Miracle-dz/UniFAR-FTUs huggingface dataset:
Computational Modeling of Word Reading , Acquired Dyslexia , and Remediation

Many researchers assume that the most appropriate way to express the systematic aspects of language is in terms of a set of rules. For instance, there is a systematic relationship between the written and spoken forms of most English words (e.g., GAVE ), and this relationship can be expressed in terms of a fairly concise set of grapheme-phoneme correspondence (GPC) rules (e.g., G , A E , V ). In addition to being able to generate accurate pronunciations of so-called regular words, such rules also provide a straightforward account of how skilled readers apply their knowledge to novel items—for example, in pronouncing word-like nonwords (e.g., MAVE ). Most linguistic domains, however, are only partially systematic. Thus, there are many English words whose pronunciations violate the standard GPC rules (e.g., HAVE ). Given that skilled readers can pronounce such exception words correctly, GPC rules alone are insufficient. More generally, skilled language performance at virtually every level of analysis— phonological, morphological, lexical, syntactic—requires both effective handling of exceptional items and the ability to generalize to novel forms.
Predicting Personalized Emotion Perceptions of Social Images

Images can convey rich semantics and induce various emotions to viewers. Most existing works on affective image analysis focused on predicting the dominant emotions for the majority of viewers. However, such dominant emotion is often insufficient in real-world applications, as the emotions that are induced by an image are highly subjective and different with respect to different viewers. In this paper, we propose to predict the personalized emotion perceptions of images for each individual viewer. Different types of factors that may affect personalized image emotion perceptions, including visual content, social context, temporal evolution, and location influence, are jointly investigated. Rolling multi-task hypergraph learning is presented to consistently combine these factors and a learning algorithm is designed for automatic optimization. For evaluation, we set up a large scale image emotion dataset from Flickr, named Image-Emotion-Social-Net, on both dimensional and categorical emotion representations with over 1 million images and about 8,000 users. Experiments conducted on this dataset demonstrate that the proposed method can achieve significant performance gains on personalized emotion classification, as compared to several state-of-the-art approaches.
Two-Stage Data Augmentation for Low-Resourced Speech Recognition

Abstract : Low resourced languages suffer from limited training data and resources. Data augmentation is a common approach to increasing the amount of training data. Additional data is synthesized by manipulating the original data with a variety of methods. Unlike most previous work that focuses on a single technique, we combine multiple, complementary augmentation approaches. The first stage adds noise and perturbs the speed of additional copies of the original audio. The data is further augmented in a second stage, where a novel fMLLR-based augmentation is applied to bottleneck features to further improve performance. A reduction in word error rate is demonstrated on four languages from the IARPA Babel program. We present an analysis exploring why these techniques are beneficial.
Hardware design of a fast, parallel Random Tree path planner

The Rapidly-Exploring Random Trees (RRT) method has been proved successful and efficient for solving path planning problems. Most recent work focuses on optimizing RRT itself and presents the results achieved from software. In this paper a dedicated hardware architecture for FPGA implementation of Rapidly-Exploring Random Tree (RRT) path planning is developed. The proposed architecture fully takes advantage of FPGAs' natural parallel computing ability. The near neighbour (NN) search speed is improved by splitting the whole search space into several sub-spaces; correspondingly the tree nodes are stored in separate block RAMs and each Block RAM owns its independent traversal query module to enhance the memory throughput. Furthermore, to speed up the space exploration, each single path planning unit consists of two cooperating RRT modules which grow trees from each end of the path. One complete path planner involves several path planning units which work in a parallel master-slave mode to increase the probability of obtaining available path. Implementation in a 2D environment shows good path planning performance of the hardware design, with a 30x speed improvement compared to a PC implementation.
Assessing Reflective Thinking within Curricular Contexts.

This 42-month project at the University of Denver (Colorado) developed materials and faculty development activities concerning application of Kitchener and King's reflective judgment theory. Testing and implementation were at the University of Denver and Bowling Green State University (Ohio) and the University of Missouri (Columbia). The model describes the development of one aspect of critical thinking--the process by which adults become better able to make decisions about complex ill-structured problems. A paper and pencil measure, the reflective thinking appraisal, was devised and tested, and an accompanying technical manual was written. Three-day workshops and consultation with 18 faculty in various disciplines led to revision of courses based on the reflective judgment model and principles of educational psychology. A manual was developed to help faculty apply the model. Evaluation of reflective judgment in pilot studies indicated significant differences in scores between younger, less educated students and older, more educated students, with reliability over the entire age range estimated at . 79. Questionnaires and interviews with participating faculty revealed a greater understanding of students' need for support in developing critical thinking skills and increased awareness of ill-structured problems in their disciplines. Appended are the reflective thinking measure, the accompanying technical manual, and the workshop evaluation form. (DB) ******************************************************************************** * Reproductions supplied by EDRS are the best that can be made * * from the original document. * ******************************************************************************** Assessing Reflective Thinking Within Curricular Contexts Grantee Organization: University of Denver College of Education Denver, CO 80208
Wavelet-Based Relevance Vector Machines for Stock Index Forecasting

