Miracle-dz/UniFAR-FTUs huggingface dataset:
Cortical processing and perceived timing

As of yet, it is unclear how we determine relative perceived timing. One controversial suggestion is that timing perception might be related to when analyses are completed in the cortex of the brain. An alternate proposal suggests that perceived timing is instead related to the point in time at which cortical analyses commence. Accordingly, timing illusions should not occur owing to cortical analyses, but they could occur if there were differential delays between signals reaching cortex. Resolution of this controversy therefore requires that the contributions of cortical processing be isolated from the influence of subcortical activity. Here, we have done this by using binocular disparity changes, which are known to be detected via analyses that originate in cortex. We find that observers require longer stimulus exposures to detect small, relative to larger, disparity changes; observers are slower to react to smaller disparity changes and observers misperceive smaller disparity changes as being perceptually delayed. Interestingly, disparity magnitude influenced perceived timing more dramatically than it did stimulus change detection. Our data therefore suggest that perceived timing is both influenced by cortical processing and is shaped by sensory analyses subsequent to those that are minimally necessary for stimulus change perception.
Microbial responses to southward and northward Cambisol soil transplant

Soil transplant serves as a proxy to simulate climate changes. Recently, we have shown that southward transplant of black soil and northward transplant of red soil altered soil microbial communities and biogeochemical variables. However, fundamental differences in soil types have prevented direct comparison between southward and northward transplants. To tackle it, herein we report an analysis of microbial communities of Cambisol soil in an agriculture field after 4 years of adaptation to southward and northward soil transplants over large transects. Analysis of bare fallow soils revealed concurrent increase in microbial functional diversity and coarse‐scale taxonomic diversity at both transplanted sites, as detected by GeoChip 3.0 and DGGE, respectively. Furthermore, a correlation between microbial functional diversity and taxonomic diversity was detected, which was masked in maize cropped soils. Mean annual temperature, soil moisture, and nitrate (NO3¯‐N) showed strong correlations with microbial communities. In addition, abundances of ammonium‐oxidizing genes (amoA) and denitrification genes were correlated with nitrification capacity and NO3¯‐N contents, suggesting that microbial responses to soil transplant could alter microbe‐mediated biogeochemical cycle at the ecosystem level.
Maintaining Stream Statistics over Sliding Windows

We consider the problem of maintaining aggregates and statistics over data streams, with respect to the last N data elements seen so far. We refer to this model as the sliding window model. We consider the following basic problem: Given a stream of bits, maintain a count of the number of 1's in the last N elements seen from the stream. We show that, using $O(\frac{1}{\epsilon} \log^2 N)$ bits of memory, we can estimate the number of 1's to within a factor of $1 + \epsilon$. We also give a matching lower bound of $\Omega(\frac{1}{\epsilon}\log^2 N)$ memory bits for any deterministic or randomized algorithms. We extend our scheme to maintain the sum of the last N positive integers and provide matching upper and lower bounds for this more general problem as well. We also show how to efficiently compute the Lp norms ($p \in [1,2]$) of vectors in the sliding window model using our techniques. Using our algorithm, one can adapt many other techniques to work for the sliding window model with a multiplicative overhead of $O(\frac{1}{\epsilon}\log N)$ in memory and a $1 +\epsilon$ factor loss in accuracy. These include maintaining approximate histograms, hash tables, and statistics or aggregates such as sum and averages.
Identification of a novel neuronal C-SRC exon expressed in human brain

Neuronal cells are known to express at least two different forms of the C-SRC proto-oncogene as a consequence of alternative splicing events which add an 18-nucleotide exon (the NI exon) between C-SRC exons 3 and 4. Here we report that a second neuronal exon of C-SRC is also present between C-SRC exons 3 and 4. This neuronal exon (the NII exon) of C-SRC was isolated from human adult and fetal brain-derived cDNAs and contains 33 nucleotides capable of encoding 11 amino acids (Gln-Thr-Trp-Phe-Thr-Phe-Arg-Trp-Leu-Gln-Arg). The human NI exon was located approximately 390 nucleotides from the end of C-SRC exon 3, whereas the NII exon was approximately 1,000 nucleotides from the beginning of C-SRC exon 4. Analysis of human brain RNA revealed that the NII exon is utilized primarily in conjunction with the NI exon to yield transcripts capable of encoding C-SRC products possessing 17 additional amino acids. These splicing events, which occur between the NI and NII exons, are predicted to alter the sixth amino acid encoded by the NI exon from an arginine to a serine residue, producing a potentially novel phosphorylation site. Analysis of the different C-SRC RNA transcripts revealed that the level of C-SRC RNA containing both NI and NII exons is similar in adult and fetal brain tissue, whereas the level of C-SRC RNA containing only the NI exon or the nonneuronal form of C-SRC RNAs is significantly higher in fetal brain tissues. These results indicate that the expression and splicing pattern of the C-SRC gene are developmentally regulated in the human brain.
FISH—family identification of sequence homologues using structure anchored hidden Markov models

