely needed to determine the nature of this rare object.
Muscle Damage and Muscle Activity Induced by Strength Training Super-Sets in Physically Active Men

Abstract Brentano, MA, Umpierre, D, Santos, LP, Lopes, AL, Radaelli, R, Pinto, RS, and Kruel, LFM. Muscle damage and muscle activity induced by strength training super-sets in physically active men. J Strength Cond Res 31(7): 1847–1858, 2017—In strength training, muscle activity is often analyzed by surface electromyography (EMG) and muscle damage through indirect markers, such as plasma concentrations of creatine kinase (CK) after exercise. However, there is little information about the influence of the strength exercises order on these parameters. The purpose of this study is to analyze the effect of strength exercises order (super-sets) in muscle activity and indirect markers of muscle damage. Twenty men were randomly assigned to one of the strength training sessions (TS). Each TS (5 sets × 8–10 repetition maximum) consisted of 2 exercises for the knee extensor muscles and 2 exercises for the horizontal shoulder flexors performed in a different order: exercises for the same muscle group grouped (grouped exercises [GE]: n = 10; 26.6 ± 3.4 years; 17.4 ± 3.4 body fat) or separated (separated exercises [SE]: n = 10; 24.9 ± 2.6 years; 15.4 ± 5.9 body fat). Muscle activity was analyzed by surface EMG (vastus lateralis [VL], vastus medialis [VM], rectus femoris [RF], pectoralis major [PM], and anterior deltoid [AD]), and the main indirect marker of muscle damage was the CK, evaluated immediately before and after the first 5 days of each TS. There was a higher EMG activity of GE in the RF (GE: 88.4% × SE: 73.6%) and AD (GE: 176.4% × SE: 100.0%), in addition to greater concentration of CK (GE: 632.4% × SE: 330.5%) after exercise. Our findings suggest that, in physically active men, implementing super-sets with GE promotes greater muscle effort and muscle damage, wherein 5 days are not enough to recover the trained muscle groups.
Wagner-like decoding for noncoherent PPM based ultra-low-power communications

Noncoherent pulse-position modulation (PPM) with simple channel codes has the potential to realize ultra-low power (ULP) wireless design. In this paper, we develop a Wagnerlike decoding rule for single-parity-check and high-rate Reed-Solomon (RS) coded PPM schemes by simply `flipping' the most unreliable received PPM symbol(s) to obtain a good balance between performance and coding complexity. The proposed algorithm can be considered as a list decoding algorithm that first generates a candidate codeword list based on the algebraic structure of the code before applying soft decisions to decode. This approach can result in more power-efficient realizations of the studied schemes. It is shown that our decoding approach can achieve near maximum likelihood decoding performance based on the trellis, while having a significantly lower decoding complexity. In addition, by exploiting the inherent advantage of PPM transmission, it is possible to reduce the candidate list to further simplify the decoding for RS-coded PPM without losing coding gain. This makes the proposed scheme more attractive for ULP communications.
Do epileptic seizures damage the brain?

Purpose of reviewThe possibility that recurrent seizures in patients with poorly controlled epilepsy may produce neuronal damage and contribute to progressive functional and cognitive declines observed in some patients with epilepsy has major clinical and therapeutic implications for urgency of treatment and effective intervention to achieve complete control. Recent findingsAdvances in magnetic resonance imaging techniques, technical and conceptual advances in experimental analysis of neuronal death at the cellular and molecular level, and long-term neuropsychological observations have provided substantial new data and insights into phenomena of seizure-induced plasticity in neural circuitry that address the question ‘Do epileptic seizures damage the brain? ’ SummaryThe emerging perspective is that seizure-induced damage should be regarded not only as neuronal loss but as adverse long-term behavioral and cognitive consequences. This perspective provides a strong rationale for development of neuroprotective treatments to forestall adverse long-term consequences of repeated seizures, and for the importance of prompt, effective intervention that achieves complete seizure control.
Asynchronous algorithms for approximate distributed constraint optimization with quality bounds

