Miracle-dz/UniFAR-FTUs huggingface dataset:
Beamforming for multiuser MIMO systems

Wireless communications systems use a multiple user scheme such as timeor frequency-division, but these do not allow truly simultaneous use of the spectrum. By deploying multiple antennas and beamforming, it is possible, in principle, for users to share the spectrum simultaneously, and this scenario is called the multiuser MIMO interference channel. This thesis presents new beamfoming design methods for this channel, derived from the convergence criteria for multi-objective optimization. Beamforming is proven to be possible for any combination of communications objective functions such as mean-square error, signal-to-interference plus noise ratio, and leakage interference. Relationships are found between the number of users and number of antennas, for different objective functions. The existence of a Nash equilibrium is guaranteed and the important networking properties of quality of service and fairness among users are accounted for. A new optimization algorithm, which is an extension of alternating optimization, is formulated for the design process. Its advantage over existing approaches is its significantly lower computational complexity. Several optimized, multi-user OFDM systems are formulated and demonstrated by simulation using statistical channel models in a multipath environment. The feedback overhead required for deploying the beamforming is quantified, showing the trade-off among complexity, minimum number of antennas required, error performance, capacity, feedback rate, and the ability to extract multi-path diversity for multiple users. When one of the users has priority access to the spectrum, the channel takes on a form of cognitive radio. This scenario is formulated as an optimization which requires solution via an evolutionary algorithm, and convergence is shown to be faster when more antennas are deployed. Finally, an architecture is presented that enables a secondary (i.e., low priority) user, whose terminals cannot directly ”see” each other, to communicate in the presence of multiple primary users. The cost is the need for all the primary users to be modified to collaborate with the secondary user, and for several MIMO relays to be installed. The secondary capacity is maximized under constraints of transmission power and interference to the primary receivers, and relay selection. This concept showcases several communications techniques including eigen-beamforming, channel selection and capacity optimization.
Scaling Up Machine Learning: Large-Scale Learning to Rank Using Boosted Decision Trees

The Web search ranking task has become increasingly important due to the rapid growth of the internet. With the growth of the Web and the number of Web search users, the amount of available training data for learning Web ranking models has also increased. We investigate the problem of learning to rank on a cluster using Web search data composed of 140,000 queries and approximately fourteen million URLs. For datasets much larger than this, distributed computing will become essential, due to both speed and memory constraints. We compare to a baseline algorithm that has been carefully engineered to allow training on the full dataset using a single machine, in order to evaluate the loss or gain incurred by the distributed algorithms we consider. The underlying algorithm we use is a boosted tree ranking algorithm called LambdaMART, where a split at a given vertex in each decision tree is determined by the split criterion for a particular feature. Our contributions are two-fold. First, we implement a method for improving the speed of training when the training data ﬁts in main memory on a single machine by distributing the vertex split computations of the decision trees. The model produced is equivalent to the model produced from centralized training, but achieves faster training times. Second, we develop a training method for the case where the training data size exceeds the main memory of a single machine. Our second approach easily scales to far larger datasets, i.e., billions of examples, and is based on data distribution. Results of our methods on a real-world Web dataset indicate signiﬁcant improvements in training speed.
English to Urdu Transliteration System

Urdu language processing applications encounter non-Urdu text specifically English text frequently. The accuracy of these systems e.g. machine translation, text-to-speech etc. is highly undermined as they are unable to handle English text. One possibility could be addition of multilingual language processing capabilities in Urdu language processing applications so that they may handle English text also along with Urdu but this approach is quite taxing. Another approach to handle English text is to transliterate it into Urdu automatically and then pass it on to the Urdu language processing applications. This paper describes English to Urdu transliteration system. First the mapping rules that are used to generate Urdu text from English transcription are discussed then syllabification, manual transliteration and Urduization phase is described and finally the issues related to Out-Of-Vocabulary (OOV) are discussed.
Parameter Estimation Method Based on Simulated Recommendation Process for Collaborative Filtering

In many E-commerce sites, recommender systems, which provide personalized recommendation from among a large number of items, are recently introduced. Collaborative filtering (CF) is one of the most successful algorithms which provide recommendations using ratings of users on items. There are two approaches such as user-based CF and item-based CF. Additionally a unifying method for user-based and item-based CF was proposed to improve the recommendation accuracy. The unifying approach uses a constant value as a weight parameter to unify both algorithms. However, because the optimal weight for unifying is actually different by the situation, the algorithm should estimate an appropriate weight dynamically, and should use it. In this research, we propose an approach for estimation of the appropriate weight based on collected ratings. Moreover, we discussed the effectiveness based on both multi-agent simulation and MovieLens dataset.
Microsoft SQL Server 2005功能包

SQL Server 2005光盘上的墨迹才刚刚干透，微软就已经发布了一个SOL Server 2005常用软件组件的功能包。 你可以从http：／／wwwmicrosoft．com／downloads／details.aspx？ familyid=d09cld60—a13c-4479-9b91—9e8b9d835cdc＆dis playlang=en找到这个功能包的下载。 虽然其中一些工具微软是直接从SOL Server 2005和Visual Studio 2005中拿出来的，但是其它一些是新的。 你可以根据需要单独下载每个工具，而以下这些是我最喜欢的。
Robust Methods for Program Evaluation.

