Miracle-dz/UniFAR-FTUs huggingface dataset:
Geodesics in heat: A new approach to computing distance based on heat flow

We introduce the heat method for computing the geodesic distance to a specified subset (e.g., point or curve) of a given domain. The heat method is robust, efficient, and simple to implement since it is based on solving a pair of standard linear elliptic problems. The resulting systems can be prefactored once and subsequently solved in near-linear time. In practice, distance is updated an order of magnitude faster than with state-of-the-art methods, while maintaining a comparable level of accuracy. The method requires only standard differential operators and can hence be applied on a wide variety of domains (grids, triangle meshes, point clouds, etc.). We provide numerical evidence that the method converges to the exact distance in the limit of refinement; we also explore smoothed approximations of distance suitable for applications where greater regularity is required.
3D ear reconstruction based on the morphable model

Existing work of 3D morphable models mainly focuses on 3D face recognition and reconstruction but ignores the ear regions which are usually captured together with the face images. However, ears are almost rigid structures, which are relatively stable to age growth and resist the facial expression variations, making themselves an effective supplement of faces for personal identification. Hence, we explore a novel framework for 3D ear reconstruction based on the 3D morphable model. This is the original work to reconstruct pure 3D ears using the 3D morphable model. Novel methods for ear pit detection and shape alignment are proposed. Model registration is achieved among 3D ear samples. Based on the proposed framework, the 3D ear model is reconstructed. Our work provides advantageous expansion for existing 3D morphable model research. Experimental results and comparison with other 3D ear reconstruction methods have verified the validity of our proposed method.
The Inherited Blindness Protein AIPL1 Regulates the Ubiquitin-Like FAT10 Pathway

Mutations in AIPL1 cause the inherited blindness Leber congenital amaurosis (LCA). AIPL1 has previously been shown to interact with NUB1, which facilitates the proteasomal degradation of proteins modified with the ubiquitin-like protein FAT10. Here we report that AIPL1 binds non-covalently to free FAT10 and FAT10ylated proteins and can form a ternary complex with FAT10 and NUB1. In addition, AIPL1 antagonised the NUB1-mediated degradation of the model FAT10 conjugate, FAT10-DHFR, and pathogenic mutations of AIPL1 were defective in inhibiting this degradation. While all AIPL1 mutants tested still bound FAT10-DHFR, there was a close correlation between the ability of the mutants to interact with NUB1 and their ability to prevent NUB1-mediated degradation. Interestingly, AIPL1 also co-immunoprecipitated the E1 activating enzyme for FAT10, UBA6, suggesting AIPL1 may have a role in directly regulating the FAT10 conjugation machinery. These studies are the first to implicate FAT10 in retinal cell biology and LCA pathogenesis, and reveal a new role of AIPL1 in regulating the FAT10 pathway.
Discriminative Strategies to Integrate Multiword Expression Recognition and Parsing

The integration of multiword expressions in a parsing procedure has been shown to improve accuracy in an artificial context where such expressions have been perfectly pre-identified. This paper evaluates two empirical strategies to integrate multiword units in a real constituency parsing context and shows that the results are not as promising as has sometimes been suggested. Firstly, we show that pre-grouping multiword expressions before parsing with a state-of-the-art recognizer improves multiword recognition accuracy and unlabeled attachment score. However, it has no statistically significant impact in terms of F-score as incorrect multiword expression recognition has important side effects on parsing. Secondly, integrating multiword expressions in the parser grammar followed by a reranker specific to such expressions slightly improves all evaluation metrics.
Dynamic model based microrobot control

In this paper, a novel control algorithm for a microrobot is proposed, which is Two-degree-of-freedom control based on dynamic model. Two-degree-of-freedom control which consists of Disturbance observer (DOB), feedback controller and feedforward controller, has been utilized widely in industry. For the application of this controller for the microrobot, dynamic characteristics of a microrobot is analyzed and modeled at first, then Two-degree-of-freedom controller is designed based on the derived dynamic model of the microrobot. Experiments verify better performance by the proposed controller over the conventional controller.
Keynote Talk: Scope, cost and effort estimation for SOA projects

Current approaches to costing Service Oriented Architecture (SOA) projects are very limited and have only been applied to specific types of SOA such as Service Development or SOA Application Development. We are developing a Scope, Cost and Effort Estimation Framework that covers a wide range of SOA projects. As the framework is being developed we are engaging with industry and government to work with them on their SOA projects to apply the framework.
Extending the CSG Tree. Warping, Blending and Boolean Operations in an Implicit Surface Modeling System

Automatic blending has characterized the major advantage of implicit surface modeling systems. Recently, the introduction of deformations based on space warping and Boolean operations between primitives has increased the usefulness of such systems. We propose a further enhancement which will extend the range of models that can be easily and intuitively defined with a skeletal implicit surface system. We describe a hierarchical method which allows arbitrary compositions of models that make use of blending, warping and Boolean operations. We call this structure the BlobTree. Blending and space warping are treated in the same way as union, difference and intersection, i.e. as nodes in the BlobTree. The traversal of the BlobTree is described along with two rendering algorithms; a polygonizer and a ray tracer. We present some examples of interesting models which can be made easily using our approach that would be very difficult to represent with conventional systems.
Increased correlation between methylation sites in epigenome-wide replication studies: impact on analysis and results.

