Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Transformations for Inference with Linear Regression: Data Transformations for Inference with Linear Regression: Clarifications and Recommendations Clarifications and Recommendations

Data transformations have been promoted as a popular and easy-to-implement remedy to address the assumption of normally distributed errors (in the population) in linear regression. However, the application of data transformations introduces non-ignorable complexities which should be fully appreciated before their implementation. This paper adds to existing Practical Research and Assessment Evaluation ( PARE ) publications on data transformations by providing a broad overview underlying the use of data transformations for the specific purpose of statistical inference and interpreting meaningful effect sizes. Data transformations not only potentially change the scale of the transformed variable; they also alter the fundamental relationships among variables while simultaneously changing the distribution of the errors. Given these repercussions, we clarify the nature of certain data transformations and strongly recommend the use of data transformations when they can enhance the interpretation of effect sizes.
Induced burstiness in generalized processor sharing queues with long-tailed traffic flows

We analyze the queueing behavior of long-tailed traffic flows under the Generalized Processor Sharing (GPS) discipline. GPS-based scheduling algorithms, such as Weighted Fair Queueing, play a major role in achieving differentiated quality-of-service in integrated-services networks. We prove that, in certain scenarios, a flow may be strongly affected by the activity of `heavier''-tailed flows, and may inherit their traffic characteristics, causing induced burstiness. This phenomenon contrasts with previous results which show that, under certain conditions, an individual flow with long-tailed traffic characteristics is effectively served at a constant rate. In particular, the flow is then essentially immune from excessive activity of flows with `heavier''-tailed traffic characteristics. The sharp dichotomy in qualitative behavior illustrates the crucial importance of the weight parameters in protecting individual flows.
Computing Bayes Factors using Thermodynamilmc Integration

—In the Bayesian paradigm, a common method for comparing two models is to compute the Bayes factor, defined as the ratio of their respective marginal likelihoods. In recent phylogenetic works, the numerical evaluation of marginal likelihoods has often been performed using the harmonic mean estimation procedure. In the present article, we propose to employ another method, based on an analogy with statistical physics, called thermodynamic integration. We describe the method, propose an implementation, and show on two analytical examples that this numerical method yields reliable estimates. In contrast, the harmonic mean estimator leads to a strong overestimation of the marginal likelihood, which is all the more pronounced as the model is higher dimensional. As a result, the harmonic mean estimator systematically favors more parameter-rich models, an artefact that might explain some recent puzzling observations, based on harmonic mean estimates, suggesting that Bayes factors tend to overscore complex models. Finally, we apply our method to the comparison of several alternative models of amino-acid replacement. We confirm our previous observations, indicating that modeling pattern heterogeneity across sites tends to yield better models than standard empirical matrices. [Bayes factor; harmonic mean; mixture model; path sampling; phylogeny; thermodynamic integration. ]
Systemy sterowania współczesnych robotów przemysłowych

This paper describes the use of genetic optimization in diagnostic system decomposition. First, an overview of diagnostic system and the reasons for its decomposition are given. Decomposition quality index is proposed. In the second part the analysis of a genetic algorithm application possibility is placed. The structure of the genome, type of genetic algorithm and genetic operators are described. Investigations on best values of key parameters is the main subject of third part. As a summary, a description of industrial application for diagnostic system decomposition on a hydrocarbon plant is placed.
Planning Reference Choices for Argumentative Teas

This paper deals with the reference choices involved in the generation of argumentative text. Since a natual segmentation of discourse into attentional spaces is needed to carry out this task, this paper first proposes an architecture for natural language generation that combines hierarchical planning and focus-guided navigation, a work in its own right. While hierarchical planning spans out an attentional hierarchy of the discourse produced, local navigation fills details into the primitive discourse spaces. The usefulness of this architecure actually goes beyond the particular domain of application for which it is developed.A piece of argumentative text such as the proof of a mathematical theorem conveys a sequence of derivations. For each step of derivation, the premises derived in the previous context and the inference method (such as the application of a particular theorem or definition) must be made clear. Although not restricted to nominal phrases, our reference decisions are similar to those concerning nominal subsequent referring expressions. Based on the work of Reichmann, this paper presents a discourse theory that handles reference choices by taking into account both textual distance as well as the attentional hierarchy.
Modeling of Oyster Larval Connectivity for CBF in Support of NOAA’S Community-Based Restoration Program & Restore America’s Estuaries Oyster and Reef Balls on Sanctuary Reefs in MD and VA - Phase Three

Recommended Citation Sisson, M. , & Shen, J. (2012) Modeling of Oyster Larval Connectivity for CBF in Support of NOAA’S Community-Based Restoration Program & Restore America’s Estuaries Oyster and Reef Balls on Sanctuary Reefs in MD and VA Phase Three. Special Reports in Applied Marine Science and Ocean Engineering (SRAMSOE) No. 433. Virginia Institute of Marine Science, College of William and Mary. https://doi.org/10.21220/V5TB4S
An Efﬁcient Representation Model of Distance Distribution Between Two Uncertain Objects

