Miracle-dz/UniFAR-FTUs huggingface dataset:
Surface simplification using quadric error metrics

Many applications in computer graphics require complex, highly detailed models. However, the level of detail actually necessary may vary considerably. To control processing time, it is often desirable to use approximations in place of excessively detailed models. We have developed a surface simplification algorithm which can rapidly produce high quality approximations of polygonal models. The algorithm uses iterative contractions of vertex pairs to simplify models and maintains surface error approximations using quadric matrices. By contracting arbitrary vertex pairs (not just edges), our algorithm is able to join unconnected regions of models. This can facilitate much better approximations, both visually and with respect to geometric error. In order to allow topological joining, our system also supports non-manifold surface models.
Regular expression sub-matching using partial derivatives

Regular expression sub-matching is the problem of finding for each sub-part of a regular expression a matching sub-string. Prior work applies Thompson and Glushkov NFA methods for the construction of the matching automata. We propose the novel use of derivatives and partial derivatives for regular expression sub-matching. Our benchmarking results show that the run-time performance is promising and that our approach can be applied in practice.
Realtime moving object detection from a freely moving monocular camera

Detection of moving objects is a key component in mobile robotic perception and understanding of the environment. In this paper, we describe a realtime independent motion detection algorithm for this purpose. The method is robust and is capable of detecting difficult degenerate motions, where the moving objects is followed by a moving camera in the same direction. This robustness is attributed to the use of efficient geometric constraints and a probability framework which propagates the uncertainty in the system. The proposed independent motion detection framework integrates seamlessly with existing visual SLAM solutions. The system consists of multiple modules which are tightly coupled so that one module benefits from another. The integrated system can simultaneously detect multiple moving objects in realtime from a freely moving monocular camera.
An experimental investigation of interference effects in human-humanoid interaction games

Investigating how people respond to and relate to robots is a multifaceted scientific challenge. This paper reports on an experimental investigation concerning movement interference effects between a human and a robot. We compare results with that obtained by Oztop et al. [1], however, in our study we used a small child-sized robot (KASPAR) with an overall human-like appearance. The experiment was conducted with both child and adult participants who interacted with a small humanoid robot using arm waving behaviours. The experimental setup was designed to be less constrained than in [1] with an emphasis on playful interaction. The experimental results did not show evidence for interference effects. This might be due to a more game-like and less constrained experimental environment or to the specific features of the robot or both. In addition to measurements of the variance of the movements, we investigated a measure for behavioural synchrony between human and robot movements based on the concept of information distance. The results of information distance analysis indicated that most of the human participants were affected by the robot's behavioural rhythms. While our experiments did not show a movement interference effect, we found behavioural adaptation of participants' movement timing to the robot's movements. Thus, the measure of behavioural synchrony that we introduced appears useful for complementing other measures (such as variance) previously used in the literature.
Journal of Emerging Trends in Computing and Information Sciences Pairing-free and Revocable Certificateless Signature against Signing Key Exposure

Certificateless public key schemes without pairing have attracted much attention for its relatively high efficiency. The revocation problem is a very necessary issue in a public key cryptosystem. However, there have been some solutions to the certificateless revocation problem; they still suffer from low efficiency or security weakness. In this paper, we propose a revocable certificateless signature scheme, which highlights the public channel for key-update, no bilinear pairing and signing key exposure resistant. Our scheme is provably secure under the difficulty assumption of the Computational Diffie-Hellman problem.
Recursive log-barrier method for on-line time-optimal robot path tracking

This paper focuses on time-optimal robot path tracking and develops an approximate, log-barrier batch solution method to rapidly solve discretized, convexly reformuled path tracking problems. Based on this batch solution method, which results in smooth actuator torques, a recursive variant is derived for on-line path tracking. The results and trade-offs in calculation time, delay and path duration are compared for the batch and recursive variant of the log-barrier method as well as for an exact solution method, by means of an experimental test case of a robot carrying out a writing task, in which the path data is generated on-line by human demonstration.
Smoking in Movies, Implicit Associations of Smoking With the Self, and Intentions to Smoke

