Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced gravity separation at the Mineral Processing Laboratory at NTNU

The mineral processing laboratory offers both traditional gravity separation equipment and enhanced gravity concentrators. The traditional gravity separation equipment are laboratory shaking tables like the Mozley Laboratory Mineral Separator, a traditional Wilfley table, and a laboratory jig, while the pilot equipment consists of a larger Wilfley table and a spiral rig system from Insmart Systems. Our enhanced gravity concentrators are the Knelson Concentrators KCMD3 and KC-MD7.5, the Kelsey Jig J200 and the Mozley C900 Multi-Gravity Separator (MGS). The Knelson Concentrators are batch separators (laboratory and pilot scale), while the Kelsey Jig and the Mozley MGS are continuous pilot scale processing equipment.
Towards non-invasive BCI controlled grasp neuroprostheses-systematic analysis of FES-induced artefacts on EEG-signals

This work aims at the investigation of the artefacts in EEG-signals caused by Functional Electrical Stimulation (FES) of arm muscles. In a first step an “in-vitro” testbed for simulation of the situation in humans has been set up. The simulation of different FESand EEG-electrode conditions revealed that the EEG amplifier impluse response is independent from electrode parameters like size, position or interelectrode distance. In a second step these results have been confirmed in healthy subjects with a low-frequency stimulation of forearm muscles. Finally, a reference artifact signal has been determined and several methods for artefact supression have been tested.
The divergence of cpcBA-IGS sequences between Dolichospermum and Aphanizomenon (Cyanobacteria) and the molecular detection of Dolichospermum flos-aquae in Taihu Lake, China

Wang Z. , Liu Y. , Xu Y. , Xiao P. and Li R. 2013. The divergence of cpcBA-IGS sequences between Dolichospermum and Aphanizomenon (Cyanobacteria) and the molecular detection of Dolichospermum flos-aquae in Taihu Lake, China. Phycologia 52: 447–454. DOI: 10.2216/12–125.1 The occurrence of bloom-forming heterocystous cyanobacteria such as Dolichospermum flos-aquae has become increasingly serious. This study was conducted to provide a detailed understanding of diversity, distribution and seasonal dynamics of Dolichospermum flos-aquae populations in Taihu Lake, China. Morphological identifications showed Dolichospermum flos-aquae and Aphanizomnon flos-aquae were the two main heterocystous cyanobacteria in Taihu Lake. Phylogenetic analysis based on cpcBA-IGS sequences, the highly variable intergenic spacer (IGS) region between two phycobilisome subunits (cpcB and cpcA), distinguished Dolichospermum from other filametous cyanobacterial genera. Therefore, the real-time PCR detection of Dolichospermum flos-aquae based on cpcBA-IGS sequences was developed to study the distribution and abundance of Dolichospermum flos-aquae. The investigation began in August 2008 and extended through September 2010. Dolichospermum flos-aquae occurred throughout the entire lake. There was an average abundance of 2.0 × 106 gene copies L−1, and the peak abundance was in the western part of Taihu Lake during late spring when it reached 4.0 × 107 copies L−1. Dolichospermum flos-aquae had a regular annual cycle; it began appearing in late autumn and it reached a peak in late spring.
Snubber circuit to suppress the voltage ringing for SiC device

In these days, SiC device is expected to improve the efficiency of power electronics application because of very low switching energy. However, the voltage ringing caused by high speed switching makes EMC problem for the system. In general, this voltage ringing could be suppressed by selecting a high gate resistance in the gate driver. On the other hand, this would cause higher switching energy and thus the SiC’s advantage would be spoiled. This paper presents C-CR snubber circuit that makes it possible to suppress the voltage ringing without increasing the semiconductor’s switching energy.
An internal tRNA-like structure regulates the life cycle of a plus-sense RNA virus

