Miracle-dz/UniFAR-FTUs huggingface dataset:
Volume-Enhanced Compatible Remeshing of 3D Models

Compatible remeshing provides meshes with common connectivity structures. The existing compatible remeshing methods usually suffer from high computational cost or poor quality. In this paper, we present a fast method for computing compatible meshes with high quality. Given two closed, oriented, and topologically equivalent surfaces and a sparse set of corresponding landmarks, we first compute a bijective inter-surface mapping, from which compatible meshes are generated. We then improve the remeshing quality by using a volume-enhanced optimization. In contrast to previous work, our method designs a fast volume-enhanced improvement procedure that directly reduces the isometric distortion of the map between the compatible meshes. Our method also tries to preserve the shapes of the input meshes by projecting the vertices of the compatible meshes onto the input surfaces. Central to this approach is the use of the monotone preconditioned conjugate gradient method, which minimizes the energies effectively and efficiently. Compared with state-of-the-art methods, our method performs about one order of magnitude faster with better remeshing quality. We demonstrate the efficiency and efficacy of our method using various model pairs.
Dual-Linearly Polarized, Electrically Small, Low-Profile, Broadside Radiating, Huygens Dipole Antenna

A dual-linearly polarized, electrically small, low-profile, broadside radiating Huygens dipole antenna is presented, that is, an advanced combination of electric and magnetic near-field resonant parasitic elements. Its prototype was fabricated and tested. The measured results are in good agreement with their simulated values. At 1.515 GHz, the prototype is electrically small (<inline-formula> <tex-math notation="LaTeX">$ka = 0.904$ </tex-math></inline-formula>) and low profile (<inline-formula> <tex-math notation="LaTeX">$0.0483\lambda _{0}$ </tex-math></inline-formula>). It exhibits high port isolation and a large front-to-back ratio (FTBR). The isolation between its two ports is demonstrated to be over 25.8 dB within its −10 dB fractional impedance bandwidth, 0.46%. When port 1 (port 2) is excited, the peak realized gain is 2.03 dBi (2.15 dBi) strictly along the broadside direction with a 12.4 dB (12.1 dB) FTBR.
LALCA: Locality-Aware Lock Contention Avoidance for NVMe-Based Scale-out Storage System

Flash-based NVMe storage devices dramatically improve I/O latency as well as I/O throughput. However, existing scale-out storage systems are designed for targeting hard disk drives and this design limitation causes significant performance degradation when they are used with NVMe devices. In this paper, we analyzed the performance of an existing scale-out storage system and identified lack of data locality and excessive lock contentions. To mitigate these problems, we present a new design based on locality aware lock contention avoidance (LALCA). LALCA proposes two techniques for scale-out storage system on high speed storage device: (1) locality-aware thread control to minimize processor context switching overhead and relevant performance degradation and (2) lock contention avoidance to remove locking problems in existing scale-out distributed storage system. With evaluation, LALCA shows not only up to 9 times performance improvement but also up to half CPU usage reduction when servicing small random I/Os.
Robust MISO Control of Propofol-Remifentanil Anesthesia Guided by the NeuroSENSE Monitor

This paper describes the design and evaluation of a controller for multi-input single-output (MISO) propofol-remifentanil anesthesia, guided by feedback from a measure of depth-of-hypnosis (DOH). DOH monitors are commonly used in clinical practice to guide anesthetic dosing, however, there is currently no widely accepted nociception monitor to guide remifentanil (analgesic) infusion. Variability in the DOH measure has been associated with insufficient analgesia, and feasibility of closed-loop control of both propofol and remifentanil infusion using DOH feedback has been demonstrated. However, DOH variability does not provide a measure of analgesia in the absence of stimulation. Consequently, control of the opioid-hypnotic balance is lost in control systems relying on DOH feedback alone. The proposed design overcomes this limitation by introducing a second, indirect control objective. This paper defines clinical design specifications to achieve adequate anesthesia in a wide range of clinical cases, proposes a modification of the habituating control framework, and presents methods to translate the clinical objectives into control objectives within this framework. The developed design methodology provides a controller that: 1) meets the clinical objectives; 2) is robust to interpatient variability, both in single-input single-output and MISO operation; 3) is robust to nonlinear drug interactions; 4) gives the user control of the opioid-hypnotic balance in the absence of stimulation and in the presence of input saturation; and 5) improves disturbance rejection following nociceptive stimulation. The MISO system performed as designed in 80 clinical cases.
Towards Smart Work Clothing for Automatic Risk Assessment of Physical Workload