We examined whether identifying with a film character who smokes increases implicit associations of the self with smoking. Undergraduate men were randomly assigned to view film clips in which the male protagonist either smoked or did not smoke. We measured subsequent levels of self-smoking associations using a reaction time task, as well as self-reported beliefs about smoking and smokers. Greater identification with the smoking protagonist predicted stronger implicit associations between the self and smoking (for both smokers and nonsmokers) and increased intention to smoke (among the smokers). Stronger implicit self-smoking associations uniquely predicted increases in smokers' intentions to smoke, over and above the effects of explicit beliefs about smoking. The results provide evidence that exposure to smoking in movies is causally related to changes in smoking-related thoughts, that identification with protagonists is an important feature of narrative influence, and that implicit measures may be useful in predicting deliberative behavior.
A KRAS-directed transcriptional silencing pathway that mediates the CpG island methylator phenotype

Approximately 70% of KRAS-positive colorectal cancers (CRCs) have a CpG island methylator phenotype (CIMP) characterized by aberrant DNA hypermethylation and transcriptional silencing of many genes. The factors involved in, and the mechanistic basis of, CIMP is not understood. Among the CIMP genes are the tumor suppressors p14ARF, p15INK4B, and p16INK4A, encoded by the INK4-ARF locus. In this study, we perform an RNA interference screen and identify ZNF304, a zinc-finger DNA-binding protein, as the pivotal factor required for INK4-ARF silencing and CIMP in CRCs containing activated KRAS. In KRAS-positive human CRC cell lines and tumors, ZNF304 is bound at the promoters of INK4-ARF and other CIMP genes. Promoter-bound ZNF304 recruits a corepressor complex that includes the DNA methyltransferase DNMT1, resulting in DNA hypermethylation and transcriptional silencing. KRAS promotes silencing through upregulation of ZNF304, which drives DNA binding. Finally, we show that ZNF304 also directs transcriptional silencing of INK4-ARF in human embryonic stem cells. DOI: http://dx.doi. org/10.7554/eLife.02313. 001
Formal modelling and transformations of processor instruction sets

Instruction sets of modern processors contain hundreds of instructions defined on a relatively small set of datapath components and distinguished by their codes and the order in which they activate these components. Optimal design of an instruction set for a particular combination of available hardware components and software requirements is crucial for system performance and is a challenging task involving a lot of heuristics and high-level design decisions. The overall design process is significantly complicated by inefficient representation of instructions, which are usually described individually despite the fact that they share a lot of common behavioural patterns. This paper presents a new methodology for compact graph representation of processor instruction sets, which gives the designer a new high-level perspective for reasoning on large sets of instructions without having to look at each of them individually. This opens the way for various transformation and optimisation procedures, which are formally defined and explained on several examples, as well as practically evaluated on an FPGA platform.
A randomized algorithm for spectral clustering

Spectral Clustering has reached a wide level of diffusion among unsupervised learning applications. Despite its practical success we believe that for a correct usage one has to face a difficult problem: given a target number of classes K the optimal K-dimensional subspace is not necessarily spanned by the first K eigenvectors of the graph Normalized Laplacian. The contribution of this paper is twofold. First, we show a bound for choosing a correct number of eigenvectors. Second, we propose a randomized spectral algorithm able to find a clustering solution. We show the efficacy of the algorithm with experiments on real world graphs. Our proposal is a scheme that naturally extends the current usage of Spectral Clustering.
Integrated Security Process Improvement Framework for Systems and Services

