Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Robust Low Rank Matrix Recovery

Low rank matrix recovery has shown its importance as a theoretic foundation in many areas of information processing. Its solutions are usually obtained in batch mode that requires to load all the data into memory during processing, and thus are hardly applicable on large scale data. Moreover, a fraction of data may be severely contaminated by outliers, which makes accurate recovery significantly more challenging. This paper proposes a novel online robust low rank matrix recovery method to address these difficulties. In particular, we first introduce an online algorithm to solve the problem of low rank matrix completion. Then we move on to low rank matrix recovery from observations with intensive outliers. The outlier support is robustly estimated from a perspective of mixture model. Experiments on both synthetic and real data are conducted to demonstrate the efficacy of our method and show its superior performance over the state-of-the-arts.
Efficient Generation \epsilon-close to G(n,p) and Generalizations

We give an efficient algorithm to generate a graph from a distribution $\epsilon$-close to $G(n,p)$, in the sense of total variation distance. In particular, if $p$ is represented with $O(\log n)$-bit accuracy, then, with high probability, the running time is linear in the expected number of edges of the output graph (up to poly-logarithmic factors). All our running times include the complexity of the arithmetic involved in the corresponding algorithms. Previous standard methods for exact $G(n,p)$ sampling (see e.g. Batagelj and Brandes, 2005) achieve similar running times, however, under the assumption that performing real number arithmetic with arbitrary accuracy takes constant time. We note that the actual accuracy required by these methods is O(n)-bit per step, which results in quadratic running times. The main idea of our $G(n,p)$ generation algorithm is a Metropolis Markov chain to sample $\epsilon$-close from the binomial distribution. This is a new method for sampling from the binomial distribution: it is of separate interest and may find other useful applications. Our analysis accounts for all necessary bit-accuracy and arithmetic, and our running times are comparable to known methods for exact binomial sampling. We further obtain efficient generation algorithms for random graphs with given arbitrary degree distributions, Inhomogeneous Random Graphs when the kernel function is the inner product, and Stochastic Kronecker Graphs. To the best our knowledge, our work can be viewed as the first effort to simulate efficient generation of graphs from classical random graph models, while taking into account implementational considerations as fundamental computational aspects, and quantifying the tradeoff between accuracy and running time in a way that can be useful in practice.
Mutagenesis within Helix 6 of the Human β1-Adrenergic Receptor Identifies Lysine324 as a Residue Involved in Imparting the High-Affinity Binding State of Agonists

Competition binding isotherms for agonists to G protein-coupled receptors (GPCR) display high and low binding affinities. Mutagenesis of lysine at position 324 in helix 6 of the wild-type (WT) human β1-adrenergic receptor (β1-AR) generated mutant receptors that had GTP-insensitive single low-affinity binding sites for agonists and reduced potencies of full or partial agonists in stimulating adenylyl cyclase. Unlike the WT β1-AR, intrinsic activities of full and partial agonists in activating the Lys324→ Ala β1-AR (K324A) mutant were correlated with their binding affinities to the K324A mutant. In assays, such as agonist-mediated phosphorylation and recycling, the K324A mutant and the WT β1-AR behaved similarly. However, in fluorescence resonance energy transfer assays that determined the proximity between the WT β1-AR or the K324A mutant to Gsα, there were significant differences. The conceptual framework of the ternary complex model could not adequately account for the behavior of the K324A mutant except under assumptions of low receptor-G protein binding affinities. The single low-affinity binding site of the K324A mutant to isoproterenol was converted by the C-terminal 11-amino-acid peptide of Gsα, which acts a GDP-bound Gsα mimic, to high- and low-affinity sites. Based upon the three-dimensional architecture of the human β1-AR, the distance between Lys324 and the Asp/Glu-Arg-Tyr motif in helix 3 was the shortest among the various amino acids in helix 6. These findings indicate that Lys324 lies in a groove between helices 3 and 6, and its mutagenesis generates a mutant receptor with very low binding affinity for the GDP-bound isoform of Gs.
Short-Term Wind-Thermal Scheduling of Electric Power System using Gravitational Search Algorithm

Wind Power and Solar energy are the two most vital energy resources in the electric power industry's transition to an environmental-friendly operation. The use of Wind Power and renewable energy in electric power sector has grown significantly in recent years. The proportion of wind energy in the pattern of world energy has been increasing since the beginning of the twenty-first century. Since wind power plays a positive role in energy saving and reducing emissions of pollutants, power companies should transport and distribute wind power electricity as much as possible. This research papers aims to present the Short-Term Wind-Thermal Scheduling of Electric Power System Using Gravitational Search Algorithm. The Effectiveness of Proposed Algorithm is Tested with IEEE Test System Consisting of Three, Six and Fifteen Unit Test System. To achieve the goal of environmental protection, Wind-Power is combined with Thermal power to satisfy time-varying load demand and incorporate transmission losses.
A fast method for single image dehazing using dark channel prior