Robust Methods for Program Evaluation by Sebastian Calonico My dissertation research focuses on different approaches to conduct robust estimation and inference in the context of program evaluation. In Chapter 1, I look at the effects of teacher and peer characteristics on student achievement in the STAR Project conducted in Tennessee in the late 1980s. As in standard linear models, the proposed approach considers two types of unobservables: school-specific effects and idiosyncratic disturbances. It generalizes previous empirical research by allowing both effects to enter the structural function nonseparably. No functional form assumptions are needed for identification. Instead, it uses an exchangeability condition in the way that covariates affect the distribution of the school-specific effects. The model permits nonparametric distributional and counterfactual analysis of heterogeneous effects: it extends policy analysis beyond marginal or discrete changes to consider distributional effects originating from a counterfactual change in the distribution of characteristics of classrooms, peers and teachers. Also, these impacts can be analyzed on any feature of the distribution of student achievement, such as quantiles and inequality measures. The empirical analysis looks at the effects of class size, teacher experience and gender composition of the classroom
On the Path Tracking Properties of Random Sensor Networks

We analyze the ability of a two dimensional sensor network to track targets moving on a one dimensional path. The sensor locations are assumed to form a spatial Poisson process of density and the sensing regions are random circles of i.i. d. radii. We first show that the sensing process induced on a straight line path by the area coverage process is a one dimensional Boolean process which in turn is the same as an M/G/ queue. This is then used to obtain two asymptotic results—a strong law and a central limit theorem for the fraction of a path that is covered by or more sensors. The asymptotic results are obtained under the same limiting regimes as that required for asymptotic coverage by a two dimensional Boolean process. Interestingly, for the asymptotic fraction of the area covered by the sensors is the same as the fraction of a path sensed. It is not clear if this generalizes for . The strong law derived above helps us obtain the sensor density that is necessary to sense a given fraction of an arbitrary path with very high probability is derived. Expectation and variance of the fraction of a path covered for finite are also obtained. We then characterize the ‘length to first sense’, and sensing continuity measures like holes and clumps. Measures that do not depend on the sensing radius like breach and support are also characterized. Finally, we discuss some generalizations of the results like characterization of the coverage process of dimensional ‘straight line paths’ by a dimensional sensor networks.
A quantitative view on fuzzy numbers

Since its introduction, fuzzy set theory has become a useful tool in the mathematical modelling of problems in Operations Research and many other fields. The number of applications is growing continuously. In this thesis we investigate a special type of fuzzy set, namely fuzzy numbers. Fuzzy numbers (which will be considered in the thesis as possibility distributions) have been widely used in quantitative analysis in recent decades. In this work two measures of interactivity are defined for fuzzy numbers, the possibilistic correlation and correlation ratio. We focus on both the theoretical and practical applications of these new indices. The approach is based on the level-sets of the fuzzy numbers and on the concept of the joint distribution of marginal possibility distributions. The measures possess similar properties to the corresponding probabilistic correlation and correlation ratio. The connections to real life decision making problems are emphasized focusing on the financial applications. We extend the definitions of possibilistic mean value, variance, covariance and correlation to quasi fuzzy numbers and prove necessary and sufficient conditions for the finiteness of possibilistic mean value and variance. The connection between the concepts of probabilistic and possibilistic correlation is investigated using an exponential distribution. The use of fuzzy numbers in practical applications is demonstrated by the Fuzzy Pay-Off method. This model for real option valuation is based on findings from earlier real option valuation models. We illustrate the use of number of different types of fuzzy numbers and mean value concepts with the method and provide a real life application.
Analysis of pictorial metaphors in comicbook art: test of the LA-MOAD theory

