Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the Stretch Factor of a Geometric Network by Edge Augmentation

Given a Euclidean graph $G$ in $\mathbb{R}^d$ with $n$ vertices and $m$ edges, we consider the problem of adding an edge to $G$ such that the stretch factor of the resulting graph is minimized. Currently, the fastest algorithm for computing the stretch factor of a graph with positive edge weights runs in $\cal{O}$$(nm+n^2 \log n)$ time, resulting in a trivial $\cal{O}$$(n^3m+n^4 \log n)$-time algorithm for computing the optimal edge. First, we show that a simple modification yields the optimal solution in $\cal{O}$$(n^4)$ time using $\cal{O}$$(n^2)$ space. To reduce the running time we consider several approximation algorithms.
Using learning environments to create meaningful work for co-op students

ABSTRACT For students in cooperative education (co-op) programs, meaningful work is a critical aspect of participation and a key component of program success. However, studies have not explored how meaningful work can be created for co-op students. The purpose of this study was to evaluate the relationship between co-op students’ perceptions of a learning environment, perceived relatedness of the work experience (to their potential careers and to their academic studies), and self-reported meaningfulness of work. Data were collected from undergraduate co-op students in a Canadian university immediately following a co-op work term. Results from testing a parallel mediation model showed that both forms of relatedness partially mediated the influence of learning environments on self-reported meaningfulness. These findings offer insight into the ways in which work terms can be designed to enhance co-op students’ work experiences.
The geometry of deformed boson algebras

We consider a phase space realisation of an infinite parameter family of deformations of the boson algebra in which the q-deformed algebras arise as special cases. The deformation parameters are identified with coefficients of non-linear terms in the normal forms expansion of a family of classical Hamiltonian systems. These deformations simply correspond to nonlinear transformations of the boson algebra. They are physically interesting, as the deformed commutators consistently quantise a class of noncanonical classical Poisson structures.
Determining the natural absorption and radiation modes of lossy periodic structures using energy absorption interferometry

Energy absorption interferometry is presented in the spectral domain in order to allow an easier representation of fields for layered media. A formulation comprising a single interface between semi-infinite media is presented and a compact representation of the fringe pattern is obtained assuming that two sources with varying relative phase are used. Limit cases of continuous lossy layers and of very fine parallel strips are considered and different full-wave approaches are discussed. Simulation results show how the structure of the absorbing medium becomes visible when the sources are placed closer to the lossy medium.
Maruyama Mindscapes, Myers-Briggs Type Indicators and Cultural Agency Mindset Types

In this paper we introduce the concept of the normative personality bedded on ideas from psychology and social psychology develop, and explains how it can be effectively characterised. There are a variety of approaches that attempt to represent personality types, but few have any substantive theory, and even fewer adopt an epistemic basis. It is explained that one of the more powerful ways of identifying personality types is through Maruyama’s Mindscape theory, a little known but powerful epistemic approach that can anticipate an agency’s patterns of behaviour and demands. It is also a construct from which coherent sets of behavioural mind-sets can emerge, though its major drawback is its lack of generic transparency. From the principles of Mindscape theory, this paper then develops Mindset theory. This has generic transparency, extends personality types beyond the capacity of Mindscape theory, and is derived from a well-endowed empirical base. Mindset Theory is based on the Sagiv-Schwartz (2007) cultural values study from which 8 Mindset types are generated that individually or in combination can characterise personality and anticipate behaviour. Mindset theory is also superior to other personality the Myers-Briggs Type Inventory schema since it has a well-developed theory, is empistemically based, and establishes a means by which patterns of behaviour can be anticipated.
On the Capacity of Operator Channels

We examine linear, bounded, fixed operators as channels between continuous input functions and continuous output functions. The time-frequency waveform channel and continuous space channel are both related to an abstract operator viewpoint of communication channels. The properties of the operator required in order to allow valid capacity estimation are examined, and the modelling of noise in a continuous setting is discussed. We outline the procedure for calculating the information theoretic capacity for this restricted class of operator channels and provide an example of operator channels in the form of a power constrained input signal.
Application of a Bioinformatics Approach to High-throughput Docking for Drug Discovery

we have developed a high-throughput docking (HTD)-based virtual screening scheme, termed HiPCDock, for drug discovery and development. To improve the statistical significance of our screening results, a bioinformatics approach, motivated by sequence alignment package BLAST, was implemented so that we can estimate the confidence of our prediction accuracy. The model was validated by docking ten known thymidine kinase (TK) binders into the enzyme and the real inhibitors showed significant statistics of low probability and expectation values compared to those random compounds extracted from ChemBank. It was also demonstrated that HiPCDock was able to efficiently recover the ten known TK inhibitors from a dataset including 990 decoy compounds randomly selected from ChemBank. Our HiPCDock is currently implemented based on only one configuration template file for job submission, which makes it very easy to use for both computational experts and experimental scientists who have little molecular docking experience. With just one command line, users can submit massive parallel docking jobs for their screening of millions of compounds against a specific target of interest. The statistical model will be very useful to guide the decision-making for lead discovery and optimization. Thus HiPCDock is an elegant, professional integrated package for highthroughput molecular docking and drug discovery. We are currently developing a web-based user interface to further simplify the process.
Interpretable Dimensionality Reduction for Classification with Functional Data

