Miracle-dz/UniFAR-FTUs huggingface dataset:
Flow cell coupled dynamic light scattering for real-time monitoring of nanoparticle size during liquid phase bottom-up synthesis

To tailor the properties of nanoparticles and nanocomposites, precise control over particle size is of vital importance. Real-time monitoring of particle size during bottom-up synthesis in liquids would allow a detailed study of particle nucleation and growth, which provides valuable insights in the mechanism of formation of the nanoparticles. Furthermore, it facilitates a rational scale-up, and would enable adequate intervention in the production process of nanoparticle dispersions to minimize the number of off-spec batches. Since real-time monitoring requires particle size measurements on dispersions in flow, conventional dynamic light scattering (DLS) techniques are not suited: they rely on single scattering and measure the Brownian motion of particles dispersed in a liquid. Here, we present a set-up that allows accurate measurements in real-time on flowing dispersions using a DLS technique based on modulated 3D cross-correlation. This technique uses two simultaneous light scattering experiments performed at the same scattering vector on the same sample volume in order to extract only the single scattering information common to both. We connected the reactor to a flow-cell in the DLS equipment using a tailor-made analysis loop, and successfully demonstrated the complete set-up through monitoring of the size of spherical silica nanoparticles during Stober synthesis in a water-alcohol mixture starting from the molecular precursor tetraethyl orthosilicate.
Variable‐density spiral‐in/out functional magnetic resonance imaging

A variable‐density spiral k‐space trajectory is introduced for brain functional magnetic resonance imaging. The proposed spiral trajectory consists of an Archimedean spiral from the k‐space origin to an arbitrary fraction r of the maximum k‐space radius, extending beyond this point with a variable‐density spiral in which the sampling density decreases as the k‐space radius increases. It, therefore, permits a reduction in readout time at the expense of undersampling only the high spatial frequencies, in which the energy in T2*‐weighted brain images is low. The trajectory was implemented in a two‐dimensional spiral‐in/out sequence, and single‐shot high‐resolution (1.72 × 1.72 mm2 in‐plane) functional magnetic resonance imaging data were acquired from human volunteers. Compared with a two‐shot fully Archimedean spiral sequence with the same spatial coverage and total scan time, the variable‐density sequence yielded greater activation magnitudes with improved temporal efficiency and minor artifacts. Magn Reson Med, 2011. © 2010 Wiley‐Liss, Inc.
Glucose Transporter 3 Potentiates Degranulation and Is Required for Platelet Activation

Objective— On activation, platelets increase glucose uptake, glycolysis, and glucose oxidation and consume stored glycogen. This correlation between glucose metabolism and platelet function is not well understood and even less is known about the role of glucose metabolism on platelet function in vivo. For glucose to enter a cell, it must be transported through glucose transporters. Here we evaluate the contribution of GLUT3 (glucose transporter 3) to platelet function to better understand glucose metabolism in platelets. Approach and Results— Platelet-specific knockout of GLUT3 was generated by crossing mice harboring GLUT3 floxed allele to a PF4 (platelet factor 4)-driven Cre recombinase. In platelets, GLUT3 is localized primarily on &agr;-granule membranes and under basal conditions facilitates glucose uptake into &agr;-granules to be used for glycolysis. After activation, platelets degranulate and GLUT3 translocates to the plasma membrane, which is responsible for activation-mediated increased glucose uptake. In vivo, loss of GLUT3 in platelets increased survival in a collagen/epinephrine model of pulmonary embolism, and in a K/BxN model of autoimmune inflammatory disease, platelet-specific GLUT3 knockout mice display decreased disease progression. Mechanistically, loss of GLUT3 decreased platelet degranulation, spreading, and clot retraction. Decreased &agr;-granule degranulation is due in part to an impaired ability of GLUT3 to potentiate exocytosis. Conclusions— GLUT3-mediated glucose utilization and glycogenolysis in platelets promotes &agr;-granule release, platelet activation, and postactivation functions.
MR Evaluation of Left Ventricular Volumes and Function: Threshold-Based 3D Segmentation Versus Short-Axis Planimetry

