Miracle-dz/UniFAR-FTUs huggingface dataset:
Multicenter Validation Of Population-Based Input Function With Non-Linear Mixed Effect Modeling For Voxel-Wise Quantification Of [18F]Fdg Metabolic Rate

Population-based input function (PBF) methods provide a less-invasive approach to the quantification of dynamic positron emission tomography (PET) images. PBF methods require the a priori creation of an input function template from a group of subjects who underwent full arterial blood sampling with the same radiotracer. The template is then calibrated using one or two blood samples from the subject under analysis. In this study we propose to generate the PBF template from a group of 8 subjects using a non-linear mixed effect approach and a new input function model. We validated our PBF approach using an independent[18F] FDG dataset of 25 subjects acquired in a different PET center. Results showed a high correlation (> 0.98) and low bias (mean percentage error=1.0 ± 3.1%) between the voxel-wise estimates of [18F] FDG net uptake rate (Ki) obtained with the measured input function and those obtained with the proposed PBF, supporting its use for the quantification of [18F] FDG images acquired in different PET centers.
Automatic Radiographic Quantification of Joint Space Narrowing Progression in Rheumatoid Arthritis Using POC

This paper is an application of image processing techniques for computer-aided diagnosis of Rheumatoid Arthritis (RA). Accurately measuring the progression of joint space narrowing (JSN) is crucial during medical treatment and in imaging biomarkers in clinical trials. In this paper, we analyze sequential radiographic images of patients who have rheumatoid arthritis in hands using image processing techniques. Phase only correlation (POC) is used to detect the progression of JSN between images. A new image processing algorithm is proposed to segment joint images so as to eliminate the mutual interference when measuring the movement of the upper and lower bones by POC. We found that the texture feature on bones will greatly affect the accuracy of POC. Median filter is used to eliminate the effect of texture, and excellent results are obtained in practice. Additionally, the progress of JSN is measured accurately in our method. This can be beneficial for doctors in the identification of disease stages.
Your Fellows Matter: Affect Analysis across Subjects in Group Videos

Automatic affect analysis has become a well established research area in the last two decades. Recent works have started moving from individual to group scenarios. However, little attention has been paid to investigating how individuals in a group influence the affective states of each other. In this paper, we propose a novel framework for cross-subjects affect analysis in group videos. Specifically, we analyze the correlation of the affect among group members and investigate the automatic recognition of the affect of one subject using the behaviours expressed by another subject in the same group. A set of experiments are conducted using a recently collected database aimed at affect analysis in group settings. Our results show that (1) people in the same group do share more information in terms of behaviours and emotions than people in different groups; and (2) the affect of one subject in a group can be better predicted using the expressive behaviours of another subject within the same group than using that of a subject from a different group. This work is of great importance for affect recognition in group settings: when the information of one subject is unavailable due to occlusion, head/body poses etc., we can predict his/her affect by employing the expressive behaviours of the other subject(s).
An AOA assisted TOA positioning system

In this paper, time of arrival (TOA) and angle of arrival (AOA) errors in four typical cellular environments are analyzed and modeled. Based on the analysis, a hybrid TOA/AOA positioning (HTAP) algorithm, which utilizes TOA and AOA information delivered by serving base stations (BS), is proposed. The performance of the related positioning algorithms is simulated. It is shown that when the MS is close to the serving BS, HTAP will produce an accurate location estimate. When MS is far from the serving BS, the location estimate obtained by HTAP can be used as an initial location in our system to help a least square (LS) algorithm converge easily. When there are more than three TOA detected, weights and TOA numbers used in the LS algorithm should be dynamically adjusted according to the distance between MS and serving BS and the propagation environment for better positioning performance.
Nonlinear Multi-Body Dynamic Modeling and Coordinated Motion Control Simulation of Deep-Sea Mining System

