Miracle-dz/UniFAR-FTUs huggingface dataset:
A Methodology for Defining and Inferring Higher Level Semantic Information from Topographic Data

Lack of high level semantic descriptions within geospatial data has been a major critique point for the handling and usage of such data. Much research has been invested into the cognitive principles of how humans conceptualise, store and work with spatial information, yet current data models continue to fail to meet the users’ needs for flexible and fit-for-use data. This paper proposes a general methodology for defining and inferring higher level semantic information in the form of functional attributes from topographic data by the means of ontologies. Based on the argument that a specific spatial structure and configuration is inherent in different land use types, this project focuses on the ontology’s reasoning capabilities for making the modelled functional information – from both the human and data point of view – explicit within the data, thus enriching its current thematic contents with higher level concepts and enabling new opportunities for on demand mapping and multi-representation.
generation approach for solving generation expansion lanning problems with high renewable energy penetration

The high penetration of renewables envisaged for future power systems will significantly increase the need for flexible operational measures and generation technologies, whose associated investment decisions must be properly planned in the long term. To achieve this, expansion models will need to incorporate unit commitment constraints, which can result in large scale MILP problems that require significant computational resources to be solved. In this context, this paper proposes a novel Dantzig–Wolfe eywords: olumn generation eneration expansion planning lexibility enewable energy integration nit commitment decomposition and a column generation approach to reduce the computational burden and overcome intractability. We demonstrate through multiple case studies that the proposed approach outperforms direct application of commercial solvers, significantly reducing both computational times and memory usage. Using the Chilean power system as a reference case, we also confirm and highlight the importance of considering unit commitment constraints in generation expansion models. © 2016 Elsevier B.V. All rights reserved.
Modern Models of Monopsony in Labor Markets: A Brief Survey

This brief survey contains a review of several new empirical papers that attempt to measure the extent of monopsony in labor markets. As noted originally by Joan Robinson, monopsonistic exploitation represents the gap between the value of a worker's marginal product and the worker's wage, and it represents both a distortion in the allocation of resources and an income transfer away from workers. The evidence surveyed from a fairly broad range of labor markets suggests that monopsony may be far more pervasive than is sometimes suggested.
Bridging the gap between real and virtual objects for tabletop games

In this paper, we present a collaborative tabletop game which combines both the real and virtual world. The main motivation for this game was to augment a traditional board game with additional digital content. Users have to work together and place domino pieces in each level. We present the implementation including the hardware components and the software implementation, describe the game design and discuss the game. Finally, we also discuss the results of our first pilot study.
Generalized Plan Recognition

This paper outlines a new theory of plan recognition that is significantly more powerful than previous approaches. Concurrent actions, shared steps between actions, and disjunctive information are all handled. The theory allows one to draw conclusions based on the class of possible plans being performed, rather than having to prematurely commit to a single interpretation. The theory employs circumscription to transform a first-order theory of action into an action taxonomy, which can be used to logically deduce the complex action(s) an agent is performing.
Journal of Modern Applied Statistical Methods Impact of Serial Correlation Misspecification with the Linear Mixed Model Impact of Serial Correlation Misspecification with the Linear Mixed Model

Linear mixed models are popular models for use with clustered and longitudinal data due to their ability to model variation at different levels of clustering. A Monte Carlo study was used to explore the impact of assumption violations on the bias of parameter estimates and the empirical type I error rates. Simulated conditions included in this study are: simulated serial correlation structure, fitted serial correlation structure, random effect distribution, cluster sample size, and number of measurement occasions. Results showed that the fixed effects are unbiased, but the random components tend to be overestimated and the empirical Type I error rates tend to be inflated. Implications for applied researchers were discussed.
Decentralized centroid estimation for multi-agent systems in absence of a common reference frame: A convergence analysis

In this paper a novel algorithm for the decentralized estimation of the network centroid for a multi-agent system deployed in a d-dimensional space is presented. It is assumed that the agents are static, do not share a common coordinate system and are only able to estimate relative distance and angle of sight with respect to themselves during pairwise asynchronous communications. It is shown that if the graph representing the network topology is rigid, then the proposed algorithm solves the network centroid estimation problem.
Estimation of Models in a Rasch Family for Polytomous Items and Multiple Latent Variables