Security of systems and services has been dominant research area in recent years as today's cloud services, big data and networked systems, especially when they provide wireless application access where personal and confidential data to be transmitted across the networked systems. Numerous tools and technologies are available to ensure system's security; however, external threats to computer systems and applications residents thereon, are also becoming more and more sophisticated and on the increase. Therefore, the key aim of this research is to integrate security engineering techniques and process with systems development life-cycle and process improvement frameworks. This paper presents a framework that consists of two components: 1 a security assessment model to looks at the existing security infrastructure of an organisation to determine its security maturity level; and 2 a security improvement maturity model to suggest an improvement mechanism for the organisation to progress from one maturity level to the next higher level. The intention is to provide a scheme to improve the organisation's Systems and network security with the aim that it becomes more efficient and effective than before.
Optimal Sailing Routes with Uncertain Weather

We consider the problem of finding a route that minimises the expected sailing time between two points on the ocean under uncertain weather conditions. This has applications in long distance offshore yacht racing. The uncertainty in the weather is modelled by a branching scenario tree that captures the serial correlation inherent in the evolution of weather systems over time. The stochastic solution method extends a deterministic dynamic programming approach to include the weather scenario as a state variable, yielding a stochastic dynamic programming algorithm. Careful attention to implementation details yields an approach that optimises with uncertainty while maintaining acceptable solution times on a PC. This paper summarises the work presented in Allsopp (1998).
Differentially private Bayesian learning on distributed data

Many applications of machine learning, for example in health care, would benefit from methods that can guarantee privacy of data subjects. Differential privacy (DP) has become established as a standard for protecting learning results. The standard DP algorithms require a single trusted party to have access to the entire data, which is a clear weakness, or add prohibitive amounts of noise. We consider DP Bayesian learning in a distributed setting, where each party only holds a single sample or a few samples of the data. We propose a learning strategy based on a secure multi-party sum function for aggregating summaries from data holders and the Gaussian mechanism for DP. Our method builds on an asymptotically optimal and practically efficient DP Bayesian inference with rapidly diminishing extra cost.
Searching for hematopoietic stem cells: evidence that Thy-1.1lo Lin- Sca-1+ cells are the only stem cells in C57BL/Ka-Thy-1.1 bone marrow

Hematopoietic stem cells (HSCs) are defined in mice by three activities: they must rescue lethally irradiated mice (radioprotection), they must self-renew, and they must restore all blood cell lineages permanently. We initially demonstrated that HSCs were contained in a rare (approximately 0.05%) subset of bone marrow cells with the following surface marker profile: Thy-1.1lo Lin- Sca-1+. These cells were capable of long-term, multi-lineage reconstitution and radioprotection of lethally irradiated mice with an enrichment that mirrors their representation in bone marrow, namely, 1,000-2,000-fold. However, the experiments reported did not exclude the possibility that stem cell activity may also reside in populations that are Thy-1.1-, Sca-1-, or Lin+. In this article stem cell activity was determined by measuring: (a) radioprotection provided by sorted cells; (b) long-term, multi-lineage reconstitution of these surviving mice; and (c) long- term, multi-lineage reconstitution by donor cells when radioprotection is provided by coinjection of congenic host bone marrow cells. Here we demonstrate that HSC activity was detected in Thy-1.1+, Sca-1+, and Lin- fractions, but not Thy-1.1-, Sca-1-, or Lin+ bone marrow cells. We conclude that Thy-1.1lo Lin- Sca-1+ cells comprise the only adult C57BL/Ka-Thy-1.1 mouse bone marrow subset that contains pluripotent HSCs.
Two (Note) Heads Are Better Than One: Pen-Based Multimodal Interaction with Music Scores

