Miracle-dz/UniFAR-FTUs huggingface dataset:
GPU Acceleration of Particle Advection Workloads in a Parallel, Distributed Memory Setting

Although there has been significant research in GPU acceleration, both of parallel simulation codes (i.e., GPGPU) and of single GPU visualization and analysis algorithms, there has been relatively little research devoted to visualization and analysis algorithms on GPU clusters. This oversight is significant: parallel visualization and analysis algorithms have markedly different characteristics -- computational load, memory access pattern, communication, idle time, etc. -- than the other two categories. In this paper, we explore the benefits of GPU acceleration for particle advection in a parallel, distributed-memory setting. As performance properties can differ dramatically between particle advection use cases, our study operates over a variety of workloads, designed to reveal insights about underlying trends. This work has a three-fold aim: (1) to map a challenging visualization and analysis algorithm -- particle advection -- to a complex system (a cluster of GPUs), (2) to inform its performance characteristics, and (3) to evaluate the advantages and disadvantages of using the GPU. In our performance study, we identify which factors are and are not relevant for obtaining a speedup when using GPUs. In short, this study informs the following question: if faced with a parallel particle advection problem, should you implement the solution with CPUs, with GPUs, or does it not matter?
Face recognition based on the band fusion of generalized phase spectrum of 2D-FrFT

In this paper, we propose a novel feature extraction method for face recognition based on two dimensional fractional Fourier transform (2D-FrFT). First, we extract the phase information of facial image in 2D-FrFT, which is called the generalized phase spectra (GPS). Then, we present an improved two-dimensional separability judgment (I2DSJ) to select appropriate order parameters for discrete fractional Fourier transform. Finally, multiple orders’ generalized phase spectrum bands (MGPSB) fusion is proposed. In order to make full use of the discriminative information from different orders for face recognition, the proposed approach merges different orders’ generalized phase spectra (GPS) of 2D-FrFT. The proposed method is no need to construct the subspace through the feature extraction methods and has less computation cost. Experimental results on the public face databases demonstrate that our method outperforms the representative methods.
on Intelligent Manufacturing and Automation , DAAAM 2014 Collaborative Project Management Framework for Partner Network Initiation

Fast changing market conditions force SMEs to collaborate dynamically with each other for carrying out projects accordingly to customer requirements in a competitive way. In order to simplify the cooperation, a Collaborative Network (CN) of enterprises forms Partner Networks (PN), where partners define the rules of collaboration and sign collaboration agreements. This paper provides a conceptual model of a sustainable realization of collaborative projects for SME-s from the machinery domain that follows criteria for involving new members to a PN. Furthermore, the authors describe the initiation of a PN joining process and introduce the business processes classifier for enterprises partners. The main objective of research is to enable the commencement of new projects, or Virtual Organization (VO) faster for the price proposal preparation. Today there is a gap exists in the state-of-the-art with respect to a comprehensive lifecycle of PN-initiation and collaborative project management, starting from its very inception. The primary purpose of this paper is to introduce a novel model that enables a faster PN initiation process; the novel process of PN initiation covers the aspects of composing the partner network, the PN-initiation lifecycle and a corresponding architecture for PN initiation. © 2015 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of DAAAM International Vienna.
Relationship between heart rate variability and acute : chronic ratio throughout a season in NCAA D 1 Men ’ s Soccer Players

