Miracle-dz/UniFAR-FTUs huggingface dataset:
Compressive Online Robust Principal Component Analysis Via n-&#x2018;1 Minimization

—This work considers online robust principal component analysis (RPCA) in time-varying decomposition problems such as video foreground-background separation. We propose a compressive online RPCA algorithm that decomposes recursively a sequence of data vectors (e.g., frames) into sparse and low-rank components. Different from conventional batch RPCA, which processes all the data directly, our approach considers a small set of measurements taken per data vector (frame). Moreover, our algorithm can incorporate multiple prior information from previous decomposed vectors via proposing an n - (cid:96) 1 minimization method. At each time instance, the algorithm recovers the sparse vector by solving the n - (cid:96) 1 minimization problem—which promotes not only the sparsity of the vector but also its correlation with multiple previously-recovered sparse vectors—and, subsequently, updates the low-rank component using incremental singular value decomposition. We also establish theoretical bounds on the number of measurements required to guarantee successful compressive separation under the assumptions of static or slowly-changing low-rank components. We evaluate the proposed algorithm using numerical experiments and online video foreground-background separation experiments. The experimental results show that the proposed method outperforms the existing methods.
Linking Knowledge Bases to Social Media Profiles

The Linked Open Data (LOD) cloud is currently a primary source of background knowledge for tasks in a wide variety of domains and across many scientific fields. The structured nature and the usage of well-defined open standards make it convenient to contribute to and build upon. However, since the major part of the LOD is ultimately crowdsourced and mostly populated and updated manually, some of the content in the LOD can become stale, inconsistent and lack coverage. Social media, on the other hand, uniquely allow the real world events to be accurately reflected with little or no delay in the form of posts and profile updates. A major downside of this vibrant source of knowledge that is contained in the social media is its lack of structure, significant noisiness and restrictive APIs that make it hard to extract, analyze and use it in the downstream tasks. In this thesis, I present the task of linking entities in a knowledge base (KB) to the corresponding social media profiles as an attempt to bridge the structured LOD cloud and the vibrant social media. As will be shown, such linking allows knowledge transfer between the two worlds: on the one hand, enabling the Semantic Web practitioners to harvest this vast amount of valuable, up-to-date data from the social media; on the other hand, the social media researchers can use the structured LOD knowledge much more efficiently, simplifying the pipelines and improving performance for tasks such as Type Prediction, Entity Linking, and User Profiling. I implement such knowledge transfer using DBpedia as a KB, since it is a cornerstone dataset in the LOD, and Twitter as a social media, due to its popularity and relative accessibility. However, approaches developed here are designed to be general and could be applied to other social media and KBs. To this end, firstly, I introduce SocialLink — a project designed to link KBs to social media profiles. SocialLink consists of (i) a linking approach that is able to produce highquality entity-profile pairs, (ii) a LOD-compliant dataset of alignments between DBpedia and Twitter, (iii) the Social Media Toolkit (SMT) system providing additional functionality on top of SocialLink. SocialLink employs a custom deep neural network-based architecture designed to efficiently exploit many modalities of data representing entities and profiles within DBpedia and Twitter.
Picante: R tools for integrating phylogenies and ecology

SUMMARY Picante is a software package that provides a comprehensive set of tools for analyzing the phylogenetic and trait diversity of ecological communities. The package calculates phylogenetic diversity metrics, performs trait comparative analyses, manipulates phenotypic and phylogenetic data, and performs tests for phylogenetic signal in trait distributions, community structure and species interactions. AVAILABILITY Picante is a package for the R statistical language and environment written in R and C, released under a GPL v2 open-source license, and freely available on the web (http://picante.r-forge.r-project.org) and from CRAN (http://cran.r-project.org).
Highly edge‐connected detachments of graphs and digraphs

Let G = (V,E) be a graph or digraph and r : V → Z+. An r‐detachment of G is a graph H obtained by ‘splitting’ each vertex ν ∈ V into r(ν) vertices. The vertices ν1,…,νr(ν) obtained by splitting ν are called the pieces of ν in H. Every edge uν ∈ E corresponds to an edge of H connecting some piece of u to some piece of ν. Crispin Nash‐Williams 9 gave necessary and sufficient conditions for a graph to have a k‐edge‐connected r‐detachment. He also solved the version where the degrees of all the pieces are specified. In this paper, we solve the same problems for directed graphs. We also give a simple and self‐contained new proof for the undirected result. © 2003 Wiley Periodicals, Inc. J Graph Theory 43: 67–77, 2003
Towards Operational Abduction from a Cognitive Perspective