Classification problems involving a categorical class label Y and a functional predictor X(t) are becoming increasingly common. Since X(t) is essentially infinite dimensional some form of dimensionality reduction is essential in these problems. Conventional data reduction techniques for functional data can be categorized into functional principal component analysis and filtering methods. However, they usually suffer from one or both of the following problems. First, they do not take the categorical response into consideration, and second, the resulting reduced data may be difficult to interpret. In this paper, we propose a dimensionality reduction method, “Functional Adaptive Classification” (FAC), specifically designed for functional classification problems. Based on a stochastic search procedure guided by the evaluation of model complexity, FAC makes use of the functional response and produces simple relationships between the reduced data, Z, and X(t). Hence, it can achieve both good classification accuracy and interpretability. Extensive simulation studies and an fMRI time course study show that FAC is extremely competitive in comparison to other potential approaches.
A reconfigurable 4.7–6.6GHz and 8.5–10.7GHz concurrent and dual-band oscillator in 65nm CMOS

A triple-mode 65nm CMOS VCO is designed to be reconfigurable to operate in two frequency bands, 4.7-6.6GHz and 8.5-10.7GHz, either concurrently or one at a time. Operated concurrently in the lower and higher bands, the VCO consumes 1.6mA at 1.2V and measures tuning ranges of 35%/23%, phase noise of -130/-128dBc/Hz at 10-MHz offset, corresponding to FOMs of -184/187dB, respectively. Operated in the lower and higher bands one at a time, the VCO consumes 0.75mA/1.43mA at 1.2V with tuning ranges of 34%/23% and phase noise of -131/-128dBc/Hz at 10-MHz offset, which correspond to FOMs of -186dB and -185dB, respectively.
Early Detection of Prostate Cancer with Classifier Ensembles

We investigate the performance of different classification models and their ability to recognize prostate cancer in an early state. We build ensembles of classification models in order to increase the classification performance. We measure the performance of our models in an extensive cross- validation procedure. The data sets come from clinical examinations and some of the classification models are already in use to support the urologists in their clinical work.
Calibration Method to Measure Accurate Bow Force for Real Violin Performances

In this paper, we present a procedure to predict bow pressing force in a violin from data acquired in real recordings. We focus on the calibration procedure that must be robust to the bow tension changes in long recordings and fast enough to not disturb the recording session. Because of this last limitation, the calibration method here proposed does not exhaustively cover all the possible bow conditions that potentially may appear in the recording. We propose the use of Support Vector Regression to predict all these missing scenarios and compute the predicted force. On the other hand, bow tension variations in long recordings produce decalibrations on the acquisition system. After analyzing their behavior, we propose a solution to compensate this effect based on post processing and a specific behavior of the performer at the beginning and the end of each phrase.
Do Global stock market cues matter in forecasting stock returns in developed and developing markets ?

Financial markets all over the world have witnessed growing integration within as well as across boundaries, spurred by deregulation, globalization and advances in information technology. However, none of the researches have investigated the trading profitability of models that employed the financial market integration information as input variables especially in the case of day trading. Moreover traditional methods employed linear correlation techniques to study the market integration though it is strongly believed that their relationship could be nonlinear. This paper examines the usefulness of international stock market price transmission information (global cues) in day trading in developed and emerging stock markets. The study investigates the performance of global stock market cues in forecasting stock prices using Support Vector Regression for seven global markets – US (Dow Jones Industrial Average, S&P500), UK (FTSE -100), India (Nifty), Singapore (Straits Times Index), Hong Kong (Hang Seng Index), China (Shanghai Stock Composite) over the period 1999-2011. The empirical analysis shows that hit ratio of the models with other market cues outperform forecast models based merely on Auto-regressive past lags and technical indicators. Shanghai stock index movement was predicted best by Hang Seng Index opening price with a hit ratio of 57.69, while, Hang Seng Index by previous day’s S&P500 closing price (54.34), FTSE by previous day’s S&P500 closing price (57.94), Straits Times Index by previous day’s Dow Jones closing price (54.44), Nifty by HSI opening price (60), S&P500 by STI closing price (55.31) and DJIA by HSI closing price (55.22) and Nifty was found to be the most predictable stock index. The study provides evidence that stock markets across the globe are integrated and that information on price transmissions across markets can induce arbitrage opportunities even in day trading.
Seizure prediction model based on method of common spatial patterns and support vector machine