Relevance vector machine (RVM) is a Bayesian version of the support vector machine, which with a sparse model representation, has appeared as a powerful tool for time series forecasting. RVM has demonstrated better performance over other methods such as neural networks or ARIMA-based models. This paper proposes a wavelet-based RVM model to forecast stock indices. The time series of explanatory variables are decomposed by the wavelet basis, and the extracted time scale features served as inputs of a RVM to perform the nonparametric regression and forecasting. Compared with the traditional GARCH model forecasts, the new method shows superior performance, and reduces the root-mean-squared forecasting errors by nearly one order.
WoT 2013: fourth international workshop on the web of things

We propose a workshop on the topic of the Web of Things, which is about extending the Internet of Things concept beyond the connection of things and considering issues like heterogeneity, scalability, and usability with respect to pervasive computing. The goal of this initiative is to reuse the architectural principles that made the Web successful and apply them to smart devices, thereby making real-world objects first-class citizens of the Web. The approach taken by the Web of Things initiative is to look at the problems and research issues that emerge when considering the interaction of heterogeneous devices within composite applications. Continuing the successful Web of Things workshop series, this workshop aims at further exploring the use of technologies and principles at the core of the Web to provide methods for a seamless integration of physical devices. In particular, our goal is to foster discussion on systems towards a real-time Web of Things and the discovery, search, and composition of services provided by Web-enabled devices.
Covariation of Soil Bacterial Composition with Plant Rarity

ABSTRACT Rare and common plants are predicted to host different rhizospheric microbes. To evaluate this prediction, we used terminal restriction fragment length polymorphism (T-RFLP) analysis to compare rhizospheric bacteria from eight native grass species whose local abundances in their natural ecosystem spanned a 15-fold range. We observed that locally rare versus common plants are associated with divergent bacterial communities.
Improved detection of overrepresentation of Gene-Ontology annotations with parent-child analysis

MOTIVATION High-throughput experiments such as microarray hybridizations often yield long lists of genes found to share a certain characteristic such as differential expression. Exploring Gene Ontology (GO) annotations for such lists of genes has become a widespread practice to get first insights into the potential biological meaning of the experiment. The standard statistical approach to measuring overrepresentation of GO terms cannot cope with the dependencies resulting from the structure of GO because they analyze each term in isolation. Especially the fact that annotations are inherited from more specific descendant terms can result in certain types of false-positive results with potentially misleading biological interpretation, a phenomenon which we term the inheritance problem. RESULTS We present here a novel approach to analysis of GO term overrepresentation that determines overrepresentation of terms in the context of annotations to the term's parents. This approach reduces the dependencies between the individual term's measurements, and thereby avoids producing false-positive results owing to the inheritance problem. ROC analysis using study sets with overrepresented GO terms showed a clear advantage for our approach over the standard algorithm with respect to the inheritance problem. Although there can be no gold standard for exploratory methods such as analysis of GO term overrepresentation, analysis of biological datasets suggests that our algorithm tends to identify the core GO terms that are most characteristic of the dataset being analyzed.
Learning Binary Codes for High-Dimensional Data Using Bilinear Projections

Recent advances in visual recognition indicate that to achieve good retrieval and classification accuracy on large-scale datasets like Image Net, extremely high-dimensional visual descriptors, e.g., Fisher Vectors, are needed. We present a novel method for converting such descriptors to compact similarity-preserving binary codes that exploits their natural matrix structure to reduce their dimensionality using compact bilinear projections instead of a single large projection matrix. This method achieves comparable retrieval and classification accuracy to the original descriptors and to the state-of-the-art Product Quantization approach while having orders of magnitude faster code generation time and smaller memory footprint.
STREAM: Elucidating How Alcohol Use Disorders are Managed in UK General Practice Today.