In this paper, we consider the problem of efﬁcient computation of distance distribution between two uncertain objects. It is important to many uncertain query evaluation (e.g., range queries, nearest-neighbour queries) and uncertain data mining (e.g., classiﬁcation, clustering and outlier detection). However, existing approaches involve distance computations between samples of two objects, which is very computationally intensive. On one hand, it is expensive to calculate and store the actual distribution of the possible distance values between two uncertain objects. On the other hand, the expected distance (the weighted average of the pair-wise distances among samples of two uncertain objects) provides very limited information and also restricts the deﬁnitions and usefulness of queries and mining tasks. In this paper, we propose several approaches to approximate the actual distance distribution between two given objects. Experiments on real data and synthetic data show that our approaches DAGO and DAGS produce approximations in a very short time with acceptable accuracy (about 90% or above). We suggest that these approaches make it practical for the research communities to deﬁne and develop more powerful queries and data mining tasks based on the distance distribution instead of the expected distance.
Animating Scanned Human Models Dongliang Zhang ? , João Oliveira ? ?

In this paper, we present a novel technique for animating the human model from whole body scanned data. In our method, the human model is represented with a layered model, its skeleton is generated automatically, and a simplified surface skin mapped to the underlying skeleton. We present methods for constructing and animating our models without manual intervention, a vertex blending technique is employed to create continuous and smooth deformation, and animation results are presented using motion capture data.
A Constant Factor Approximation for the Single Sink Edge Installation Problem ∗

We present the first constant approximation to the single sink buy-at-bulk network design problem, where we have to design a network by buying pipes of different costs and capacities per unit length to route demands at a set of sources to a single sink. The distances in the underlying network form a metric. This result improves the previous bound of O(log |R|), where R is the set of sources. We also present a better constant approximation to the related Access Network Design problem. Our algorithms are randomized and combinatorial. As a subroutine in our algorithm, we use an interesting variant of facility location with lower bounds on the amount of demand an open facility needs to serve. We call this variant load balanced facility location, and present a constant factor approximation for it, while relaxing the lower bounds by a constant factor. ∗This paper combines the work in two conference papers [12, 13], which appeared in the 41st IEEE Symposium on the Foundations of Computer Science, 2000 and the 33rd ACM Symposium on Theory of Computing, 2001 respectively. †Department of Computer and Information Sciences, University of Pennsylvania. Email: sudipto@cis.upenn. edu. Research supported by an NSF CAREER award and a Sloan Foundation Fellowship. ‡Department of Computer Science, University of California, Los Angeles CA 90095-1596. Email: awm@cs.ucla. edu §Department of Computer Science, Duke University, Durham NC 27708. Email: kamesh@cs.duke. edu. Research supported by NSF via a CAREER award and grant CNS-0540347.
Dataset and metrics for predicting local visible diferences

KRZYSZTOF WOLSKI, MPI Informatik DANIELE GIUNCHI, University College London NANYANG YE, University of Cambridge PIOTR DIDYK, Saarland University, MMCI, MPI Informatik, Università della Svizzera italiana KAROL MYSZKOWSKI, MPI Informatik RADOSŁAW MANTIUK, West Pomeranian University of Technology, Szczecin HANS-PETER SEIDEL, MPI Informatik ANTHONY STEED, University College London RAFAŁ K. MANTIUK, University of Cambridge
C-STREAM: A COROUTINE-BASED ELASTIC STREAM PROCESSING ENGINE

C-STREAM: A COROUTINE-BASED ELASTIC STREAM PROCESSING ENGINE Semih Şahin M.S. in Computer Engineering Advisor: Assoc. Prof. Dr. Buğra Gedik June, 2015 Stream processing is a computational paradigm for on-the-fly processing of live data. This paradigm lends itself to implementations that can provide high throughput and low latency, by taking advantage of various forms of parallelism that is naturally captured by the stream processing model of computation, such as pipeline, task, and data parallelism. In this thesis, we describe the design and implementation of C-Stream, which is an elastic stream processing engine. C-Stream encompasses three unique properties. First, in contrast to the widely adopted event-based interface for developing stream processing operators, C-Stream provides an interface wherein each operator has its own control loop and rely on data availability APIs to decide when to perform its computations. The self-control based model significantly simplifies development of operators that require multi-port synchronization. Second, C-Stream contains a multi-threaded dynamic scheduler that manages the execution of the operators. The scheduler, which is customizable via plug-ins, enables the execution of the operators as co-routines, using any number of threads. The base scheduler implements back-pressure, provides data availability APIs, and manages preemption and termination handling. Last, C-Stream provides elastic parallelization. It can dynamically adjust the number of threads used to execute an application, and can also adjust the number of replicas of data-parallel operators to resolve bottlenecks. We provide an experimental evaluation of C-Stream. The results show that C-Stream is scalable, highly customizable, and can resolve bottlenecks by dynamically adjusting the level of data parallelism used.
Extending the Network: the Influence of Offline Friendship to Twitter Network

