Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining deep learning algorithm with scene recognition and haptic feedback for 4D-VR cinema

Adding multiple tactile sensations is a quick and useful method of enhancing immersion in virtual reality (VR) games by providing strong feedback to players, such as kinesthesia and cutaneous feedback. Our previous work on SoEs [Chen et al. 2016] and BoEs [Han et al. 2017] has shown the potential of using haptic feedback for VR controllers that provide feedback through heat, air, vibration, and reaction force. Additionally, our another work on AoEs [Han et al. 2017] and AoEs+ [Han et al. 2018] employed integration devices to simulate two virtual environments simultaneously in a room-scale physical space with each offering multiple tactile sensations.
Eigenvalue Corrected Noisy Natural Gradient

Variational Bayesian neural networks combine the flexibility of deep learning with Bayesian uncertainty estimation. However, inference procedures for flexible variational posteriors are computationally expensive. A recently proposed method, noisy natural gradient, is a surprisingly simple method to fit expressive posteriors by adding weight noise to regular natural gradient updates. Noisy K-FAC is an instance of noisy natural gradient that fits a matrix-variate Gaussian posterior with minor changes to ordinary K-FAC. Nevertheless, a matrix-variate Gaussian posterior does not capture an accurate diagonal variance. In this work, we extend on noisy K-FAC to obtain a more flexible posterior distribution called eigenvalue corrected matrix-variate Gaussian. The proposed method computes the full diagonal re-scaling factor in Kronecker-factored eigenbasis. Empirically, our approach consistently outperforms existing algorithms (e.g., noisy K-FAC) on regression and classification tasks.
Style Decomposition for Improved Neural Style Transfer

Universal Neural Style Transfer (NST) methods are capable of performing style transfer of arbitrary styles in a style-agnostic manner via feature transforms in (almost) real-time. Even though their unimodal parametric style modeling approach has been proven adequate to transfer a single style from relatively simple images, they are usually not capable of effectively handling more complex styles, producing significant artifacts, as well as reducing the quality of the synthesized textures in the stylized image. To overcome these limitations, in this paper we propose a novel universal NST approach that separately models each sub-style that exists in a given style image (or a collection of style images). This allows for better modeling the subtle style differences within the same style image and then using the most appropriate sub-style (or mixtures of different sub-styles) to stylize the content image. The ability of the proposed approach to a) perform a wide range of different stylizations using the sub-styles that exist in one style image, while giving the ability to the user to appropriate mix the different sub-styles, b) automatically match the most appropriate sub-style to different semantic regions of the content image, improving existing state-of-the-art universal NST approaches, and c) detecting and transferring the sub-styles from collections of images are demonstrated through extensive experiments.
Up-Regulation of hsa-miR-210 Promotes Venous Metastasis and Predicts Poor Prognosis in Hepatocellular Carcinoma

Objective: To investigate the potential biomarkers for venous metastasis of hepatocellular carcinoma (HCC), and briefly discuss their target genes and the signaling pathways they are involved in. Materials and Method: The dataset GSE6857 was downloaded from GEO. Significantly differentially expressed miRNAs were identified using the R package “limma,” After that, the survival analysis was conducted to discover the significance of these up-regulated miRNAs for the prognosis of HCC patients. Additionally, miRNAs which were up-regulated in venous metastasis positive HCC tissues and were significant for the prognosis of HCC patients were further verified in clinical samples using RT-qPCR. The miRNAs were then analyzed for their correlations with clinical characteristics including survival time, AFP level, pathological grade, TNM stage, tumor stage, lymph-node metastasis, distant metastasis, child-pugh score, vascular invasion, liver fibrosis and race using 375 HCC samples downloaded from the TCGA database. The target genes of these miRNAs were obtained using a miRNA target gene prediction database, and their functions were analyzed using the online tool DAVID. Results: 15 miRNAs were differentially expressed in samples with venous metastasis, among which 7 were up-regulated in venous metastasis positive HCC samples. As one of the up-regulated miRNAs, hsa-miR-210 was identified as an independent prognostic factor for HCC. Using RT-qPCR, it was evident that hsa-miR-210 expression was significantly higher in venous metastasis positive HCC samples (p = 0.0036). Further analysis indicated that hsa-miR-210 was positively associated with AFP level, pathological grade, TNM stage, tumor stage and vascular invasion. A total of 168 hsa-miR-210 target genes, which are mainly related to tumor metastasis and tumor signaling pathways, were also predicted in this study. Conclusion: hsa-miR-210 might promote vascular invasion of HCC cells and could be used as a prognostic biomarker.
A Fast Security Evaluation of Support Vector Machine Against Evasion Attack