ABSTRACT Motion can be depicted using pictorial devices that are literal (representing features present in the real world) and metaphorical (representing features not present in the real world). How are literal and metaphorical pictorial devices used in comic book art? The Literal Additive Metaphorical One-And-Done (LA-MOAD) theory proposes that literal devices function additively while metaphorical devices function non-cumulatively. We test the generalisability of the LA-MOAD theory by analysing the pictorial devices used to depict jumping, falling, swinging and flying in 400 Silver- and Bronze-Age comic books. As predicted by the LA-MOAD theory, literal devices were used additively; artists preferred to use many literal devices. On the other hand, metaphorical devices were not used additively; artists preferred to use only one metaphorical device. The LA-MOAD theory has implications for comic book art as well as for how our cognitive system processes metaphorical information in general.
Clustering Algorithm Using a Center of Gravity for Grid-based Sample

Cluster analysis has been widely used in many applications, such as data analysis, pattern recognition, image processing, etc. But clustering requires many hours to get clusters that we want, because it is more primitive, explorative and we make many data an object of cluster analysis. In this paper we propose a new clustering method, 'Clustering algorithm using a center of gravity for grid-based sample'. It reduces running time by using grid-based sample and keeps accuracy by using representative point, a center of gravity.
Interactive Economic Models from the Wolfram Demonstrations Project

The Wolfram Demonstrations Project is a growing online collection of free interactive illustrations from a broad range of ﬁelds, including economics. Each demonstration is a computer model, or applet , with interactive controls that users can manipulate to explore the effects of changing parameters of the model. Demonstrations can include two-and three-dimensional graphics and formatted text and often display sophisticated plots of data and mathematical functions. Each demonstration has its own informational Web page, which includes an animated preview of the demonstration, a synopsis of the topic being displayed, and links to more information. Running a demonstration requires downloading the free Mathematica Player, or Mathematica 6, a technical computing environment commonly available on college campuses. The site’s search interface is the primary way of locating content, but related demonstrations are also linked together, allowing the user to browse collections of similar material. The economics section (http://demonstrations.wolfram.com/ topics.html) is subdivided into microeconomics, macroeconomics, and high school economics and ﬁnance. Subtopics are further reﬁned as the collection expands. Each demonstration includes a complete source code, so users with Mathematica 6 can edit the source code to modify a demonstration. Minor modi-ﬁcations, such as changing colors or thicknesses of curves, can be done with little Mathematica experience. Mathematica code is intuitive and readable, so learning to create original demonstrations by reading the source code of existing ones is also relatively easy.
Adaptive Tutoring in Virtual Learning Worlds

To enhance the learning success of the learners in the Virtual Learning Worlds (VLW) and effective of VLW, the aspects like precognition and learning aptitude of the learners play a key role. The constructivistic approach based VLW not only offers learning, but also great experience by exploring through reality based virtual worlds. VLWs can be extended with such a Learning Environment. They are Novices or Beginner and need more explanations and instructions to understand a topic and resolve a given problem. An Adaptive Tutoring System tries to find out the differences in precognitions and learning aptitudes and offers the learning task depending on these parameters. In the following paper a new system is designed for adapting a VLE to learners’ need and presenting the learning tasks based on the recommended pedagogical approach.
Stratégies pour l'anonymisation systématique d'un corpus d'interactions plurilingues

Dans le champ de l'analyse des interactions textuelles, les chercheurs desirant partager leurs corpus font face a de grandes difficultes pour en eliminer les marques d'identification des personnes physiques. La loi europeenne suggere pourtant que ces marques soient soigneusement retirees avant toute publication. Dans la suite des outils de la plateforme Calico dediee a l'analyse des interactions en ligne, nous proposons ici un procede interactif d'anonymisation systematique, fonctionnant sans dictionnaire a priori et donc applicable a toute langue. Ce procede a ete applique a un premier corpus plurilingue issu de Galanet. Cet article souligne les difficultes de l'anonymisation et presente les premiers resultats de cette experience. Au-dela de la transformation elle-meme, nous proposons deux strategies de fouille afin de detecter de nouvelles graphies pouvant reveler des donnees personnelles.
Advanced Image Processing with DirectX® 9 Pixel Shaders

With the introduction of the ps_2_0 pixel shader model in DirectX 9.0, we are able to significantly expand our ability to use consumer graphics hardware to perform image processing operations. This is due to the longer program length, the ability to sample more times from the input image(s) and the addition of floating point internal data representation. In the first ShaderX book, we used the ps_1_4 pixel shader model in DirectX 8.1 to perform basic image processing techniques such as simple blurs, edge detection, transfer functions and morphological operators [Mitchell02]. In this chapter, we will extend our image processing toolbox to include color space conversion, a better edge detection filter called the Canny filter, separable Gaussian and median filters, and a real-time implementation of the Fast Fourier Transform.
Substitution by Image: The Very Idea