Title of dissertation: AN INTERNAL tRNA-LIKE STRUCTURE REGULATES THE LIFE CYCLE OF A PLUS-SENSE RNA VIRUS John Crisler McCormack, III, Doctor of Philosophy, 2007 Dissertation directed by: Professor Anne E. Simon Department of Cell Biology and Molecular Genetics Turnip crinkle virus (TCV) is a 4054 b plus-sense RNA virus that belongs to the genus Carmovirus in the Family Tombusviridae. The 3’ terminal 200 b of TCV are predicted to fold into 5 hairpins labeled in the 3’ to 5’ direction as the promoter (Pr), hairpin 5 (H5), hairpin 4b (H4b), hairpin 4a (H4a), and hairpin 4 (H4), using 3’ UTR phylogenetic comparisons with other carmoviruses and the RNA structural prediction program, mfold. H5 was found to be a highly-conserved structure containing a large symmetrical loop (LSL) that formed a tertiary interaction between the 3’ side of the LSL and the 3’ terminal nucleotides using compensatory mutational analysis in vivo. In plants, LSL mutations resulted in a mutation frequency that was increased by as much as 12-fold without inducing error catastrophe. The original mutations frequently reverted and led to second site alterations biased for uridylate to cytidylate and adenylate to guanylate changes. These results suggest that H5 may function as a chaperone to properly fold the RdRp. The TCV 5’ UTR, which binds 40S ribosomal subunits, contains two short segments exhibiting IRES activity that function synergistically with the 3’ terminal region to enhance cap-independent translation in vivo. In the TCV 3’ UTR, H4a, H4b, H5, and flanking sequences, form an internal tRNA-like structure (iTLS) that binds 60S ribosomal subunits and the P-site of salt washed 80S ribosomes. The iTLS may therefore mediate assembly of 80S ribosomes, which are then transported to the 5’ end for translation of virally-encoded proteins. Phylogenetic comparisons of carmovirus 3’ UTRs revealed that Cardamine chlorotic fleck virus (CCFV) and Japanese iris necrotic ring virus (JINRV) are capable of forming the 5 elemental features comprising the iTLS. Ribosome binding and plant cell culture assays showed that only the CCFV iTLS bound 80S ribosomes and could functionally replace the TCV iTLS. These results suggest that closely-related members of the same viral genus may utilize different strategies for cap-independent translation. AN INTERNAL tRNA-LIKE STRUCTURE REGULATES THE LIFE CYCLE OF A PLUS-SENSE RNA VIRUS by John Crisler McCormack, III Thesis submitted to the Faculty of the Graduate School of the University of Maryland, College Park in partial fulfillment of the requirements for the degree of Doctor of Philosophy 2007 Advisory Committee: Professor Anne E. Simon, Chair Professor Barbara L. Thorne Associate Professor Jonathan D. Dinman Associate Professor James N. Culver Assistant Professor Daniel R. Perez ©Copyright by John Crisler McCormack, III 2007 In memory of my father, John Crisler McCormack, II (June 24, 1943-September 30, 2007)
Carotenoids in Avian Nutrition and Embryonic Development. 1. Absorption, Availability and Levels in Plasma and Egg Yolk

Avian embryo development is associated with a progressive accumulation in certain tissues of lipids which are rich in polyunsaturated fatty acids (PUFA), making such tissues vulnerable to free radical attack and lipid peroxidation. Antioxidant systems are expressed in the embryonic tissues and are responsible for maintaining antioxidant protection during different stages of embryoonic development, in particular at hatching time when oxidative stress is highly pronounced (Surai, 1999). The antioxidant systems of the avian embryo are based on the antioxidant enzymes superoxide dismutase (SOD), glutathione peroxidase (GSH-Px) and catalase (Surai, 1999a ; Surai, 2000a), and fat-soluble and water-soluble natural antioxidants (Surai, 1999). Vitamin E is considered to be the main player in the antioxidant defence at the cellular and subcellular levels (Niki,1996). In recent years it has become evident that in the cell the association of vitamin E with other natural antioxidants which are able to recycle this vitamin from its oxidized form is the major biochemical mechanism in maintaining the efficiency of antioxidant defence (Surai, 1999; Surai, 2000). Different antioxidant components occupy distinct cellular and extracellular compartments and among them, active recycling plays a crucial role. For example, oxidized vitamin E (tocopheroxyl radical) has been shown to be regenerated by ascorbate, glutathione (GSH), lipoic acid, or ubiquinols. Furthermore glutathione disulfides (GSSG) can be regenerated by glutathione reductase and enzymic pathways have also been identified for the recycling of the ascorbate radical and dehydroascorbate. The reducing agents that are used to fuel these recycling reactions (NADH and NADPH) are ultimately derived from the oxidation of nutrients (Chap et al., 1999). The exact role of carotenoids within this system is still under discussion but evidence is accumulating concerning antioxidant properties of carotenoids and their role in antioxidant defence directly or via interactions with other antioxidants increasing their efficiency. In this respect, development of effective HPLC-based methods for carotenoid analysis, in association with the introduction of new assays for antioxidant activities in vitro, promise new advances in this area.
Coherent Ising Machines with Error Correction Feedback