The Rasch family of models considered in this paper includes models for polytomous items and multiple correlated latent traits, as well as for dichotomous items and a single latent variable. An R package is described that computes estimates of parameters and robust standard errors of a class of log-linear-by-linear association (LLLA) models, which are derived from a Rasch family of models. The LLLA models are special cases of log-linear models with bivariate interactions. Maximum likelihood estimation of LLLA models in this form is limited to relatively small problems; however, pseudo-likelihood estimation overcomes this limitation. Maximizing the pseudo-likelihood function is achieved by maximizing the likelihood of a single conditional multinomial logistic regression model. The parameter estimates are asymptotically normal and consistent. Based on our simulation studies, the pseudo-likelihood and maximum likelihood estimates of the parameters of LLLA models are nearly identical and the loss of efficiency is negligible. Recovery of parameters of Rasch models fit to simulated data is excellent.
Recognizing Your Digital Friends

Personal relationships are more and more managed over digital communication media, and electronic social networks in particular. Digital identity, conceived as a way to characterize and recognize persons on the Internet, has thus taken center stage, although this concept still remains vague in many of its aspects. This work aims at shedding some light on this topic, by sketching a basic conceptual framework, analyzing the issues for Internet users, and proposing possible solutions that promote a better use of digital identity.
Final Project Proposal Timothy Anderson EE 368 – Digital Image Processing Overview

Title: Efficient Brain MRI Segmentation via Neuromechanics-based Edge Optimization Project members: Timothy Anderson (timmya@stanford.edu) Android Device: None. Note: I am planning to also use this project for a final project in ME 234 (Introduction to Neuromechanics) and CEE 362G (Stochastic Inverse Modeling). As explained below, I believe this project suitably combines concepts from all three of these areas, and using a project for multiple classes will allow me to do a more in-depth exploration of the topic, as well as leave me with more time for research and graduate school/fellowship applications.
Undersampling Strategy Based on Clustering to Improve the Performance of Splice Site Classification in Human Genes

The recognition of splice sites plays an important role in the annotation of the eukaryotic genes structure. The detection of such sites is a highly imbalanced classification task because the number of negatives examples found in the DNA sequences is much higher than the number of positive ones. One possible strategy to deal with this particularity is to use training sets more balanced than the original dataset. It is necessary then to choose which part of the majority examples will be taken to compose those sets. Aiming at increasing the learning ability in this problem, we propose a new under sampling procedure. In this strategy, the negative examples used to train the classifier are selected based on clusters obtained from this majority class. The experimental results show that, for the splice site problem, it is possible to increase classification performance when compared to simpler under sampling techniques.
An extreme rotation measure in the high-redshift radio galaxy PKS B0529–549

We present the results of a radio polarimetric study of the high-redshift radio galaxy PKS B0529−549 (z= 2.575), based on high-resolution 12-mm and 3-cm images obtained with the Australia Telescope Compact Array. The source is found to have a rest-frame Faraday rotation measure of −9600 rad m^(−2), the largest seen thus far in the environment of a z > 2 radio galaxy. In addition, the rest-frame Faraday dispersion in the screen responsible for the rotation is calculated to be 5800 rad m^(−2), implying rotation measures as large as −15 400 rad m^(−2). Using supporting near-infrared (near-IR) imaging from the Very Large Telescope, we suggest that the rotation measure originates in the Lyα halo surrounding the host galaxy, and estimate the magnetic field strength to be ~10 μG. We also present a new optical spectrum of PKS B0529−549 obtained with the New Technology Telescope, and propose that the emission-line ratios are best described by a photoionization model. Furthermore, the host galaxy is found to exhibit both hot dust emission at 8.0 μm and significant internal visual extinction (~1.6 mag), as inferred from Spitzer Space Telescope near/mid-IR imaging.
Resolution threshold analysis of music algorithm in radar range imaging

Super-resolution algorithms used in radar imaging, e.g., MUltiple SIgnal Classification (MUSIC), can help us to get much higher resolution image beyond what is limited by the signal's bandwidth. We focus on MUSIC imaging algorithm in the paper and investigate the uniqueness and effectiveness conditions of the MUSIC algorithm when used in 1-D radar range imaging. Unlike conventional radar resolution analysis, we introduced the concept of resolution threshold from Direction of Arrival (DOA) into the MUSIC radar range imaging, we derive an approximate expression of theoretical resolution threshold for 1-D MUSIC imaging algorithm through the approach of asymptotic and statistical analysis to the null spectrum based on the perturbation theory of algebra and matrix theories. Monte Carlo simulations are presented to verify the work.
Using peer-to-peer communication to improve the performance of distributed computing on the Internet