In this paper, a nonlinear integrated multi-body dynamic (MBD) model of a deep-sea mining (DSM) system was developed. A particular nonlinear interaction model between the mining vehicle and the seabed sediment was derived. The fluid resistance and the resistance torque acting on the vehicle were obtained by the computational fluid dynamics (CFD) numerical simulations. In addition, a rigid-flexible coupled discrete element method (DEM) was adopted to model an extremely long mining riser efficiently and accurately. Developed by the C# program, a user-defined subprogram was intended to establish the DEM model of the riser. A motion control system model for trajectory tracking of the vehicle was developed in which the fuzzy control algorithm and the fuzzy adaptive PID control algorithm were used to control the speed and speed ratio of the tracks, respectively. Furthermore, the collaborative simulation between the dynamic model and the control model was achieved, and a coordinated motion mode for the total mining system was proposed and simulated. Accordingly, the trajectory tracking accuracy and slip of the vehicle, the spatial state change of the riser, and the interaction forces among subsystems were analyzed. This paper can provide a valuable theoretical basis and technical reference for the integrated design, performance prediction, and operation control of the practical DSM system.
Parallel Optimization of Fiber Bundle Segmentation for Massive Tractography Datasets

We present an optimized algorithm that performs automatic classification of white matter fibers based on a multi-subject bundle atlas. We implemented a parallel algorithm that improves upon its previous version in both execution time and memory usage. Our new version uses the local memory of each processor, which leads to a reduction in execution time. Hence, it allows the analysis of bigger subject and/or atlas datasets. As a result, the segmentation of a subject of 4,145,000 fibers is reduced from about 14 minutes in the previous version to about 6 minutes, yielding an acceleration of 2.34. In addition, the new algorithm reduces the memory consumption of the previous version by a factor of 0.79.
Primary Workshopping Ideas for Media Literacy

Children at the Armadale Primary School increase their confidence and ability to understand the complexity of written texts by approaching the teaching of form and structure through visual avenues, such as television. Their knowledge and comprehension in the area of Viewing, readily transfers to the written text and they can quickly see similar patterns between visual texts and those in written stories and non-fiction pieces.
A Scheduling Method of Moldable Parallel Tasks Considering Speedup and System Load on the Cloud

The moldable parallel task (MPT) is a kind of parallel task that their sub-tasks hold the resources exclusively, which has been widely used in different areas. Our paper focuses on the scheduling of moldable tasks when every sub-task supports time-slice. The time-slice is a consecutive time that the sub-task holds the resources exclusively. After every time-slice, the sub-task can be canceled, suspended, or continued. We give the model of MPTs and propose a scheduling method for MPTs: MC (a heuristic scheduling method supporting time-slice model on the cloud). The simulation results show that, even under a forecast accuracy of system load under 90% and 95%, MC reduces average waiting time and average execution time; at the same time, MC has a lower value in the percentages of unfinished tasks.
Barrier-enhanced InGaAs/InAlAs photodetectors using quantum-well intermixing

A thin barrier-enhancement region on quantum well InGaAs/InAlAs MSM photodetector is incorporated in between the two-dimensional quantum channel and the three-dimensional metal contact. One-dimensional and uniform strong electric field throughout the transport direction in the quantum channel is obtained by contacting this quantum channel laterally. This region is modeled by a quantum mechanical boundary condition in the numerical carrier transport simulation. Effects of this thin boundary on InGaAs/InAlAs MSM photodetector dark and transient current responses are presented.
Temperature Estimation with Time Series Analysis from Air Quality Data Set

With the expansion of the data size, data mining techniques are gaining more and more importance. Data mining consists of methods such as classification, clustering, time series estimation and association rule. In this study, a time series analysis is carried out in order to make an estimation for the future in accordance with the structure of the data set. Time series are series in which the variables are recorded in chronological order. The data set was created by recording the gas concentrations in the air at a time interval. These data are used to estimate the changes in air quality. Three types of time series analysis training algorithm are used in the study. The results given by the algorithms are close to each other and high performance has been determined. As a result of experimental studies, it is observed that time series analysis is sufficient to estimate air quality.
Interactive ray tracing on reconfigurable SIMD MorphoSys