Diminishing awareness is a consequence of the information explosion: disciplines are becoming increasingly specialized; individuals and groups are becoming ever more insular. This article considers how awareness can be enhanced via operational abductive systems. The goal is to generate and justify suggestions which can span disparate islands of knowledge. Knowledge representation is motivated from a cognitive perspective. Words and concepts are represented as vectors in a high dimensional semantic space automatically derived from a text corpus. Various mechanisms will be presented for computing suggestions from semantic space: information flow, semantic similarity, pre-inductive generalization. The overall goal of this article is to introduce semantic space to the model-based reasoning and abduction community and to illustrate its potential for principled, operational abduction by semi-automatically replicating the Swanson Raynaud/fish oil discovery in medical text.
Macroeconomic Uncertainty and Private Investment in LDCs: An Empirical Investigation

The impact of uncertainty on investment has attracted considerable attention in the analytical and empirical macroeconomic literature. On theoretical grounds, however, uncertainty can affect investment through different channels, some of which operate in mutually opposing directions. Thus, the sign of its overall effect is therefore ambiguous, and can be assessed only empirically. This paper uses a large panel data set on developing countries to provide a thorough assessment of the impact of macroeconomic uncertainty on private investment. The paper draws a distinction between sample variability and uncertainty, constructs alternative measures of the volatility of innovations to five key macroeconomic variables – inflation, growth, the terms of trade, the real exchange rate and the price of capital goods—and examines their association with aggregate private investment. These constructed measures are then added to an empirical investment equation that is estimated employing alternative panel data econometric methods, allowing for simultaneity, country-specific effects and parameter heterogeneity across countries. The results underscore the robustness of the investment-uncertainty link. JEL classification codes: E22, C23
A LOSSLESS COMPRESSION ALGORITHM BASED ON PREDICTIVE CODING FOR VOLUMETRIC MEDICAL DATASETS

In this paper, a novel lossless compression method for volumetric medical datasets based on 3D adaptive prediction is presented. While almost all existing methods are based on a three-dimensional wavelet transform, this method, referred to as LSE-3D, evaluates the performance of an algorithm that operates on a least-square prediction basis. This leads to a different method with approximately similar results on compression performance, although with some advantages on computational cost.
Type-Based Decentralized Detection in Wireless Sensor Networks

Distributed detection strategies for wireless sensor networks are studied under the assumption of spatially and temporally independent and identically distributed (i.i.d.) observations at the sensor nodes. Both intelligent (with knowledge of observation statistics) and dumb (oblivious of observation statistics) sensors are considered. Two types of communication channels are studied: a parallel access channel (PAC) in which each sensor has a dedicated additive white Gaussian noise (AWGN) channel to a decision center, and a multiple-access channel (MAC) in which the decision center receives a coherent superposition of the sensor transmissions. Our results show that the MAC yields significantly superior detection performance for any network power constraint. For intelligent sensors, uncoded (finite duration) communication of local log-likelihood ratios over the MAC achieves the optimal error exponent of the centralized (noise-free channel) benchmark as the number of nodes increases, even with sublinear network power scaling. Motivated by this result, we propose a distributed detection strategy for dumb sensors-histogram fusion-in which each node appropriately quantizes its temporal data and communicates its type or histogram to the decision center. It is shown that uncoded histogram fusion over the MAC is also asymptotically optimal under sublinear network power scaling with an additional advantage: knowledge of observation statistics is needed only at the decision center. Histogram fusion achieves exponential decay in error probability with the number of nodes even under a finite total network power. In principle, a vanishing error probability at a slower subexponential rate can be attained even with vanishing total network power in the limit. These remarkable power/energy savings with the number of nodes are due to the inherent beamforming gain in the MAC
Limits on super-resolution and how to break them

We analyze the super-resolution reconstruction constraints. In particular we derive a sequence of results which all show that the constraints provide far less useful information as the magnification factor increases. It is well established that the use of a smoothness prior may help somewhat, however for large enough magnification factors any smoothness prior leads to overly smooth results. We therefore propose an algorithm that learns recognition-based priors for specific classes of scenes, the use of which gives far better super-resolution results for both faces and text.
Towards a Business Intelligence Systems Development Methodology: Drawing on Decision Support and Executive Information Systems