Digitizing early music sources requires new ways of dealing with musical documents. Assuming that current technologies cannot guarantee a perfect automatic transcription, our intention is to develop an interactive system in which user and software collaborate to complete the task. Since conventional score post-editing might be tedious, the user is allowed to interact using an electronic pen. Although this provides a more ergonomic interface, this interaction must be decoded as well. In our framework, the user traces the symbols using the electronic pen over a digital surface, which provides both the underlying image (ofﬂine data) and the drawing made by the e-pen (online data) to improve classiﬁcation. Applying this methodology over 70 scores of the target musical archive, a dataset of 10 230 bimodal samples of 30 different symbols was obtained and made available for research purposes. This paper presents experimental results on classiﬁcation over this dataset, in which symbols are recognized by combining the two modalities. This combination of modes has demonstrated its good performance, decreasing the error rate of using each modality separately and achieving an almost error-free performance.
PRELIMINARY INVESTIGATION OF THE ROBUSTNESS OF MAXIMALLY STABLE EXTREMAL REGIONS (MSER) MODEL FOR THE AUTOMATIC REGISTRATION OF OVERLAPPING IMAGES

Various researchers in Digital Image processing have developed keen interest in the automation of object detection, description and extraction process used for various applications and this has led to the development of series of Feature detection and extraction models one of which is the Maximally Stable Extremal Regions Feature Algorithm (MSER). This paper investigates the robustness of MSER algorithm (a blob-like and affine-invariant feature detector) for the detection and extraction of corresponding features used for the automatic registration of series of overlapping images. The robustness investigation was carried out in three different registration campaigns using overlapping images extracted from google earth and image pair acquired from an Unmanned Aerial Vehicle (UAV). Sum of Square Difference (SSD) and Bilinear interpolation models were used to establish the similarity measure between the images to be registered, resampling of the pixel-values and computation of non-integer coordinates respectively while Random Sampling Consensus (RANSAC) algorithm was used to exclude the outliers and to compute the transformation matrix using affine transformation function. The results obtained from this preliminary investigation shows that the processing speed of MSER is quite high for Automatic Image Registration with a relatively high accuracy. While an accuracy of 61.54% was obtained from the first campaign with a processing time of 11.92 seconds, the second campaign gave an accuracy of 52.02% with a processing time of 11.20 seconds and the third campaign produced an accuracy of 55.62% with a processing time of 3.27 seconds. The obtained speed and accuracy shows that MSER is a very robust model and as such, can be deployed as the feature detection and extraction model in the development of an automatic image registration scheme.
Image segmentation using encoder-decoder architecture and region consistency activation

An Encoder-Decoder Neural Network Architecture is combined with a novel strategy to improve global label consistency, to come with an improved image segmentation model. Label Distribution predictions extracted from the SegNet Network is investigated and used in the project for image labeling. An algorithm called Region Consistency Activation (RCA) to improve the global label consistency is implemented. RCA is based on a novel transformation between Ultra metric Contour Map (UCM) and the Probability of Regions Consistency (PRC). These algorithms were rigorously tested on the CamVid dataset. Superior performances were achieved compared with the state-of-the-art methods on this dataset.
Chunk-Based Bi-Scale Decoder for Neural Machine Translation

In typical neural machine translation (NMT), the decoder generates a sentence word by word, packing all linguistic granularities in the same time-scale of RNN. In this paper, we propose a new type of decoder for NMT, which splits the decode state into two parts and updates them in two different time-scales. Specifically, we first predict a chunk time-scale state for phrasal modeling, on top of which multiple word time-scale states are generated. In this way, the target sentence is translated hierarchically from chunks to words, with information in different granularities being leveraged. Experiments show that our proposed model significantly improves the translation performance over the state-of-the-art NMT model.
A Glucagon-Like Peptide-1 Analog Reverses the Molecular Pathology and Cardiac Dysfunction of a Mouse Model of Obesity

