Miracle-dz/UniFAR-FTUs huggingface dataset:
Preposition Semantic Classification via Treebank and FrameNet

This paper reports on experiments in classifying the semantic role annotations assigned to prepositional phrases in both the Penn Treebank and FrameNet. In both cases, experiments are done to see how the prepositions can be classified given the dataset's role inventory, using standard word-sense disambiguation features. In addition to using traditional word collocations, the experiments incorporate class-based collocations in the form of WordNet hypernyms. For Treebank, the word collocations achieve slightly better performance: 78.5% versus 77.4% when separate classifiers are used per preposition. When using a single classifier for all of the prepositions together, the combined approach yields a significant gain at 85.8% accuracy versus 81.3% for word-only collocations. For FrameNet, the combined use of both collocation types achieves better performance for the individual classifiers: 70.3% versus 68.5%. However, classification using a single classifier is not effective due to confusion among the fine-grained roles.
Personalized Marketing Messages in an Online Banking Context: Does Anybody Notice?

In the 21 century the amount of research in personalization has grown exponentially. Much of this increased interest is linked with the Internet and other information technologies that enable efficient servicing of customers even on one-to-one basis. This paper reports on a research project, the first part of which studied via focus groups the perceptions of a Nordic bank’s customers towards various personalized marketing messages in their online bank. We employed the Elaboration Likelihood Model (ELM) of persuasion as a theoretical framework. In the second part, a 9-week field experiment at an online bank was conducted. In this experiment, customers were shown personalized banner advertisements when they logged in to their online bank service. Three different types of financial products were promoted to three groups of customers. We compared the effectiveness of the personalized banners vs. default banners via click-stream analyses. Moreover, these online promotions were compared with traditional direct-mail promotions. Our results are encouraging. The lifts of personalized banners compared to a default banner varied between 12 and 120. The pullpercentages, which measure the actual sales, were also higher than in the direct-mail promotions in two of the experiment’s three product cases.
The U.S. economy in 1990 and 1991: continued expansion likely

This paper reports an optimistic forecast of U.S. output and inflation trends in 1990_91. Generated by a Bayesian vector autoregression (BVAR) model of the U.S. economy using data available on November 30, 1989, the forecast is more optimistic than a consensus forecast. The key to the model's greater optimism for real growth is its outlook for strong consumer spending. The model's optimism is defended by examining historical precedents as well as comparing the track records of the model and consensus forecasts. The model's measures of forecast uncertainty, however, suggest that its predictions should be taken cautiously. An appendix explains how the model can be used to generate conditional forecasts.
Asymptotic Properties for a Class of Partially Identified Models

We propose inference procedures for partially identified population features for which the population identification region can be written as a transformation of the Aumann expectation of a properly defined set valued random variable (SVRV). An SVRV is a mapping that associates a set (rather than a real number) with each element of the sample space. Examples of population features in this class include sample means and best linear predictors with interval outcome data, and parameters of semiparametric binary models with interval regressor data. We extend the analogy principle to SVRVs, and show that the sample analog estimator of the population identification region is given by a transformation of a Minkowski average of SVRVs. Using the results of the mathematics literature on SVRVs, we show that this estimator converges in probability to the identification region of the model with respect to the Hausdorff distance. We then show that the Hausdorff distance between the estimator and the population identification region, when properly normalized by square-root-of-n, converges in distribution to the supremum of a Gaussian process whose covariance kernel depends on parameters of the population identification region. We provide consistent bootstrap procedures to approximate this limiting distribution. Using similar arguments as those applied for vector valued random variables, we develop a methodology to test assumptions about the true identification region and to calculate the power of the test. We show that these results can be used to construct a confidence collection, that is a collection of sets that, when specified as null hypothesis for the true value of the population identification region, cannot be rejected by our test.
Decisional Architecture for Text Warehousing: ETL-Text Process and Multidimensional Model TWM