AIM To show that an increased correlation between CpGs after selection through an epigenome-wide association studies (EWAS) might translate into biased replication results. METHODS Pairwise correlation coefficients between CpGs selected in two published EWAS, the top hits replication, Bonferroni p-values, Benjamini-Hochberg (BH) false discovery rate (FDR) and directional FDR r-values were calculated in the NINFEA cohort data. Exposures' random permutations were performed to show the empirical p-value distributions. RESULTS The average pairwise correlation coefficients between CpGs were enhanced after selection for the replication (e.g., from 0.12 at genome-wide level to 0.26 among the selected CpGs), affecting the empirical p-value distributions and the usual multiple testing control. CONCLUSION Bonferroni and Benjamini-Hochberg FDR are inappropriate for the EWAS replication phase, and methods that account for the underlying correlation need to be used.
Combining CNNs and Pattern Matching for Question Interpretation in a Virtual Patient Dialogue System

For medical students, virtual patient dialogue systems can provide useful training opportunities without the cost of employing actors to portray standardized patients. This work utilizes word- and character-based convolutional neural networks (CNNs) for question identification in a virtual patient dialogue system, outperforming a strong word- and character-based logistic regression baseline. While the CNNs perform well given sufficient training data, the best system performance is ultimately achieved by combining CNNs with a hand-crafted pattern matching system that is robust to label sparsity, providing a 10% boost in system accuracy and an error reduction of 47% as compared to the pattern-matching system alone.
Sample‐Based Manifold Filtering for Interactive Global Illumination and Depth of Field

We present a fast reconstruction filtering method for images generated with Monte Carlo–based rendering techniques. Our approach specializes in reducing global illumination noise in the presence of depth‐of‐field effects at very low sampling rates and interactive frame rates. We employ edge‐aware filtering in the sample space to locally improve outgoing radiance of each sample. The improved samples are then distributed in the image plane using a fast, linear manifold‐based approach supporting very large circles of confusion. We evaluate our filter by applying it to several images containing noise caused by Monte Carlo–simulated global illumination, area light sources and depth of field. We show that our filter can efficiently denoise such images at interactive frame rates on current GPUs and with as few as 4–16 samples per pixel. Our method operates only on the colour and geometric sample information output of the initial rendering process. It does not make any assumptions on the underlying rendering technique and sampling strategy and can therefore be implemented completely as a post‐process filter.
A Task Ontology Driven Approach for Live Geoprocessing in a Service‐Oriented Environment

In a service‐oriented environment, Web geoprocessing services can provide geoprocessing functions for a variety of applications including Sensor Web. Connecting Sensor Web and geoprocessing services together shows great potentail to support live geoprocessing using real‐time data inputs. This article proposes a task ontology driven approach to live geoprocessing. The task in the ontology contains five aspects: task type, task priority, task constraints, task model, and task process. The use of the task ontology in driving live geoprocessing includes the following steps: (1) Task model generation, which generates a concrete process model to fulfill user demands; (2) Process model instantiation, which transforms the process model into an executable workflow; (3) Workflow execution: the workflow engine executes the workflow to generate value‐added data products using Sensor Web data as inputs. The approach not only helps create semantically correct connections between Sensor Web and Web geoprocessing services, but also provides sharable problem solving knowledge using process models. A prototype system, which leverages Web 2.0, Sensor Web, Semantic Web, and geoprocessing services, is developed to demonstrate the applicability of the approach.
Psychiatric morbidity in multiple sclerosis: a clinical and MRI study

SYNOPSIS This study reports the psychiatric morbidity in 116 patients with definite multiple sclerosis (MS). The presence of brain pathology was investigated using magnetic resonance imaging (MRI). A group of 48 physically disabled controls suffering from rheumatic or neurological conditions not involving the brain was used for comparison. Normative MRI data were obtained from a group of 40 healthy volunteers. Psychiatric morbidity was significantly higher in the MS group with nearly half of the patients scoring as cases at interview. There was no evidence to suggest that psychiatric symptoms in isolation were the first manifestation of MS in the present material. The presence of psychiatric illness at interview was significantly related to the degree of social stress as perceived by the patient. The severity of MRI abnormalities was greater in those with longer histories and greater physical disability, but did not show a significant relationship with global measures of psychiatric disability. Of the various psychiatric symptoms elation was significantly correlated with the presence of widespread MRI abnormalities, while flattening of affect, delusions and thought disorder correlated with the degree of pathology in the temporo-parietal region.
Index and Recognition for the Shape Contour of Plant Leaves