The purpose of this study was twofold: (a) to examine the relationship between heart rate variability (HRV) and acute:chronic workload ratio (ACWR)-based training load (TL) metrics and (b) to examine relationships across various A:C ratio-based TL metrics. Heart rate variability in 23 male college soccer players (mean ± SD; age, 21 ± 1 years; body mass, 80.3 ± 5.8 kg; height, 181.9 ± 6.5 cm; %body fat, 11.9 ± 2.0%; and V[Combining Dot Above]O2max, 51.9 ± 5.0 ml·kg−1·min−1) was measured at 5 time points: week(W)1, W3, W7, W12, and W14 during the 2015 NCAA men's soccer season. Heart rate variability was calculated from beat to beat intervals using a heart rate monitor. Players donned a global position satellite–enabled device that measured the following TL metrics: session time (ST), Player Load (PL), PL·min−1, and total distance (TD). Acute:chronic workload ratio was calculated for each TL metric: ACWRbased ST (ACWRST), ACWR-based PL (ACWRPL), ACWR-based PL·min−1 (ACWRPLM), and ACWR-based TD (ACWRTD): ACWR = week average TLs/mo average (30 ± 1 days) TLs. Relationships between HRV and ACWR-based each TL metric were evaluated using mixed effects models. Tukey pairwise comparisons were used to examine differences between types of ACWR-based TL metrics. An increase in ACWRST significantly reduced HRV throughout a season (−7.4 ± 3.6 m·s−1; p = 0.04). There were significant differences between ACWRPLM and ACWRST, ACWRPL and ACWRTD at W1, ACWRPLM and ACWRST at W3 (p < 0.05). In conclusion, ACWRST, ACWRPL, and ACWRTD were significantly different from ACWRPLM. ACWRST was found to significantly predict HRV; higher ACWRST was significantly associated with lower HRV. Therefore, tracking of the ACWR using ST may help to optimize athlete's physiological state throughout a season.
A new Kohn-Vogelius type formulation for inverse source problems

In this paper we propose a Kohn-Vogelius type formulation for an inverse source problem of partial differential equations. The unknown source term is to be determined from both Dirichlet and Neumann boundary conditions. We introduce two different boundary value problems, which depend on two different positive real numbers $\alpha$ and $\beta$, and both of them incorporate the Dirichlet and Neumann data into a single Robin boundary condition. This allows noise in both boundary data. By using the Kohn-Vogelius type Tikhonov regularization, data to be fitted is transferred from boundary into the whole domain, making the problem resolution more robust. More importantly, with the formulation proposed here, satisfactory reconstruction could be achieved for rather small regularization parameter through choosing properly the values of $\alpha$ and $\beta$. This is a desirable property to have since a smaller regularization parameter implies a more accurate approximation of the regularized problem to the original one. The proposed method is studied theoretically. Two numerical examples are provided to show the usefulness of the proposed method.
A Projected Inverse Dynamics Approach for Dual-arm Cartesian Impedance Control

We propose a method for dual-arm manipulation of rigid objects, subject to external disturbance. The problem is formulated as a Cartesian impedance controller within a projected inverse dynamics framework. We use the constrained component of the controller to enforce contact and the unconstrained controller to accomplish the task with a desired 6-DOF impedance behaviour. Furthermore, the proposed method optimises the torque required to maintain contact, subject to unknown disturbances, and can do so without direct measurement of external force. The techniques are evaluated on a single-arm wiping a table and a dual-arm platform manipulating a rigid object of unknown mass and with human interaction.
Image segmentation on adaptive sub-region smoothing

To improve the performance of the active contour segmentation on real images, a new segmentation method is proposed. In this model, we construct a function about Gaussian variance according to sub-regions intensity. Further, to avoid the curve vanishing, we design the convergence condition based on the confidence level of segmentation sub-regions. Experimental results show that the proposed method is less sensitive to noise and can suppress inhomogeneous intensity regions efficiently.
Neurofibromatosis type 1 and attention deficit hyperactivity disorder: a case study and literature review

Background: The cognitive profile of children with neurofibromatosis type 1 (NF1) and attention deficit hyperactivity disorder (ADHD) has been well characterized, but few studies have evaluated the cognitive abilities of adults with NF1 and ADHD. Objectives: We investigated 1) the cognitive profile of an adult patient with NF1 and inattention problems, 2) changes in his cognition after 14 months of follow-up, and 3) whether the patient exhibited comorbid NF1 and ADHD or secondary ADHD-like symptoms. Methods: We administered neuropsychological tests of executive function, attention, verbal and visual memory, visuospatial function, and language during two evaluations separated by 14 months. Results: We found no changes in sustained attention, language, or verbal memory. Visual memory, verbal learning, selective attention inhibitory control, and problem solving declined over time, whereas visual search, psychomotor speed, visuospatial function, and mental flexibility improved. Conclusion: Our patient exhibited a cognitive profile characteristic of both NF1 and ADHD, leading to the hypothesis that the patient had comorbid ADHD instead of secondary ADHD-like symptoms. More studies are necessary to characterize the cognition of patients with NF1 and ADHD.
Book Reviews