The FISH server is highly accurate in identifying the family membership of domains in a query protein sequence, even in the case of very low sequence identities to known homologues. A performance test using SCOP sequences and an E-value cut-off of 0.1 showed that 99.3% of the top hits are to the correct family saHMM. Matches to a query sequence provide the user not only with an annotation of the identified domains and hence a hint to their function, but also with probable 2D and 3D structures, as well as with pairwise and multiple sequence alignments to homologues with low sequence identity. In addition, the FISH server allows users to upload and search their own protein sequence collection or to quarry public protein sequence data bases with individual saHMMs. The FISH server can be accessed at .
Identification and Biochemical Characterization of Small-Molecule Inhibitors of West Nile Virus Serine Protease by a High-Throughput Screen

ABSTRACT West Nile virus and dengue virus are mosquito-borne flaviviruses that cause a large number of human infections each year. No vaccines or chemotherapeutics are currently available. These viruses encode a serine protease that is essential for polyprotein processing, a required step in the viral replication cycle. In this study, a high-throughput screening assay for the West Nile virus protease was employed to screen ∼32,000 small-molecule compounds for identification of inhibitors. Lead inhibitor compounds with three distinct core chemical structures (1 to 3) were identified. In a secondary screening of selected compounds, two compounds, belonging to the 8-hydroxyquinoline family (compounds A and B) and containing core structure 1, were identified as potent inhibitors of the West Nile virus protease, with Ki values of 3.2 ± 0.3 μM and 3.4 ± 0.6 μM, respectively. These compounds inhibited the dengue virus type 2 protease with Ki values of 28.6 ± 5.1 μM and 30.2 ± 8.6 μM, respectively, showing some selectivity in the inhibition of these viral proteases. However, the compounds show no inhibition of cellular serine proteases, trypsin, or factor Xa. Kinetic analysis and molecular docking of compound B onto the known crystal structure of the West Nile virus protease indicate that the inhibitor binds in the substrate-binding cleft. Furthermore, compound B was capable of inhibiting West Nile virus RNA replication in cultured Vero cells (50% effective concentration, 1.4 ± 0.4 μM; selectivity index, 100), presumably by inhibition of polyprotein processing.
Physical Co-registration of Magnetic Resonance Imaging and Ultrasound in vivo

Physical co-registration of Magnetic Resonance Imaging and Ultrasound in vivo Firas Moosvi Master of Science Graduate Department of Medical Biophysics University of Toronto 2012 The use of complementary non-invasive imaging modalities has been proposed to track disease progression, particularly cancer, while simultaneously evaluating therapeutic efficacy. A major obstacle is a limited ability to compare parameters obtained from different modalities, especially those from exogenous contrast agents or tracers. We hypothesize that combining Magnetic Resonance Imaging (MRI) and Ultrasound (US) will improve characterization of the tumour microenvironment. In this study, we describe a co-registration apparatus that facilitates the acquisition of a priori co-registered MR and US images in vivo. This apparatus was validated using phantom data and it was found that the US slices can be selected to an accuracy of ± 100μm translationally and ± 2◦ rotationally. Additionally, it was shown that MRI and US may provide complimentary information about the tumour microenvironment, but more work needs to be done to assess repeatability of dynamic contrast enhanced MRI and US.
Monte Carlo Planning Method Estimates Planning Horizons during Interactive Social Exchange

Reciprocating interactions represent a central feature of all human exchanges. They have been the target of various recent experiments, with healthy participants and psychiatric populations engaging as dyads in multi-round exchanges such as a repeated trust task. Behaviour in such exchanges involves complexities related to each agent’s preference for equity with their partner, beliefs about the partner’s appetite for equity, beliefs about the partner’s model of their partner, and so on. Agents may also plan different numbers of steps into the future. Providing a computationally precise account of the behaviour is an essential step towards understanding what underlies choices. A natural framework for this is that of an interactive partially observable Markov decision process (IPOMDP). However, the various complexities make IPOMDPs inordinately computationally challenging. Here, we show how to approximate the solution for the multi-round trust task using a variant of the Monte-Carlo tree search algorithm. We demonstrate that the algorithm is efficient and effective, and therefore can be used to invert observations of behavioural choices. We use generated behaviour to elucidate the richness and sophistication of interactive inference.
A probabilistic approach based on dynamical systems to learn and reproduce gestures by imitation