Traditional machine learning techniques may suffer from evasion attack in which an attacker intends to have malicious samples to be misclassified as legitimate at test time by manipulating the samples. It is crucial to evaluate the security of a classifier during the development of a robust system against evasion attack. Current security evaluation for Support Vector Machine (SVM) is very time-consuming, which largely decreases its availability in applications with big data. In this paper, we propose a fast security evaluation of support vector machine against evasion attack. It calculates the security of an SVM by the average distance between a set of malicious samples and the hyperplane. Experimental results show strong correlation between the proposed security evaluation and the current one. Current security measure min-cost-mod runs 24,000 to 551,000 times longer than our proposed one on six datasets.
First-person-view drone flying in mixed reality

This paper presents a study aimed at creating an aerial mixed reality environment by using a first-person-view drone. We develop a drone with a stereoscopic camera based on human binocular vision. Then, we present a novel mixed reality environment with bidirectional interaction between the real and virtual worlds. Our approach is effective to perceive the depth of obstacles and provides a safe and exciting environment for drone flying.
Morphological and Biochemical Diversity of Shallot Landraces Preserved Along the Croatian Coast

Shallots are a valuable minor Allium crop, and are propagated vegetatively and maintained in home gardens across generations along the Croatian coast and island areas. Shallot landraces growing along the Croatian coast fall into three genotypes: Allium cepa Aggregatum group (2n = 2x = 16), A. × proliferum (Moench) Schard. (2n = 2x = 16), and A. × cornutum Clementi ex Vis. (2n = 3x = 24), among which A. × cornutum is the most widespread. The aim of this study was to differentiate shallot accessions collected from local farmers using morphological markers. Also, the chemical composition including phenolic content, phenolic profile, total antioxidant capacity, and mineral composition, of shallot accessions was compared with that of the local landraces of common onion, and with market available shallot and common onion cultivars. Based on morphological observations and using multivariate classification, shallot landraces were classified into three distinct groups. Properties, based on which A. × cornutum can be differentiated from A. cepa Aggregatum and A. × proliferum, are stamen morphology, stamen length, leaf and scape vegetative properties, number of bulbs in cluster, cluster mass, and bulb diameter. Flower diameter and flower pedicel length differentiate A. × cornutum and A. × proliferum from A. cepa Aggregatum. Significant variability was observed in the biochemical profiles across tested accessions. Compared with the commercial common onion cultivars, local shallot accessions have higher bulb N, P, and K content. The major phenolic compounds identified in shallots were quercetin-4′-glucoside and quercetin-3,4′-diglucoside. Additionally, several other minor phenolic compounds were also identified. Morphological and biochemical profiles were evaluated using Partial Least Square (PLS) analysis. Specific morphological traits and biochemical markers for possible species identification are proposed.
Chimeric RNA in Cancer and Stem Cell Differentiation

Gene fusions are considered hallmarks of cancer which can be produced by chromosomal rearrangements. These DNA-level fusion events may result in the expression of chimeric RNAs; however, chimeric RNAs can be also produced by intergenic splicing events. Chimeric transcripts created by the latter mechanism are regulated at the transcriptional level and thus present additional modes of action and regulation. They have demonstrated importance in normal cell physiology, and their dysregulation can induce oncogenesis and impact cell differentiation. In this review, we outline proven mechanisms through which intergenically spliced chimeric RNAs are involved in carcinogenesis. We highlight their similarity to canonical chimeric RNAs resulting from gene fusions as well as their unique qualities. Additionally, we review known roles of chimeric RNA in cell differentiation and propose means through which chimeric RNAs may be valuable as stage-specific markers or as targets for expression profiling.
ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples

Deep neural networks (DNNs) have been demonstrated to be vulnerable to adversarial examples. Specifically, adding imperceptible perturbations to clean images can fool the well trained deep neural networks. In this paper, we propose an end-to-end image compression model to defend adversarial examples: ComDefend. The proposed model consists of a compression convolutional neural network (ComCNN) and a reconstruction convolutional neural network (ResCNN). The ComCNN is used to maintain the structure information of the original image and purify adversarial perturbations. And the ResCNN is used to reconstruct the original image with high quality. In other words, ComDefend can transform the adversarial image to its clean version, which is then fed to the trained classifier. Our method is a pre-processing module, and does not modify the classifier’s structure during the whole process. Therefore it can be combined with other model-specific defense models to jointly improve the classifier’s robustness. A series of experiments conducted on MNIST, CIFAR10 and ImageNet show that the proposed method outperforms the state-of-the-art defense methods, and is consistently effective to protect classifiers against adversarial attacks.
Application of Small Epigenetic Modulators in Pediatric Medulloblastoma

Medulloblastoma is one of the most frequent among pediatric brain tumors, and it has been classified in various subgroups. Some of them already benefit from quite good therapeutic options, whereas others urgently need novel therapeutic approaches. Epigenetic modulators have long been studied in various types of cancer. Within this review, we summarize the main preclinical studies regarding epigenetic targets (such as HDAC, SIRT, BET, EZH2, G9a, LSD1, and DNMT) inhibitors in medulloblastoma. Furthermore, we shed light on the increasing number of applications of drug combinations as well as hybrid compounds involving epigenetic mechanisms. Nevertheless, in the studies published so far, mainly un-specific or old modulators have been used, and the PKs (brain permeability) have not been well-evaluated. Thus, these findings should be considered as a starting point for further improvement and not as a final result.
The Approach to Managing Provenance Metadata and Data Access Rights in Distributed Storage Using the Hyperledger Blockchain Platform

The paper suggests a new approach based on blockchain technologies and smart contracts to creation of a distributed system for managing provenance metadata, as well as access rights to data in distributed storages, which is fault-tolerant, safe and secure from the point of view of preservation of metadata records from accidental or intentional distortions. The implementation of the proposed approach is based on the permissioned blockchains and on the Hyperledger Fabric blockchain platform in conjunction with Hyperledger Composer.
NLRX1 Facilitates Histoplasma capsulatum-Induced LC3-Associated Phagocytosis for Cytokine Production in Macrophages

LC3-associated phagocytosis (LAP) is an emerging non-canonical autophagy process that bridges signaling from pattern-recognition receptors (PRRs) to autophagic machinery. LAP formation results in incorporation of lipidated LC3 into phagosomal membrane (termed LAPosome). Increasing evidence reveals that LAP functions as an innate defense mechanism against fungal pathogens. However, the molecular mechanism involved and the consequence of LAP in regulating anti-fungal immune response remain largely unexplored. Here we show that Histoplasma capsulatum is taken into LAPosome upon phagocytosis by macrophages. Interaction of H. capsulatum with Dectin-1 activates Syk and triggers subsequent NADPH oxidase-mediated reactive oxygen species (ROS) response that is involved in LAP induction. Inhibiting LAP induction by silencing LC3α/β or treatment with ROS inhibitor impairs the activation of MAPKs-AP-1 pathway, thereby reduces macrophage proinflammatory cytokine response to H. capsulatum. Additionally, we unravel the importance of NLRX1 in fungus-induced LAP. NLRX1 facilitates LAP by interacting with TUFM which associates with autophagic proteins ATG5-ATG12 for LAPosome formation. Macrophages from Nlrx1−/− mice or TUFM-silenced cells exhibit reduced LAP induction and LAP-mediated MAPKs-AP-1 activation for cytokine response to H. capsulatum. Furthermore, inhibiting ROS production in Nlrx1−/− macrophages almost completely abolishes H. capsulatum-induced LC3 conversion, indicating that both Dectin-1/Syk/ROS-dependent pathway and NLRX1-TUFM complex-dependent pathway collaboratively contribute to LAP induction. Our findings reveal new pathways underlying LAP induction by H. capsulatum for macrophage cytokine response.
New Features on the Expression and Trafficking of mGluR1 Splice Variants Exposed by Two Novel Mutant Mouse Lines