The trope of the ‘sucked oranges’, which was first used in the 1930s to describe the systematic exploitation of South Indian labourers on Malaya’s plantation frontier, has since inspired numerous scholarly works on the subject (Aiyar 1938: 22; Jain 1970; Wiebe and Mariappen 1978). Andrew Willford’s sensitive and detailed portrayal of Tamil plantation residents differs from these past approaches in its bold attempt to tell the story of these sucked oranges in their own words. It shifts the focus from labourers to residents, shedding new light on the community’s efforts to hold on to the last remaining vestiges of its identity amidst the Malaysian state’s relentless seizure of plantation lands to make way for development projects aimed at furthering Malay pride. This process, which began in the 1960s, acquired renewed momentum with the government’s announcement of ‘Vision 2020’ in 1991. Drawing on ethnographic fieldwork conducted primarily in the state of Selangor, the book’s value lies in its discussion of the ambivalence characterizing plantation residents’ responses to their displacement; their ability, for instance, to exist simultaneously in states of alienation and empowerment. Tamils and the Haunting of Justice picks up from Willford’s first book, Cage of Freedom: Tamil Identity and the Ethnic Fetish in Malaysia (2006), by elaborating on the violence of evictions and forced resettlements as well as the narratives of victimhood that these experiences elicited. The crux of the discussion is the looming question of what the future holds for the plantation residents, now robbed of the physical landscapes which had formerly given them at least their subaltern identity. As the residents of the last plantations struggle to reconcile their memories of a relatively peaceful past with the prospect of an uncertain future, the author presents this unsettling question for the reader’s urgent consideration. The discussion focuses on three states of displacement: communities facing a future of retrenchment and resettlement; those that had been reduced to ‘squatter’ status; and, finally, former plantation residents resettled in low-cost housing. Their responses to these tiers of displacement are explored over the course of ten chapters. At the outset, the author clarifies that his is not a historical focus, although he is interested in recovering a subaltern community’s sense of its history (p. 15). Chapter 2 begins the narrative with an overview of the politics of development in Malaysia and the implications for Tamil estate communities. Chapters 3 and 4 examine the related issues of nostalgia and compensation. These chapters’ discussion of the Tamils’ recourse to spirituality as a means of coping with TRaNS: Trans –Regional and –National Studies of Southeast Asia Vol. 4, No. 2 (July) 2016: 419–437. © Institute for East Asian Studies, Sogang University 2016
Enhanced carrier mobility of multilayer MoS2 thin-film transistors by Al2O3 encapsulation

We report the effect of Al2O3 encapsulation on the carrier mobility and contact resistance of multilayer MoS2 thin-film transistors by statistically investigating 70 devices with SiO2 bottom-gate dielectric. After Al2O3 encapsulation by atomic layer deposition, calculation based on Y-function method indicates that the enhancement of carrier mobility from 24.3 cm2 V−1 s−1 to 41.2 cm2 V−1 s−1 occurs independently from the reduction of contact resistance from 276 kΩ·μm to 118 kΩ·μm. Furthermore, contrary to the previous literature, we observe a negligible effect of thermal annealing on contact resistance and carrier mobility during the atomic layer deposition of Al2O3. These results demonstrate that Al2O3 encapsulation is a useful method of improving the carrier mobility of multilayer MoS2 transistors, providing important implications on the application of MoS2 and other two-dimensional materials into high-performance transistors.
Key-Skeleton-Pattern Mining on 3D Skeletons Represented by Lie Group for Action Recognition