We present a probabilistic approach to learn robust models of human motion through imitation. The association of Hidden Markov Model (HMM), Gaussian Mixture Regression (GMR) and dynamical systems allows us to extract redundanci es across multiple demonstrations and build time-independen t models to reproduce the dynamics of the demonstrated movements . The approach is first systematically evaluated and compared with other approaches by using generated trajectories shar ing similarities with human gestures. Three applications on di fferent types of robots are then presented. An experiment with the iCub humanoid robot acquiring a bimanual dancing motion is first presented to show that the system can also handle cyclic moti n. An experiment with a 7 DOFs WAM robotic arm learning the motion of hitting a ball with a table tennis racket is presented to highlight the possibility to encode several variations o f a movement in a single model. Finally, an experiment with a HOAP-3 humanoid robot learning to manipulate a spoon to feed the Robota humanoid robot is presented to demonstrate the ca pability of the system to handle several constraints simultan eously.
Distributed asynchronous policy iteration in dynamic programming

We consider the distributed solution of dynamic programming (DP) problems by policy iteration. We envision a network of processors, each updating asynchronously a local policy and a local cost function, defined on a portion of the state space. The computed values are communicated asynchronously between processors and are used to perform the local policy and cost updates. The natural algorithm of this type can fail even under favorable circumstances, as shown by Williams and Baird [WiB93]. We propose an alternative and almost as simple algorithm, which converges to the optimum under the most general conditions, including asynchronous updating by multiple processors using outdated local cost functions of other processors.
Investigation of Carbon Fiber Architecture in Braided Composites Using X-Ray CT Inspection

During the fabrication of braided carbon fiber composite materials, process variations occur which affect the fiber architecture. Quantitative measurements of local and global fiber architecture variations are needed to determine the potential effect of process variations on mechanical properties of the cured composite. Although non-destructive inspection via X-ray CT imaging is a promising approach, difficulties in quantitative analysis of the data arise due to the similar densities of the material constituents. In an effort to gain more quantitative information about features related to fiber architecture, methods have been explored to improve the details that can be captured by X-ray CT imaging. Metal-coated fibers and thin veils are used as inserts to extract detailed information about fiber orientations and inter-ply behavior from X-ray CT images.
Collaborative Enterprise Integrated Modelling

Enterprise modelling focuses on the construction of structured de- scriptions, the enterprise models, of sets of aspects relevant to the activity of an enterprise. Nowadays, knowledge engineering provides powerful methodolo- gies and tools to support enterprise modeling in the production of formal models. These methods however present two main limitation: First, they are based on a waterfall process of transformation of informal knowledge into formal state- ments; Second, they tend to separate the modelling of the different aspects with- out providing enough support to their final integration. In this paper we propose a more flexible framework that supports an agile collaboration between the actors involved in the modelling activities, without sticking to a pre-defined protocol of interaction. In addition we propose an integrated meta-model that specifies the integration of single components of an articulated enterprise model as well as their relations, and we report our experience of using the framework and the meta-model in the production of five enterprise models within the APOSDLE EU-project.
Converting PDF to HTML approach based on text detection

Converting PDF document to HTML document with the same layout format is a very important and interesting research problem. After the conversion, it is easy for PDF document to be browsed online and information extracted. Based on the extraction result of the PDF document of the open source tool PDFBox, the paper described a method that can detect the layout information of the PDF document and convert the PDF document to HTML page effectively.
A realtime hand gesture recognition based on Haar wavelet representation

Nowadays, sign language is commonly used as a communication language for auditory handicapped people. In addition to voice and controller pads, hand gestures can also be an effective way of communication between humans and robots or even between auditory handicapped people and robots. To be an effective sign recognition system, it should be glove-free, fast, small database and accurate. In this project, we aim to propose a hand gesture recognition system which performs realtime recognition. We reduce the database size by standardizing the orientation of hands using the idea of principal axis. To facilitate the matching process, we propose a codeword scheme based on the features of hand gestures. We also propose the use of a new measurement metric with a penalty score which enhances the recognition accuracy. Experimental results have proved the success of the proposed hand gesture recognition system in terms of time, database size, and accuracy.
Advances in sheet metal forming analyses: dealing with mechanical anisotropy from crystallographic texture