Business Intelligence (BI) systems are important IT platforms providing decision support in many enterprises, but there is a lack of independent BI-specific systems development methodologies. Further, while BI has been acknowledged as a successor to Executive Information Systems (EIS), there is little empirical evidence underpinning this view. This paper presents a case study of a BI systems development project in a large Australian healthcare organisation to argue that the development challenges faced by BI systems developers are largely similar to the challenges addressed by EIS development methodologies, and that an EIS development methodology is a useful starting point for designing a development methodology specifically for BI systems.
Louis Working Paper Series Too Big to Cheat : Efficiency and Investment in Partnerships

This paper studies the efficient arrangement among several agents that are subject to idiosyncratic, privately observed taste shocks affecting their marginal utility of current consumption. Agents accumulate capital and have access to a technology to produce goods. The framework deviates from previous literature, which assumed that (i) there is a continuum of agents and (ii) agents are exogenously endowed with output every period with no investment opportunities. A new method is proposed to solve for the optimal allocation that takes advantage of the fact that the utility possibility set is convex. Pareto weights play the role of promised utility in Abreu, Pearce, and Stacchetti (1990). The method is applied to study efficiency in a partnership between the founder, who faces the taste shock, and the partner, who provides funds but do not face shocks. New insights are derived. Under private information the ownership structure determines to what extent private information matters. If the founder’s share of the partnership is too big his incentives to cheat vanish. Additionally, efficiency implies that, while incentive constraints bind, equity shares must fluctuate to alleviate information problems. In the long run, there are two possible extreme structures: (1) the founder’s equity share becomes sufficiently large to make the incentive problem irrelevant and (2) the founder’s equity share converges to zero. Two alternative economies are studied to understand the role of key assumptions: (i) an endowment economy and (ii) an economy in which both partners face taste shocks. It turned out that to obtain the main results allowing for a production economy is crucial but having only one agent with shock is not.
Too Big to Cheat : Efficiency and Investment in Partnerships ∗

Many economic activities are organized as partnerships. These are ventures formed with capital contributions by partnership members that in exchange obtain a share of ownership. The design of the partnership rules dictates how much of the profits are distributed among the members and how much are reinvested. In this paper, we study the optimal design of partnerships under the assumption that one of the members (the founder) privately observes shocks to his liquidity needs. When the ownership share of the founder is large enough, private information is immaterial and the ownership structure remains constant over time. The founder has no incentives to misreport because a fraction of the increase in his payouts after reporting high liquidity needs is financed by disinvesting in the partnership. When the founder’s ownership share is not big enough, the ownership structure of the partnership must vary over time to prevent misreporting. In the long run, only two possible outcomes have positive probability: Either (i) the ownership shares of the founder converge to zero (i.e., “immiserization,” as in other models of private information), or (ii) the founder’s share of the partnership becomes too big to cheat (i.e., his incentives to misreport vanish) and thus both partners own a constant positive share of the partnership forever.
ULF pulsation evidence of the plasmapause: 1. Spectral studies of Pc 3 and Pc 4 pulsations near L = 4

Power spectra and digital dynamic power spectra of geomagnetic field fluctuations observed at a latitudinal network of stations near L = 4 were computed for the entire day of January 5, 1972, in order to study magnetospheric ULF pulsations on a nonstatistical basis. It was found that there are three dominant spectral peaks in the Pc 3–4 frequency range and another peak in the Pc 5 range. The peaks in the Pc 3–4 range are approximately higher harmonics of the spectral peak in the Pc 5 range. The contours of the peak intensities in the spectral bands have been determined as a function of geomagnetic latitude and universal time. These contour maps clearly show that the locations of the intensity maximums of the Pc 3 and Pc 4 peaks gradually change equatorward and poleward in parallel throughout the day and maintain the relationship that the intensity maximum of the Pc 4 peak is always located at a lower latitude than the intensity maximum of the Pc 3 peak. From these observational results it is suggested that the harmonic relationship results from the spectrum of the driving source and that the difference in the location of the intensity maximums of the Pc 3 and Pc 4 frequency bands arises from a sharp decrease in the phase velocity of the Alfven waves just inside the plasmapause. It is pointed out that these dynamic spectral analyses of Pc 3–4 pulsations near L = 4 open the possibility of studying the dynamics of the plasmapause by ground-based ULF techniques.
Air quality effects of urban trees and parks

Parks are significant parts of the urban landscape and comprise about 6% of city and town areas in the conterminous United States. These urban parks are estimated to contain about 370 million trees with a structural value of approximately $300 billion. The number of park trees varies by region of the country, but they can produce significant air quality effects in and near parks, related to air temperatures, air pollution, ultraviolet radiation, and carbon dioxide (a dominant greenhouse gas related to global climate change). Additional open space and other vacant lands in cities, which may contain trees and other vegetation, contribute significant additional benefits. Effects of parks and open space at the city scale can vary significantly depending on the amount of parkland and amount of tree cover within the parkland.
RATA.Gesture: A gesture recognizer developed using data mining

