Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Quality By Design: A Goal-oriented Approach

A successful information system is the one that meets its design goals. Expressing these goals and subsequently translating them into a working solution is a major challenge for information systems engineering. This thesis adopts the concepts and techniques from goal-oriented (software) requirements engineering research for conceptual database design, with a focus on data quality issues. Based on a real-world case study, a goal-oriented process is proposed for database requirements analysis and modeling. It spans from analysis of high-level stakeholder goals to detailed design of a conceptual databases schema. This process is then extended specifically for dealing with data quality issues: data of low quality may be detected and corrected by performing various quality assurance activities; to support these activities, the schema needs to be revised by accommodating additional data requirements. The extended process therefore focuses on analyzing and modeling quality assurance data requirements. A quality assurance activity supported by a revised schema may involve manual work, and/or rely on some automatic techniques, which often depend on the specification and enforcement of data quality rules. To address the constraint aspect in conceptual database design, data quality rules are classified according to a number of domain and application independent properties. This classification can be used to guide rule designers and to facilitate building of a rule repository. A quantitative framework is then proposed for measuring and comparing DQ rules according to one of these properties: effectiveness; this framework relies on derivation of formulas that represent the effectiveness of DQ rules under different probabilistic assumptions. A semi-automatic approach is also presented to derive these effectiveness formulas.
White matter hyperintensities influence distal cortical β‐amyloid accumulation in default mode network pathways

Cerebral small vessel disease (SVD) has been suggested to contribute to the pathogenesis of Alzheimer's disease (AD). Yet, the role of SVD in potentially contributing to AD pathology is unclear. The main objective of this study was to test the hypothesis that WMHs influence amyloid β (Aβ) levels within connected default mode network (DMN) tracts and cortical regions in cognitively unimpaired older adults.
Centralization and democratization: Managing crisis communication in health care delivery

Background Communication is an essential organizational process for responding to adversity. Managers are often advised to communicate frequently and redundantly during crises. Nonetheless, systematic investigation of how information receivers perceive organizational communication amid crises has remained lacking. Purpose The aim of this study was to characterize features of effective internal crisis communication by examining how information-sharing processes unfolded during the initial stage of the COVID-19 pandemic. Methodology Between June and August 2020, we conducted 55 semistructured interviews with emergency department workers practicing in a variety of roles. We analyzed interview transcripts following constructivist constant comparative methods. Results Our findings revealed that at the onset of COVID-19 pandemic response, emergency department workers struggled with immense fear and anxiety amid high uncertainty and equivocality. Frequent and redundant communication, however, resulted in information delivery and uptake problems, worsening anxiety, and interpersonal tension. These problems were ameliorated by the emergence of contextual experts who centralized and democratized communication. Centralization standardized information received across roles, work schedules, and settings while decoupling internal communication from turbulence in the environment. Democratization made information accessible in a way that all could understand. It also ensured information senders’ receptiveness to feedback from information receivers. Centralization and democratization together worked to reduce sensed uncertainty and equivocality, which reduced anxiety and interpersonal tension. Conclusion Establishing frequent and redundant communication strategies does not necessarily address the anxiety and interpersonal tension produced by uncertainty and equivocality in crises. Practice Implications Centralization and democratization of crisis communication can reduce anxiety, improve coordination, and promote a safer workplace and patient care environment.
On Sequential Point Estimation in a Uniform Distribution with Adjusted Non-Sufficient Estimators : A Comparative Study and Real Data Illustration

Abtsrcat The purely sequential minimum risk point estimation procedure for the unknown parameter θ (> 0) in a Uniform (0; θ) population has been discussed in this article. This is developed under a squared error loss plus a linear cost function of sampling. The unknown parameter θ is estimated by means of four different estimators in the stopping rule, where as in the loss function two different unbiased estimators of θ are proposed. The unbiased estimators are randomly stopped versions of Χn:n and X¯n in either loss function. Performances of such estimators are compared. Clearly, using a randomly stopped version of X¯ n would amount to some loss of in- formation when compared with a corresponding randomly stopped largest sample order statistic under both loss functions and the stopping rules. In this paper, we explore a novel approach to recover any loss of information by fine-tuning the loss function and then properly tailoring the associated sequential methodologies. We examine how the sequential risks of our newly proposed methodologies would compare with those associated with the ex- isting sequential estimators and illustrate our methodologies with the help of a real dataset.
Atypical cognitive training-induced learning and brain plasticity and their relation to insistence on sameness in children with autism