The human skeleton can be considered as a tree system of rigid bodies connected by bone joints. In recent researches, substantial progress has been made in both theories and experiments on skeleton-based action recognition. However, it is challenging to accurately represent the skeleton and precisely eliminate noisy skeletons from the action sequence. This paper proposes a novel skeletal representation, which is composed of two subfeatures to recognize human action: static features and dynamic features. First, to avoid scale variations from subject to subject, the orientations of the rigid bodies in a skeleton are employed to capture the scale-invariant spatial information of the skeleton. The static feature of the skeleton is defined as a combination of the orientations. Unlike previous orientation-based representations, the orientation of a rigid body in the skeleton is defined as the rotations between the rigid body and the coordinate axes in three-dimensional space. Each rotation is mapped to the special orthogonal group SO(3). Next, the rigid-body motions between the skeleton and its previous skeletons are utilized to capture the temporal information of the skeleton. The dynamic feature of the skeleton is defined as a combination of the motions. Similarly, the motions are represented as points in the special Euclidean group SE(3). Therefore, the proposed skeleton representation lies in the Lie group (SE(3)×⋯×SE(3), SO(3)×⋯×SO(3)), which is a manifold. Using the proposed representation, an action can be considered as a series of points in this Lie group. Then, to recognize human action more accurately, a new pattern-growth algorithm named MinP-PrefixSpan is proposed to mine the key-skeleton-patterns from the training dataset. Because the algorithm reduces the number of new patterns in each growth step, it is more efficient than the PrefixSpan algorithm. Finally, the key-skeleton-patterns are used to discover the most informative skeleton sequences of each action (skeleton sequence). Our approach achieves accuracies of 94.70%, 98.87%, and 95.01% on three action datasets, outperforming other relative action recognition approaches, including LieNet, Lie group, Grassmann manifold, and Graph-based model.
Bathymetric Artifacts in Sea Beam Data: How to Recognize Them and What Causes Them

Sea Beam multibeam bathymetric data have greatly advanced understanding of the deep seafloor. However, several types of bathymetric artifacts have been identified in Sea Beam's contoured output. Surveys with many overlapping swaths and digital recording on magnetic tape of Sea Beam's 16 acoustic returns made it possible to evaluate actual system performance. The artifacts are not due to the contouring algorithm used. Rather, they result from errors in echo detection and processing. These errors are due to internal factors such as side lobe interference, bottom-tracking gate malfunctions, or external interference from other sound sources (e.g., 3.5 kHz echo sounders or seismic sound sources). Although many artifacts are obviously spurious and would be disregarded, some (particularly the “omega” effects described in this paper) are more subtle and could mislead the unwary observer. Artifacts observed could be mistaken for volcanic constructs, abyssal hill trends, hydrothermal mounds, slump blocks, or channels and could seriously affect volcanic, tectonic, or sedimentological interpretations. Misinterpretation of these artifacts may result in positioning errors when seafloor bathymetry is used to navigate the ship. Considering these possible geological misinterpretations, a clear understanding of the Sea Beam system's capabilities and limitations is deemed essential.
Robust and efficient unsupervised anomaly detection in complex and dynamic environments

Creating value from data is the ultimate goal of all sensing platforms within the Internet of Things (IoT). This value is derived from data analytics that can support effective and timely decision making for a variety of business applications. An important part of this process is anomaly detection, which is typically used for two main purposes. First, anomaly detection can be used to clean data prior to applying pattern classification or prediction techniques. This purpose is often referred to as outlier removal or data cleansing instead of anomaly detection. Second and more importantly, anomaly detection can be used to detect events that have practical impact, such as detecting intrusions or identifying faults. Anomaly detection for the second purpose has attracted considerable attention in the last decade to detect events with usually negative effects. Such events should be detected and dealt with as soon as possible to minimise their impact. The wide use of sensors that collect data from our smart-phones to advanced monitoring systems has created new applications, which introduce new challenges for machine learning and data mining techniques. The sensor data is being generated at increasingly higher rates and in higher dimensional data streams. In this context, the unavailability of labelled training data, changes in normal behaviour over time, and time and memory constraints demand new methods for unsupervised anomaly detection. In this thesis, we look at the challenges for anomaly detection in this context, and propose novel methods to cope with these emerging challenges. We first motivate the need for unsupervised model-based anomaly detection and provide a comprehensive survey of methods from this view. We then enhance a widely used semi-supervised model-based anomaly detection technique to be able to perform unsupervised anomaly detection in a robust and efficient manner. This method builds
Private Stochastic Convex Optimization: Optimal Rates in (cid:96) 1 Geometry