A nonequilibrium open‐dissipative neural network, such as a coherent Ising machine based on mutually coupled optical parametric oscillators, has been proposed and demonstrated as a novel computing machine for hard combinatorial optimization problems. However, there is a challenge in the previously proposed approach: The machine can be trapped by local minima which increases exponentially with a problem size. This leads to erroneous solutions rather than correct answers. In this paper, it is shown that it is possible to overcome this problem partially by introducing error detection and correction feedback mechanism. The proposed machine achieves efficient sampling of degenerate ground states and low‐energy excited states via its inherent exploration property during a solution search process.
Driver Identification through Stochastic Multi-State Car-Following Modeling

Intra-driver and inter-driver heterogeneity has been confirmed to exist in human driving behaviors by many studies. In this study, a joint model of the two types of heterogeneity in car-following behavior is proposed as an approach of driver profiling and identification. It is assumed that all drivers share a pool of driver states; under each state a car-following data sequence obeys a specific probability distribution in feature space; each driver has his/her own probability distribution over the states, called driver profile, which characterize the intradriver heterogeneity, while the difference between the driver profile of different drivers depict the inter-driver heterogeneity. Thus, the driver profile can be used to distinguish a driver from others. Based on the assumption, a stochastic car-following model is proposed to take both intra-driver and inter-driver heterogeneity into consideration, and a method is proposed to jointly learn parameters in behavioral feature extractor, driver states and driver profiles. Experiments demonstrate the performance of the proposed method in driver identification on naturalistic car-following data: accuracy of 82.3% is achieved in an 8-driver experiment using 10 car-following sequences of duration 15 seconds for online inference. The potential of fast registration of new drivers are demonstrated and discussed.
Bi-direction Context Propagation Network for Real-time Semantic Segmentation

Spatial details and context correlations are two types of important information for semantic segmentation. Generally, shallow layers tend to contain more spatial details, while deep layers are rich in context correlations. Aiming to keep both advantages, most of current methods choose to forward-propagate the spatial details from shallow layers to deep layers, which is computationally expensive and substantially lowers the model's execution speed. To address this problem, we propose the Bi-direction Context Propagation Network (BCPNet) by leveraging both spatial and context information. Different from the previous methods, our BCPNet builds bi-directional paths in its network architecture, allowing the backward context propagation and the forward spatial detail propagation simultaneously. Moreover, all the components in the network are kept lightweight. Extensive experiments show that our BCPNet has achieved a good balance between accuracy and speed. For accuracy, our BCPNet has achieved 68.4 \% mIoU on the Cityscapes test set and 67.8 \% mIoU on the CamVid test set. For speed, our BCPNet can achieve 585.9 FPS (or 1.7 ms runtime per image) at $360 \times 640$ size based on a GeForce GTX TITAN X GPU card.
Classification and Clustering of arXiv Documents, Sections, and Abstracts, Comparing Encodings of Natural and Mathematical Language