Content-based image retrieval methods are used to construct systems to retrieve and classify plants' leaves. The target of this paper is to balance matching veracity and computing efficiency and to improve the process of extracting feature. This paper focuses on the shape feature of the image. The work we have finished includes in the process of choosing feature and using numerical sequence of image features to solve the question about the irrespective of image feature’s circumrotating transformation. In the design process, we adopt general content-based image retrieval framework. So, result of this paper is used to plant leaves’ classifying and recognizing.
Eurographics/ Ieee-vgtc Symposium on Visualization 2008 Visual Clustering in Parallel Coordinates

Parallel coordinates have been widely applied to visualize high-dimensional and multivariate data, discerning patterns within the data through visual clustering. However, the effectiveness of this technique on large data is reduced by edge clutter. In this paper, we present a novel framework to reduce edge clutter, consequently improving the effectiveness of visual clustering. We exploit curved edges and optimize the arrangement of these curved edges by minimizing their curvature and maximizing the parallelism of adjacent edges. The overall visual clustering is improved by adjusting the shape of the edges while keeping their relative order. The experiments on several representative datasets demonstrate the effectiveness of our approach.
Ngram and Bayesian Classification of Documents for Topic and Authorship

Large, real world, data sets have been investigated in the context of Authorship Attribution of real world documents. Ngram measures can be used to accurately assign authorship for long documents such as novels. A number of 5 (authors) x 5 (movies) arrays of movie reviews were acquired from the Internet Movie Database. Both ngram and naive Bayes classifiers were used to classify along both the authorship and topic (movie) axes. Both approaches yielded similar results, and authorship was as accurately detected, or more accurately detected, than topic. Part of speech tagging and function-word lists were used to investigate the influence of structure on classification tasks on documents with meaning removed but grammatical structure intact.
In-Plane Permeability of Stitched MMWK Laminates in Resin Transfer Molding

Resin Transfer Molding (RTM) involves the injection of liquid resin into a closed mold preloaded with dry preforms. For planar composite structures, the in-plane permeability should be determined prior to the injection process in order to predict the fluid flow and fill time progress. In this work, the in-plane permeability of multi-layer multi-directional warp knit (MMWK) laminates stitched using Kevlar fiber were measured using radial flow visualization experiments. The visualization demonstrated that the in-plane permeability of MMWK laminates is highly anisotropic and significantly influenced by the Kevlar stitches. Results showed that the major principal permeability, k x , decreased with an increase in stitch density. Although the stitches resulted in an increase in minor principal permeability, k y , it seems that the increase in stitch density did not result in an obvious change in k y .
Bipartitionnement d'un tableau de contingence

Resume. La recherche simultanee de partitions sur l’ensemble de lignes et l’ensemble de colonnes d’un tableau de donnees a donne naissance a des methodes de classification simultanee ou bipartitionnement. On parle aussi de la classification croisee ou la classification par blocs. Plusieurs algorithmes de bipartitionnement ont ete proposes dans la litterature selon le type de tableau des donnees. Nous nous interessons dans ce papier a l’algorithme Croki2 de classification croisee des tableaux de contingence. Nous proposons dans ce papier une variante plus rapide de cet algorithme que nous comparons a la version originale a travers des experimentations sur des donnees presentant une structure de biclasses generees artificiellement selon une methodologie que nous detaillons.
A novel higher-order semantic kernel for text classification

In conventional text categorization algorithms, documents are symbolized as “bag of words” (BOW) with the fact that documents are supposed to be independent from each other. While this approach simplifies the models, it ignores the semantic information between terms of each document. In this study, we develop a novel method to measure semantic similarity based on higher-order dependencies between documents. We propose a kernel for Support Vector Machines (SVM) algorithm using these dependencies which is called Higher-Order Semantic Kernel. With the aim of presenting comparative performance of Higher-Order Semantic Kernel we performed many experiments not only with our algorithm but also with existing traditional first-order kernels such as Polynomial Kernel, Radial Basis Function Kernel, and Linear Kernel. The experiments using Higher-Order Semantic Kernel on several well-known datasets show that classification performance improves significantly over the first-order methods.
Prediction‐based fingerprints of protein–protein interactions