In this paper, we propose an improved method for single image haze removal based on the dark channel prior. The dark channel prior is a kind of statistics of outdoor haze-free images. It estimates the transmission map and gives out a superior result. While refining the transmission map with soft matting is computation consuming and this prior is invalid for near white scenes. We simplify the refining step by locally processing and the region to be refined is located by dealing with the coarse dark channel. By computing the difference of intensities between the highest channel and the lowest, we reestimate the transmission map with a method derived from underwater dehazing. Results show that our method has a good ability of haze removal with lower computation and the output of near white scenes is more realistic.
Adaptive tracking control of fully actuated port-Hamiltonian mechanical systems

In the presence of parameter uncertainty tracking control can result in significant tracking errors. To overcome this problem adaptive control is applied, which estimates and compensates for the errors of the uncertain parameters. A new adaptive tracking control scheme is presented for standard fully actuated port-Hamiltonian mechanical systems. The adaptive control is such that the closed loop error system is still port-Hamiltonian and asymptotically stable.
The Use of Didactic Sequences and the Teaching of L1: An Analysis of an Institutional Program of Teaching Writing at School.

A competitive program called “Writing the Future (Escrevendo o Futuro)”, developed with the aim of improving written production at Brazilian public schools may gain importance in a context where meaningful reading and writing skills are considered key factors to social inclusion and to personal, educational and professional achievements. This paper will begin by presenting an overview of the Brazilian Competitive Program “Writing the Future (Escrevendo o Futuro)” developed using a genrebased approach, before proceeding with the following objectives: a) to analyze written texts produced by students who used the Program’s didactic sequence as a learning tool of the didactic intervention work, and b) to debate critically teaching writing at school with such a program. Didactic sequences are defined as a group of school activities systematically organized within a class project (Dolz & Schneuwly, 1998: 93) aimed at developing students’ language capacities. The texts were analyzed according to two dimensions: a) the criteria established by the program itself; b) the language plans that constitute any text (according to Bronckart, 2003). As a preliminary result, the levels of (in)adequacy of the texts produced in the genre taught in the didactic sequence may be stressed, as well as the type of difficulty of linguistic accuracy in general. Another result is the kind of pedagogic practice underlying the proposal, together with the different dimensions of teacher education it may involve.
Human resource practices and affective organisational commitment: A sectoral comparative study

Orientation: Organisational commitment (OC) is often depicted as a desirable employee attitude and relates to organisational performance. Little research has been carried out on the (Human Resource) HR practices–OC relationship within the South African context, and specifically with regard to this relationship across various sectors. Research purpose: The purpose of this contribution is to specify the HR practices–OC relationship, within South Africa, and specifically across various sectors. Motivation for the study: It addresses the important matter of OC, a necessary requirement for sustaining organisations, through effective delivery of HR practices. Research design, approach and method: This study is based on a cross-sectional survey design, collecting primary data on HR practices and OC from South African employees in public and private sector organisations, as well as state-owned entities. Main findings: Minimal practically significant mean scored differences were detected among HR practices delivered per sector. Furthermore, a positive link between effective HR practices and OC was found within each of the three sectors, but this relationship was sector-specific. Practical implications: The findings of this study offer a new, contextualised perspective on the HR practices–OC relationship. The data gathered could assist practitioners in reshaping HR policies – and particularly practices – to suit their specific sector. Contribution and value add: The findings of this study are expected to offer valuable insight into the deferential management along sector lines. They also reiterate the importance of localised research and caution practitioners not to transfer research findings to local settings without a thorough investigation of local research.
Assessing the impact of European Integration on sectoral trade in services