In this paper, we show how selecting and combining encodings of natural and mathematical language affect classification and clustering of documents with mathematical content. We demonstrate this by using sets of documents, sections, and abstracts from the arXiv preprint server that are labeled by their subject class (mathematics, computer science, physics, etc.) to compare different encodings of text and formulae and evaluate the performance and runtimes of selected classification and clustering algorithms. Our encodings achieve classification accuracies up to 82.8% and cluster purities up to 69.4% (number of clusters equals number of classes), and 99.9% (unspecified number of clusters) respectively. We observe a relatively low correlation between text and math similarity, which indicates the independence of text and formulae and motivates treating them as separate features of a document. The classification and clustering can be employed, e.g., for document search and recommendation. Furthermore, we show that the computer outperforms a human expert when classifying documents. Finally, we evaluate and discuss multi-label classification and formula semantification.
Navigated Weighting to Improve Inverse Probability Weighting for Missing Data Problems and Causal Inference

The inverse probability weighting (IPW) is broadly utilized to address missing data problems including causal inference but may suffer from large variances and biases due to propensity score model misspecification. To solve these problems, I propose an estimation method called the navigated weighting (NAWT), which utilizes estimating equations suitable for a specific pre-specified parameter of interest (e.g., the average treatment effects on the treated). Since these pre-specified parameters determine the relative importance of each unit as a function of propensity scores, the NAWT prioritizes important units in the propensity score estimation to improve efficiency and robustness to model misspecification. I investigate its large-sample properties and demonstrate its finite sample improvements through simulation studies and an empirical example. An R package nawtilus which implements the NAWT is developed and available from the Comprehensive R Archive Network (this http URL).
Destined to decline? Intrinsic susceptibility of the threatened estuary stingray to anthropogenic impacts

Evaluating the extinction risk of poorly known fishes is one of the key problems in marine conservation biology. Elasmobranchs are widely recognised to be highly susceptible to anthropogenic impacts, with ecological specialists being particularly at risk. The estuary stingray Dasyatis fluviorum is endemic to near-shore, estuarine and riverine habitats along the eastern coast of Australia. The present study aimed to obtain basic data on the biology and demography of D. fluviorum to inform conservation evaluation of the species. Age and growth data were collected from rays caught in Moreton Bay, Queensland. Maturity occurred at 630mm disc width (WD) (13.4 years) in females and 412mm WD (7.0 years) in males, with maximum age estimates of 21 and 16 years from females and males, respectively. These parameters produced a positive population growth of 1.02year � 1 in a stochastic demographic model based on indirect estimates of mortality. The life history parameters of D. fluviorum confer a high to very high vulnerability to population decline, emphasising the need for directed conservation efforts for the species. Without management intervention, even minor levels of additive anthropogenic mortality are likely to lead to the species' conservation status declining further to Endangered based on IUCN classification criteria.
A model for the collapse of the edge when two transitions routes compete

The transition to turbulence in many shear flows proceeds along two competing routes, one linked with finite-amplitude disturbances and the other one originating from a linear instability, as in e.g. boundary layer flows. The dynamical systems concept of edge manifold has been suggested in the subcritical case to explain the partition of the state space of the system. This investigation is devoted to the evolution of the edge manifold when a linear stability is added in such subcritical systems, a situation poorly studied despite its prevalence in realistic fluid flows. In particular the fate of the edge state as a mediator of transition is unclear. A deterministic three-dimensional model is suggested, parametrised by the linear instability growth rate. The edge manifold evolves topologically, via a global saddle-loop bifurcation, from the separatrix between two attraction basins to the mediator between two transition routes. For larger instability rates, the stable manifold of the saddle point increases in codimension from 1 to 2 after an additional local saddle node bifurcation, causing the collapse of the edge manifold. As the growth rate is increased, three different regimes of this model are identified, each one associated with a flow case from the recent hydrodynamic literature. A simple nonautonomous generalisation of the model is also suggested in order to capture the complexity of spatially developing flows.
Positive Desch-Schappacher perturbations of bi-continuous semigroups on AM-spaces

In this paper, we consider positive Desch-Schappacher perturbations of bi-continuous semigroups on AM-spaces with an additional property concerning the additional locally convex topology. As an example, we discuss perturbations of the left-translation semigroup on the space of bounded continuous functions on the real line and on the space of bounded linear operators.
Evaluating Neural Morphological Taggers for Sanskrit