Distributed Constraint Optimization (DCOP) is a popular framework for cooperative multi-agent decision making. DCOP is NP-hard, so an important line of work focuses on developing fast incomplete solution algorithms for large-scale applications. One of the few incomplete algorithms to provide bounds on solution quality is k-size optimality, which defines a local optimality criterion based on the size of the group of deviating agents. Unfortunately, the lack of a general-purpose algorithm and the commitment to forming groups based solely on group size has limited the use of k-size optimality. This paper introduces t-distance optimality which departs from k-size optimality by using graph distance as an alternative criteria for selecting groups of deviating agents. This throws open a new research direction into the tradeoffs between different group selection and coordination mechanisms for incomplete DCOP algorithms. We derive theoretical quality bounds for t-distance optimality that improve known bounds for k-size optimality. In addition, we develop a new efficient asynchronous local search algorithm for finding both k-size and t-distance optimal solutions --- allowing these concepts to be deployed in real applications. Indeed, empirical results show that this algorithm significantly outperforms the only existing algorithm for finding general k-size optimal solutions, which is also synchronous. Finally, we compare the algorithmic performance of k-size and t-distance optimality using this algorithm. We find that t-distance consistently converges to higher-quality solutions in the long run, but results are mixed on convergence speed; we identify cases where k-size and t-distance converge faster.
Acquiring and Revising Preferences in a Critique-Based Mobile Recommender System

Many e-commerce Web sites offer numerous services, so a product search could return an overwhelming set of options. Without system support, filtering irrelevant products, comparing alternatives, and selecting the best option can be difficult or impossible - especially for users connecting to the Web through a mobile device. Few Web-based recommender systems have been designed for mobile users. A critique-based recommendation methodology aids the acquisition and revision of user preferences in a mobile recommender system. We designed a product recommendation methodology and implemented it in MobyRek, a mobile-phone recommender system that helps users search for travel products. MobyRek supports limited asking and answering of questions and is based mostly on critiques.
Nodule volume change estimation in thoracic CT using sphere fitting , morphological segmentation and image registration

Three methods are presented to assess the relative volume change of a pulmonary nodule between two chest CT scans when the nodule location in both scans is provided. The first method fits a sphere around both nodules and computes the volume of dense tissue in that sphere. The second method segments both nodules using thresholding, component labeling and morphological processing. The third method applies non-rigid registration to transform the first to the second scan and applies that transformation to a segmentation of the nodule in the first scan to obtain a segmentation of the nodule in the second scan. All methods are applied to 50 nodule pairs from the VOLCANO’09 challenge. These cases are divided by a radiologist in stable and growing pairs. All methods produce lower mean volume change for the stable cases compared to the growing nodules, but the distributions overlap considerably. Moreover, the correlation between the volume change estimates produced by the three methods is modest. This shows that nodule volume change assessment is a complicated problem.
Intonation Modeling of Slovak Speech Using MOMEL/INTSINT Framework

Synthesis of adequate prosody from text is considered as one of the crucial issues in order to achieve high quality speech synthesis. This problem has not been addressed yet in our speech synthesis system. In this paper we present our first attempt to model intonation, with selected tools - MOMEL and INTSINT. They present low-level coding of F0 contour and more abstract description of speech melody respectively. Finally, we present our experiments in coding INTSINT symbols from MOMEL targets and our results in re-synthesizing the targets from the symbols. The results are comparable to those in other studies regarding different languages.
Approximate nearest neighbor queries in fixed dimensions

Given a set of n points in d-dimensional Euclidean space, S ⊂ E, and a query point q ∈ E, we wish to determine the nearest neighbor of q, that is, the point of S whose Euclidean distance to q is minimum. The goal is to preprocess the point set S, such that queries can be answered as efficiently as possible. We assume that the dimension d is a constant independent of n. Although reasonably good solutions to this problem exist when d is small, as d increases the performance of these algorithms degrades rapidly. We present a randomized algorithm for approximate nearest neighbor searching. Given any set of n points S ⊂ E, and a constant ǫ > 0, we produce a data structure, such that given any query point, a point of S will be reported whose distance from the query point is at most a factor of (1 + ǫ) from that of the true nearest neighbor. Our algorithm runs in O(log n) expected time and requires O(n log n) space. The data structure can be built in O(n) expected time. The constant factors depend on d and ǫ. Because of the practical importance of nearest neighbor searching in higher dimensions, we have implemented a practical variant of this algorithm, and show empirically that for many point distributions this variant of the algorithm finds the nearest neighbor in moderately large dimension significantly faster than existing practical approaches.
Variational Autoencoder ( VAE ) is model comprised of two multilayer perceptrons : one acts as a density network