MorphoSys is a reconfigurable SIMD architecture. In this paper, a BSP-based ray tracing is gracefully mapped onto MorphoSys. The mapping highly exploits ray-tracing parallelism. A straightforward mechanism is used to handle irregularity among parallel rays in BSP. To support this mechanism, a special data structure is established, in which no intermediate data has to be saved. Moreover, optimizations such as object reordering and merging are facilitated. Data starvation is avoided by overlapping data transfer with intensive computation so that applications with different complexity can be managed efficiently. Since MorphoSys is small in size and power efficient, we demonstrate that MorphoSys is an economic platform for 3D animation applications on portable devices.
Comparison of discrete and continuous HMMs in a CSR task over the telephone

Attention is given to a comparison of the performance of discrete and continuous density hidden Markov models (DDHMMs and CDHMMs) on a 786-word E-mail inquiry task performed by the speaker-independent word recognition component of a speech understanding system. This comparison between DDHMMs and CDHMMs has also been carried out by training speaker-dependent models. The authors also present the results of a set of experiments carried out with the aim at automatically selecting a suitable set of subword unit models by a clustering procedure. The recognizer gives word accuracy (WA) rates of 67.8% and 75.3% by using DDHMMs and CDHMMs, respectively, without any linguistic constraints. On the same task, WA rates of 87.1% and 85.9% have been obtained in the speaker-dependent mode. <<ETX>>
Optimizing Contextual Feature Learning for Mitosis Detection with Convolutional Recurrent Neural Networks

Automatic detection of mitosis in cell videos is essential for research in many fields including stem cell biology and pharmacology. Current state-of-the-art graph-based and deep learning models for mitosis detection rely on candidate sequence extraction that locates the mitotic events at the center of the input frame for optimal contextual feature learning. We propose a method to detect mitosis, by extending convolutional long short-term memory (LSTM) neural networks to remove the candidate sequence extraction step. Our method maintains a high detection accuracy by using the entire video frames as the input, instead of small crops from the original frames and this, acts to preserve the complete contextual features of mitotic events. We evaluated our method on a dataset of stem cell phase-contrast microscopy videos. Under conditions of a temporal tolerance of 1 and 3 frames, our method achieved a detection F1-score of 0.880 and 0.911, which outperformed state-of-the-art benchmark methods by approximately 0.15 in F1-score.
An Effective Order-Aware Hybrid Genetic Algorithm for Capacitated Vehicle Routing Problems in Internet of Things

The Internet of Things integrates lots of capacitated vehicles in smart logistics. The routing for capacitated vehicles is a combinatorial optimization problem which has been widely studied in recent years. This paper proposes an effective order-aware hybrid genetic algorithm for the capacitated vehicle routing problem in the Internet of Things. The method is characterized by an improved initialization strategy and a problem-specific crossover operator. The former combines the sweep algorithm with randomness to harmonize the contradiction between diversity and convergence, while the latter integrates neighborhood search heuristics to find the offspring with the best fitness and check constraints simultaneously. A large number of simulations have been carried out, and the results validated the effectiveness of our algorithm.
Performance Analysis for MmWave MIMO-SCMA Systems using Lens Antenna Array

The recent concept of beamspace multiple input multiple output (MIMO) can significantly reduce the number of required radio frequency (RF) chains in millimeter wave (mmWave) massive MIMO systems without obvious performance loss. However, the number of supported users cannot be larger than the number of RF chains in hybrid MIMO architectures. To break this fundamental limit, we introduce the concept of sparse code multiple access (SCMA) with beamspace MIMO, then a useful upper bound on the average symbol error probability is obtained through the union bound. The bound is tight in beamspace mmWave MIMO-SCMA systems for high SNR regions. The analytical result is compared with simulations, and the results confirm the effectiveness of the analysis for mmWave MIMO-SCMA channels.
How do practising teachers understand creativity

