Miracle-dz/UniFAR-FTUs huggingface dataset:
A Functional Wavelet-Kernel Approach for Continuous-time Prediction

We consider the prediction problem of a continuous-time stochastic process on an entire time-interval in terms of its recent past. The approach we adopt is based on functional kernel nonparametric regression estimation techniques where observations are segments of the observed process considered as curves. These curves are assumed to lie within a space of possibly inhomogeneous functions, and the discretized times series dataset consists of a relatively small, compared to the number of segments, number of measurements made at regular times. We thus consider only the case where an asymptotically non-increasing number of measurements is available for each portion of the times series. We estimate conditional expectations using appropriate wavelet decompositions of the segmented sample paths. A notion of similarity, based on wavelet decompositions, is used in order to calibrate the prediction. Asymptotic properties when the number of segments grows to infinity are investigated under mild conditions, and a nonparametric resampling procedure is used to generate, in a flexible way, valid asymptotic pointwise confidence intervals for the predicted trajectories. We illustrate the usefulness of the proposed functional wavelet-kernel methodology in finite sample situations by means of three real-life datasets that were collected from different arenas.
A new feature selection method based on relief and SVM-RFE

In original data, there may exist redundant features, irrelevant features, noisy features besides informative features. Extracting informative features while eliminating the others is the goal of feature selection. This paper proposed a new feature selection algorithm based on Relief algorithm and SVM-RFE algorithm, and it is strongly targeted to eliminate the unnecessary features. Finally, We test the proposed method on three data sets from UCI, and treat accuracy, size of optimal subset, time-cost as evaluations, the experimental results show that the proposed algorithm has a better performance than Relief algorithm and SVM-RFE algorithm except time-cost.
Complex networks and local externalities: A strategic approach

In this paper, we illustrate a new approach to the study of how local externalities shape agents’ strategic behavior when the underlying network is volatile and complex. We consider a large population who interacts as specified by a random network with a given degree distribution. Motivated by the complexity of the induced network, we assume that the only precise information agents have is local, i.e. it is restricted to their immediate neighborhood. Each agent chooses an investment level, which in turn imposes a payoff externality on her neighbors that is captured by a (local) Cobb-Douglas production/payoff function. We find that, in the unique interior equilibrium, the induced externality is positive or negative, depending on whether investment costs are (respectively) above or below a certain threshold. This also has implications on the nature of the equilibrium strategy, which is increasing in the degree, in the first case, and decreasing in the second. Finally, we also characterize how the equilibrium changes when the network topology varies and becomes more connected, or when its degree distribution becomes more polarized.
A perspective geometry approach to user-perspective rendering in hand-held video see-through augmented reality

Video see-through Augmented Reality (V-AR) displays a video feed overlaid with information, co-registered with the displayed objects. In this paper we consider the type of V-AR that is based on a hand-held device with a fixed camera. In most of the VA-R applications the view displayed on the screen is completely determined by the orientation of the camera, i.e., the device-perspective rendering; the screen displays what the camera sees. The alternative method is to use the relative pose of the user's view and the camera, i.e., the user-perspective rendering. In this paper we present an approach to the user perspective V-AR using 3D projective geometry. The view is adjusted to the user's perspective and rendered on the screen, making it an augmented window. We created and tested a running prototype based on our method.
Wideband Slot Antenna for Low-Profile Hand-held Terminal Applications

A quarter-wave transversal slot in the center of a handheld terminal ground plane is proposed and evaluated as a wideband antenna for low profile mobile phones. By feeding the slot with a microstrip line, a triple-resonance response is obtained from the chassis, slot and microstrip stub interaction, providing sufficient impedance bandwidth and radiation efficiency to cover the 0.9-2.7 GHz frequency range. While the introduction of a slot in the ground plane has many advantages over e.g. PIFA or patch structures, both in terms of performance and cost, the circuit floor planning becomes more complicated. To address this problem, a possible solution in terms of module partitioning and signal line routing is suggested. In addition, simulations of parasitic antenna to signal line coupling and techniques to reduce this effect are presented
A Survey : Outlier Detection in Streaming Data Using Clustering Approached