The aim of this article is to provide a plausible conceptual model of a specific use of images described as substitution in recent art-historical literature. I bring to light the largely implicit shared commitments of the art historians’ discussion of substitution, each working as they do in a different idiom, and I draw consequences from these commitments for the concept of substitution by image—the major being the distinction between nonportraying substitution and substitution by portrayal. I then develop an argument that substitution by image in the desired, nonportraying sense needs to be thought of in terms of a figurative representation of an image’s subject as a generic object, what I will call its figurative instantiation. Not every image [Bild] must also be a portrayal [Abbild].
Comparisons of Tahoe, Reno, and Sack TCP

The capability to provide selective acknowledgement (SACK) of received data h as been used in a number of transport protocols including NETBLT [CLZ87], XTP [SDW92], RDP [VHS84] and VMTP [C he88]. Although there have been proposals for adding SACK to TCP [BJ88, BJZ90] SACK was later removed from the TCP R FCs (Request For Comments) [BBJ92] pending further research. In this report, we illustrate some of the benefits of ad ding SACK to “Reno” implementations of TCP. We use simulations to show that a SACK option with at most three SACK b locks can be of substantial benefit, relative to TCP without SACK. We have been running simulations of Reno TCP with SA CK in our simulator for over five years [Flo91]. Our assumption is that the important issue is that SACK be added to TCP, an d th t it is not of critical importance whether that addition is in the form of a SACK option with three SACK blocks, a S ACK option with six SACK blocks, or other proposed formats (such as those suggested by Matt Mathis or Keshav).
Sparse Models for Sparse Data: Methods, Limitations, Visualizations, and Ensembles

Author(s): Purdy, David Gregory | Advisor(s): Yu, Bin | Abstract: Significant recent advances in many areas of data collection and processing have introduced many challenges for modeling such data. Data sets have exploded in the number of observations and dimensionality. The explosion in dimensionality has led to advances in the modeling of high dimensional data with regularized and sparse models. One of the more interesting and challenging varieties of high dimensional data are sparse data sets. Sparse data sets arise from many important areas involving human-computer interaction, such as text and language processing, and human-human interaction, such as social networks.Our motivation in this thesis is to explore the use of sparse models for applications involving sparse data. In some cases, we have made improvements over previous methods that fundamentally involved dense models fitted on, and applied to, sparse data. In other cases, we have adapted sparse models developed for dense data sets. Along the way, we have encountered a recurring issue: due to both subsampling and regularization, we are faced with a problem that sparse models may not adequately capture the full dimensionality of such data and may be inadequate for prediction on test data.The utility of sparse models have been demonstrated in contexts with very high dimensional dense data. In this dissertation, we shall examine two applications and modeling methods involving sparse linear models and sparse matrix decompositions. Our first application involves natural language processing and ranking, the second involves recommendation systems and matrix factorization.In Chapter 2, we developed a novel and powerful visualization system. We named our system Bonsai as it enables a curated process of developing trees that partition the joint space of data and models. By exploring the product space of the space of training data, the space of modeling parameters, and the space of test data, we can explore how our models are developed based on the constraints imposed and the data they attempt to model or predict. More generally, we believe we have introduced a very fruitful means of exploring a multiplicity of models and a multiplicity of data samples.Chapter 3 is based on our work in the Netflix Prize competition. In contrast to others' use of dense models for this sparse data, we sought to introduce modeling methods with tunable sparsity. In this work, we found striking difficulties in modeling the data with sparse models, and identified concerns about the utility of sparse models for sparse data.In conclusion, this thesis presents several methods, and limitations of such methods, for modeling sparse data with sparse models. These limitations are suggestive of new directions to pursue. In particular, we are optimistic that future research in modeling methods may find new ways to tune models for density, when applied to sparse data, just as much research on models for dense data has involved tuning models for sparsity.
Basic Statistics Using Excel and Megastat

Chapter 1 IntroductionChapter 2 Descriptive StatisticsChapter 3 Frequency DistributionsChapter 4 Probability ConceptsChapter 5 Discrete Probability DistributionsChapter 6 Normal DistributionChapter 7 Sampling and Sampling DistributionsChapter 8 Confidence IntervalsChapter 9 Hypothesis Testing ConceptsChapter 10 Hypothesis Testing ApplicationsChapter 11 Analysis of VarianceChapter 12 Linear Regression AnalysisChapter 13 Multiple RegressionChapter 14 Chi-Square ApplicationsChapter 15 Time-Series AnalysisChapter 16 Summary and IntegrationAppendix A: Excel Statistical FunctionsAppendix B: Hypothesis Test SummariesAppendix C: Glossary and Key EquationsAppendix D: TablesAppendix E: Tutorial List