Abstract Progress in computer simulation of sheet forming operations depends on accurate characterisation of the sheet strength. The strength exhibits anisotropy arising from its crystallographic texture. Approaches to incorporating representations for strength anisotropy in finite element formulations based on polycrystal plasticity and texture are reviewed. The approaches include ones that employ analytical (closed form) representations of the yield surface or plastic potential and others based on piecewise (numerical) representations. An approach based on directly embedding polycrystal plasticity without a macroscopic yield surface per se is summarised. Applications of the various approaches are presented, some related to forming operations such as deep drawing and others to formability tests such as the limiting dome height test.
Parallax-tolerant image stitching based on mesh optimization

Image stitching with large parallax has long been an important and challenging issue in computer graph and vision. Most studies focused on finding the best-fitting homography to align and seam the input images. Images with large parallax can not be mapped over the whole overlapping regions or aircrafts like ghosting and distortion may occur. In this paper, We combine the global 2D homography method with local warping based on mesh optimization. Instead of finding the best-fitting mapping relationships among input images, we align them loosely by SIFT feature points, and then refine the local areas with mesh optimization. Four constraints are considered during local warping, including the matching error, regular error, scale error and linearity error. Finally, for the image sequence, a tree-structure stitching method is proposed to reduce the accumulated error during the continuous aligning and seaming. The comparison experiments show that, our parallax-tolerant image stitching based on mesh optimization can handle parallax well.
Data-Driven Baseline Estimation of Residential Buildings for Demand Response

The advent of advanced metering infrastructure (AMI) generates a large volume of data related with energy service. This paper exploits data mining approach for customer baseline load (CBL) estimation in demand response (DR) management. CBL plays a significant role in measurement and verification process, which quantifies the amount of demand reduction and authenticates the performance. The proposed data-driven baseline modeling is based on the unsupervised learning technique. Specifically we leverage both the self organizing map (SOM) and K-means clustering for accurate estimation. This two-level approach efficiently reduces the large data set into representative weight vectors in SOM, and then these weight vectors are clustered by K-means clustering to find the load pattern that would be similar to the potential load pattern of the DR event day. To verify the proposed method, we conduct nationwide scale experiments where three major cities’ residential consumption is monitored by smart meters. Our evaluation compares the proposed solution with the various types of day matching techniques, showing that our approach outperforms the existing methods by up to a 68.5% lower error rate.
Size-Correction and Principal Components for Interspecific Comparative Studies

Phylogenetic methods for the analysis of species data are widely used in evolutionary studies. However, preliminary data transformations and data reduction procedures (such as a size-correction and principal components analysis, PCA) are often performed without first correcting for nonindependence among the observations for species. In the present short comment and attached R and MATLAB code, I provide an overview of statistically correct procedures for phylogenetic size-correction and PCA. I also show that ignoring phylogeny in preliminary transformations can result in significantly elevated variance and type I error in our statistical estimators, even if subsequent analysis of the transformed data is performed using phylogenetic methods. This means that ignoring phylogeny during preliminary data transformations can possibly lead to spurious results in phylogenetic statistical analyses of species data.
The Rényi redundancy of generalized Huffman codes

Huffman's algorithm gives optimal codes, as measured by average codeword length, and the redundancy can be measured as the difference between the average codeword length and Shannon's entropy. If the objective function is replaced by an exponentially weighted average, then a simple modification of Huffman's algorithm gives optimal codes. The redundancy can now be measured as the difference between this new average and A. Renyi's (1961) generalization of Shannon's entropy. By decreasing some of the codeword lengths in a Shannon code, the upper bound on the redundancy given in the standard proof of the noiseless source coding theorem is improved. The lower bound is improved by randomizing between codeword lengths, allowing linear programming techniques to be used on an integer programming problem. These bounds are shown to be asymptotically equal. The results are generalized to the Renyi case and are related to R.G. Gallager's (1978) bound on the redundancy of Huffman codes. >
Optically Transparent Composites Reinforced with Networks of Bacterial Nanofibers

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Skeleton-based Walking Motion Analysis Using Hidden Markov Model and Active Shape Models

This paper proposes a skeleton-based human walking motion analysis system which consists of three major phases. In the first phase, it extracts the human body skeleton from the background and then obtains the body signatures. In the second phase, it analyzes the training sequences to generate statistical models. In the third phase, it uses the trained models to recognize the input human motion sequence and calculate the motion parameters. The experimental results demonstrate how our system can recognize the motion type and describe the motion characteristics of the image sequence. Finally, the synthesized motion sequences are illustrated. The major contributions of this paper are: (1) development of a skeleton-based method and use of Hidden Markov Models (HMM) to recognize the motion type; (2) incorporation of the Active Shape Models (ASMs) and the body structure characteristics to generate the motion parameter curves of the human motion.