We extend Stochastic Gradient Variational Bayes to perform posterior inference for the weights of Stick-Breaking processes. This development allows us to define a Stick-Breaking Variational Autoencoder (SB-VAE), a Bayesian nonparametric version of the variational autoencoder that has a latent representation with stochastic dimensionality. We experimentally demonstrate that the SB-VAE, and a semisupervised variant, learn highly discriminative latent representations that often outperform the Gaussian VAE’s.
Workload and main activities of consultative ICU nurses: long‐term experience in a large teaching hospital in the Netherlands

Background: Early recognition and prompt treatment of deteriorating patients outside the intensive care unit (ICU) improves hospital survival. Over the past decade, consultative services have been implemented in many institutions. This service is frequently performed by ICU nurses, while little information is available on the workload and type of activities these ICU nurses actually perform.
Aggregation, Foraging, and Formation Control of Swarms with Non-Holonomic Agents Using Potential Functions and Sliding Mode Techniques ∗†

In this article we consider the aggregation, foraging, and formation control of swarms whose agents are moving in 2-dimensions with non-holonomic unicycle agent dynamics. We approach these problems using artificial potentials and sliding mode control. The main contribution is extension of the recent results (mainly for aggregation)in the literature based on a similar approach for simple integrator agent dynamics models to a significantly more realistic and more difficult setting with non-holonomic unicycle agent dynamics models. In particular, we design continuous-time control schemes via a constructive analysis based on artificial potential functions and sliding mode control techniques. The effectiveness of the proposed designs are demonstrated analytically as well as via a set of simulation results.
On channel estimation and detection for multicarrier signals in fast and selective Rayleigh fading channels

Time-domain channel estimation and detection techniques are presented for multicarrier signals in a fast and frequency-selective Rayleigh fading channel. As a consequence of the time-varying channel, the orthogonality between subcarriers is destroyed in conventional frequency-domain approaches, resulting in interchannel interference, which increases an irreducible error floor in proportion to the normalized Doppler frequency. An important feature of the proposed technique is the ability to exploit the time-selective channel as a provider of time diversity. This enables us to achieve performance superior to any other structure without increasing bandwidth or incorporating redundancy, in order to reduce the complexity of the estimator, we apply the theory of optimal low rank approximation to a minimum mean squared error channel estimator and present a theoretical calculation of mean squared error and simulations to confirm that the estimator is robust to changes in channel characteristics.
Multiple Two-Phase Data Processing with MapReduce

MapReduce, proposed as a programming model, has been widely adopted in the field of text processing over large datasets with the capability of exploiting the distributed resources and processing the large-scale data. Attributed to its simplicity and scalability, the success seems to have the potential to make Big Data processing by cloud computing available. Nevertheless, such promise is accompanied by the difficulty of fitting the applications into MapReduce. This is because MapReduce is limited to the kind of applications that every input key-value pair is independent of each other. In this paper, we extend the general applicability of MapReduce by allowing the dependence within a set of input key-value pairs but preserving independence among all sets. Such this new modeling paradigm intends MapReduce to shift processing the independent input key-value pairs to processing the independent sets. However, the advancement in the applicability brings the intricate problem of how two-stage processing structure, inherent in MapReduce, handles the dependence within a set of input key-value pairs. To tackle this problem, we propose the design pattern called two-phase data processing. It expresses the application in two phases not only to match the two-stage processing structure but to exploit the power of MapReduce through the cooperation between the mappers and reducers. In addition, we present the design methodology-multiple two-phase data processing-to offer advice on processing the independent sets. The experiment of background subtraction, a part of video surveillance, proves that the new modeling paradigm broadens the possibilities of MapReduce and demonstrates how our design methodology guides the applications to the implementation.
Deadlock avoidance for distributed real-time and embedded systems