Work-related musculoskeletal and cardiovascular disorders are still prevalent in today’s working population. Nowadays, risk assessments are usually performed via self-reports or observations, which have relatively low reliability. Technology developments in textile electrodes (textrodes), inertial measurement units, and the communication and processing capabilities of smart phones/tablets provide wearable solutions that enable continuous measurements of physiological and musculoskeletal loads at work with sufficient reliability and resource efficiency. In this paper, a wearable system integrating textrodes, motion sensors, and real-time data processing through a mobile application was developed as a demonstrator of risk assessment related to different types and levels of workload and activities. The system was demonstrated in eight subjects from four occupations with various workload intensities, during which the heart rate and leg motion data were collected and analyzed with real-time risk assessment and feedback. The system showed good functionality and usability as a risk assessment tool. The results contribute to designing and developing future wearable systems and bring new solutions for the prevention of work-related disorders.
Lossy Compression for Embedded Computer Vision Systems

Computer vision applications are rapidly gaining popularity in embedded systems, which typically involve a difficult tradeoff between vision performance and energy consumption under a constraint of real-time processing throughput. Recently, hardware (FPGA and ASIC-based) implementations have emerged, which significantly improves the energy efficiency of vision computation. These implementations, however, often involve intensive memory traffic that retains a significant portion of energy consumption at the system level. To address this issue, we are the first researchers to present a lossy compression framework to exploit the tradeoff between vision performance and memory traffic for input images. To meet various requirements for memory access patterns in the vision system, a line-to-block format conversion is designed for the framework. Differential pulse-code modulation-based gradient-oriented quantization is developed as the lossy compression algorithm. We also present its hardware design that supports up to 12-scale 1080p@60fps real-time processing. For histogram of oriented gradient-based deformable part models on VOC2007, the proposed framework achieves a 49.6%–60.5% memory traffic reduction at a detection rate degradation of 0.05%–0.34%. For AlexNet on ImageNet, memory traffic reduction achieves up to 60.8% with less than 0.61% classification rate degradation. Compared with the power consumption reduction from memory traffic, the overhead involved for the proposed input image compression is less than 5%.
Electrochemical Sensing of Biotin Using Nafion-Modified Boron-Doped Diamond Electrode

Nafion formed on the surface of a boron-doped diamond electrode allows for a chemosensing system for biotin. The modified electrode is capable of oxidizing biotin and offers a detection limit of 5 nM, the average normal level of biotin in blood plasma. The developed method was successfully applied to determine biotin in human plasma samples and a popular health product as two popular models.
TNT: A Solver for Large Dense Least-Squares Problems that Takes Conjugate Gradient from Bad in Theory, to Good in Practice

Since its inception by Gauss, the least-squares problem has frequently arisen in science, mathematics, and engineering. Iterative methods, such as Conjugate Gradient Normal Residual (CGNR), have been popular for solving sparse least-squares problems, but have historically been regarded as undesirable for dense applications due to poor convergence. We contend that this traditional "common knowledge" should be reexamined. Preconditioned CGNR, and perhaps other iterative methods, should be considered alongside standard methods when addressing large dense least-squares problems. In this paper we present TNT, a dynamite method for solving large dense least-squares problems. TNT implements a Cholesky preconditioner for the CGNR fast iterative method. The Cholesky factorization provides a preconditioner that, in the absence of round-off error, would yield convergence in a single iteration. Through this preconditioner and good parallel scaling, TNT provides improved performance over traditional least-squares solvers allowing for accelerated investigations of scientific and engineering problems. We compare a parallel implementation of TNT to parallel implementations of other conventional methods, including the normal equations and the QR method. For the small systems tested (15000 15000 or smaller), it is shown that TNT is capable of producing smaller solution errors and executing up to 16 faster than the other tested methods. We then apply TNT to a representative rock magnetism inversion problem where it yields the best solution accuracy and execution time of all tested methods.
Content-driven, unsupervised clustering of news articles through multiscale graph partitioning

The explosion in the amount of news and journalistic content being generated across the globe, coupled with extended and instantaneous access to information through online media, makes it difficult and time-consuming to monitor news developments and opinion formation in real time. There is an increasing need for tools that can pre-process, analyse and classify raw text to extract interpretable content; specifically, identifying topics and content-driven groupings of articles. We present here such a methodology that brings together powerful vector embeddings from Natural Language Processing with tools from Graph Theory that exploit diffusive dynamics on graphs to reveal natural partitions across scales. Our framework uses a recent deep neural network text analysis methodology (Doc2vec) to represent text in vector form and then applies a multi-scale community detection method (Markov Stability) to partition a similarity graph of document vectors. The method allows us to obtain clusters of documents with similar content, at different levels of resolution, in an unsupervised manner. We showcase our approach with the analysis of a corpus of 9,000 news articles published by Vox Media over one year. Our results show consistent groupings of documents according to content without a priori assumptions about the number or type of clusters to be found. The multilevel clustering reveals a quasi-hierarchy of topics and subtopics with increased intelligibility and improved topic coherence as compared to external taxonomy services and standard topic detection methods.
An Optimization Formulation of Converter Control and Its General Solution for the Four-Leg Two-Level Inverter