Objectives:Our study aimed to evaluate a three-dimensional (3D) threshold-based, region-growing segmentation algorithm for the assessment of left ventricular (LV) volumes in cardiac MRI. Materials and Methods:Two-dimensional cine steady-state free precession short-axis scans (TR 3 milliseconds, TE 1.5 milliseconds, FA 60°, section thickness 8 mm) covering the entire LV without slice gap were acquired in 44 consecutive patients on a 1.5 Tesla MR system (Magnetom Avanto, Siemens, Germany). LV volumes were assessed with an in-house written 3D threshold-based, interslice region-growing segmentation algorithm, and were compared with manual tracing and short-axis planimetry with semiautomatic contour detection (Argus™ software, Siemens, Germany). Results:Three-dimensional threshold-based segmentation was feasible in 41 of 44 patients. Results of threshold-based segmentation were not statistically significantly different to manual tracing for the assessment of end-diastolic (EDV: 104.5 ± 32.3 mL vs. 104.7 ± 33.26 mL, P > 0.05), end-systolic (ESV: 34.5 ± 17.2 mL vs. 33.8 ± 17.45 mL, P > 0.05), and stroke (SV: 69.9 ± 25.0 mL vs. 70.9 ± 25.0 mL, P > 0.05) volumes. Bland-Altmann plots showed systematic lower EDV (−12.0 ± 6.7 mL), ESV (−4.8 ± 3.7 mL), and SV (−7.3 ± 7.6 mL), and a systematic higher ejection fraction (0.4% ± 5.0%) for threshold-based segmentation compared with short-axis planimetry with semiautomatic contour detection. Threshold-based segmentation showed an excellent reproducibility with low absolute differences between 2 consecutive analyses (EDV: 0.2 ± 2.2 mL; ESV: 0.5 ± 1.9 mL; SV: 0.6 ± 3.4 mL; EF: 0.5% ± 2.1%). Evaluation times were significantly shorter for threshold-based segmentation (60 ± 16 vs. 278 ± 31 seconds/669 ± 157 seconds, P < 0.0001). Conclusion:Automated threshold-based segmentation of the left ventricle allows fast and reproducible assessment of LV volumes in cardiac MRI. Exclusion of papillary muscles and myocardial trabeculations from LV volumes leads to systematically lower LV volumes compared with short axis planimetry with semi-automatic contour detection.
Multi-resolution for disparity estimation with convolutional neural networks

Disparity estimation is the process of obtaining the depth information from the left and right views of a particular scene. A recent work based on convolutional neural network (CNN) has achieved state-of-the-art performance for the disparity estimation task. However, this network has some limitations for measuring small and large disparities, which compromises the accuracy of the obtained results. In this paper, a multi-resolution framework with a three-phase strategy to generate high quality disparity maps is proposed, which handles both small and large displacements and retains the details of the scene. The first phase up/down-samples the images to several different resolutions to improve the matching process between CNN feature maps where scaled information is obtained for objects with various sizes and distances. The second phase uses a deep CNN to estimate the disparity maps using the resampled versions, and each version is suitable for a specific range of disparities. Finally, the best fitting disparity map is adaptively selected. To the best of our knowledge, our framework is the first to exploit multiple resolutions of the stereo pair with convolutional neural network for disparity estimation. Significant performance gain is achieved with this proposed method, the mean absolute error is reduced to 3.40 from 5.66, the DispNetC performance for the Sintel dataset.
Programme 10 : 30 Registration opens Tea / Coffee 11 : 00 Welcome and opening remarks