This paper presents the design and evaluation of a distributed computing platform on the Internet that employs peer-to-peer communication for transferring data directly among participants without consuming scheduler's bandwidth. The aim is to reduce network congestion at the scheduler by utilizing available peer-to-peer bandwidth. The peer-to-peer model is evaluated and compared to the client/server model in different system settings. The impacts of various factors, including the dynamic nature of a peer-to-peer system, on the performance and scalability are studied. The experimental results show that when peer-to-peer communication is applied, even partially, the bandwidth demands on the scheduler significantly reduced. This results in improved system's performance and scalability, and better support for data-intensive applications.
Beyond Sweatshops: Positive Deviancy and Global Labour Practices

Disputes concerning global labour practices are at the core of contemporary debates regarding globalization. Attention frequently focuses on the real or alleged unjust exploitation of workers in developing economies by multinational corporations (MNCs) and their suppliers. Critics charge MNCs with the unjust exploitation of workers in developing nations and seek laws restricting the use of sweatshop labour (Gray 1998, Kaplan 2001, Featherstone 2002). Many economists and sociologists retort that the existence of sweatshops is an important and inevitable feature of economic development, and that laws that seek to restrict the production of goods in sweatshops will harm the very people they were intended to help (Maitland 1997, Krugman 1999, Kristof 2000, Henderson 2001, Norberg 2003). However, insufficient attention has been paid to firms that engage in truly good and beneficial activities with regard to their global workforces, where the result is not a ‘sweatshop’ environment but is instead a safe and healthy workplace where labourers are treated with respect. Workers have basic rights that should not be violated, notwithstanding the geographical locale of their workplace. In this essay, we argue that the labour practices of ‘positive MNC deviants’ can serve as models for other MNCs that wish to respect human rights while taking advantage of the economic benefits of a global workforce. Positive deviants are MNCs that deviate from specific norms in praiseworthy ways. While positive deviancy can occur in reference to both strategic and legal norms, in this essay we are primarily concerned with positive deviance from ethical norms. In particular, we call attention to those positive MNC deviants in the footwear and apparel sector that demonstrate enhanced respect for basic human rights and corresponding workers’ rights. These positive deviants have used creative approaches to global labour practices in order to move beyond sweatshops and to provide workers with wages and working conditions that respect their basic human dignity. The organization of this essay is as follows. First, we outline the conventional debate over sweatshops. Second, we provide a summary of the basic human and workers’ rights that ought to be respected by employers. Third, we present a pioneering account of positive deviancy and explain why our view is preferable to two alternative views that have recently been defended in the literature. Next, we use recent empirical field studies of MNC factories in developing nations conducted by one of the present authors, together with other recent research, as a basis for arguing that MNCs are capable of voluntarily respecting the basic rights of workers while remaining economically competitive. Finally, we provide a summary of the numerous strategic Respectively, Assistant Professor of Philosophy, University of Tennessee, Knoxville, TN, USA and Associate Vice President and Professor of Business Ethics, DePaul University, Chicago, IL, USA.
Quark Delocalization, Color Screening and Nuclear Intermediate Range Attraction II

The center of mass motion effect on the quark delocalization model is to make the effective baryon-baryon (BB) interactions a little less attractive but only a minor correction. The long-range behavior of the color screening potential is not critical for obtaining an intermediate attractive BB interaction. The cluster model and the molecular single quark-basis give exactly the same result.
Formal Specification of UML Use Case Diagram-A CASL based approach

Unified Modelling Language (UML) gives a modelling approach to design a system. Use Case diagram is one of the behavioural approach of UML which describe the behavioural pattern of the system. It has been observed that UML diagrams are not formally specified. Formal specification gives a specific way to design a system as a whole using mathematical notation. This paper proposed formal specification of UML use case diagram using the Common Algebraic Specification Language (CASL) in object oriented paradigm. Keywords— Unified Modelling Language (UML), Use Case diagram, formal specification, non-formal models, Common Algebraic Specification Language (CASL), Object oriented Software.
IGR J16479–4514: the first eclipsing supergiant fast X-ray transient?

Supergiant fast X-ray transients (SFXTs) are a new class of high-mass X-ray binaries recently discovered with INTEGRAL. Hours-long outbursts from these sources have been observed on numerous occasions at luminosities of ∼10 36 –10 37 erg s −1 , whereas their low-level activity at ∼10 32 –10 34 erg s −1 has not been deeply investigated yet due to the paucity of long pointed observations with high-sensitivity X-ray telescopes. Here, we report on the first long (∼32 ks) pointed XMM–Newton observation of IGR J16479−4514, a member of this new class. This observation was carried out in 2008 March, shortly after an outburst from this source, with the main goal of investigating its low-level emission and physical mechanisms that drive the source activity. Results from the timing, spectral and spatial analysis of the EPIC-PN XMM– Newton observation show that the X-ray source IGR J16479−4514 underwent an episode of sudden obscuration, possibly an X-ray eclipse by the supergiant companion. We also found evidence for a soft X-ray extended halo around the source that is most readily interpreted as due to scattering by dust along the line of sight to IGR J16479−4514. We discuss this result in the context of the gated accretion scenarios that have been proposed to interpret the behaviour of SFXT.
A Descriptor for Handwritten Strokes Off-Line Analysis: A Preliminary Study