In the public field like network intrusion detection, credit card fraud detection, stock market analysis . The theory of outlier detection is used for detecting the outlying in the medical data. As we know that streaming data fails to scan the multiple items and also the new concepts may keep evolving in coming data over time hence the outlier detection plays the challenging role in the streaming data. The irrelevant attributes can be termed as noisy attribute at the time of working with the data streams and such attribute magnify the challenge. In this paper, we propose the method for streaming data, the method is known as unsupervised outlier detection method. Clustering is one of the unsupervised data mining task, hence this method is based on the theory of clustering and label data is not required in this method. In the proposed method for taking the advantage of both the density based outlier detection and density based outlier detection the scheme combined the application of both density based clustering and partitioned based clustering. Depending upon their respective relevance in mining task the proposed scheme assign weights to attribute. This weighted attributes are very helpful to reduce or remove the effect of noisy attributes. For concept evaluation the proposed scheme is incremental and adaptive for keeping the view challenges of streaming data. Our proposed approach outperforms other existing approach in terms of outlier detection rate, and the increasing percentage of outlier.
Characterization of a Protocatechuate Catabolic Gene Cluster from Rhodococcus opacus 1CP: Evidence for a Merged Enzyme with 4-Carboxymuconolactone-Decarboxylating and 3-Oxoadipate Enol-Lactone-Hydrolyzing Activity

ABSTRACT The catechol and protocatechuate branches of the 3-oxoadipate pathway, which are important for the bacterial degradation of aromatic compounds, converge at the common intermediate 3-oxoadipate enol-lactone. A 3-oxoadipate enol-lactone-hydrolyzing enzyme, purified from benzoate-grown cells of Rhodococcus opacus(erythropolis) 1CP, was found to have a larger molecular mass under denaturing conditions than the corresponding enzymes previously purified from γ-proteobacteria. Sequencing of the N terminus and of tryptic peptides allowed cloning of the gene coding for the 3-oxoadipate enol-lactone hydrolase by using PCR with degenerate primers. Sequencing showed that the gene belongs to a protocatechuate catabolic gene cluster. Most interestingly, the hydrolase gene, usually termed pcaD, was fused to a second gene, usually termedpcaC, which encodes the enzyme catalyzing the preceding reaction, i.e., 4-carboxymuconolactone decarboxylase. The two enzymatic activities could not be separated chromatographically. At least six genes of protocatechuate catabolism appear to be transcribed in the same direction and in the following order: pcaH andpcaG, coding for the subunits of protocatechuate 3,4-dioxygenase, as shown by N-terminal sequencing of the subunits of the purified protein; a gene termed pcaB due to the homology of its gene product to 3-carboxy-cis,cis-muconate cycloisomerases; pcaL, the fused gene coding for PcaD and PcaC activities; pcaR, presumably coding for a regulator of the IclR-family; and a gene designated pcaFbecause its product resembles 3-oxoadipyl coenzyme A (3-oxoadipyl-CoA) thiolases. The presumed pcaI, coding for a subunit of succinyl-CoA:3-oxoadipate CoA-transferase, was found to be transcribed divergently from pcaH.
The Nogo-B-PirB Axis Controls Macrophage-Mediated Vascular Remodeling