The present paper contributes to the existing literature analyzing the relationship between intra EU trade in services and European Integration by taking into consideration a potential endogeneity bias of the EU dummy and a correct specification of multilateral resistance terms in a panel data set covering the years 2000-2010. Our results offer evidence for a high positive impact of European integration on aggregate services trade between member states while we find a negative effect of monetary integration. However, there exist notable differences at the sector level. According to our results, European integration has positive effects especially for business services, travel and EDV services. Analyzing the evolvement of the sectoral EU-effects over time shows that exports of EDV and OBS have steadily increased due to European integration.
Constructing Efficient and Fault-Tolerant Unstructured Peer-To-Peer Networks

by William F. Acosta Peer-to-peer (P2P) networks are decentralized, distributed networks where the participating nodes self-organize and communicate with each other to support a distributed application. P2P networks have been used to support such diverse applications as file sharing, distributed storage and backup, and content distribution. Because P2P networks are decentralized, the manner in which the participating nodes make local decisions about the management of the P2P overlay has significant impact on the functionality and performance of the system as a whole. In this thesis, we explore the nature and design of unstructured P2P systems. We show that a well-connected overlay for unstructured P2P systems can be fault tolerant and support efficient searches. To achieve this goal, we construct an overlay that has high expansion, and thus good connectivity. Because analyzing the expansion of a graph is NP-hard, we describe the use of techniques from spectral graph theory such as Laplacian eigenvalue analysis to determine bounds on the expansion of a graph. We use these techniques to examine the overlays generated by various algorithms and compare their connectivity to that of a known good expander graph. We show that a realistic hybrid algorithm that considers both proximity and connectivity information generates overlays that have low communication costs as well as have the high expansion and connectivity required to support efficient search. The high expansion results in an William F. Acosta overlay that can tolerate the instantaneous failure of up to 30% of most highly connected nodes while still resolving all search queries with few messages. We use this algorithm, which we call Makalu, to address the problem of searching efficiently in an unstructured P2P network. We analyze the range of replication ratios required for good search performance using Makalu. We observe that, on average, for very low replication ratios, the search in Makalu can generate many duplicate queries in order to locate the desired object. We show that high expansion forces duplicate messages beyond a threshold which we call the Convergence Boundary. For more realistic replication ratios of 0.5% or higher, we show that the overlay topology generated by Makalu can successfully resolve all queries within four hops and only 6% duplicate messages on a 10,000 node network. Additionally , we show that Makalu overlays can support an indexed identifier-based searches with the use of attenuated Bloom filters. The high expansion of the Makalu overlay greatly increases the accuracy of …
Optimizing Resource allocation while handling SLA violations in Cloud Computing platforms

In this paper, we study a resource allocation problem in the context of Cloud Computing, in which a set of Virtual Machines (VM) has to be allocated on a set of Physical Machines (PM). Each VM has a given demand (e.g. CPU demand), and each PM has a capacity. However, VMs only use a fraction of their demand. The aim is to exploit the difference between the demand of the VM and its actual resource usage, to achieve a higher utilization on the PMs. However, the resource consumption of the VMs might change over time (while staying under its original demand), implying sometimes expensive “SLA violations” when the demand of some VMs is not satisfied because of overloaded PMs. Thus, while optimizing the global resource utilization of the PMs, it is necessary to ensure that at any moment a VM's need evolves, a few number of migrations (moving a VM from PM to PM) is sufficient to find a new configuration in which all the VMs' consumptions are satisfied. We model this problem using a fully dynamic bin packing approach and we present an algorithm ensuring a global utilization of the resources of 66%. Moreover, each time a PM is overloaded, at most one migration is sufficient to fall back in a configuration with no overloaded PM, and at most 3 different PMs are concerned by required migrations that may occur to keep the global resource utilization correct. This allows the platform to be highly resilient to a great number of changes.
Sequential frequency reuse with power control for OFDMA systems

Orthogonal frequency division multiple access (OFDMA) is a promising technique for high data rate communications in future cellular systems. Since frequency resources are universally reused in every cell in a system, a typical OFDMA system tries to maximize the spectral efficiency. Users located near the cell-edge tend to have the weakest signal strength. So they might experience severe inter-cell interferences (ICIs). In this paper, we propose a sequential frequency reuse (SqFR) that reduces ICIs by a sequential sub-channel allocation. By giving more power to sub-carriers allocated to cell-edge users, our SqFR significantly enhances the performance of cell-edge users. The performance of the proposed SqFR is investigated via the analysis and simulations. Simulation results show that proposed SqFR improves the performance of cell-edge users in an OFDMA system under both homogeneous and heterogeneous traffic conditions. Copyright © 2011 John Wiley & Sons, Ltd.
Multilevel thresholding by fast PNN-based algorithm

We propose a fast PNN-based O(N log N) time algorithm for multilevel non-parametric thresholding, where N denotes the size of the image histogram. The proposed PNN based multilevel thresholding algorithm is considerably faster than optimal thresholding. On a set of 8-16 bits per pixel real images, experimental results also reveal that the proposed method provides better quality than the Lloyd-Max quantizer alone. Since the time complexity of the proposed thresholding algorithm is log-linear, it is applicable in real-time image processing applications.
Distributed compression and fusion of nonnegative sparse signals for multiple-view object recognition