This paper presents research relating to the Performance strand of the *FAST project. An emerging focus of the research on this project has related to the Performance (and Creation) of music. In this paper we briefly discuss two of the demonstrator projects that have been developed as part of the research of the project. We discuss a game-like musical piece called ‘Climb! ’, which allowed the composer to integrate musical codes into their composition that could be triggered by the performers, and ‘Numbers into Notes’ an experimental digital humanities project that developed music creation software into the future based on Ada Lovelace’s writings. I. SOUNDING SEMANTICS OUT Contemporary musical performance has taken advantage or a range of new technological innovations that have enabled composers, performances and audiences to engage with music in new and innovative ways. By experimenting with such technologies we are able to unpack and research the use and development of such technologies in the context of musical performance and creation, and by doing understand the nature and use of semantics in this area. This may be meaning as interpreted by the audience or performer, meta-data as used at a system level or the use of a codified system for compositional purposes.
Calculating Intrinsic and Extrinsic Camera Parameters Based on the PnP Problem

The classical PnP problem is premised on given intrinsic camera parameters. However, for unknown intrinsic camera parameters, given n space points in a world coordinate system and their coordinates in an image coordinate system, the extrinsic camera parameters can be determined. Regarding the existence and uniqueness of a solution for the classical PnP problem, for 4 control points in a plane and an uncalibrated camera, a set of linear equations can be solved based on the correspondence between the space points and the image points. The results show that this approach is feasible and has high calculation precision.
Java/DSM: A Platform for Heterogeneous Computing

In this paper we describe a system for programming heterogeneous computing environments based upon Java and software distributed shared memory (DSM). Compared with existing approaches for heterogeneous computing, our system transparently handles both the hardware differences and the distributed nature of the system. It is therefore much easier to program. Java is a good candidate for heterogeneous programming because of its portability. Java provides the remote method invocation (RMI) mechanism for distributed computing. However, our early experience with RMI showed that the programmer must expend significant effort on such problems as data replication and the optimization of the remote interface to improve communication efficiency. Furthermore, the need for reference marshaling is not completely eliminated by RMI's effort to facilitate the sharing of linked structures between machines. A DSM system provides a shared memory abstraction over a group of physically distributed machines. It automatically handles data communication between machines and eliminates the need for the programmer to write message-passing code. A multithreaded Java program written for a single machine will require fewer changes to run on a Java/DSM combination than with RMI. We have been implementing a JDK-1.0. 2 based parallel Java Virtual Machine on the TreadMarks DSM system. Our implementation includes a distributed garbage collector and supports the Java API with very few changes. In this paper we describe our motivation and the implementation, and report our early experience with programming under both RMI and DSM. © 1997 John Wiley & Sons, Ltd.
Matrix Completion via Factorizing Polynomials

Predicting unobserved entries of a partially observed matrix has found wide applicability in several areas, such as recommender systems, computational biology, and computer vision. Many scalable methods with rigorous theoretical guarantees have been developed for algorithms where the matrix is factored into low-rank components, and embeddings are learned for the row and column entities. While there has been recent research on incorporating explicit side information in the low-rank matrix factorization setting, often implicit information can be gleaned from the data, via higher-order interactions among entities. Such implicit information is especially useful in cases where the data is very sparse, as is often the case in real-world datasets. In this paper, we design a method to learn embeddings in the context of recommendation systems, using the observation that higher powers of a graph transition probability matrix encode the probability that a random walker will hit that node in a given number of steps. We develop a coordinate descent algorithm to solve the resulting optimization, that makes explicit computation of the higher order powers of the matrix redundant, preserving sparsity and making computations efficient. Experiments on several datasets show that our method, that can use higher order information, outperforms methods that only use explicitly available side information, those that use only second-order implicit information and in some cases, methods based on deep neural networks as well.
Compact Formulas for the Electrical Resistance of Semiconducting and Metallic Single Wall Carbon Nanotubes

We present compact analytic formulas with appropriate accuracy for the resistance of semiconducting and metallic single wall carbon nanotubes (s-SWCNTs and m-SWCNTs) as functions of their diameters and lengths, and the temperature. Based on these relations, we investigate the dependence of their resistances and corresponding temperature coefficients on the SWNTs' diameters. Taking the tube to tube random variation in diameters of horizontally-aligned SWCNTs, we calculate the equivalent resistances and effective TCRs for arrays of both s-SWCNTs and m-SWCNTs.
Cognitive Control Structures in the Imitation Learning of Spatial Sequences and Rhythms—An fMRI Study

