Miracle-dz/UniFAR-FTUs huggingface dataset:
Minimum Steiner trees, roots of a polynomial, and other magic

The paper is a collection of observations and ideas on a surprising similarity between Steiner points on one hand, and roots of a complex polynomial on the other. These ideas can also be used to derive a simple practical method to generate good suboptimal Steiner trees. However, the main purpose of the paper is to point out a vast area of problems still open to further research.
A simple theorem prover based on symbolic trajectory evaluation and BDD's

Formal hardware verification based on symbolic trajectory evaluation shows considerable promise in verifying medium to large scale VLSI designs with a high degree of automation. However, in order to verify today's designs, a method for composing partial verification results is needed. This paper presents a theory of composition for symbolic trajectory evaluation and shows how implementing this theory using a specialized theorem prover is very attractive. Symbolic trajectory evaluation is used to prove low level properties of a circuit, and these properties are combined using the prover. Providing a powerful and flexible interface to a coherent system (with automatic assistance in parts) reduces the load on the human verifier. This hybrid approach, coupled with powerful and simple data representation, increases the range of circuits which can be verified using trajectory evaluation. The paper concludes with two examples. One example is the complete verification of a 64 b multiplier which takes approximately 15 minutes on a SPARC 10 machine. >
Medical applications of multi-field volume rendering and VR techniques

This paper reports on a new approach for visualizing multi-field MRI or CT datasets in an immersive environment with medical applications. Multi-field datasets combine multiple scanning modalities into a single 3D, multivalued, dataset. In our approach, they are classified and rendered using real-time hardware accelerated volume rendering, and displayed in a hybrid work environment, consisting of a dual power wall and a desktop PC. For practical reasons in this environment, the design and use of the transfer functions is subdivided into two steps, classification and exploration. The classification step is done at the desktop, taking advantage of the 2D mouse as a high accuracy input device. The exploration process takes place on the powerwall. We present our new approach, describe the underlying implementation issues, report on our experiences with different immersive environments, and suggest ways it can be used for collaborative medical diagnosis and treatment planning.
Defect detection of backlight panel surfaces using independent component analysis filtering scheme

In this study, a filter-design scheme based on Independent Component Analysis (ICA) is proposed for defect inspection in backlight panels. In a backlight panel image, the gray levels of defects and the background are very similar and result in a low-contrast image which makes the defect detection task difficult. The proposed method is based on an ICA filtering scheme that computes the output response of energy from the convolution of an inspection image with the ICA filter. The ICA model with prior constraints is applied to determine the filter so that the convolved responses of an inspection image have the variance as small as possible. In this study, Particle Swarm Optimization (PSO) algorithm is used to solve for the constrained ICA model. Experimental results have shown that the proposed method can effectively detect defects in low-contrast backlight panel images.
Reflection of a Horizon Image on the Surface of Water

We have created a piece of software that renders the reflection of a ”landscape” texture on a wavy fluid surface. The fluid is represented by a height field and a quad mesh. Waves are created by using wave particles, non-physical entities that determine the influence a wave has on the fluid around it. The scene is ray traced by sending a ray through each pixel, bouncing it off the water and against the texture backdrop. The color of the texture is quieried and influences the color of the water surface. Surface normals are calulated by interpolating vertex normals across each face. The ray tracing is also parallelized, and speeds up to an aymptotic point, leveling off at the number of cores of the machine being used. We produce very attractive non-photo-realistic renderings of landscapes reflecting off a liquid surface.
Firm Valuation with Endogenous Growth Opportunities

This paper provides a valuation framework for a firm with endogenous growth opportunities. Three interesting results are obtained. First, it is demonstrated that the firm’s cost of capital is a weighted average of two components; the average cost of distributions and the average cost of investments, where the latter is shown to be the higher between the two. Second, we find that in optimum the marginal rate of return on investment is equal to the average cost of distributions. Third, we suggest a compact formula for the value of a firm with endogenous growth opportunities. Our framework can be used to check the efficiency of investments at the firm and macroeconomic levels. JEL Classification: D21, D24, D92, E22, E23, G12, G31.
MCJoin: a memory-constrained join for column-store main-memory databases