Objective Nogo-B mediates vascular protection and facilitates monocyte- and macrophage-dependent vascular remodeling. PirB is an alternate receptor for Nogo-B, but a role for the Nogo-PirB axis within the vascular system has not been previously reported. We examined whether Nogo-B or PirB play a role in regulating macrophage-mediated vascular remodeling and hypothesized that endothelial Nogo-B regulates vein graft macrophage infiltration via its alternate receptor PirB. Methods Vein grafts were performed using Nogo and PirB wild type and knockout mice. Human vein grafts were similarly analyzed. The hindlimb ischemia model was performed in PirB wild type and knockout mice. Accompanying in vitro work included isolation of macrophages from PirB wild type and knockout mice. Results Increased Nogo-B and PirB mRNA transcripts and protein expression were observed within mouse and human vein grafts. Both Nogo knockout and PirB knockout vein grafts showed increased wall thickness and increased numbers of F4/80-positive macrophages. Macrophages derived from PirB knockout mice had increased adhesion to fibronectin, increased EC-specific binding, and increased numbers of mRNA transcripts of M2 markers as well as MMP3 and MMP9. PirB knockout vein grafts had increased active MMP9 compared to wild type vein grafts. PirB knockout mice had increased recovery from hindlimb ischemia and increased macrophage infiltration compared to wild type mice. Conclusions Vein graft adaptation shows increased expression of both Nogo-B and PirB. Loss of PirB, or its endothelial ligand Nogo-B, results in increased inflammatory cell infiltration and vein graft wall thickening. These findings suggest that PirB regulates macrophage activity in vein grafts and that Nogo-B in the vein graft limits macrophage infiltration and vein graft thickening. PirB may play a more general role in regulating macrophage responses to vascular injury. Macrophage inhibition via Nogo-PirB interactions may be an important mechanism regulating vein graft adaptation to the arterial circulation.
Time-sensitive query auto-completion

Query auto-completion (QAC) is a common feature in modern search engines. High quality QAC candidates enhance search experience by saving users time that otherwise would be spent on typing each character or word sequentially. Current QAC methods rank suggestions according to their past popularity. However, query popularity changes over time, and the ranking of candidates must be adjusted accordingly. For instance, while halloween might be the right suggestion after typing ha in October, harry potter might be better any other time. Surprisingly, despite the importance of QAC as a key feature in most online search engines, its temporal dynamics have been under-studied. In this paper, we propose a time-sensitive approach for query auto-completion. Instead of ranking candidates according to their past popularity, we apply time-series and rank candidates according their forecasted frequencies. Our experiments on 846K queries and their daily frequencies sampled over a period of 4.5 years show that predicting the popularity of queries solely based on their past frequency can be misleading, and the forecasts obtained by time-series modeling are substantially more reliable. Our results also suggest that modeling the temporal trends of queries can significantly improve the ranking of QAC candidates.
Towards Adaptive Quest Narrative in Shared, Persistent Virtual Worlds

In this paper, we discuss motivations for studying interactive narrative in shared, persistent worlds using the established conventions of quest-based MMORPGs. We present a framework for categorizing the various techniques used in these games according to the interaction between the world model and the quest model. Using this framework we generalize recent games to present a more dynamic world model, and investigate extensions to the quest model to support storytelling through adaptive quest narratives.
Comparative analysis of seven multiple protein sequence alignment servers: clues to enhance reliability of predictions

MOTIVATION The prediction reliability of seven multiple alignment servers currently available on the Internet (ClustalW, MAP, PIMA, Block Maker, MSA, MEME and Match-Box) has been evaluated in terms of power (sensitivity) and confidence (selectivity). Therefore, the alignments obtained have been respectively compared to refined structural alignments for 20 families of related proteins with low levels of identity. RESULTS Results clearly show that any powerful method remains reliable when the rate of identity falls. For some methods, power and confidence decrease linearly with the rate of identity, while other methods emphasize reliability at the cost of a lower power. Increasing the number of related sequences included in the alignment may either improve or decrease the quality of the predictions substantially. For some methods, the gain in power or in confidence is quite systematic; for others, the effect of the addition of homologous sequences is highly unpredictable. Extracting the consensus between two different methods may increase the overall confidence of the predictions tremendously. Our conclusions induce users of sequence alignment methods on the Internet to select the most suitable technique according to their requirements in terms of selectivity and sensitivity. AVAILABILITY The aligned sequences of the 20 alignments of structure can be obtained automatically by sending the message 'send: cabios_tests. txt' by e-mail to 'matchbox@biq.fundp.ac.be'. CONTACT eric.depiereux@fundp.ac.be
R-Spondin 3 Regulates Dorsoventral and Anteroposterior Patterning by Antagonizing Wnt/β-Catenin Signaling in Zebrafish Embryos