The recognition of protein interaction sites is an important intermediate step toward identification of functionally relevant residues and understanding protein function, facilitating experimental efforts in that regard. Toward that goal, the authors propose a novel representation for the recognition of protein–protein interaction sites that integrates enhanced relative solvent accessibility (RSA) predictions with high resolution structural data. An observation that RSA predictions are biased toward the level of surface exposure consistent with protein complexes led the authors to investigate the difference between the predicted and actual (i.e., observed in an unbound structure) RSA of an amino acid residue as a fingerprint of interaction sites. The authors demonstrate that RSA prediction‐based fingerprints of protein interactions significantly improve the discrimination between interacting and noninteracting sites, compared with evolutionary conservation, physicochemical characteristics, structure‐derived and other features considered before. On the basis of these observations, the authors developed a new method for the prediction of protein–protein interaction sites, using machine learning approaches to combine the most informative features into the final predictor. For training and validation, the authors used several large sets of protein complexes and derived from them nonredundant representative chains, with interaction sites mapped from multiple complexes. Alternative machine learning techniques are used, including Support Vector Machines and Neural Networks, so as to evaluate the relative effects of the choice of a representation and a specific learning algorithm. The effects of induced fit and uncertainty of the negative (noninteracting) class assignment are also evaluated. Several representative methods from the literature are reimplemented to enable direct comparison of the results. Using rigorous validation protocols, the authors estimated that the new method yields the overall classification accuracy of about 74% and Matthews correlation coefficients of 0.42, as opposed to up to 70% classification accuracy and up to 0.3 Matthews correlation coefficient for methods that do not utilize RSA prediction‐based fingerprints. The new method is available at http://sppider.cchmc. org. Proteins 2007. © 2006 Wiley‐Liss, Inc.
CorrLog: Correlated Logistic Models for Joint Prediction of Multiple Labels

In this paper, we present a simple but effective method for multi-label classification (MLC), termed Correlated Logistic Models (Corrlog), which extends multiple Independent Logistic Regressions (ILRs) by modeling the pairwise correlation between labels. Algorithmically, we propose an efficient method for learning parameters of Corrlog, which is based on regularized maximum pseudolikelihood estimation and has a linear computational complexity with respect to the number of labels. Theoretically, we show that Corrlog enjoys a satisfying generalization bound which is independent of the number of labels. The effectiveness of Corrlog on modeling label correlations is illustrated by a toy example, and further experiments on real data show that Corrlog achieves competitive performance compared with popular MLC algorithms.
A 6.4Gb/s near-ground single-ended transceiver for dual-rank DIMM memory interface systems

The emergence of cloud computing has driven the demand for high-density, low-latency and high-speed memory interfaces. For such applications the use of multiple dual-inline memory modules (DIMMs) with multiple ranks enables time-efficient processing of high-volume data. However, the deterioration of the channel frequency response due to the presence of DIMM connectors and multiple ranks makes it challenging to perform low-power read and write (R/W) operations at high-speed. Recent works have demonstrated the use of near-ground signaling (NGS) for low-power operation and signal-integrity enhancement with the aid of transmit supply regulation. In contrast to their differential nature, this paper introduces a single-ended NGS transceiver that achieves 6.4Gb/s R/W operations with the aid of low-power equalization and in-situ reference-voltage calibration over a 3.5" total FR4 PCB routing with more than 25mm of package traces in a dual-rank DIMM memory interface system.
The Paleotopography Reconstruction of late Cretaceous Dabie Orogen by Low-Temperature Thermochronological Age-Elevation Relationships

The paleotopography reconstruction of orogen has an important research value on the study of mountain building and the study of orogen’s regional control over the paleoclimate change. Considering the Dabie orogen’s regional difference in exhumation, this article models the post-orogenic exhumation rates and the relief evolution rates by low-temperature thermochronology and age-elevation relationships. The result shows that the mean exhumation rates（0.07～0.08km/Ma）of the middle block, the southwest block, and the southeast block are higher than the rate（0.06 km/Ma）of the northwest block and the northeast block. The relief evolution rate of the northeast block (3.5) is the highest, the middle block (3.0) follows, the southeast block (2.5) goes after, and those of the north-west block and south-west block（2.0,1.5）are the lowest. This is might be related to the thermal doming extensional structure of Dabie orogen in Cretaceous.
Programming deterministic reactive systems with Synchronous Java

A key issue in the development of reliable embedded software is the proper handling of reactive control-flow, which typically involves concurrency. Java and its thread concept have only limited provisions for implementing deterministic concurrency. Thus, as has been observed in the past, it is challenging to develop concurrent Java programs without any deadlocks or race conditions. To alleviate this situation, the Synchronous Java (SJ) approach presented here adopts the key concepts that have been established in the world of synchronous programming for handling reactive control-flow. Thus SJ not only provides deterministic concurrency, but also different variants of deterministic preemption. Furthermore SJ allows concurrent threads to communicate with Esterel-style signals. As a case study for an embedded system usage, we also report on how the SJ concepts have been ported to the ARM-based Lego Mindstorms NXT system.