There exists a need for high performance, read-only main-memory database systems for OLAP-style application scenarios. Most of the existing works in this area are centered around the domain of column-store databases, which are particularly well suited to OLAP-style scenarios and have been shown to overcome the memory bottleneck issues that have been found to hinder the more traditional row-store database systems. One of the main database operations these systems are focused on optimizing is the JOIN operation. However, all these existing systems use join algorithms that are designed with the unrealistic assumption that there is unlimited temporary memory available to perform the join. In contrast, we propose a Memory Constrained Join algorithm (MCJoin) which is both high performing and also performs all of its operations within a tight given memory constraint. Extensive experimental results show that MCJoin outperforms a naive memory constrained version of the state-of-the-art Radix-Clustered Hash Join algorithm in all of the situations tested, with margins of up to almost 500%.
Crime Prediction and Classiﬁcation in San Francisco City

—To be better prepared to respond to criminal activity, it is important to understand patterns in crime. In our project, we analyze crime data from the city of San Francisco, drawn from a publicly available dataset. At the outset, the task is to predict which category of crime is most likely to occur given a time and place in San Francisco. To overcome the limitations imposed by our limited set of features, we enrich our data by adding information from the United States Census to it. We also attempt to make our classiﬁcation task more meaningful by merging multiple classes into larger classes. Finally, we report and reﬂect on our results with different classiﬁers, and dwell on avenues for future work.
Results from Droxo. I. The variability of fluorescent Fe 6.4 keV emission in the young star Elias 29

Aims. We study the variability of the Fe 6.4 KeV emission line from the Class I young stellar object Elias 29 in theOph cloud. Methods. We analysed the data from Elias 29 collected by XMM-Newton during a nine-day, nearly continuous observation of the � Oph star-forming region (the Deep Rho-Oph X-ray Observation, named Droxo). The data were subdivided into six homogeneous time intervals, and the six resulting spectra were individu ally analysed Results. We detect significant variability in the equivalent width of the Fe 6.4 keV emission line from Elias 29. The 6.4 keV line is absent during the first time interval of observation and ap pears at its maximum strength during the second time interval (90 ks after Elias 29 undergoes a strong flare). The X-ray thermal em ission is unchanged between the two observation segments, while line variability is present at a 99.9% confidence level. Give n the significant line variability in the absence of variatio ns in the X-ray ionising continuum and the weakness of the photoionising continuum from the star's thermal X-ray emission, we suggest t hat the fluorescence may be induced by collisional ionisation from a n (unseen) population of non-thermal electrons. We speculate on the possibility that the electrons are accelerated in a reconne ction event of a magnetically confined accretion loop, conne cting the young star to its circumstellar disk.
Suboptimal comparison of AF and DF relaying for fixed target error probability

In this paper, multi-node hybrid Cooperative cellular communication is considered with Amplify and Forward (AF) and Decode and Forward (DF) replaying protocols. Most of researchers have evaluated the performance of cooperative communication either with DF or AF relaying protocols. Considering single DF relay at midway between Source and Destination, for fixed target error probability we have evaluated the performance of multiple AF relays with shortest path relay selection criteria. The analysis is further extended to compare the energy consumption per bit for selected multiple AF and single DF relays.
APPLICATION OF DATA MINING TECHNIQUES FOR BUILDING SIMULATION PERFORMANCE PREDICTION ANALYSIS

Simulation exercises covering long periods (e.g. annual simulations) can produce large quantities of data. The result data set is often primarily used to determine key performance parameters such as the frequency binning of internal temperatures. Efforts to obtain an understanding for reasons behind the predicted building performance are often only carried out to a limited extent and simulation is therefore not used to its full potential. This paper describes how data mining can be used to enhance the analysis of results obtained from a simulation exercise. It identifies clustering as a particular useful analysis technique and illustrates its potential in enhancing the analysis of building simulation performance predictions.
A method of self-localization of robot based on infrared landmark

Aiming at the problem of robot self-localization based on vision, this paper proposed a method for robot's localization based on infrared landmark. First select the suitable infrared source. Second calculate the camera extrinsic parameters using the algorithm based on P3P. Third give the calculation method of robot's pose to the landmark based on the camera extrinsic. Experiment results validate the feasibility and effectiveness of the proposed method.
A stochastic model of computer-human interaction for learning dialogue strategies

Recent progress in the (cid:12)eld of spoken natural language understanding expanded the scope of spoken language systems to include mixed initiative dialogue. Currently there are no agreed upon theoretical foundations for the design of such systems. In this work we propose a stochas- tic model of computer-human interactions. This model can be used for learning and adaptation of the dialogue strategy and for objective evaluation.
Comparison between Different Intensity Normalization Methods in 123I-Ioflupane Imaging for the Automatic Detection of Parkinsonism