In this paper, we propose a decisional architecture composed of three phases. Firstly, a new ETL process for textual data, named ETL-Text, is defined, secondly, a multidimensional text warehouse model, denoted TWM, is designed, and finally, OLAP processing component, named Text-OLAP, is deployed. The proposed TWM model is associated with new dimension types including a meta-data dimension and a semantic dimension. It includes also a new attribute measure based on the language model widely used in information retrieval (IR) area. To validate our approach, we develop a prototype composed of several modules that illustrate the phases of the decisional architecture. Moreover, we use the 20 Newsgroups corpus to carry out our experimentation.
Recognizing Static Signs from the Brazilian Sign Language: Comparing Large-Margin Decision Directed Acyclic Graphs, Voting Support Vector Machines and Artificial Neural Networks

In this paper, we explore and detail our experiments in a high-dimensionality, multi-class image classification problem often found in the automatic recognition of Sign Languages. Here, our efforts are directed towards comparing the characteristics, advantages and drawbacks of creating and training Support Vector Machines disposed in a Directed Acyclic Graph and Artificial Neural Networks to classify signs from the Brazilian Sign Language (LIBRAS). We explore how the different heuristics, hyperparameters and multi-class decision schemes affect the performance, efficiency and ease of use for each classifier. We provide hyperparameter surface maps capturing accuracy and efficiency, comparisons between DDAGs and 1-vs-1 SVMs, and effects of heuristics when training ANNs with Resilient Backpropagation. We report statistically significant results using Cohen's Kappa statistic for contingency tables.
Squaring the terms of an ℓth order linear recurrence

is (an(c0, . . . , cl−1; p1, . . . , pl))n≥0, or briefly (an)n≥0, where the pi are constant coefficients, with given aj = cj for all j = 0, 1, . , l − 1, and n ≥ l; in such a context, (an)n≥0 is called an l-sequence. In the case l = 2, this sequence is called Horadam’s sequence and was introduced, in 1965, by Horadam [4, 5], and it generalizes many sequences (see [1, 6]). Examples of such sequences are the Fibonacci numbers (Fn)n≥0, the Lucas numbers (Ln)n≥0, and the Pell numbers (Pn)n≥0, when one has the following initial conditions: p1 = p1 = c1 = 1, c0 = 0; p1 = p2 = c1 = 1, c0 = 2; and p1 = 2, p2 = c1 = 1, c0 = 0; respectively. In 1962, Riordan [8] found the generating function for powers of Fibonacci numbers. He proved that the generating function Fk(x) = ∑ n≥0 F k n x n satisfies the recurrence relation
A System for Region Search and Exploration

With the increasing popularity of mobile devices and location based services, massive amount of geo-textual data (e.g., geo-tagged tweets) is being generated everyday. Compared with traditional spatial data, the textual dimension of geo-textual data greatly enriches the data. Meanwhile, the spatial dimension of geo-textual data also adds a semantically rich new aspect to textual data. The large volume, together with its rich semantics, calls for the need for data exploration. First, it has many applications to retrieve a region for exploration that satisfies user-specified conditions (e.g., the size and shape of the region) while maximizing some other conditions (e.g., the relevance to the query keywords of the objects in the region). Second, it is useful to mine and explore the topics of the geo-textual data within a (specified or retrieved) region and perhaps a timespan. This demonstration proposal presents the main ideas of our system, the RegIon Search and Exploration System (RISE), for efficiently supporting region search and exploration, and our demonstration plan.
Gate defined wires in HgTe quantum wells: from Majorana fermions to spintronics