This research sought to explore teachers' views, in terms of the assertion that creative development is important within the national curriculum. It aimed to identify the extent to which creative development is supported within the current curriculum and whether more should be done to encourage and promote its practice. Finally, it investigates the potential barriers that appear to restrict creative development in schools in the UK. Existing literature covering multiple facets of creativity, such as how academics define creativity, establishing whether it can be taught and identifying whether there is a current 'teach to test' culture, was explored and the literature review highlighted that previous research into the development of creativity in schools has often failed to consider the perspectives of teachers. Consequently, the current research sought to address this by gaining the views of teachers across the UK. The results showed that 88% of respondents viewed creative development as either 'very important' or 'extremely important' and 95% of teachers believed that the national curriculum should do more to encourage creative practice. Finally, 71% of all participants asserted that the largest barriers to creative development were limited teaching time and excessive exam pressures.
Molecular Dynamic Simulation of the Effect of Nanocoating on Two-Phase Evaporative Heat and Mass Transfer

Two-phase cooling such as thin film evaporation is becoming increasingly popular for thermal management of high powered electronics due to the high latent heat associated with the phase change process. Nanoengineered surfaces have been shown to improve two-phase heat transfer performance through enhanced wettability and reduced interfacial thermal resistance. However, how interfacial resistance varies with surface wettability and how such resistance can affect thin-film evaporative transport is still not well understood. In this study, we investigate the evaporative transport characteristics and wetting state of an evaporating thin liquid film on both smooth and nanocoated surfaces using Molecular Dynamics (MD)simulations. The surface wettability between liquid argon and silicon (100) surface coated with 0, 1, and 3 layers of graphene is characterized using equilibrium molecular dynamics methods. The associated interfacial thermal resistances and the evaporation rates are explored using non-equilibrium molecular dynamics methods, in which a hot and cold solid substrate are implemented to facilitate the evaporation and condensation of liquid argon molecules.
4 perspectives on Web information systems

In recent literature, many authors argue that "Web development" is new and different. The arguments are, however, both debatable and debated, and often it is neither clear what is meant by Web development, nor what it is supposed to be different from. Often-suggested special characteristics of Web development do not seem special after all. The author presents a model describing four archetypical perspectives on Web information systems; the perspectives being characterized by different views on what the system is intended to communicate and on the direction of the communication. The author argues that these perspectives (information provider, information system, advertisement, and community) can explain some of the quite opposite opinions on Web development one can find in the literature.
An ultra low phase noise W-band GaAs-based PHEMT MMIC CPW VCO

A W-band voltage control oscillator (VCO) using 0.1m AlGaAs/InGaAs/GaAs PHEMT MMIC technology with ultra low phase noise is presented. This VCO demonstrated an operation frequency centered at 97 GHz with a tuning range of 2GHz and an output power of 1 mW. The measured single side-band phase noise is -88 dBc/Hz at 1 MHz offset. To the best of our knowledge, this phase noise performance is not only the best among the previously reported results for HEMT MMIC VCO at this frequency, but also rivals those for most VCOs using HBTs.
Parallel molecular dynamics on a torus network

The paper presents some theoretical results concerning molecular dynamics simulations on parallel networks. Specifically, it gives rules which, depending on the system to be simulated and on the processor network, gives the optimal mapping for a class of algorithms. It also shows that multi-particle potentials can efficiently be implemented when geometric parallelism is used. The paper demonstrates the approach by showing some results of simulations of water/oil/surfactant and of polymer systems on a toroidal network of transputers. Furthermore, it compares timing results of some simulations performed on this network with those performed on a Cray single-processor machine. <<ETX>>
Towards Understanding the Challenges Facing Effective Trust-Aware Recommendation

a method for generating semi-synthetic social data collections, which we use to study trust-aware recommendation. Specifically, we examine the effects of social graph degree distri- bution on user-based collaborative filtering that substitutes trusted users for conventional neighbors. Our semi-synthetic data collec- tions are created via a naive pruning process that maps a user-item matrix onto various social graphs with the degree distributions of real-world Web-based social systems. Our goal is to extend our understanding of the challenges facing effective trust-aware rec- ommendation beyond the current possibilities, which are limited by data set availability. The improvement offered by trust-aware recommendation is shown to have substantial dependence on the degree distribution of the social graph.
Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints

We study reinforcement learning (RL) with linear function approximation under the adaptivity constraint. We consider two popular limited adaptivity models: the batch learning model and the rare policy switch model, and propose two efficient online RL algorithms for episodic linear Markov decision processes, where the transition probability and the reward function can be represented as a linear function of some known feature mapping. In specific, for the batch learning model, our proposed LSVI-UCB-Batch algorithm achieves an $\tilde O(\sqrt{d^3H^3T} + dHT/B)$ regret, where $d$ is the dimension of the feature mapping, $H$ is the episode length, $T$ is the number of interactions and $B$ is the number of batches. Our result suggests that it suffices to use only $\sqrt{T/dH}$ batches to obtain $\tilde O(\sqrt{d^3H^3T})$ regret. For the rare policy switch model, our proposed LSVI-UCB-RareSwitch algorithm enjoys an $\tilde O(\sqrt{d^3H^3T[1+T/(dH)]^{dH/B}})$ regret, which implies that $dH\log T$ policy switches suffice to obtain the $\tilde O(\sqrt{d^3H^3T})$ regret. Our algorithms achieve the same regret as the LSVI-UCB algorithm (Jin et al., 2019), yet with a substantially smaller amount of adaptivity. We also establish a lower bound for the batch learning model, which suggests that the dependency on $B$ in our regret bound is tight.
Hodge-theoretic analysis on manifolds with boundary, heatable currents, and Onsager's conjecture in fluid dynamics

We use Hodge theory and functional analysis to develop a clean approach to heat flows and Onsager's conjecture on Riemannian manifolds with boundary, where the weak solution lies in the trace-critical Besov space $B_{3,1}^{\frac{1}{3}}$. We also introduce heatable currents as the natural analogue to tempered distributions and justify their importance in Hodge theory.
Percolation description of charge transport in amorphous oxide semiconductors

The charge transport mechanism in amorphous oxide semiconductors (AOS) is a matter of controversial debates. Most theoretical studies so far neglected the percolation nature of the phenomenon. In this article, a recipe for theoretical description of charge transport in AOSs is formulated using the percolation arguments. Comparison with the previous theoretical studies shows a superiority of the percolation approach. The results of the percolation theory are compared to experimental data obtained in various InGaZnO materials revealing parameters of the disorder potential in such AOS.
Improving Neural Relation Extraction with Implicit Mutual Relations

Relation extraction (RE) aims at extracting the relation between two entities from the text corpora. It is a crucial task for Knowledge Graph (KG) construction. Most existing methods predict the relation between an entity pair by learning the relation from the training sentences, which contain the targeted entity pair. In contrast to existing distant supervision approaches that suffer from insufficient training corpora to extract relations, our proposal of mining implicit mutual relation from the massive unlabeled corpora transfers the semantic information of entity pairs into the RE model, which is more expressive and semantically plausible. After constructing an entity proximity graph based on the implicit mutual relations, we preserve the semantic relations of entity pairs via embedding each vertex of the graph into a low-dimensional space. As a result, we can easily and flexibly integrate the implicit mutual relations and other entity information, such as entity types, into the existing RE methods.Our experimental results on a New York Times and another Google Distant Supervision datasets suggest that our proposed neural RE framework provides a promising improvement for the RE task, and significantly outperforms the state-of-the-art methods. Moreover, the component for mining implicit mutual relations is so flexible that can help to improve the performance of both CNN-based and RNN-based RE models significant.