This paper presents preliminary results of the study of a new descriptor for identity verification based on handwriting strokes. The descriptor is obtained from the relationship between the line that joins the darkest points of the image and the line of the skeleton of the stroke. It was discovered that the relative distance between the line of the minimum gray level values in relation to the skeleton is not only characteristic of the author but it also varies from one person to another. The promising results of this study lead to the belief that it is feasible to develop a method of identity verification based on the descriptor presented.
Efficient error models for fault-tolerant architectures and the Pauli twirling approximation

The design and optimization of realistic architectures for fault-tolerant quantum computation requires error models that are both reliable and amenable to large-scale classical simulation. Perhaps the simplest and most practical general-purpose method for constructing such an error model is to twirl a given completely positive channel over the Pauli basis, a procedure we refer to as the Pauli twirling approximation (PTA). In this work we test the accuracy of the PTA for a small stabilizer measurement circuit relevant to fault-tolerant quantum computation, in the presence of both intrinsic gate errors and decoherence, and find excellent agreement over a wide range of physical error rates. The combined simplicity and accuracy of the PTA, along with its direct connection to the chi matrix of process tomography, suggests that it be used as a standard reference point for more refined error model constructions.
Parallax error compensation for head-mounted gaze trackers based on binocular data

The parallax error caused by the spatial offset between the eye monitoring camera and the scene camera is a common challenge for head-mounted gaze trackers. To address this problem, the parallax error is investigated based on the epipolar geometry at first and two parallax compensation methods for binocular gaze tracking setups are proposed in this paper to ensure the accurate gaze point estimation over a wide range of distances without on-line manual adjustments. A comparative study is conducted on these two methods, and simulation results demonstrate that our proposed compensation methods can be effectively applied on the binocular gaze trackers.
Two loop Kahler potential in beta-deformed N=4 SYM theory

In = 2 superconformal field theories the Kahler potential is known to be tree level exact. The β-deformation of = 4 SU(N) SYM reduces the amount of supersymmetry to = 1, allowing for non-trivial, superconformal loop corrections to the Kahler potential. We analyse the two-loop corrections on the Coulomb branch for a complex deformation. For an arbitrary chiral field in the Cartan subalgebra we reduce the problem of computing the two-loop Kahler potential to that of diagonalising the mass matrix, we then present the result in a manifestly superconformal form. The mass matrix diagonalisation is performed for the case of the chiral background that induces the breaking pattern SU(N) → SU(N−2) × U(1)2. Then, for the gauge group SU(3), the Kahler potential is explicitly computed to the two-loop order.
Generating Referring Expressions: Boolean Extensions of the Incremental Algorithm

This paper brings a logical perspective to the generation of referring expressions, addressing the incompleteness of existing algorithms in this area. After studying references to individual objects, we discuss references to sets, including Boolean descriptions that make use of negated and disjoined properties. To guarantee that a distinguishing description is generated whenever such descriptions exist, the paper proposes generalizations and extensions of the Incremental Algorithm of Dale and Reiter (1995).
Learning-based mitotic cell detection in histopathological images

Breast cancer grading of histological tissue samples by visual inspection is the standard clinical practice for the diagnosis and prognosis of cancer development. An important parameter for tumor prognosis is the number of mitotic cells present in histologically stained breast cancer tissue sections. We propose a hierarchical learning workflow for automated mitosis detection in breast cancer. From an initial training set a pixel-wise classifier is learned to segment candidate cells, which are then classified into mitotic and non-mitotic cells using object shape and texture features. Our workflow banks on two open source biomedical image analysis software: “ilastik” and “CellCognition” which provide a user user friendly interface to powerful learning algorithms, with the potential of making the pathologist work an easier task. We evaluate our approach on a dataset of 35 high-resolution histopathological images from 5 different specimen (provided by International Conference for Pattern Recognition 2012 contest on Mitosis Detection in Breast Cancer Histological Images). Based on the candidate segmentation our approach achieves an area-under Precision-Recall-curve of 70% on an annotated dataset, with good localization accuracy, little parameter tuning and small user effort. Source code is provided.