Stochastic convex optimization over an (cid:96) 1 - bounded domain is ubiquitous in machine learning applications such as LASSO but remains poorly understood when learning with differential privacy. We show that, up to logarithmic factors the optimal excess population loss of any ( ε, δ ) -differentially private optimizer is (cid:112) log( d ) /n + √ d/εn. The upper bound is based on a new algorithm that combines the iterative localization approach of Feldman et al. (2020a) with a new analysis of private regularized mirror descent. It applies to (cid:96) p bounded domains for p ∈ [1 , 2] and queries at most n 3 / 2 gradients improving over the best previously known algorithm for the (cid:96) 2 case which needs n 2 gradients. Further, we show that when the loss functions satisfy additional smoothness assumptions, the excess loss is upper bounded (up to logarithmic factors) by (cid:112) log( d ) /n + (log( d ) /εn ) 2 / 3 . This bound is achieved by a new variance-reduced version of the Frank-Wolfe algorithm that requires just a single pass over the data. We also show that the lower bound in this case is the minimum of the two rates mentioned above.
Approximating multivariate posterior distribution functions from Monte Carlo samples for sequential Bayesian inference

An important feature of Bayesian statistics is the possibility to do sequential inference: the posterior distribution obtained after seeing a first dataset can be used as prior for a second inference. However, when Monte Carlo sampling methods are used for the inference, we only have one set of samples from the posterior distribution, which is typically insufficient for accurate sequential inference. In order to do sequential inference in this case, it is necessary to estimate a functional description of the posterior probability from the Monte Carlo samples. Here, we explore whether it is feasible to perform sequential inference based on Monte Carlo samples, in a multivariate context. To approximate the posterior distribution, we can use either the apparent density based on the sample positions (density estimation) or the relative posterior probability of the samples (regression). Specifically, we evaluate the accuracy of kernel density estimation, Gaussian mixtures, vine copulas and Gaussian process regression; and we test whether they can be used for sequential Bayesian inference. Additionally, both the density estimation and the regression methods can be used to obtain a post-hoc estimate of the marginal likelihood. In low dimensionality, Gaussian processes are most accurate, whereas in higher dimensionality Gaussian mixtures or vine copulas perform better. We show that sequential inference can be computationally more efficient than joint inference, and we also illustrate the limits of this approach with a failure case. Since the performance is likely to be case-specific, we provide an R package mvdens that provides a unified interface for the density approximation methods.
Dynamic Coverage Verification in Mobile Sensor Networks via Switched Higher Order Laplacians

In this paper, we study the problem of verifying dynamic coverage in mobile sensor networks using certain switched linear systems. These switched systems describe the flow of discrete differential forms on time-evolving simplicial complexes. The simplicial complexes model the connectivity of agents in the network, and the homology groups of the simplicial complexes provides information about the coverage properties of the network. Our main result states that the asymptotic stability the switched linear system implies that every point of the domain covered by the mobile sensor nodes is visited infinitely often, hence verifying dynamic coverage. The enabling mathematical technique for this result is the theory of higher order Laplacian operators, which is a generalization of the graph Laplacian used in spectral graph theory and continuous-time consensus problems.
The Detour Polynomials of Ladder Graphs

The detour distance (,) Duv between two distinct vertices u and v of a connected graph G is the length of a longest uv - path in G. The detour index () ddG of G is defined by { ,} (,) uv Duv ∑ , and the detour polynomial of G is (,)