This thesis studies how to prevent deadlocks in distributed real-time and embedded systems. Deadlocks are undesirable states of concurrent systems, characterized by a set of processes in a circular wait state, in which each process is blocked trying to gain access to a resource held by the next one in the chain. Solutions can be classified into three categories: • Deadlock detection is an optimistic approach. It assumes that deadlocks are infrequent and detects and corrects them at runtime. This technique is not applicable to real-time systems since the worst case running time is long. Moreover, in embedded systems actions cannot be undone. • Deadlock prevention is a pessimistic approach. The possibility of a deadlock is broken statically at the price of restricting concurrency. • Deadlock avoidance takes a middle route. At runtime each allocation request is examined to ensure that it cannot lead to a deadlock. Deadlock prevention is commonly used in distributed real-time and embedded systems but, since concurrency is severely limited, an efficient distributed deadlock avoidance schema can have a big practical impact. However, it is commonly accepted (since the mid 1990s) that the general solution for distributed deadlock avoidance is impractical, because it requires maintaining global states and global atomicity of actions. The communication costs involved simply outweigh the benefits gained from avoidance over prevention. This thesis presents an efficient distributed deadlock avoidance schema that requires no communication between the participant sites, based on a combination of static analysis and runtime protocols. This solution assumes that the possible sequences of remote calls are available for analysis at design time. This thesis also includes protocols that guarantee liveness, and mechanisms to handle distributed priority inversions.
Using Syntactic Information to Identify Plagiarism

Using keyword overlaps to identify plagiarism can result in many false negatives and positives: substitution of synonyms for each other reduces the similarity between works, making it difficult to recognize plagiarism; overlap in ambiguous keywords can falsely inflate the similarity of works that are in fact different in content. Plagiarism detection based on verbatim similarity of works can be rendered ineffective when works are paraphrased even in superficial and immaterial ways. Considering linguistic information related to creative aspects of writing can improve identification of plagiarism by adding a crucial dimension to evaluation of similarity: documents that share linguistic elements in addition to content are more likely to be copied from each other. In this paper, we present a set of low-level syntactic structures that capture creative aspects of writing and show that information about linguistic similarities of works improves recognition of plagiarism (over tfidf-weighted keywords alone) when combined with similarity measurements based on tfidf-weighted keywords.
Direct and indirect roles for Nodal signaling in two axis conversions during asymmetric morphogenesis of the zebrafish heart

The Nodal signaling pathway plays a conserved role in determining left-sided identity in vertebrates with this early left-right (L/R) patterning influencing the asymmetric development and placement of visceral organs. We have studied the role of Nodal signaling in asymmetric cardiac morphogenesis in zebrafish and describe two distinct rotations occurring within the heart. The first is driven by an asymmetric migration of myocardial cells during cardiac jogging, resulting in the conversion of the L/R axis to the dorsal-ventral (D/V) axis of the linear heart. This first rotation is directly influenced by the laterality of asymmetric gene expression. The second rotation occurs before cardiac looping and positions the original left cells exposed to Nodal signaling back to the left of the wild-type (WT) heart by 48 hours postfertilization (hpf). The direction of this second rotation is determined by the laterality of cardiac jogging and is not directly influenced by asymmetric gene expression. Finally, we have identified a role for Nodal signaling in biasing the location of the inner ventricular and outer atrial curvature formations. These results suggest that Nodal signaling directs asymmetric cardiac morphogenesis through establishing and subsequently reinforcing laterality information over the course of cardiac development.
DOJ: dynamically parallelizing object-oriented programs

We present Dynamic Out-of-Order Java (DOJ), a dynamic parallelization approach. In DOJ, a developer annotates code blocks as tasks to decouple these blocks from the parent execution thread. The DOJ compiler then analyzes the code to generate heap examiners that ensure the parallel execution preserves the behavior of the original sequential program. Heap examiners dynamically extract heap dependences between code blocks and determine when it is safe to execute a code block. We have implemented DOJ and evaluated it on twelve benchmarks. We achieved an average compilation speedup of 31.15 times over OoOJava and an average execution speedup of 12.73 times over sequential versions of the benchmarks.