Children with autism spectrum disorders (ASDs) often display atypical learning styles; however, little is known regarding learning-related brain plasticity and its relation to clinical phenotypic features. Here, we investigate cognitive learning and neural plasticity using functional brain imaging and a novel numerical problem-solving training protocol. Children with ASD showed comparable learning relative to typically developing children but were less likely to shift from rule-based to memory-based strategy. While learning gains in typically developing children were associated with greater plasticity of neural representations in the medial temporal lobe and intraparietal sulcus, learning in children with ASD was associated with more stable neural representations. Crucially, the relation between learning and plasticity of neural representations was moderated by insistence on sameness, a core phenotypic feature of ASD. Our study uncovers atypical cognitive and neural mechanisms underlying learning in children with ASD, and informs pedagogical strategies for nurturing cognitive abilities in childhood autism.
A MATHEMATICAL MODEL OF DENGUE INTERNAL TRANSMISSION PROCESS

In this paper we formulate a mathematical model of internal process of dengue virus transmission in the human body. We analyze the dynamic of dengue virus using a system of differential equation. We obtain a local stability of equilibrium for this model base on a threshold parameter. In particular, we prove the stability result for a free-virus and abundance of virus-states. Finally, numerical simulation of the model are performed to study the behaviour of the system for a short period of time. DOI : http://dx.doi. org/10.22342/jims.13. 1.79. 123-132
A new algorithm for real-time ellipse detection

We present a robust ellipse detector based on robust edge detection and Hough transform for real-time applications. The algorithm consists of three steps: we first perform edge detection, we than use a rule-based approach to compute edge convexity, and finally we decompose the parameter space of the Hough transform to achieve computational efficiency. We present our experiments, which show indeed that the proposed detector is able to detect elliptic features with an excellent accuracy under various image conditions and achieve a high speed at about 10 frames per second on a Pentium-III 500 MHz PC.
Modeling of C-terminal tail of human STING and its

3 Stimulator of Interferon Genes (STING) plays a significant role in a cell’s 4 intracellular defense against pathogens or self-DNA by inducing inflammation or 5 apoptosis through a pathway known as cGAS-cGAMP-STING. STING uses one of its 6 domains, the C-Terminal Tail (CTT) to recruit the members of the pathway. However, 7 the structure of this domain has not been solved experimentally. STING conformation is 8 open and more flexible when inactive. When STING gets activated by cGAMP, its 9 conformation changes to a closed state covered by 4 beta-sheets over binding site. This 10 conformational change leads to its binding to Tank-binding kinase 1 (TBK1). TBK1 then 11 phosphorylates STING aiding its entry to the cell’s nucleus. 12 In this study, we focused on the loop modeling of the CTT domain in both the 13 active and inactive STING conformations. After the modeling step, the active and inactive 14 STING structures were docked to one of the cGAS-cGAMP-STING pathway members, 15 TBK1, to observe the differences of binding modes. CTT loop stayed higher in the active 16 structure, while all the best-scored models, active or inactive, ended up around the same 17 position with respect to TBK1. However, when the STING poses are compared with the 18 cryo-EM image of the complex structure, the models in the active structure chain B 19 displayed closer results to the complex structure. 20 21
ROME: Robustifying Memory-Efficient NAS via Topology Disentanglement and Gradient Accumulation

Albeit being a prevalent architecture searching approach, differentiable architecture search (DARTS) is largely hindered by its substantial memory cost since the entire supernet resides in the memory. This is where the single-path DARTS comes in, which only chooses a single-path submodel at each step. While being memory-friendly, it also comes with low computational costs. Nonetheless, we discover a critical issue of single-path DARTS that has not been primarily noticed. Namely, it also suffers from severe performance collapse since too many parameter-free operations like skip connections are derived, just like DARTS does. In this paper, we propose a new algorithm called RObustifying Memory-Efficient NAS (ROME) to give a cure. First, we disentangle the topology search from the operation search to make searching and evaluation consistent. We then adopt Gumbel-Top2 reparameterization and gradient accumulation to robustify the unwieldy bi-level optimization. We verify ROME extensively across 15 benchmarks to demonstrate its effectiveness and robustness.
Zeroth-Order Algorithms for Nonconvex Minimax Problems with Improved Complexities

In this paper, we study zeroth-order algorithms for minimax optimization problems that are nonconvex in one variable and strongly-concave in the other variable. Such minimax optimization problems have attracted significant attention lately due to their applications in modern machine learning tasks. We first design and analyze the Zeroth-Order Gradient Descent Ascent (\texttt{ZO-GDA}) algorithm, and provide improved results compared to existing works, in terms of oracle complexity. Next, we propose the Zeroth-Order Gradient Descent Multi-Step Ascent (\texttt{ZO-GDMSA}) algorithm that significantly improves the oracle complexity of \texttt{ZO-GDA}. We also provide stochastic version of \texttt{ZO-GDA} and \texttt{ZO-GDMSA} to handle stochastic nonconvex minimax problems, and provide oracle complexity results.
A registration method for 2D blade profile