Neural sequence labelling approaches have achieved state of the art results in morphological tagging. We evaluate the efficacy of four standard sequence labelling models on Sanskrit, a morphologically rich, fusional Indian language. As its label space can theoretically contain more than 40,000 labels, systems that explicitly model the internal structure of a label are more suited for the task, because of their ability to generalise to labels not seen during training. We find that although some neural models perform better than others, one of the common causes for error for all of these models is mispredictions due to syncretism.
Ezrin Becomes Restricted to Outer Cells Following Asymmetrical Division in the Preimplantation Mouse Embryo

During preimplantation development in the mouse, two phenotypically distinct cell populations appear at the 16-cell stage: nonpolarized inner cells that give rise to the inner cell mass and polarized outer cells that give rise mainly to the trophectoderm. The divergence of these two cell lineages is due to asymmetrical cell divisions during the transition from the 8- to the 16 cell stage which can occur following blastomere polarization. During compaction, at the 8-cell stage, cytoplasmic organelles accumulate in the apical domain, a surface pole of microvilli forms, and blastomeres flatten onto one another. During the division from the 8- to the 16-cell stage, the only asymmetrical structure maintained is the pole of microvilli. At the 16-cell stage, only blastomeres inheriting a large part of this apical structure can reestablish a polarized organization. The mechanisms involved in the formation and stabilization of the apical pole of microvilli are still unknown. Ezrin is an actin-associated protein that has been proposed to play a role in the formation of microvillous structures. This led us to study the expression of ezrin during early development of the mouse embryo. We observed that ezrin mRNA and protein are present in the mouse oocyte and throughout preimplantation embryo development, although the amount of protein present decreases continuously during early development, particularly after the 8-cell stage, at the time of compaction. Two isoforms of ezrin phosphorylated on tyrosine residues are present during all of preimplantation development while a third non-tyrosine-phosphorylated isoform appears at the 8-cell stage and its relative amount increases from the 8-cell stage to the blastocyst stage. Before compaction, ezrin is distributed around the cell cortex. However ezrin becomes restricted to the microvilli of the apical pole after compaction. At later stages, ezrin is found in the microvilli of the apical surface of outer cells. Finally, ezrin remains associated with the microvillous pole during the transition from the 8- to 16-cell stage and is found only in the outer cells after division. Thus, ezrin is the first cytocortical protein described that is totally segregated in outer cells at the 16-cell stage after an asymmetrical division.
Hyperspectral Unmixing Network Inspired by Unfolding an Optimization Problem

The hyperspectral image (HSI) unmixing task is essentially an inverse problem, which is commonly solved by optimization algorithms under a predefined (non-)linear mixture model. Although these optimization algorithms show impressive performance, they are very computational demanding as they often rely on an iterative updating scheme. Recently, the rise of neural networks has inspired lots of learning based algorithms in unmixing literature. However, most of them lack of interpretability and require a large training dataset. One natural question then arises: can one leverage the model based algorithm and learning based algorithm to achieve interpretable and fast algorithm for HSI unmixing problem? In this paper, we propose two novel network architectures, named U-ADMM-AENet and U-ADMM-BUNet, for abundance estimation and blind unmixing respectively, by combining the conventional optimization-model based unmixing method and the rising learning based unmixing method. We first consider a linear mixture model with sparsity constraint, then we unfold Alternating Direction Method of Multipliers (ADMM) algorithm to construct the unmixing network structures. We also show that the unfolded structures can find corresponding interpretations in machine learning literature, which further demonstrates the effectiveness of proposed methods. Benefit from the interpretation, the proposed networks can be initialized by incorporating prior information about the HSI data. Different from traditional unfolding networks, we propose a new training strategy for proposed networks to better fit in the HSI applications. Extensive experiments show that the proposed methods can achieve much faster convergence and competitive performance even with very small size of training data, when compared with state-of-art algorithms.