Abstract Although many approaches to digital ink recognition have been proposed, most lack the flexibility and adaptability to provide acceptable recognition rates across a variety of problem spaces. This project uses a systematic approach of data mining analysis to build a gesture recognizer for sketched diagrams. A wide range of algorithms was tested, and those with the best performance were chosen for further tuning and analysis. Our resulting recognizer, RATA.Gesture, is an ensemble of four algorithms. We evaluated it against four popular gesture recognizers with three data sets; one of our own and two from other projects. Except for recognizer–data set pairs (e.g., PaleoSketch recognizer and PaleoSketch data set) the results show that it outperforms the other recognizers. This demonstrates the potential of this approach to produce flexible and accurate recognizers.
Effects of soil incorporation and setbacks on herbicide runoff from a tile-outlet terraced field

ABSTRACT: Label changes defining the correct use of atrazine (6-chloro-N-ethyl-N9-(-(1-methylethyl)-1,3,5-triazine-2,4-diamine) and cyanazine (2-[[4-chloro-6-(ethylamino)-1,3,5-trianzin-2-yl]amino]-2-methylpropionitrile) have forced producers to place a buffer area or an application setback around standpipes or surface inlets in a tile-outlet terraced field. An alternative best management practice (BMP) to the setback requirement, in the way of soil incorporation, was evaluated for possible adoption without the perceived increased economic costs and hardship of the use of the setbacks. Both inflow to and outflow of runoff from tile-outlet terrace subwatersheds with different herbicide management practices were monitored under natural rainfall to determine how they affected the herbicide concentrations and losses in runoff being discharged. Results showed that the setback did not reduce off-site herbicide transport when compared to no-setback treatments beyond that expected due to the reduction in area treated. Incorporation of the herbicides without a setback were found to be just as effective in reducing the herbicides lost in outflow as when using a setback.
Ieee Transactions on Evolutionary Computation Intrinsic Motivation Systems for Autonomous Mental Development

— Exploratory activities seem to be intrinsically rewarding for children. Infants engage in these activities for their own sake. Intrinsic motivation systems are computational systems trying to capture this drive towards novel or curious situations. After discussing related research coming in developmental psychology, neuroscience, developmental robotics and active learning, this article presents the mechanism of Intelligent Adaptive Curiosity, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress. This drive makes the robot focus on situations which are neither too predictable nor too unpredictable thus permitting autonomous mental development. The complexity of the robot's activities autonomously increases and a complex developmental sequence self-organize without being constructed in a supervised manner. Two experiments are presented illustrating the stage-like organization emerging with this mechanism. In one of them, a physical robot is placed on a baby play mat with objects that it can learn to manipulate. Experimental results show that the robot first spends time in situations which are easy to learn, then shifts its attention progressively to situations of increasing difficulty, avoiding situations in which nothing can be learnt. Finally, these various results are discussed in relation to more complex forms of behavioural organization and data coming from developmental psychology. I. THE CHALLENGE OF AUTONOMOUS MENTAL DEVELOPMENT All humans develop in an autonomous open-ended manner through lifelong learning. So far, no robot has this capacity. Building such a robot is one of the greatest challenges to robotics today, and is the long-term goal of the growing field of developmental robotics ([1], [2]). This article explores a possible route towards such a goal. Our approach is inspired from developmental psychology and our ambition is to build systems featuring some of the fundamental aspects of infant's development. More precisely two remarkable properties of human infant development inspire us. First of all, development involves the progressive increase of the complexity of the activities of children with an associated increase of their capabilities. Moreover, infants' activities have always a complexity which is well fitted to their current capabilities. Children undergo a developmental sequence during which each new skill is acquired only when associated cognitive and morphological structures are ready. For example, children learn first to roll over, then to crawl and sit, and only when these skills are operational, they begin to learn how to stand. Development is progressive and incremental. Taking inspiration from these observations, some roboticists …
Campsite Impacts in Four Wildernesses in the South-Central United States

Campsite impacts were studied in four wildernesses in the South-Central United States - Caney Creek, AR, Upper Buffalo, AR, Hercules Glades, MO, and Garden of the Gods, IL. Compared with wildernesses in other regions, campsite densities in these wildernesses are low to moderate, while impact intensities on individual campsites are low. Implications for monitoring procedures, management strategies, and educational programs are suggested.