Metabotropic glutamate receptors (mGluRs) couple to G-proteins to modulate slow synaptic transmission via intracellular second messengers. The first cloned mGluR, mGluR1, regulates motor coordination, synaptic plasticity and synapse elimination. mGluR1 undergoes alternative splicing giving rise to four translated variants that differ in their intracellular C-terminal domains. Our current knowledge about mGluR1 relates almost entirely to the long mGluR1α isoform, whereas little is known about the other shorter variants. To study the expression of mGluR1γ, we have generated by means of the CRISPR/Cas9 system a new knock-in (KI) mouse line in which the C-terminus of this variant carries two short tags. Using this mouse line, we could establish that mGluR1γ is either untranslated or in amounts that are undetectable in the mouse cerebellum, indicating that only mGluR1α and mGluR1β are present and active at cerebellar synapses. The trafficking and function of mGluR1 appear strongly influenced by adaptor proteins such as long Homers that bind to the C-terminus of mGluR1α. We generated a second transgenic (Tg) mouse line in which mGluR1α carries a point mutation in its Homer binding domain and studied whether disruption of this interaction influenced mGluR1 subcellular localization at cerebellar parallel fiber (PF)-Purkinje cell (PC) synapses by means of the freeze-fracture replica immunolabeling technique. These Tg animals did not show any overt behavioral phenotype, and despite the typical mGluR1 perisynaptic distribution was not significantly changed, we observed a higher probability of intrasynaptic diffusion suggesting that long Homers regulate the lateral mobility of mGluR1. We extended our ultrastructural analysis to other mouse lines in which only one mGluR1 variant was reintroduced in PC of mGluR1-knock out (KO) mice. This work revealed that mGluR1α preferentially accumulates closer to the edge of the postsynaptic density (PSD), whereas mGluR1β has a less pronounced perijunctional distribution and, in the absence of mGluR1α, its trafficking to the plasma membrane is impaired with an accumulation in intracellular organelles. In conclusion, our study sets several firm points on largely disputed matters, namely expression of mGluR1γ and role of the C-terminal domain of mGluR1 splice variants on their perisynaptic clustering.
A Survey of the BCube Data Center Network Topology

With the development of cloud computing and data center, In the present society, the data center network has become an indispensable network. And the data center network has played a very important role in transportation, economy and social work. So the study of server-centric data center networks is now the focus of researchers at home and abroad . And the BCube is one of the most classic network topology. This paper mainly introduces the data center network topology of the switch-centric represented by Fat-tree and the server-centric represented by BCube. This paper mainly introduces Fat-tree and BCube, BCube as the classic server-centric data center network topology, which is of great significance for the future development of data center network topologies.
Motion regeneration using motion texture and autoencoder

Motion analysis and recognition frequently suffer from noisy motion capture data not only because of systematic noises of imaging devices but also because of motion dependent non-systematic errors such as self occlusions and motion dynamics extraction failure from visual data. In this work, we propose a motion regeneration method that extracts only statistically significant and distinct characteristics of human body motion and synthesizes a new motion data. To this end, we convert 3D human body motion to 2D motion texture that is easily applicable to well-trained deep convolutional network. An autoencoder is trained with our 2D motion textures to learn only essential characteristics of human body motion in encoded space discarding systematic noises and unexpected non-systematic errors that are nothing to do with the description of particular motion. For the verification of the effectiveness of our regenerated motion, we perform motion classification test on public body motion dataset using our Long-Short Term Memory(LSTM) based method.
Elementary Evaluation of the Zeta and Related Functions: An Approach From a New Perspective