Twitter effectively provides a communication platform that allows users to strengthen or augment relationships with their close ones. It is a common scene to see groups of people in continuing group communication from offline to online using tools such as Twitter. Furthermore, it is also possible to meet new friends via online encounter. Under these circumstances, there would be friends who are both offline and online, or only in online. Many of earlier Twitter network studies focused on the network effects’ direction going from online to offline network. This paper explores the opposite direction, going from offline to online network. It investigates the peer’s friendly relationship intricacies that emerge when a known friendly offline relationship influences its subsequently established Twitter online relationship. An empirical data set of 2,193 pairs of Twitter user accounts were examined.
Print and roll : A technique for rapid production of stretchable liquid alloy circuits

Liquid alloy circuitry is an exciting new field of research. It is one of the technologies that strives to make a commercial production of reliable, stretchable circuits possible. The making of liquid alloy circuits is, today, a somewhat tedious handicraft that is time-consuming and not suited for mass production. In this diploma thesis a new method to produce liquid alloy circuitry is presented; print and roll. The circuits consists of Galinstan paths embedded in polydimethylsiloxane (PDMS). Conductive paths are printed in a two step sequence on semi-cured PDMS and then covered with uncured PDMS and exposed to a final curing step. Print and roll produces circuits that are of equal quality as the circuits made with the old method, with a speed and ease superior to the old method. Furthermore advantages and disadvantages with printing on partly cured PDMS substrates are discussed. Partly cured PDMS substrates is important for the print and roll process since it enables the use of uncured PDMS to cover the printed circuit. Using uncured PDMS as a cover-material makes it possible to print the circuits on flat substrates and to use a pick and place machine to place components on the circuit-paths. Some tests with pick and place placing of both large and small components were conducted with varying results.
Variation

Variation afflicts the design, manufacture, and operation of integrated circuits. Techniques and tools are needed in three areas to address variation: statistical metrology, advanced process control, and design for manufacturability. First, statistical metrology seeks to characterize and model variations and their sources. Advanced metrology helps to understand geometric and material property variations, while variation test structures and test circuits enable study of the impact of specific or aggregate variations on performance. Second, advanced process control attempts to reduce process variation through sensing and feedback/feedforward control during fabrication. Third, design for manufacturability (DFM) seeks methods to improve performance and yield given process and environmental variation, through robust design, increased regularity, and other approaches. Finally, linkages between these areas, particularly between statistical metrology and DFM, will be important and empowering.
AIS Electronic

In the era of big data, medical researchers attempt to utilize some analysis techniques like machine learning and text mining on their large-scale corpora to save valuable labor work and time. Consequently, many data analysis platforms are built to support medical professionals such as Pubtator, GeneWays, BioContext, etc. These platforms are helpful to medical entities recognition and relation extraction, but there is not an integrated platform to support researchers’ various needs, and medical projects are isolated from each other, which is hard to be shared and reused. As a result, we present an integrated system containing ‘name entity recognition’, ‘document categorization’ and ‘association extraction’. Besides, we add the concept of ‘socialization’ making projects reusable for further analyses. A case study of chronic kidney disease was adopted to indicate the effectiveness of the proposed system.
Design and Implementation of a Scalable Encryption Processor with Embedded Variable DC / DC Converter

This work describes the design and implementation of an energy-efficient, scalable encryption processor that utilizes variable voltage supply techniques and a highefficiency embedded variable output DC/DC converter. The resulting implementation dissipates 134nJ/bit @ VDD = 2.5V, when encrypting at its maximum rate of 1Mb/s using a maximum datapath width of 512 bits. The embedded converter achieves an efficiency of 96% at this peak load. The processor is 2-3 orders of magnitude more energy efficient than optimized assembly code running on a low-power processor such as the StrongARM.
One-Way Mirrors and Weak-Signaling in Online Dating: A Randomized Field Experiment - Online E-Companion Appendix