AIMS To characterize a contemporaneous cohort of UK general practice patients with alcohol use disorder and describe their management. METHODS Prospective observational study in 25 primary care practices in England and Scotland. Adult patients with known or suspected alcohol dependence (Alcohol Use Disorders Identification Test score ≥8) were followed for 6 months and were profiled using baseline data. Changes over time in treatment for alcohol dependence (strategy, goals, modality) and the level of compliance were examined. RESULTS Targeted case identification was a feasible means of identifying patients with DSM-IV confirmed alcohol dependence in general practice. In the cohort (n = 218), mean total alcohol consumption was high (men: 86.9 g/day; women: 62.7 g/day) and psychiatric co-morbidities were common (depression: 25.8% of patients; anxiety disorders: 25.8% of patients). At baseline, about half of patients reported ongoing or planned treatment for alcohol dependence. Over 6 months' observation, treatment modalities did not change and most patients retained their original stated treatment goal of abstinence or reduced alcohol intake. CONCLUSIONS Targeted case identification in general practice identifies patients with alcohol use disorder in need of treatment.
HRM and temporary workers’ well-being: a study in Portugal and Brazil

Purpose – Temporary agency workers (TAWs) are regarded as experiencing employment conditions that tend to facilitate high strain. In this study, the authors view this situation as not being inevitable and dependent on the opportunity of having positive experiences in the professional context. The purpose of this paper is to propose that perceptions of the human resource management (HRM) practices system are positively related to work engagement, which in turn, is positively related to context free well-being. Design/methodology/approach – These hypotheses were tested with two samples of TAWs, from Portugal (n=241) and Brazil (n=228), contracted by the same international agency. Data were analyzed with multiple group analyses. Findings – Results confirmed that in both the Portuguese and Brazilian samples the HRM practices system is positively related to TAWs’ context free well-being and their work engagement mediates this relationship. However, the authors also verified that the relationship between work e.
Natural Language Multiprocessing: A Case Study

This paper presents two case studies of parallelization of large Natural Language Processing (NLP) applications using a parallel logic programming system (called "ACE") that automatically exploits implicit parallelism. The first system considered is Artwork, a system for semantic disambiguation, speech act resolution, and temporal reference resolution. The second system is ULTRA, a multilingual translation system. Both applications were originally developed in Prolog without any consideration for parallel processing. The results obtained confirm that NLP is a ripe area for exploitation of parallelism. Most previous work on parallelism in NLP focused primarily on parallelizing the parsing phase of language processing. The case studies presented here show that parallelism is also present in the semantic and discourse processing phases, which are often the most computationally intensive part of the application.
Power System Stability Control Based on the Characteristics of the Observed Information via Excitation Control

This paper presents a method for tuning power system stabilizer (PSS) parameters to control low-frequency oscillations. The dynamics of low-frequency oscillations can be identified by wide area phasor measurements. Hence, the theory builds a low-order model that reflects the dynamics of the full system, based on observation components that are included in system oscillations. Since the model reflects the real structure of an excitation control such as PSS and Automatic Voltage Regulator (AVR), also it can tune AVR parameters at the same time. As a result, the PSS parameters tuning can damp effectively low-frequency oscillations.
It ’ s Not Magic : I Can Prove It

Our work presents a new approach to developing, analyzing, and proving correctness of programs intended for execution on a quantum computer. We provide tools to write quantum as well as classical specifications, develop quantum and classical solutions for them, and analyses various properties of quantum specifications and quantum programs, such as implementability, time and space complexity, and probabilistic error analysis uniformly, all in the same framework. The work also develops a formal framework for specifying, implementing, and analyzing quantum pseudo-telepathy: an intriguing phenomenon which manifests itself when quantum information theory is applied to communication complexity.
Global Distribution and Density of Constructed Impervious Surfaces

We present the first global inventory of the spatial distribution and density of constructed impervious surface area (ISA). Examples of ISA include roads, parking lots, buildings, driveways, sidewalks and other manmade surfaces. While high spatial resolution is required to observe these features, the new product reports the estimated density of ISA on a one-km2 grid based on two coarse resolution indicators of ISA – the brightness of satellite observed nighttime lights and population count. The model was calibrated using 30-meter resolution ISA of the USA from the U.S. Geological Survey. Nominally the product is for the years 2000-01 since both the nighttime lights and reference data are from those two years. We found that 1.05% of the United States land area is impervious surface (83,337 km2) and 0.43 % of the world's land surface (579,703 km2) is constructed impervious surface. China has more ISA than any other country (87,182 km2), but has only 67 m2 of ISA per person, compared to 297 m2 per person in the USA. The distribution of ISA in the world's primary drainage basins indicates that watersheds damaged by ISA are primarily concentrated in the USA, Europe, Japan, China and India. The authors believe the next step for improving the product is to include reference ISA data from many more areas around the world.
Unsupervised Category Discovery in Images Using Sparse Neural Coding

We present an unsupervised method for learning and recognizing object categories from unlabeled images. Motivated by the existence of highly selective, sparsely firing cells observed in the human medial temporal l obe (MTL), we apply a sparse generative model to the outputs of a biologically faithful model of the primate ventral visual system. In our model, a network of nonlinear neurons learns a sparse representation of its inputs throug h an unsupervised expectation-maximization process. In recognition, this model is used in a maximum-likelihood manner to classify unseen images, and we find units emerging from learning that respond selectively to specific image categories. A significant advantage of this approach is that there is no ne ed to specify the number of categories present in the training set. We present classification accuracy using three different evaluation metrics.
Therapeutic Enhancement of ER Stress by Insulin-Like Growth Factor I Sensitizes Myeloma Cells to Proteasomal Inhibitors