Fast and accurate registration research has important theory significance and engineering application value in improving digital measurement accuracy and efficiency. Aiming at solving registration precision and registration speed problem, the extraction scheme of contour dominant point, correspondence establishment method and the objective function of registration are discussed in the paper. Compared with other extraction ones, the scheme can extract typical characters of the blade contour effectively. It is essential to sample measuring points which can represent the entire blade with sufficient confidence and accuracy. Unlike the registration method that only minimizes the one-way distance between the data points and the initial fitted curves, the weighted mutual (two-way) distances between the template profile curves and the data points as the objective function is considered in the paper. The registration algorithm based on iterative closest point algorithm is introduced in details. Using experimental method, the validation of proposed model registration algorithm is verified. Two experimental examples were used to demonstrate registration precision and effectiveness.
Panchromatic HST/WFC3 Imaging Studies of Young, Rapidly Evolving Planetary Nebulae. I. NGC 6302

We present the results of a comprehensive, near-UV-to-near-IR Hubble Space Telescope (HST) Wide Field Camera 3 (WFC3) imaging study of the young planetary nebula (PN) NGC 6302, the archetype of the class of extreme bilobed, pinched-waist PNe that are rich in dust and molecular gas. The new WFC3 emission-line image suite clearly defines the dusty toroidal equatorial structure that bisects NGC 6302's polar lobes, and the fine structures (clumps, knots, and filaments) within the lobes. The most striking aspect of the new WFC3 image suite is the bright, S-shaped 1.64 μm [Fe ii] emission that traces the southern interior of the east lobe rim and the northern interior of the west lobe rim, in point-symmetric fashion. We interpret this [Fe ii] emitting region as a zone of shocks caused by ongoing, fast (∼100 km s−1), collimated, off-axis winds from NGC 6302's central star(s). The [Fe ii] emission and a zone of dusty, N- and S-rich clumps near the nebular symmetry axis form wedge-shaped structures on opposite sides of the core, with boundaries marked by sharp azimuthal ionization gradients. Comparison of our new images with earlier HST/WFC3 imaging reveals that the object previously identified as NGC 6302's central star is a foreground field star. Shell-like inner lobe features may instead pinpoint the obscured central star’s actual position within the nebula’s dusty central torus. The juxtaposition of structures revealed in this HST/WFC3 imaging study of NGC 6302 presents a daunting challenge for models of the origin and evolution of bipolar PNe.
A TRANSITORY INTERFACE COMPONENT FOR THE IN-CONTEXT VISUALIZATION AND ADJUSTMENT OF A VALUE A Thesis by ANDREW WEBB

A Transitory Interface Component for the In-Context Visualization and Adjustment of a Value. (August 2007) Andrew Webb, B.S., Texas A&M University Chair of Advisory Committee: Dr. Andruid Kerne Some agent-based systems depend on eliciting ratings from the user. However, the user’s willingness to provide ratings is limited due to requisite demands of attention and effort. From a human-centered view, we redefine providing ratings as expressing interest. We develop a new interface component for parameter setting, the In-Context Slider, which reduces physical effort and demand on attention by using fluid mouse gestures and in-context interaction. We hypothesize that such an interface should make interest expression easier for the user. We evaluated the In-Context Slider as an interest expression component compared with a more typical interface. Participants performed faster with the In-Context Slider. They found it easier to use and more natural for expressing interest. We then integrated the In-Context Slider in the agent-based system, combinFormation. We compared the In-Context Slider with combinFormation’s previous interest expression interface. Of the participants that effectively used both interfaces, most expressed more interest with the In-Context Slider. Participants’ experience reports described the InContext Slider as easier to use while developing collections to answer open-ended information discovery questions.
Testing the weighted salience model of conceptual combination

Testing the Weighted Salience Model of Conceptual Combination. (December 2003) Merryl Joy Patterson, B.S., Indiana University; M.S., Texas A&M University Chair of Advisory Committee: Dr. Steven M. Smith In two experiments the Weighted Salience Model (WSM) of conceptual combination was examined. Several of the hypotheses set forth in the WSM were evaluated, including the importance of salience of constituent features, differential interpretation strategies based on similarity, an initial reliance on the modifier as opposed to the head, and a context effect of salience reorganization. Results confirmed that the hierarchy of output dominance within constituent features was important in determining features in final combinations. Additionally, similar pairs were defined with property interpretations more frequently than were dissimilar pairs, and dissimilar pairs were defined with relation interpretations more frequently than were similar pairs. Context effects were demonstrated through the finding that target features were found more often in primed than unprimed pairs. The hypothesis of modifier superiority was not confirmed. These findings indicate that the WSM adds to the current understanding of conceptual combination through a reliance on output dominance and the importance of context. Despite these strengths, changes to the WSM may be necessary if future studies fail to support the importance of the modifier over the head noun.
Pre-trained Language Models for Text Generation: A Survey