The growing popularity of online dating websites is altering one of the most fundamental human activities: finding a date or a marriage partner. Online dating platforms offer new capabilities, such as extensive search, big-data based mate recommendations and varying levels of anonymity, whose parallels do not exist in the physical world. Yet, little is known about the causal effects of these new features. In this study we examine the impact of a particular anonymity feature, which is unique to online environments, on matching outcomes. This feature allows users to browse profiles of other users anonymously, by being able to check out a potential mate's profiles while not leaving any visible online record of the visit. While this feature may decrease search costs and allow users to search without inhibition, it also eliminates "weak signals" of interest for their potential mates that may play an important role in establishing successful communication. We run a randomized field experiment on a major North American online dating website, where 50,000 of 100,000 randomly selected new users are gifted the ability to anonymously view profiles of other users. Compared to the control group, the users treated with anonymity become disinhibited, in that they view more profiles, and are more likely to view same-sex and inter-racial mates. However, based on our analysis, we demonstrate causally that weak signaling is a key mechanism in achieving higher levels of matching outcomes. Anonymous users, who lose the ability to leave a weak signal, end up having fewer matches as compared to their non-anonymous counterparts. This effect of anonymity is particularly strong for women who tend not to make the first move and instead rely on the counter-party to initiate the communication. Further, the reduction in quantity of matches by anonymous users is not compensated by a corresponding increase in quality of matches.
D IFFUSION C ONVOLUTIONAL R ECURRENT N EURAL

Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain. Traffic forecasting is one canonical example of such learning task. The task is challenging due to (1) complex spatial dependency on road networks, (2) non-linear temporal dynamics with changing road conditions and (3) inherent difficulty of long-term forecasting. To address these challenges, we propose to model the traffic flow as a diffusion process on a directed graph and introduce Diffusion Convolutional Recurrent Neural Network (DCRNN), a deep learning framework for traffic forecasting that incorporates both spatial and temporal dependency in the traffic flow. Specifically, DCRNN captures the spatial dependency using bidirectional random walks on the graph, and the temporal dependency using the encoder-decoder architecture with scheduled sampling. We evaluate the framework on two real-world large scale road network traffic datasets and observe consistent improvement of 12% 15% over state-of-the-art baselines.
MIREX : AUTOREGRESSIVE MFCC MODELS FOR GENRE CLASSIFICATION IMPROVED BY HARMONIC-PERCUSSION SEPARATION

In this abstract we describe our entry for the task of music genre classification, part of the MIREX 2010 competition. We demonstrate that classification performance can be improved when the music signal is separated into harmonic and percussive signals before the features are extracted. We briefly explain why this separation is particularly beneficial to the MFCC feature extraction. For each of the signal types, MFCCs are calculated, and a multivariate autoregressive (MAR) model is used to model the dynamics of the MFCCs. The MAR-features are combined with another set of features calculated by the Marsyas algorithm, also calculated on each signal type. Finally an SVM-classifier is trained on the features, and it is verified that the separation leads to better performance.
Technology readiness in a B 2 B online retail context : an examination of antecedents and outcomes

This paper develops and empirically tests a model that examines the role of Technology Readiness (TR) in the Business-to-Business (B2B) context. The research investigates how retailers’ TR, and its antecedents, impacts their evaluation of a credence based B2B service, namely web solution service providers (WSSPs). It responds to previous research calls by extending the TR construct from the business-to-consumer (B2C) perspective that is traditional in the extant literature into the B2B domain. The findings of a survey conducted with 133 firms in the retail industry are that TR is an important contributor to the eventual achievement of service quality and satisfaction. The contributions of this study are; first it develops and validates a measure of TR in a B2B context; second it investigates the antecedents of TR in this domain, demonstrating the impact of past inexperience, industry trust and switching costs on firms’ level of technology readiness to adopt online operations; and third, it finds that service quality and satisfaction are outcomes of TR. The key management implication for WSSPs is the need to address the TR levels of existing and potential clients if they wish to deliver successful e-business solutions to them. Their clients’ TR can be better managed by making the offering more easily understood, building relational rapport, reducing risk perceptions and adopting a client centric perspective throughout the process.
Technology readiness in a B2B online retail context: an examination of antecedents and outcomes

This paper develops and empirically tests a model that examines the role of Technology Readiness (TR) in the Business-to-Business (B2B) context. The research investigates how retailers’ TR, and its antecedents, impacts their evaluation of a credence based B2B service, namely web solution service providers (WSSPs). It responds to previous research calls by extending the TR construct from the business-to-consumer (B2C) perspective that is traditional in the extant literature into the B2B domain. The findings of a survey conducted with 133 firms in the retail industry are that TR is an important contributor to the eventual achievement of service quality and satisfaction. The contributions of this study are; first it develops and validates a measure of TR in a B2B context; second it investigates the antecedents of TR in this domain, demonstrating the impact of past inexperience, industry trust and switching costs on firms’ level of technology readiness to adopt online operations; and third, it finds that service quality and satisfaction are outcomes of TR. The key management implication for WSSPs is the need to address the TR levels of existing and potential clients if they wish to deliver successful e-business solutions to them. Their clients’ TR can be better managed by making the offering more easily understood, building relational rapport, reducing risk perceptions and adopting a client centric perspective throughout the process.