Imitation learning involves the acquisition of novel motor patterns based on action observation (AO). We used event-related functional magnetic resonance imaging to study the imitation learning of spatial sequences and rhythms during AO, motor imagery (MI), and imitative execution in nonmusicians and musicians. While both tasks engaged the fronto-parietal mirror circuit, the spatial sequence task recruited posterior parietal and dorsal premotor regions more strongly. The rhythm task involved an additional network for auditory working memory. This partial dissociation supports the concept of task-specific mirror mechanisms. Two regions of cognitive control were identified: 1) dorsolateral prefrontal cortex (DLPFC) was found to be more strongly activated during MI of novel spatial sequences, which allowed us to extend the 2-level model of imitation learning by Buccino et al. (2004) to spatial sequences. 2) During imitative execution of both tasks, the posterior medial frontal cortex was robustly activated, along with the DLPFC, which suggests that both regions are involved in the cognitive control of imitation learning. The musicians' selective behavioral advantage for rhythm imitation was reflected cortically in enhanced sensory-motor processing during AO and by the absence of practice-related activation differences in DLPFC during rhythm execution.
PACE OF G ENERATIVE M ODELS

Several recent papers have treated the latent space of deep generative models, e.g., GANs or VAEs, as Riemannian manifolds. The argument is that operations such as interpolation are better done along geodesics that minimize path length not in the latent space but in the output space of the generator. However, this implicitly assumes that some simple metric such as L2 is meaningful in the output space, even though it is well known that for, e.g., semantic comparison of images it is woefully inadequate. In this work, we consider imposing an arbitrary metric on the generator’s output space and show both theoretically and experimentally that a feature-based metric can produce much more sensible interpolations than the usual L2 metric. This observation leads to the conclusion that analysis of latent space geometry would benefit from using a suitable, explicitly defined metric.
Homozygosity Mapping and Targeted Sanger Sequencing Reveal Genetic Defects Underlying Inherited Retinal Disease in Families from Pakistan

Background Homozygosity mapping has facilitated the identification of the genetic causes underlying inherited diseases, particularly in consanguineous families with multiple affected individuals. This knowledge has also resulted in a mutation dataset that can be used in a cost and time effective manner to screen frequent population-specific genetic variations associated with diseases such as inherited retinal disease (IRD). Methods We genetically screened 13 families from a cohort of 81 Pakistani IRD families diagnosed with Leber congenital amaurosis (LCA), retinitis pigmentosa (RP), congenital stationary night blindness (CSNB), or cone dystrophy (CD). We employed genome-wide single nucleotide polymorphism (SNP) array analysis to identify homozygous regions shared by affected individuals and performed Sanger sequencing of IRD-associated genes located in the sizeable homozygous regions. In addition, based on population specific mutation data we performed targeted Sanger sequencing (TSS) of frequent variants in AIPL1, CEP290, CRB1, GUCY2D, LCA5, RPGRIP1 and TULP1, in probands from 28 LCA families. Results Homozygosity mapping and Sanger sequencing of IRD-associated genes revealed the underlying mutations in 10 families. TSS revealed causative variants in three families. In these 13 families four novel mutations were identified in CNGA1, CNGB1, GUCY2D, and RPGRIP1. Conclusions Homozygosity mapping and TSS revealed the underlying genetic cause in 13 IRD families, which is useful for genetic counseling as well as therapeutic interventions that are likely to become available in the near future.
Computerized 3D Modeling for Rapid Artificial Bone Substitute Manufacturing