Records of brain electrical activity from intracranial and scalp EEG of seven patients with different types of epilepsy are analyzed to predict the epileptic seizure onset. A method based on the CSP and SVM is introduced. This is an efficient method to predict epileptic seizures: from 52 pre-seizure signals, the seizure onsets in 23 of those are predicted. Through this method, we propose a seizure prediction model which gets an accuracy rate represented by predictions/seizures of 5/20-5/5 and a pseudo-prediction rate of 1.6-10.9 per hour.
Java Garbage Collector Performance Measurements

Advances in compiler and virtual machine technologies support spread of programming environments with automated memory management, such as Java. These environments rely on garbage collection to reclaim memory that is no longer used. While in the domain of garbage collection research, every proposed garbage collector improvement is accompanied with detailed analysis of the collector overhead [Detlefs et al., 2004; Zhang and Seltzer, 2001; Blackburn, 2006], in the domain of performance modeling, the collector overhead is usually treated implicitly as a constant background factor [Xu et al., 2006; Becker et al., 2009; Kuperberg et al., 2008]. A potential reason for this treatment is lack of available information on the overhead of the garbage collector implementation. Although some implementations are open source, it is rather difficult to get such information from the code. For example, the garbage collector implementation of OpenJDK counts for more than 50000LOC. While this particular implementation is relatively well documented [Sun Microsystems, 2004], not all the information can be obtained from the documentation, and worse, the documentation contains misleading claims, such as that the JVM automatically adjusts total collector overhead to 1% of execution time [Sun Microsystems]. In order to provide a more precise collector overhead performance model, we have conducted many experiments to assess selected aspects of the garbage collector performance. Due to size constraints, the experiments are mostly reported online [Babka et al., 2009; Q-ImPrESS Consortium], and selected aspects are described in individual papers [Libič et al., 2009]. In this paper, we do not try to provide generalized claims, because the results of the experiments are bound to a particular virtual machine and collector. We do, however, compare two widely used collector implementations in the paper, and also attach notes on possible approaches towards modeling.
Exxon Valdez Oil Spill Restoration Project Final Report Relations Between Dolly Varden Populations and Between Coastal Cutthroat Trout Populations in Prince William Sound Restoration Project 98145 Final Report

We examined genetic, meristic, and otolith microchemistry variation in coastal cutthroat trout and Dolly Varden charr in Prince William Sound. Geographically proximate aggregations of coastal cutthroat trout around Orca Bay were genetically similar and had moderate rates of genetic exchange. Genetic divergence increased in geographically more distant populations from Columbia Bay, Unakwik Inlet, Gunboat Lakes, and Green Island, which had reduced levels of genetic exchange and diversity. This pattern was best explained by isolation by distance. Dolly Varden found below barriers were characterized by high genetic exchange and minor genetic differences with no strong geographical patterns of differentiation, but populations above barriers were genetically different. Otolith microchemistry did not show differences in Sr/Ca ratios of otolith primordial between aboveand below-barrier groups but did show patterns consistent with resident or anadromous life histories. Inferences about population histories, abundances, and patterns of dispersal from these data were the basis of recommendations for recovery programs for these species.
Smartphone Usage Restriction During Crossing Roads and Railways

In late years, a number of people walk carelessly while continuing watching a smartphone screen, which often causes fatal accidents. In this poster abstract, we propose a system to restrain smartphone usage at the time of crossing road and railroad based on the combination of the present and past location of a pedestrian, walking direction and walking speed by means of smartphone-mounted sensors. A geographical database that includes roads and railway information is also combined. We achieved a recall of 0.75 against the judgment of ``crossing'' situation.
EEG single-channel seizure recognition using Empirical Mode Decomposition and normalized mutual information

In this document features taken from Empirical Mode Decomposition (EMD) are selected by mutual information for the discrimination between letal and Seizure-Free EEG single-channel signals. Some features are based on the instantaneous or average frequency and amplitude of each EMD component. Also, skewness, kurtosis and Shannon's entropy are taken as features from the energy obtained using the Teager Energy Operator (TEO). TEO is calculated over each EMD component. Then a subset of relevant and non-redundant features is selected by normalized mutual information. Finally these selected features are used to train a linear Bayes classifier, and a 5-fold cross validation is performed for different clinical cases. We used a publicly available database to compare each feature extraction approach. Accuracies around 98% are reached by the implemented methodology.
An initial evaluation of the Global Review Form as an approach to measuring individual change