Visual surveillance in complex urban environments requires an intelligent system to automatically track and identify multiple objects of interest in a network of distributed cameras. The ability to perform robust object recognition is critical to compensate adverse conditions and improve performance, such as multi-object association, visual occlusion, and data fusion with hybrid sensor modalities. In this paper, we propose an efficient distributed data compression and fusion scheme to encode and transmit SIFT-based visual histograms in a multi-hop network to perform accurate 3-D object recognition. The method harnesses an emerging theory of (distributed) compressive sensing to encode high-dimensional, nonnegative sparse signals via random projection, which is unsupervised and independent to the sensor modality. A multi-hop protocol then transmits the compressed visual data to a base-station computer, which preserves a constant bandwidth regardless of the number of active camera nodes in the network. Finally, the multiple-view object features are simultaneously recovered via ℓ1-minimization as an efficient decoder. The efficacy of the algorithm is validated using up to four Berkeley CITRIC camera motes deployed in a realistic indoor environment. The substantial computation power on the CITRIC mote also enables fast compression of SIFT-type visual features extracted from object images.
Observer Perception of Dominance and Mirroring Behavior in Human-Robot Relationships

How people view relationships between humans and robots is an important consideration for the design and acceptance of social robots. Two studies investigated the effect of relational behavior in a human-robot dyad. In Study 1, participants watched videos of a human confederate discussing the Desert Survival Task with either another human confederate or a humanoid robot. Participants were less trusting of both the robot and the person in a human-robot relationship where the robot was dominant toward the person than when the person was dominant toward the robot; these differences were not found for a human pair. In Study 2, participants watched videos of a human confederate having an everyday conversation with either another human confederate or a humanoid robot. Participants who saw a confederate mirror the gestures of a robot found the robot less attractive than when the robot mirrored the confederate; the opposite effect was found for a human pair. Exploratory findings suggest that human-robot relationships are viewed differently than human dyads. Categories and Subject Descriptors H.1. 2 [Models and Principles]: User/Machine Systems - human factors, software psychology General Terms Design, Human Factors
Investigate the Effect of Blog Browsing Intention on Loyalty toward TV Show From the Perspectives of Experiential Marketing and Idolatry

Blog popularity has surged over the last few years and gained considerable attention by practitioners and researchers. However, the effects of commercial blogs on brick-and-mortar have not been sufficiently investigated. This study thus intended to investigate the effect of blog browsing intention on loyalty toward TV show from the perspective of consumer experience and idolatry. Based on data collected from 370 respondents, the results indicated these perspectives employed in this study are appropriate for elucidating an individual’s blog browsing behavior and loyalty toward TV show. Besides, the blog browsing intention has positive moderating effects on the relationship between consumer experiences and loyalty formation. Several implications for marketers were also proposed.
Co-Clustering-Based Clustering and Segmentation for Pattern Discovery from Time Course Data

—Time course data may inherit critical temporal ordering in contiguous (i.e., neighboring) time slot. Traditional one-way k-means clustering algorithms handle time points independently, ignoring the internal time locality. Although co-clustering algorithms can discover latent local patterns, the discovered patterns are not necessary to be in a continuous time order. Therefore, this paper targets to extend an existing co-clustering framework to be applicable to time course data so that time-dependent local segment patterns over specific intervals can be captured. While following the general co-clustering framework of the alternating optimization process, the proposed algorithms employ clustering on instance dimension and segmentation on time dimension. Both batch and incremental updates at boundary time points are proposed to search for a sequence of time segments. Eight time course datasets and two specific data normalization schemes are considered in the experimental study. Clustering similarity performance among k-means, one existing co-clustering, and the two proposed clustering segmentation algorithms is compared.
Study on Working Memory Characteristics of Different Nerve Types of College Students

The purpose of this study was to investigate the differences in working memory among different nerve types of college students. A total of 562 college students were tested with 80•8 Nerve Type Test Scale and sorted according to the test scores, then each of the 21 subjects were randomly selected from the 27% and the last 27% subjects. 42 participants were required to finish Operation span task and Rotation span task. Results showed that: there was a significant difference in working memory capacity between the high and low score group of the nerve types. Those findings suggest that in contrast to the low score of the nerve types , the high score group has more working memory capacity, faster and higher accuracy processing speed.