This brief proposes an optimization formulation of the control problem for power electronic converters. A benefit of the approach is a systematic method for the control of high-switch-count static converters. In the case of the three-phase, four-leg, two-level inverter, the framework provides a characterization of all the possible solutions that yield a maximal extension of the inverter linearity range. The method makes it possible to recover well-known modulation strategies as well as to discover some new ones having different properties and distinct advantages. The characteristics resulting from different design choices are evaluated in simulations, with consideration being given to the linearity range, total harmonic distortion, and switching losses. Key principles of extension of the proposed method to multilevel, multileg converters are given, as well as motivations for a field-programmable gate array-based hardware implementation enabling real-time pulsewidth modulation control.
Motion-triggered video cameras reveal spatial and temporal patterns of red fox foraging on carrion provided by mountain lions

Carrion is a rich, ephemeral resource vital to biodiversity and ecosystem health. In temperate ecosystems in which cold temperatures and snowfall influence the accessibility and availability of small prey and seasonal mast crops, carrion may also be a limiting resource for mesocarnivores like red foxes (Vulpes vulpes), which are too small to predate ungulates. Using motion-triggered video cameras and generalized linear mixed models, we studied the spatial and temporal patterns of red fox scavenging at 232 mountain lion kills in the southern Greater Yellowstone Ecosystem (GYE) from 2012–2015. We found that red foxes scavenged mountain lion kills across all habitats throughout the year, however, red fox behaviors varied with season. In winter, we documented red foxes at a greater proportion of mountain lion kills (70.3% in winter vs. 48.9% in summer), and in greater numbers (1.83 foxes per kill in winter vs. 1.16 in summer). In winter, red foxes fed longer (= 102.7 ± 138.3 minutes feeding in winter vs. = 39.7 ± 74.0 in summer), and they more often scavenged while the mountain lion was nearby. We speculated that red foxes may have increased risk taking in winter due to hunger driven by resource scarcity. Our research highlighted an important ecological relationship between red foxes and mountain lions in the GYE. Mountain lions tolerate high levels of scavenging, so the frequency and intensity of red fox scavenging at their kills may not impact mountain lions, but instead facilitate the dispersion and benefits of resources created by this apex predator. Large carnivores, and mid-trophic felids like mountain lions in particular, are essential producers of carrion vital to biodiversity and ecosystem health. In turn, scavengers play critical roles in distributing these resources and increasing the heterogeneity of resources that support biodiversity and ecosystem structure, as well as ecological resilience.
The Neglected Contribution of Memory Encoding in Spatial Cueing: A New Theory of Costs and Benefits

Spatial cueing is thought to indicate the resource limits of visual attention because invalidly cued items are reported more slowly and less accurately than validly cued items. However, limited resource accounts cannot explain certain findings, such as dividing attention without costs, or attentional benefits without invalidity costs. The current study presents a new account of exogenous cueing, namely the memory encoding cost (MEC) theory, which integrates attention and memory encoding to explain costs and benefits evoked by a spatial cue. Unlike conventional theories that focus on the role of attention in yielding spatial cueing effects, the MEC theory argues that some cueing effects are caused by a combination of attentional facilitation evoked by the cue, but also the cost of encoding the cue into memory. The crucial implication of this theory is that limitations in attentional deployment may not necessarily be the cause of invalidity costs. MEC generates a number of predictions that we test here, providing five convergent lines of evidence that cue encoding plays a key role in producing cueing effects. Furthermore, the MEC suggests a common mechanism underlying cueing costs and the attentional blink, and we simulate the core empirical findings of the current study with an existing attentional blink model. The model was able to simulate these effects primarily through manipulation of a single parameter that corresponds to memory encoding. The MEC theory thus simplifies our theoretical understanding of attentional effects by linking the attentional blink and some varieties of spatial cueing costs to a common mechanism.
Algebraic Multigrid Solver Using Coarse Grid Aggregation with Independent Aggregation