Intensity normalization is an important pre-processing step in the study and analysis of DaTSCAN SPECT imaging. As most automatic supervised image segmentation and classification methods base their assumptions regarding the intensity distributions on a standardized intensity range, intensity normalization takes on a very significant role. In this work, a comparison between different novel intensity normalization methods is presented. These proposed methodologies are based on Gaussian Mixture Model (GMM) image filtering and mean-squared error (MSE) optimization. The GMM-based image filtering method is achieved according to a probability threshold that removes the clusters whose likelihood are negligible in the non-specific regions. The MSE optimization method consists of a linear transformation that is obtained by minimizing the MSE in the non-specific region between the intensity normalized image and the template. The proposed intensity normalization methods are compared to: i) a standard approach based on the specific-to-non-specific binding ratio that is widely used, and ii) a linear approach based on the α-stable distribution. This comparison is performed on a DaTSCAN image database comprising analysis and classification stages for the development of a computer aided diagnosis (CAD) system for Parkinsonian syndrome (PS) detection. In addition, these proposed methods correct spatially varying artifacts that modulate the intensity of the images. Finally, using the leave-one-out cross-validation technique over these two approaches, the system achieves results up to a 92.91% of accuracy, 94.64% of sensitivity and 92.65 % of specificity, outperforming previous approaches based on a standard and a linear approach, which are used as a reference. The use of advanced intensity normalization techniques, such as the GMM-based image filtering and the MSE optimization improves the diagnosis of PS.
On the calculation of normals in free‐surface flow problems

The use of boundary-conforming finite element methods is considered for the solution of surface-tension-dominated free-surface flow problems in three dimensions. This class of method is based upon the use of a moving mesh whose velocity is driven by the motion of the free surface, which is in turn determined via a kinematic boundary condition for the normal velocity. The significance of the method used to compute the normal direction at the finite element node points for a C0 piecewise-polynomial free surface is investigated. In particular, it is demonstrated that the concept of mass-consistent normals on an isoparametric quadratic tetrahedral mesh is flawed. In this case an alternative, purely geometric, normal is shown to lead to a far more robust numerical algorithm. Copyright © 2004 John Wiley & Sons, Ltd.
Unsupervised activity clustering to estimate energy expenditure with a single body sensor

Body sensor networks (BSNs) have provided the opportunity to monitor energy expenditure (EE) in daily life and with that information help reduce sedentary behavior and ultimately improve human health. Current approaches for EE estimation using BSNs require tedious annotation of activity types and multiple body sensor nodes during data collection and high accuracy activity classifiers during post processing. These drawbacks impede deploying this technology in daily life — the primary motivation of using BSNs to monitor EE. With the goal of achieving the highest EE estimation accuracy with the least invasiveness and data collection effort, this paper presents an unsupervised, single-node solution for data collection and activity clustering. Motivated by a previous finding that clusters of similar activities tend to have similar regression models for estimating EE, we apply unsupervised clustering to implicitly group activities with homogeneous features and generate specific regression models for each activity cluster without requiring manual annotation. The framework therefore does not require specific activity classification, hence eliminating activity type labels. With leave-one-subject-out cross-validation across 10 subjects, an RMSE of 0.96 kcal/min was achieved, which is comparable to the activity-specific model and improves upon a single regression model.
A nearly-optimal index rule for scheduling of users with abandonment

We analyze a comprehensive model for multi-class job scheduling accounting for user abandonment, with the objective of minimizing the total discounted or time-average sum of linear holding costs and abandonment penalties. We assume geometric service times and Bernoulli abandonment probabilities. We solve analytically the case in which there are 1 or 2 users in the system to obtain an optimal index rule. For the case with more users we use recent advances from the restless bandits literature to obtain a new simple index rule, denoted by AJN, which we propose to use also in the system with arrivals. In the problem without abandonment, the proposed rule recovers the cµ-rule which is well-known to be optimal both without and with arrivals. Under certain conditions, our rule is equivalent to the cµ/θ-rule, which was recently proposed and shown to be asymptotically optimal in a multi-server system with overload conditions. We present results of an extensive computational study that suggest that our rule is almost always superior or equivalent to other rules proposed in the literature, and is often optimal.
Re: "Confidence intervals for biomarker-based human immunodeficiency virus incidence estimates and differences using prevalent data".