bone substitutes manufacturing is highly important due to a vast number casualties are stepped to the society as a result of mainly traffic accidents, natural disasters and civil wars. Though casualties are grouped into several categories, a considerable number of patients is fallen into the bone associated injuries. It is also notable that especially the traffic related accidents and natural disasters may occur in populated regions. Due to financial reasons all the hospitals in the developing countries cannot maintain sophisticated scanning equipments along with their software solutions. Therefore having a lightweight software solution that facilitates bone profiling will be beneficial for patients and it also helps surgeons to prepare a care plan depending on the disorder. However, the artificial tools that are inserted to the human body can vary upon the injury. Hence, they should be highly customizable. Though computerized 3D modeling started around two decades ago, a few tools are available to assist surgeons in such situations. The available applications and techniques have limited functionalities thus, the manufactured bone grafts may not perfectly be suited to the lesion or injury. In this paper we propose a minimally invasive procedures to model bone grafts. In which, quality control methods for noise removals and 3D data compression mechanisms are coupled to the software solution that runs even on typical personal computer systems. The end result of the 3D modeled bone can be employed to extract the cavity, clip regions of interest and even to test the manufactured bone graft before the surgical procedure. Thereby, the process of manufacturing the prosthetic and the clinical procedures will be efficient and reliable.
Joint Job Partitioning and Collaborative Computation Offloading in Multi-User Wireless Networks

Computation offloading to nearby devices offers resources-constrained mobile devices the opportunity to reduce local computation load and energy consumption when supporting applications with intensive computation load. In this paper, we consider joint job partitioning and collaborative computation offloading in a multi-user wireless network, aiming at minimizing the energy consumption of the overall network under the completion time constraint of each application. The problem is formulated as a binary integer linear programming problem and then transformed into a weighted bipartite graph matching problem. The Kuhn-Munkres algorithm is firstly adopted to obtain the optimal partition and scheduling. Three distributed scheduling algorithms are then introduced. The first one is referred to as stable matching, where each mobile node chooses matching collaborator or requester based on its own interests. Next an asynchronous greedy algorithm is proposed, where every requester-collaborator pair cooperatively reduces their total energy consumption. Finally, based on the greedy algorithm, a modified version, referred to as Maximum Differential Energy Matching (MDEM) algorithm is devised, which minimizes the overall network energy cost by relaxing the stability criterion in order to benefit energy consumption of all nodes. Simulation results show that the MDEM algorithm achieves near-optimal performance in terms of total energy consumption for the network.
Printability of Sized Kenaf (Hibiscus cannabinus) Papers

This article reviews the effect of chitosan, cationic starch and polyvinyl alcohol as sizing agents to improve printability properties of kenaf handsheets. The polymers were incorporated into the sheets by spray application. This study clearly demonstrated that the use of chitosan could improve the printability and print quality of kenaf paper in terms of water and oil absorption, ink penetration, print density and gloss for offset printing. Both PVA-sizing and cationic starch sizing gave print densities and ink penetrations than were intermediate between chitosan and the unsized control sheet, which gave the lowest values for these properties. Kenaf paper sized with chitosan gave the best mix of desirable printing paper properties and was superior to kenaf papers sized with either cationic starch or PVA.
Supplement to: Diversity of meso-scale architecture in human and non-human connectomes

In this supplement we describe four additional inter-regional, non-human connectome datasets which we analyzed using the same methods and techniques as the human data. Whereas the human connectome data described in the main text were reconstructed in vivo from diffusion weighted images using deterministic tractography algorithms, the procedures used to reconstruct the non-human data were more varied, ranging from retrograde tract tracing to meta-analysis. These data allow us to assess whether the differences we observed between the WSBM and Qmax techniques are a consequence of the procedures used to reconstruct the connectome data. We also report a similar set of analyses carried on the C. elegans chemical synapse network. Unlike the region-level networks described in the main text and earlier in this supplement, the analysis of C. elegans allows us to probe the meso-scale structure of a complete neural system at the cellular level. In general, the analyses we present in this supplement corroborate those in the main text. We also report several confirmatory and robustness analyses. These include showing that the WSBM algorithm consistently converges to similar solutions, a comparison of communities obtained using empirical and randomly rewired networks, and a demonstration that our reported results do not depend trivially on the partition of the human brain in parcels (network nodes).