The algebraic multigrid (AMG) method is known to be one of the most efficient linear solvers, as it has O(n) complexity and also offers domain parallelism. However, its coarse grids sometimes become more unstructured than the finer grids, which leads to performance degradation, especially when AMG is executed on highly parallel machines. Here, we propose the Coarse Grid Aggregation (CGA) approach that doesn't need coarser level matrix re-distribution. Our approach actually connects process domains using information of edges between process domains. It can adjusts the degree of parallelization for coarser grids by changing the interpolation matrix P and the block row widths of the coarser level matrix. We propose independent aggregation with CGA to enhance the performance of AMG by up to a factor of 3 for the Poisson equation with varying heterogeneous diffusion coefficients. The performance is evaluated on a Fujitsu-FX10 that has more than four thousand nodes consisting of 16-core SPARC64 IXfx CPU. In addition, we check the performance behavior depending on the CGA parameter that is the number of unknowns to be left on one process domain at coarser levels, and consider the tuning of this parameter.
Novel Types of Hypermodified Fluorescent Phyllobilins from Breakdown of Chlorophyll in Senescent Leaves of Grapevine (Vitis vinifera)

Abstract The tetrapyrrolic chlorophyll catabolites (or phyllobilins, PBs) were analyzed in yellow fall leaves of the grape Chardonnay, a common Vitis vinifera white wine cultivar. The major fractions in leaf extracts of V. vinifera, tentatively assigned to PBs, were isolated and their structures elucidated. The dominant fraction is a dioxobilin‐type non‐fluorescent Chl‐catabolite of a previously observed type. Two less polar fluorescent PBs were characterized as a novel dioxobilin‐type fluorescent Chl‐catabolite with a bicyclo‐1′,6′‐glycosyl architecture, and its new fluorescent formyloxobilin‐type analogue. The discovery of persistent hypermodified fluorescent PBs with the architecture of bicyclo‐[17.3. 1]‐PBs (bcPBs), suggests the activity of an unknown enzyme that forges the 20‐membered macroring at the tetrapyrrolic core of a fluorescent PB. bcPBs may play specific physiological roles in grapevine plants and represent endogenous anti‐infective agents, as found similarly for other organic bicyclo‐[n.3. 1]‐1′,6′‐glycosyl derivatives.
Performance Analysis of Non-Orthogonal Multiple Access With Underlaid Device-to-Device Communications

Non-orthogonal multiple access (NOMA) and device-to-device (D2D) communications are promising technologies for 5G cellular networks. In this paper, we investigate the impact of the integration of D2D communications with a downlink NOMA system. In a cluster of two cellular users and a D2D pair, we extract power control strategies for the base station (BS), which allow for cellular users to achieve a higher sum rate and higher individual rates in NOMA compared to an orthogonal multiple access (OMA). The D2D link has the same rate in both OMA and NOMA systems in our proposed scheme. We further derive the probability that both users obtain higher rates in NOMA under a fixed power control strategy. We provide numerical results that illustrate the performance of the proposed power control policies and demonstrate the accuracy of the derived closed-form expression for the probability that both cellular users obtain higher rates in NOMA. We also study the effects of the BS transmit power level and the power portion assigned to each user on the probability that both users outperform in NOMA through numerical results.
A Conceptual Model of E-learning Systems Success and Its Implication for Future Research

Institutions in developing countries are adopting the exact replica of e-learning approaches used by that of developed countries with the expectations of reaping the same benefits enjoyed by institutions in developed countries. If used effectively, e-learning can provide numerous benefits to the institutions such as accessing education at any time and place, enhances interactivity, scalability, flexibility, and thereby improved student’s academic performance through creating a supportive and conducive e- learning environment for learning [1]. However, studies show that the majority of these benefits are enjoyed by institutions in the developed countries than the developing once [2]. In Ethiopian higher level educational institutions, e-learning is in an incipient phase. There are only a few institutions that use e-learning in the teaching and learning process making its effect on a larger scale immeasurable and unknown. In spite of large amount of investments for e-learning systems by Ethiopian universities, many of these e-learning initiatives are not meeting their desired objectives. With the objective of exploring success factors for e-learning systems and develop a conceptual model for e-learning systems success in Ethiopian universities and colleges, the researchers conducted a qualitative case study in 12 Ethiopian higher level educational institutions adopting e-learning systems in their teaching and learning process. Focus group interviews, in-depth personal interviews, document analysis and observation were used to triangulate the findings. We used open coding as a main data analysis technique. NVivo 11 was used to quantitatively analyze the qualitative data. The result is a proposed conceptual model of e-learning systems success and its implications for future research. This model is comprised of eight major e-learning systems success categories and twenty-nine sub-categories. The model will facilitate both academic and practitioner interest in measuring e-learning systems in developing countries and serve as a framework for further empirical research.