The zeta and related functions are explicitly known at either even values as for the ζ(s), λ(s) and η(s) functions or at odd function values as for the Dirichlet β(s) function. In this paper, the values at integers are obtained for the zeta and related functions by applying new analytical tool that naturally arises within a new theoretical setting. This allows a simple and elementary derivation of the function values at integer points from quite a different point of view. It is based on the hypothesis that negative numbers might be beyond infinity, advanced by Wallis and Euler. I will first define the basic concepts of our theory: (1) a new ordering relation on the set of integer numbers; (2) a new class of real functions, called regular, and the definition of sum that extends the classical definition to the case when the upper limit of summation is less than the lower; (3) a set of conditions imposed on regular functions that defines a new regular method for summation of infinite series.
Hair modeling from a single anime-style image

Recently, research of modeling realistic hair from a real-life portrait image has achieved extraordinary results. Modeling cartoon hair from an anime-style image, in contrast, has not drawn much attention from researchers. Yeh et al. [2015] proposed a 2.5D approach which determines a layering order and estimates hidden portions of hair segments from an anime-style image. However, their results are only suitable for a single view angle. In this work, we propose a novel approach to model cartoon hair from an anime-style image which simulates an artist's workflow and has a broader range of view angles than the method proposed by Yeh et al. [2015].
Algorithms and Hardness for Diameter in Dynamic Graphs

The diameter, radius and eccentricities are natural graph parameters. While these problems have been studied extensively, there are no known dynamic algorithms for them beyond the ones that follow from trivial recomputation after each update or from solving dynamic All-Pairs Shortest Paths (APSP), which is very computationally intensive. This is the situation for dynamic approximation algorithms as well, and even if only edge insertions or edge deletions need to be supported. This paper provides a comprehensive study of the dynamic approximation of Diameter, Radius and Eccentricities, providing both conditional lower bounds, and new algorithms whose bounds are optimal under popular hypotheses in fine-grained complexity. Some of the highlights include: - Under popular hardness hypotheses, there can be no significantly better fully dynamic approximation algorithms than recomputing the answer after each update, or maintaining full APSP. - Nearly optimal partially dynamic (incremental/decremental) algorithms can be achieved via efficient reductions to (incremental/decremental) maintenance of Single-Source Shortest Paths. For instance, a nearly $(3/2+\epsilon)$-approximation to Diameter in directed or undirected graphs can be maintained decrementally in total time $m^{1+o(1)}\sqrt{n}/\epsilon^2$. This nearly matches the static $3/2$-approximation algorithm for the problem that is known to be conditionally optimal.
Structured Information Retrieval Strategies for Localising Software Changes

During software maintenance and evolution, developers need to deal with a large number of change requests by modifying existing code or adding code into the system. An efficient tackling of change request calls for an accurate localising of software changes, i.e. identifying which code are problematic and where new files should be added for any type of change request at hand, such as a bug report or a feature request. Existing automatic techniques for this change localisation problem are limited in two aspects: on the one hand, they are only limited to tackle a specific type of change request; on the other hand, they are focused on finding files that should be modified for a change request, yet barely capable of recommending what files or packages might be newly created. To address the limitations, we are inspired to propose a generalised change localisation approach to identify the to-be-modified files (mostly for bugs), and at the same time point out where new files or packages should be created (mostly for new feature requests) for an arbitrary type of change request. In order to tackle the key challenge of predicting to-be-created program elements, our proposed SeekChanges approach leverages the hierarchical package structure for Java projects, and model the change localisation problem as a structured information retrieval (IR) task. A systematic investigation of three structured IR strategies is carried out for scoring and ranking both the files that should be modified and the software packages in which the new files should be created to address change requests. Extensive experiments on four open source Java projects from the Apache Software Foundation demonstrate that structured IR strategies have a good performance on recommending newly created files, while the overall performance of localising change requests is equally satisfactory.