The Wnt/β-catenin or canonical Wnt signaling pathway plays fundamental roles in early development and in maintaining adult tissue homeostasis. R-spondin 3 (Rspo3) is a secreted protein that has been implicated in activating the Wnt/β-catenin signaling in amphibians and mammals. Here we report that zebrafish Rspo3 plays a negative role in regulating the zygotic Wnt/β-catenin signaling. Zebrafish Rspo3 has a unique domain structure. It contains a third furin-like (FU3) domain. This FU3 is present in other four ray-finned fish species studied but not in elephant shark. In zebrafish, rspo3 mRNA is maternally deposited and has a ubiquitous expression in early embryonic stages. After 12 hpf, its expression becomes tissue-specific. Forced expression of rspo3 promotes dorsoanterior patterning and increases the expression of dorsal and anterior marker genes. Knockdown of rspo3 increases ventral-posterior development and stimulates ventral and posterior marker genes expression. Forced expression of rspo3 abolishes exogenous Wnt3a action and reduces the endogenous Wnt signaling activity. Knockdown of rspo3 results in increased Wnt/β-catenin signaling activity. Further analyses indicate that Rspo3 does not promote maternal Wnt signaling. Human RSPO3 has similar action when tested in zebrafish embryos. These results suggest that Rspo3 regulates dorsoventral and anteroposterior patterning by negatively regulating the zygotic Wnt/β-catenin signaling in zebrafish embryos.
Coxsackievirus-Induced miR-21 Disrupts Cardiomyocyte Interactions via the Downregulation of Intercalated Disk Components

Intercalated disks (ICDs) are substantial connections maintaining cardiac structures and mediating signal communications among cardiomyocytes. Deficiency in ICD components such as desmosomes, fascia adherens and gap junctions leads to heart dysfunction. Coxsackievirus B3 (CVB3) infection induces cardiac failure but its pathogenic effect on ICDs is unclear. Here we show that CVB3-induced miR-21 expression affects ICD structure, i.e., upregulated miR-21 targets YOD1, a deubiquitinating enzyme, to enhance the K48-linked ubiquitination and degradation of desmin, resulting in disruption of desmosomes. Inhibition of miR-21 preserves desmin during CVB3 infection. Treatment with proteasome inhibitors blocks miR-21-mediated desmin degradation. Transfection of miR-21 or knockdown of YOD1 triggers co-localization of desmin with proteasomes. We also identified K108 and K406 as important sites for desmin ubiquintination and degradation. In addition, miR-21 directly targets vinculin, leading to disturbed fascia adherens evidenced by the suppression and disorientation of pan-cadherin and α-E-catenin proteins, two fascia adherens-components. Our findings suggest a new mechanism of miR-21 in modulating cell-cell interactions of cardiomyocytes during CVB3 infection.
Low Complexity MIMO Detection Based on the Slowest Descent Method

This contribution presents a novel multiple-input multiple-output (MIMO) detection scheme for quadrature amplitude modulation (QAM). The proposed method is based on the slowest descent (SD) method and is suitable for both iterative and non-iterative detectors. Similar to other low-complexity detection schemes, such as sphere decoding;, the new SD approach restricts the maximization/marginalization to a small set of candidate vectors. Simulation results indicate that the method achieves a close-to-optimal performance, with a complexity that scales only linearly with the number of transmit antennas
Comment on Smaers et al. (2016): A nonviable phylogenetic comparative method hampered by circularity, inaccuracy, and bias