We introduce a promising new platform for Majorana zero-modes and various spintronics applications based on gate-defined wires in HgTe quantum wells. Due to the Dirac-like band structure for HgTe the physics of such systems differs markedly from that of conventional quantum wires. Most strikingly, we show that the subband parameters for gate-defined HgTe wires exhibit exquisite tunability: modest gate voltage variation allows one to modulate the Rashba spin-orbit energies from zero up to ~30K, and the effective g-factors from zero up to giant values exceeding 600. The large achievable spin-orbit coupling and g-factors together allow one to access Majorana modes in this setting at exceptionally low magnetic fields while maintaining robustness against disorder. As an additional benefit, gate-defined wires (in HgTe or other settings) should greatly facilitate the fabrication of networks for refined transport experiments used to detect Majoranas, as well as the realization of non-Abelian statistics and quantum information devices.
The single surgeon learning curve of laparoscopic liver resection

Abstract The aim of the study was to evaluate the single-surgeon learning curve (SSLC) in laparoscopic liver surgery over an 11-year period with risk-adjusted (RA) cumulative sum control chart analysis. Laparoscopic liver resection (LLR) is a challenging and highly demanding procedure. No specific data are available for defining the feasibility and reproducibility of the SSLC regarding a consistent and consecutive caseload volume over a specified time period. A total of 319 LLR performed by a single surgeon between June 2003 and May 2014 were retrospectively analyzed. A difficulty scale (DS) ranging from 1 to 10 was created to rate the technical difficulty of each LLR. The risk-adjusted cumulative sum control chart (RA-CUSUM) analysis evaluated conversion rate (CR), operative time (OT) and blood loss (BL). Perioperative morbidity and mortality were also analyzed. The RA-CUSUM analysis of the DS identified 3 different periods: P1 (n = 91 cases), with a mean DS of 3.8; P2 (cases 92–159), with a mean DS of 5.3; and P3 (cases 160–319), with a mean DS of 4.7. P2 presented the highest conversion and morbidity rates with a longer OT, whereas P3 showed the best results (P < 0.001). Fifty cases were needed to achieve a significant decrease in BL. The overall morbidity rate was 13.8%; no perioperative mortality was observed. According to our analysis, at least 160 cases (P3) are needed to complete the SSLC performing safely different types of LLR. A minimum of 50 cases can provide a significant decrease in BL. Based on these findings, a longer learning curve should be anticipated to broaden the indications for LLR.
Modified Current Control Schemes for High-Performance Permanent-Magnet AC Drives With Low Sampling to Operating Frequency Ratio

These days, high-frequency permanent-magnet synchronous motors (PMSMs) are used in industry; their maximum synchronous frequency is higher than 1 kHz. However, the sampling and switching frequencies of the digital inverter that consists of the digital controller and pulsewidth-modulation voltage source inverters are limited due to the cost and efficiency reasons. As a result, the current regulation loop has some problems when the high-frequency PMSM runs around its maximum speed and the ratio of the sampling frequency over the synchronous frequency, F ratio = (f samp/fr), is small. In this paper, the problems of the current regulation loop caused by small F ratio in the high-frequency PMSM drive are carefully investigated. Two problems are discussed in this paper: the stability problem and the sampling error. In addition, solutions for the problems are developed; a complex proportional and integral controller with predicted active damping terms is developed to stabilize the current regulation loop, and a model-based error estimator is offered to compensate the sampling error. The developed solutions are verified by simulations and experiments.
Optimal SNR-based selection relaying scheme with M-PSK modulation in cooperative wireless systems

Error propagation from the relay to the destination is a major drawback of digital relaying protocols that substantially diminishes the cooperative diversity gain of the system. Appropriately designed selection relaying schemes can effectively mitigate this phenomenon and improve the diversity order, specifically when the source-relay channel state information (CSI) is not available at the destination. In this paper, we investigate the SNR-based selection relaying scheme and present a closed-form expression for the end-to-end symbol error probability (SEP) of the system for M-PSK modulation. The optimal threshold which minimizes the end-to-end SEP and provides full diversity order is also presented in closed-form. In our analysis, we examine the error propagation scenario in detail and derive a closed-form expression for the SEP under this scenario. Numerical results demonstrate that the SNR-based selection relaying scheme by employing the optimal threshold, without using error detection codes that reduce the bandwidth efficiency, effectively mitigates the error propagation and significantly improves the diversity order.
Visual Knowledge Discovery using Deep Learning