We recently reported on the estimation of confidence intervals for biomarker-based human immunodeficiency virus (HIV) incidence estimates and differences using prevalent data (1). There is a growing interest in the use of Bayesian methods in epidemiology (2). Our proposed Monte Carlo method (1) can be viewed as a semi-Bayes approach, with prior information on the duration T between positive highly sensitive and less sensitive enzyme immunoassays but no prior information on the incidence of HIV. Here, we describe how our proposed Monte Carlo method can be extended easily to create fully Bayesian credible intervals in place of a confidence interval. A fully Bayesian approach would allow one to utilize prior information regarding the incidence, as well as T. Extensions of our method to prevalent infection and missing data on the less sensitive enzyme immunoassay are presented elsewhere (3). Let N be the number of subjects tested, A be the number with recent HIV infection, and B be the number with established HIV infection, as previously described (1). We assume that A follows a Poisson distribution with parameter k 3 PPT, where k is the incidence and PPT is the pseudoperson-time defined as (N – B)(T/365.25). Similar to Berry et al. (4), if we assume a conjugate Gamma(a, b) prior distribution for the biomarker-based incidence, given by
Inference of functional regions in proteins by quantification of evolutionary constraints

Likelihood estimates of local rates of evolution within proteins reveal that selective constraints on structure and function are quantitatively stable over billions of years of divergence. The stability of constraints produces an intramolecular clock that gives each protein a characteristic pattern of evolutionary rates along its sequence. This pattern allows the identification of constrained regions and, because the rate of evolution is a quantitative measure of the strength of the constraint, of their functional importance. We show that results from such analyses, which require only sequence alignments, are consistent with experimental and mutational data. The methodology has significant predictive power and may be used to guide structure–function studies for any protein represented by a modest number of homologs in sequence databases.
Researching a Decade of ICT in Post-Secondary Education

This paper reviews published research on information and communication technology (ICT) in post-secondary education since 1998 and identifies the extent to which this research addresses government policy, indicating areas in which further research would be beneficial to meet the challenges contained in government reports. The paper is written to inform government agencies and researchers of the gaps in the research to date. The published papers were wide-ranging, informative and enriching in scope and evidenced the important role of ICT in post-secondary education. The majority of publications were based on empirical inquiry of successful small-scale case studies to guide present and future practice, though less research was evidenced to support other government directives, for example, the role of ICT in cross-institutional collaboration and the role of ICT to support sharing of teaching and resources. There was limited research on theoretical inquiry, providing original perspectives, ideas and ideals to shape future thinking and few cross-sector and inter-institutional studies identifying the role of ICT to improve teaching and learning. It was concluded that future research would benefit from a greater balance between empirical research and theoretical enquiry and to identify more strategically significant cross-institutional research to support the government's vision of making Hong Kong a regional education hub. Introduction In recent years, many initiatives have been taken to promote the use of information and communication technologies (ICT) in the post-secondary sector in Hong Kong (higher education, adult continuing education and vocational education and training). For instance, Higher Education in Hong Kong (University Grants Committee, 2002) recognised the potential of electronically-based teaching and learning to 'transform bricks and mortar institutions' by providing new environments to 'facilitate the tuition, support and management of learners on-and off-campus' and 'the development of collaborative, inter-institutional teaching' (p. 25). The University Grants Committee (UGC), the government advisory body on funding and strategic development of higher education encourages this collaboration and use of ICTs through such initiatives as the Teaching Development Grants and through the Restructuring and Collaboration Fund to support cost effective collaboration and to encourage the sharing of teaching and resources between institutions
PathLog: a Query Language for Schemaless Databases of Partially Labeled Objects

In the paper we deal with the problem of modeling and querying information in schemaless databases of partially labeled objects (PLO-DB). Partially labeled objects are used for modeling data within repositories integrating both structured and semistructured data. The proposed PLO (Partially Labeled Objects) data model originates from the OEM data model and extends it by allowing partial labeling of objects. For PLO-DB we propose a rule-oriented query language PathLog, which data manipulation paradigm is based on set-theoretical operations and on rules (in Datalog style) involving Skolem functions and referential variables. The main contribution of the paper is the development of a formalized and consistent model of schemaless databases of partially labeled objects and its query language PathLog.
Modularity in Meta-Languages

A meta-language for semantics has a high degree of modularity when descriptions of individual language constructs can be formulated independently using it, and do not require reformulation when new constructs are added to the described language. The quest for modularity in semantic meta-languages has been going on for more than two decades. Here, most of the main meta-languages for operational, denotational, and hybrid styles of semantics are compared regarding their modularity. A simple bench-mark is used: describing the semantics of a pure functional language, then extending the described language with references, exceptions, and concurrency constructs. For each style of semantics, at least one of the considered meta-languages appears to provide a high degree of modularity.