Background— Cardiac consequences of obesity include inflammation, hypertrophy, and compromised energy metabolism. Glucagon-like peptide-1 is an incretin hormone capable of cytoprotective actions that reduces inflammation and endoplasmic reticulum stress in other tissues. Here we examine the cardiac effects of the glucagon-like peptide-1 analog liraglutide in a model of obesity, independent of changes in body weight. Methods and Results— C57Bl6 mice were placed on a 45% high-fat diet (HFD) or a regular chow diet. Mice on HFD developed 46±2% and 60±2% greater body weight relative to regular chow diet–fed mice at 16 and 32 weeks, respectively (both P<0.0001), manifesting impaired glucose tolerance, insulin resistance, and cardiac ceramide accumulation by 16 weeks. One-week treatment with liraglutide (30 µg/kg twice daily) did not reduce body weight, but reversed insulin resistance, cardiac tumor necrosis factor-&agr; expression, nuclear factor kappa B translocation, obesity-induced perturbations in cardiac endothelial nitric oxide synthase, connexin-43, and markers of hypertrophy and fibrosis, in comparison with placebo-treated HFD controls. Liraglutide improved the cardiac endoplasmic reticulum stress response and also improved cardiac function in animals on HFD by an AMP-activated protein kinase–dependent mechanism. Supporting a direct mechanism of action, liraglutide (100 nmol/L) prevented palmitate-induced lipotoxicity in isolated mouse cardiomyocytes and primary human coronary smooth muscle cells and prevented adhesion of human monocytes to tumor necrosis factor-&agr;–activated human endothelial cells in vitro. Conclusions— Weight-neutral treatment with a glucagon-like peptide-1 analog activates several cardioprotective pathways, prevents HFD-induced insulin resistance and inflammation, reduces monocyte vascular adhesion, and improves cardiac function in vivo by activating AMP-activated protein kinase. These data support a role for glucagon-like peptide-1 analogs in limiting the cardiovascular risks of obesity.
Transition Thresholds for Binarization of Historical Documents

This paper extends the transition method for binarization based on transition pixels, a generalization of edge pixels. This method originally computes transition thresholds using the quantile thresholding algorithm, that has a critical parameter. We achieved an automatic version of the transition method by computing the transition thresholds with the Rosin's algorithm. We experimentally tested four variants of the transition method combining the density and cumulative distribution functions of transition values, with gray-intensity thresholds based on the normal and lognormal density functions. The results of our experiments show that these unsupervised methods yields superior binarization compared with top-ranked algorithms.
hnRNP K Coordinates Transcriptional Silencing by SETDB1 in Embryonic Stem Cells

Retrotransposition of endogenous retroviruses (ERVs) poses a substantial threat to genome stability. Transcriptional silencing of a subset of these parasitic elements in early mouse embryonic and germ cell development is dependent upon the lysine methyltransferase SETDB1, which deposits H3K9 trimethylation (H3K9me3) and the co-repressor KAP1, which binds SETDB1 when SUMOylated. Here we identified the transcription co-factor hnRNP K as a novel binding partner of the SETDB1/KAP1 complex in mouse embryonic stem cells (mESCs) and show that hnRNP K is required for ERV silencing. RNAi-mediated knockdown of hnRNP K led to depletion of H3K9me3 at ERVs, concomitant with de-repression of proviral reporter constructs and specific ERV subfamilies, as well as a cohort of germline-specific genes directly targeted by SETDB1. While hnRNP K recruitment to ERVs is dependent upon KAP1, SETDB1 binding at these elements requires hnRNP K. Furthermore, an intact SUMO conjugation pathway is necessary for SETDB1 recruitment to proviral chromatin and depletion of hnRNP K resulted in reduced SUMOylation at ERVs. Taken together, these findings reveal a novel regulatory hierarchy governing SETDB1 recruitment and in turn, transcriptional silencing in mESCs.
The women's movement in Papua New Guinea as a vehicle to enhance women's participation in development

This case study examines the nature and level of women's participation in development in contemporary Papua New Guinea (PNG) through the framework of the local women's movement. Mainstream socioeconomic indicators and a review of the macro forces at play reveal the disadvantaged position of women, and expose the remarkable barriers to participation they are facing. Yet, despite those challenges, women across the country are mobilized through their membership in diverse women's groups, from the remote village group to the National Council of Women. Because of its extensive presence and network, the women's movement is seen as an important vehicle through which the problem of unequal participation in development can be addressed. Central to the case study is the question of how development and participation are defined.