Most recent efforts in computer vision harness the ever increasing availability of information on the Internet. In this work, we are interested in developing a framework that automatically mines images from the Internet and develop “knowledge” from those images. We adopt CNNs to discover the high level concepts in images, and use introspection with NLP techniques to describe those concepts in words. Finally, we show that the system can successfully discover some interesting relationships between concepts.
A comparative study on fuzzy and PI speed controllers for field-Oriented induction motor drive

This paper presents simulation results obtained from a feedforward vector controlled induction motor drive under varying operating conditions like step change in speed command and step change in torque command. Here fuzzy logic controller is used for controlling the speed. The performance of fuzzy logic controller has been investigated and compared with the conventional PI controller based drive. The simulation results show that fuzzy controller is superior to PI controller under dynamic condition.
Adding a vector unit to a superscalar processor

The focus of this paper is on adding a vector unit to a superscalar core, as a way to scale current state of the art superscalar processors. The proposed architecture has a vector register file that shares functional units both with the integer datapath and with the floatingpoint datapath. A key point in our proposal is the design of a high performance cache interface that delivers high bandwidth to the vector unit at a low cost and low latency. We propose a double-banked cache with alignment circuitry to serve vector accesses and we study two cache hierarchies: one feeds the uector unit from the Ll; the other from the L.2. Our results show that large IPC values (higher than IO in some cases) can be achieved. Moreover the scalability of our architecture simply requires addition of functional units, without requiring more issue bandwidth. As a consequence, the proposed vector unit achieves high performance for numerical and multimedia codes with minimal impact on the cycle time of the processor or on the performance of integer codes.
Frontal cortex and behavior

Few subjects in neurology have been associated with as much enigma and paradox as the behavioral affiliations of prefrontal cortex. While some authors have attributed the highest integrative faculties of the human mind to this part of the brain 11, 4, 91, others have emphasized the surprising paucity of cognitive deficits in patients with substantial frontal lobe damage 111, 191. At least two sources of difficulty are traditionally identified in this area of research: the nature of the clinical material, and the complexity of the behavioral deficits. Much of the literature on prefrontal cortex, for example , has been based on patients with massive head trauma, slowly growing tumors, aneurysmal ruptures, or extensive surgical procedures performed to treat intractable seizures or severe psychiatric disease. The lesions are often bilateral and commonly extend beyond the frontal lobes. The pitfalls of basing brain-behavior correlations on this type of clinical material are considerable. It is not clear why patients with unilateral strokes confined to prefrontal cortex are so poorly represented in the literature. Either such cases are too infrequent, or the associated deficits continue to elude coherent description or perhaps even clinical detection. The behavioral changes associated with frontal cortex damage introduce additional difficulties as they tend to be exceedingly complex, variable, difficult to define in technical terms, and almost impossible to quantitate by available tests. The recent work of Lher-mitte t21, 221, while it suffers from the customary limitations with respect to patient material, introduces two important innovations: a novel behavioral correlate of frontal lobe damage and a colorful methodology for allowing such complex observations. Lhermitte's findings and his imaginative interpretation deserve to be analyzed within the larger context of contemporary research on the frontal lobes. The frontal lobes occupy approximately one-third of the human cerebral hemispheres. The cortical surface of the frontal lobes is commonly divided into three components. The first is a motor-premotor component, which includes area 4, area 6 , the supplementary motor area (medial aspect of area 6), parts of the frontal eye fields (caudal area S), and parts of Broca's region (area 44). Depending on its exact location, damage to this component of the frontal lobe results in weakness, alteration of muscle tone, release of grasp reflexes, incontinence, akinesia, mutism, aprosody, apraxia, and some of the motor components in unilateral neglect and Broca's aphasia. The second component is located in the caudal-basal-medial part of the frontal lobe …
Rate Allocation with Lifetime Maximization and Fairness for Data Aggregation in Sensor Networks