Text Generation aims to produce plausible and readable text in human language from input data. The resurgence of deep learning has greatly advanced this field, in particular, with the help of neural generation models based on pre-trained language models (PLMs). Text generation based on PLMs is viewed as a promising approach in both academia and industry. In this paper, we provide a survey on the utilization of PLMs in text generation. We begin with introducing two key aspects of applying PLMs to text generation: 1) how to design an effective PLM to serve as the generation model; and 2) how to effectively optimize PLMs given the reference text and to ensure that the generated texts satisfy special text properties. Then, we show the major challenges that have arisen in these aspects, as well as possible solutions for them. We also include a summary of various useful resources and typical text generation applications based on PLMs. Finally, we highlight the future research directions which will further improve these PLMs for text generation. This comprehensive survey is intended to help researchers interested in text generation problems to learn the core concepts, the main techniques and the latest developments in this area based on PLMs.
Graphene Plasmonics : A Platform for Strong Light Matter Interactions

Graphene Plasmonics: A Platform for Strong Light Matter Interactions Frank H. L. Koppens,* Darrick E. Chang, and F. Javier García de Abajo* ICFO-Institut de Ciencies Fotoniques, Mediterranean Technology Park, 08860 Castelldefels (Barcelona), Spain California Institute of Technology, Pasadena, California 91125, United States Instituto de Optica—CSIC, Serrano 121, 28006 Madrid, Spain Optoelectronics Research Centre, University of Southampton, Southampton SO17 1BJ, U.K.
UC Merced Proceedings of the Annual Meeting of the Cognitive Science Society

We present data from three experiments addressing how much theory of mind reasoning is involved in production and interpretation of ambiguous referential expressions in an artiﬁcial language task, and how this interacts with the cost and availability of alternative utterances. When an unambiguous alternative is not available, listeners tend to draw simple Quantity inferences reminiscent of scalar implicatures (Grice, 1975). When an unambiguous alternative is available, fewer inferences are observed, but gradiently more as the cost of unambiguous alternatives increase. We outline a novel game theoretic model of pragmatic reasoning based on probabilistic back-and-forth reasoning about interlocutors’ rational choices and beliefs. The model provides a good ﬁt to the data and raises interesting issues for future research.
UC Merced Proceedings of the Annual Meeting of the Cognitive Science Society

Reinforcement learning has achieved broad and successful application in cognitive science in part because of its general formulation of the adaptive control problem as the maximization of a scalar reward function. The computational reinforcement learning framework is motivated by correspondences to animal reward processes, but it leaves the source and nature of the rewards unspeciﬁed. This paper advances a general computational framework for reward that places it in an evolutionary context, formulating a notion of an optimal reward function given a ﬁtness function and some distribution of environments. Novel results from computational experiments show how traditional notions of extrinsically and intrinsically motivated behaviors may emerge from such optimal reward functions. In the experiments these rewards are discovered through automated search rather than crafted by hand. The precise form of the optimal reward functions need not bear a direct relationship to the ﬁtness function, but may nonetheless confer signiﬁcant advantages over rewards based only on ﬁtness.
UC Merced Proceedings of the Annual Meeting of the Cognitive Science Society Title

One of the largest remaining unsolved mysteries in cognitive science is how the rapid input of spoken language i s mapped onto phonological and lexical representations over time. Attempts at psychologically-tractable computational models of spoken word recognition tend either to ignore ti me or to transform the temporal input into a spatial represe ntation. This is the approach taken in TRACE (McClelland & E lman, 1986), the model of spoken word recognition that ha s the broadest and deepest coverage of phenomena in speec h perception, spoken word recognition, and lexical pa rsing of multi-word sequences. TRACE reduplicates featural, phonemic, and lexical inputs at every time step in a potentially very large memory trace, and has rich interconnections (excitatory forward and backward connections between levels and inhibitory links wit hin levels). This leads to a rather extreme proliferati on of units and connections that grows dramatically as the lexi con or the memory trace grows. Our starting point is the obser vation that models of visual object recognition – including vis ual word recognition – have long grappled with the fundament al problem of how to model spatial invariance in human object recognition. We introduce a model that combines one aspect of TRACE – time-specific phoneme representations – and higher-level representations that have been used in visual word recognition – spatially(here, temporally-) i ndependent diphone and lexical units. This reduces the number of units and connections required by several orders of magni tude relative to TRACE. In this first report, we demonst rate that the model (dubbed TISK, for Time-Invariant String K ernel) achieves reasonable accuracy for the basic TRACE le xicon and successfully models the time course of phonolog ical activation and competition. We close with a discuss ion of phenomena that the model does not yet successfully imulate (and why), and with novel predictions that follow f rom this architecture.