Smaers, Mongle & Kandler (2016) (Biological Journal of the Linnean Society, 118: 78-94) introduced a new phylogenetic comparative method, multiple variance Brownian motion (mvBM), for reconstructing ancestral states given a phylogenetic tree and continuous trait data. The authors conducted a simulation study and argued that mvBM outperforms constant variance Brownian motion (BM) when rates of evolution vary across the phylogeny. In this comment, we argue that mvBM is not a viable statistical method because it is fundamentally a circular analysis that overfits phylogenetic branch lengths to the data. We further argue that the comparison of mvBM to BM under conditions where the assumptions of BM are clearly violated is not an informative performance analysis, and that the simulation study of Smaers et al. (2016) exaggerates the performance of mvBM by focusing on a narrow range of simulation conditions and reporting aggregated accuracy metrics that obscure severe inaccuracy and bias in its ancestral state estimates. Our arguments are supported by simulation results. We conclude that mvBM is not a viable phylogenetic comparative method.
Active Sensing for Robotics – A Survey

This work surveys the major methods for model-based active sensing in robotics. Active sensing in robotics incorporates the following aspects: (i) where to position sensors, and (ii) how to make decisions for next actions, in order to maximize information gain and minimize costs. We concentrate on the second aspect: “Where should the robot move at the next time step? ”. The emphasis here is on Bayesian solutions to this problem. Pros and cons of the major methods are discussed. Special attention is paid to different criteria for decision making.
Virtual Cpu Scheduling Techniques for Kernel Based Virtual Machine (Kvm)

In a multi-CPU Virtual Machine(VM), virtual CPUs (VCPUs) are not guaranteed to be scheduled simultaneously. Operating System (OS) constructs, such as busy-wait (for e.g., spin locks) are written with an assumption of CPUs running concurrently on bare-metal wastes lot of CPU time. The Hardware assisted Pause Loop Exit (PLE) feature detects unnecessary busyloop constructs in guest VMs and traps to the VCPU scheduler a.k. a PLE handler to choose a best VCPU candidate to run. The existing approach (before the optimization mentioned in the paper) does a directed yield1 to a random VCPU and needs more intelligence. We also need to carefully consider the over-commit ratio2 while designing the VCPU scheduling algorithm. For e.g., trapping to the PLE handler is an overhead during under-commit cases. The existing approach lacks the over-commit ratio awareness. Hence we need effective scheduling of VCPUs to boost the performance of VMs. We present three major improvements to old VCPU scheduling technique that include choosing a better VCPU for directed yield and optimizing for under-commit cases. All these approaches have been accepted into Linux kernel. These changes potentially bring around 300-400% improvements to I/O intensive cloud VMs (large under-committed guests) and up to 25% improvement to over-committed CPU intensive VMs.
An adaptive Markov model for text categorization

Existing methods for text categorization assume that a document is a bag of words. While computationally efficient, such a representation is unable to capture sequential information. In this paper, a document is looked upon as a sequence of characters or words and the preprocessing for text categorization, such as word segmentation and feature selection, is not demanded. Statistical dependencies among the neighboring terms of a sequence are captured by different order Markov models. We proposed a sequence classification methods based on adaptive Markov model. Our method blends the Markov models with different order values together for text categorization automatically and effectively. We present an extensive experimental evaluation of our method on an English collections and one Chinese corpus. The results show the high recall and precision of our method.
PROBE: Predictive robust estimation for visual-inertial navigation

Navigation in unknown, chaotic environments continues to present a significant challenge for the robotics community. Lighting changes, self-similar textures, motion blur, and moving objects are all considerable stumbling blocks for state-of-the-art vision-based navigation algorithms. In this paper we present a novel technique for improving localization accuracy within a visual-inertial navigation system (VINS). We make use of training data to learn a model for the quality of visual features with respect to localization error in a given environment. This model maps each visual observation from a predefined prediction space of visual-inertial predictors onto a scalar weight, which is then used to scale the observation covariance matrix. In this way, our model can adjust the influence of each observation according to its quality. We discuss our choice of predictors and report substantial reductions in localization error on 4 km of data from the KITTI dataset, as well as on experimental datasets consisting of 700 m of indoor and outdoor driving on a small ground rover equipped with a Skybotix VI-Sensor.