Purpose: Multiple myeloma is a clonal plasma cell disorder in which growth and proliferation are linked to a variety of growth factors, including insulin-like growth factor type I (IGF-I). Bortezomib, the first-in-class proteasome inhibitor, has displayed significant antitumor activity in multiple myeloma. Experimental Design: We analyzed the impact of IGF-I combined with proteasome inhibitors on multiple myeloma cell lines in vivo and in vitro as well as on fresh human myeloma cells. Results: Our study shows that IGF-I enhances the cytotoxic effect of proteasome inhibitors against myeloma cells. The effect of bortezomib on the content of proapoptotic proteins such as Bax, Bad, Bak, and BimS and antiapoptotic proteins such as Bcl-2, Bcl-XL, XIAP, Bfl-1, and survivin was enhanced by IGF-I. The addition of IGF-I to bortezomib had a minor effect on NF-κB signaling in MM.1S cells while strongly enhancing reticulum stress. This resulted in an unfolded protein response (UPR), which was required for the potentiating effect of IGF-I on bortezomib cytotoxicity as shown by siRNA-mediated inhibition of GADD153 expression. Conclusions: These results suggest that the high baseline level of protein synthesis in myeloma can be exploited therapeutically by combining proteasome inhibitors with IGF-I, which possesses a “priming” effect on myeloma cells for this family of compounds. Clin Cancer Res; 19(13); 3556–66. ©2013 AACR.
The whiteboard: Tracking usability issues: to bug or not to bug?

What does your group do to record and track usability problems? How do you determine their severity and communicate them to the rest of the project team? If you've ever been stymied by this challenge, read on! Chauncey Wilson and Kara Pernice Coyne have developed two approaches, somewhat different in implementation but highly compatible in concept. Although the difference may seem non-trivial, it strikes me that the two methods apply to different kinds of organizations. Chauncey's may work better when the usability group is small, new, or struggling to gain credibility; Kara's, when it is larger and well established. See what you think. Most development organizations track software bugs and their severity in a corporate database, which is shared with product development and tech support teams. We find, however, that these same organizations seldom have a standard method, if any, of tracking usability issues. Usability practitioners communicate usability problems through reports, highlight tapes, and formal briefings, but are these methods adequate for tracking usability problems through successive cycles of product development? We diverge in our specific approaches to tracking. Chauncey believes that it is essential to track usability issues in the official corporate bug database. Bugs that don't get into the bug database will not be considered " official " bugs or be visible to the product development community. Although Kara agrees that it is possible to track usability issues in a bug database, she believes that usability problems are better tracked in a different
Near-Optimal Entrywise Sampling for Data Matrices

We consider the problem of selecting non-zero entries of a matrix $A$ in order to produce a sparse sketch of it, $B$, that minimizes $\|A-B\|_2$. For large $m \times n$ matrices, such that $n \gg m$ (for example, representing $n$ observations over $m$ attributes) we give sampling distributions that exhibit four important properties. First, they have closed forms computable from minimal information regarding $A$. Second, they allow sketching of matrices whose non-zeros are presented to the algorithm in arbitrary order as a stream, with $O(1)$ computation per non-zero. Third, the resulting sketch matrices are not only sparse, but their non-zero entries are highly compressible. Lastly, and most importantly, under mild assumptions, our distributions are provably competitive with the optimal offline distribution. Note that the probabilities in the optimal offline distribution may be complex functions of all the entries in the matrix. Therefore, regardless of computational complexity, the optimal distribution might be impossible to compute in the streaming model.
TouchCuts and TouchZoom: enhanced target selection for touch displays using finger proximity sensing

Although touch-screen laptops are increasing in popularity, users still do not comfortably rely on touch in these environments, as current software interfaces were not designed for being used by the finger. In this paper, we first demonstrate the benefits of using touch as a complementary input modality along with the keyboard and mouse or touchpad in a laptop setting. To alleviate the frustration users experience with touch, we then design two techniques, TouchCuts, a single target expansion technique, and ,i>TouchZoom,/i>, a multiple target expansion technique. Both techniques facilitate the selection of small icons, by detecting the finger proximity above the display surface, and expanding the target as the finger approaches. In a controlled evaluation, we show that our techniques improve performance in comparison to both the computer mouse and a baseline touch-based target acquisition technique. We conclude by discussing other application scenarios that our techniques support.