Abstract Background: Routine assessment of individual change in forensic mental health services is increasingly recognised as important. However, existing tools have been criticised and their periodic use make them unsuited to directly measure the impact of interventions. This paper describes the initial evaluation of the Global Review Form (GRF) as a framework for measuring change over time. Specifically, measurement properties, feasibility and usefulness in routine practice are examined. Method: 28 male service users in three distinct areas of an adult secure service (low secure, locked rehabilitation and high relational support housing) were rated over a 20-week period by their multidisciplinary teams. Findings: The GRF showed promising construct validity and appropriate stability and sensitivity to change across time. It enabled measurement and understanding of individual change over time. Staff feedback suggested the GRF is a useable and practical outcome measuring tool. Conclusions: The GRF shows promise for use as a routine outcome monitoring tool within forensic mental health services.
MODIFIED GOLDEN CODES FOR IMPROVED ERROR RATES THROUGH LOW COMPLEX SPHERE DECODER

In recent years, the golden codes have proven to exhibit a superior performance in a wireless MIMO (Multiple Input Multiple Output) scenario than any other code. However, a serious limitation associated with it is its increased decoding complexity. This paper attempts to resolve this challenge through suitable modification of golden code such that a less complex sphere decoder could be used without much compromising the error rates. In this paper, a minimum polynomial equation is introduced to obtain a reduced golden ratio (RGR) number for golden code which demands only for a low complexity decoding procedure. One of the attractive approaches used in this paper is that the effective channel matrix has been exploited to perform a single symbol wise decoding instead of grouped symbols using a sphere decoder with tree search algorithm. It has been observed that the low decoding complexity of O (q 1.5 ) is obtained against conventional method of O (q 2.5 ). Simulation analysis envisages that in addition to reduced decoding, improved error rates is also obtained.
Geometric Descent Algorithms for Attitude Determination Using the Global Positioning System

Thispaperdescribesasetofnumericalgradient-basedoptimizationalgorithmsforsolvingtheGlobalPositioning System (GPS)-based attitude determination problem. We pose the problem as one of minimizing the function tr(H NH T Qi 2H W) with respect to the rotation matrix H , where N, Q, and W are given 3 £ 3 matrices, and tr(¢ ) denotes thematrix trace. Both the method of steepest descent and Newton’ s method are generalized to the rotation group by taking advantage of its underlying Lie group structure. Analytic solutions to the line search procedure are also derived. Results of numerical experiments for the class of geometric descent algorithms proposed here are presented and compared with those of traditional vector space-based constrained optimization algorithms.
The Log of Gravity

Abstract Although economists have long been aware of Jensen's inequality, many econometric applications have neglected an important implication of it: under heteroskedasticity, the parameters of log-linearized models estimated by OLS lead to biased estimates of the true elasticities. We explain why this problem arises and propose an appropriate estimator. Our criticism of conventional practices and the proposed solution extend to a broad range of applications where log-linearized equations are estimated. We develop the argument using one particular illustration, the gravity equation for trade. We find significant differences between estimates obtained with the proposed estimator and those obtained with the traditional method.
SuperRare: an Object-oriented Saliency Algorithm Based on Superpixels Rarity

SuperRare is a new object-oriented attention algorithm based on the notion of rarity: rare regions are worthy of attention. The main novelty of the model is to use superpixels of several sizes instead of simple pixels. This approach allows SuperRare to react efficiently to salient objects of any size. The model is validated on Jian Li’s database, a database considering both large, medium and small salient objects. It is compared with other 9 recent state-of-the-art object-oriented saliency models. Even if the proposed algorithm is solely based on color rarity, we reach and even surpass the state-of-the-art models for the detection of salient objects. We discuss the influence of the color space on our model and how we have tried to limit this influence.
Scan and 3 D Model Alignments in Indoor Scenes for Augmented Reality

While localization and reconstruction techniques mature, to achieve the effects shown in the Minecraft game we still need to fill the semantic gap. For example, computer needs to understand the 3D layout of the room such as floors, support planes and walls in order to place the virtual objects. Beyond that, if we can recognize what categories (sofa, table, bookshelf etc.) the real objects are, the application can benefit from it and support deeper immersion of virtual objects/characters into the scene. Ideally we can have virtual character navigating in the room and sit on the sofa beside us! One way to achieve such goal is to have a “semantic” reconstruction of the scene, i.e. we align scan data (depth images or point clouds) with 3D models, which can be either from our shape database or from models reconstructed from previous scans.