We consider the rate allocation problem for data aggregation in wireless sensor networks with two objectives: 1) maximizing the lifetime of a local aggregation cluster and 2) achieving fairness among all data sources. The two objectives are generally correlated with each other and usually they cannot be maximized simultaneously. We adopt a lexicographic method to solve this multi-objective programming problem. First, we recursively induce the maximum lifetime for the local aggregation cluster. Under the given maximum lifetime, we then formulate the problem of maximizing fairness as a convex optimization problem, and derive the optimal rate allocation strategy. We also present low-complexity algorithms that a local aggregation cluster can use to determine the optimal rate allocation. Our simulation results validate our analytical results and illustrate the effectiveness of the approach.
A Phenomics-Based Strategy Identifies Loci on APOC1, BRAP, and PLCG1 Associated with Metabolic Syndrome Phenotype Domains

Despite evidence of the clustering of metabolic syndrome components, current approaches for identifying unifying genetic mechanisms typically evaluate clinical categories that do not provide adequate etiological information. Here, we used data from 19,486 European American and 6,287 African American Candidate Gene Association Resource Consortium participants to identify loci associated with the clustering of metabolic phenotypes. Six phenotype domains (atherogenic dyslipidemia, vascular dysfunction, vascular inflammation, pro-thrombotic state, central obesity, and elevated plasma glucose) encompassing 19 quantitative traits were examined. Principal components analysis was used to reduce the dimension of each domain such that >55% of the trait variance was represented within each domain. We then applied a statistically efficient and computational feasible multivariate approach that related eight principal components from the six domains to 250,000 imputed SNPs using an additive genetic model and including demographic covariates. In European Americans, we identified 606 genome-wide significant SNPs representing 19 loci. Many of these loci were associated with only one trait domain, were consistent with results in African Americans, and overlapped with published findings, for instance central obesity and FTO. However, our approach, which is applicable to any set of interval scale traits that is heritable and exhibits evidence of phenotypic clustering, identified three new loci in or near APOC1, BRAP, and PLCG1, which were associated with multiple phenotype domains. These pleiotropic loci may help characterize metabolic dysregulation and identify targets for intervention.
Living with it: a grounded action on family involvement in compulsory treatment for severe addiction

ii Acknowledgements . viii Whakatauki . x Chapter 1: The Research Problem . 2 Background to the Research . 2 Background Context . 4 Addiction Treatment . 4 Patient profile . 8 Outline of current application process . 9 Review of the Alcoholism and Drug Addiction Act 1966. 9 New Zealand legislative environment . 11 Mental Health (Compulsory Assessment & Treatment) Act 1992 . 14 The Mental Health & Addiction Treatment Divide . 17 Summary . 23 Significance of the Study . 24 Population changes . 24 Drinking patterns . 25 Treatment paradigms not meeting needs . 26 Meeting the needs of Māori . 28 Early engagement and the role of family and whānau . 30 Clarifying Key Concepts . 32 Structure of the Thesis . 33 Chapter 2 Initial Literature Review . 36 Background: Other Jurisdictions . 37 The Mental Health (Compulsory Assessment and Treatment) Act 1992 New Zealand . 42 Addiction Treatment in Aotearoa, New Zealand . 44 Coercion versus mandated treatment . 48 Family and whānau involvement . 49 Chapter 3 Research Methodology . 52 Introduction . 52 Grounded Theory Methodology . 52 Grounded Action . 56 Key Components of Glaserian Grounded Theory & Action . 57 Memoing . 58 Theoretical sampling and data saturation . 58 Constant comparative method . 60 Rationale for Choosing Glaserian Grounded Theory-Action . 60