Miracle-dz/UniFAR-FTUs huggingface dataset:
Linguistically Mediated Visual Search

During an individual's normal interaction with the environment and other humans, visual and linguistic signals often coincide and can be integrated very quickly. This has been clearly demonstrated in recent eyetracking studies showing that visual perception constrains on-line comprehension of spoken language. In a modified visual search task, we found the inverse, that real-time language comprehension can also constrain visual perception. In standard visual search tasks, the number of distractors in the display strongly affects search time for a target defined by a conjunction of features, but not for a target defined by a single feature. However, we found that when a conjunction target was identified by a spoken instruction presented concurrently with the visual display, the incremental processing of spoken language allowed the search process to proceed in a manner considerably less affected by the number of distractors. These results suggest that perceptual systems specialized for language and for vision interact more fluidly than previously thought.
A Grounded Theory Study of Decision-Making within Informal Work Environments

wonderment, which was used by the researcher to guide the initial stages of this thesis, is a principle of grounded theory that is used to initiate a research and develop a general idea on what the research is attempting to understand. It is directional in purpose and allows the research problem to be discovered through grounded theory’s principles on data analysis, namely, open coding, selective coding, and theoretical coding. (Berge, et al. 2012). Glaser (1992, p.22) was emphatic that a researcher adopting grounded theory principles should only enter a research with “abstract wonderment of what is going on that is an issue and how it is handled”. The general and broad research question, ‘What data-emergent theory can help explain the impediments to effective decision-making within UMR’s informal environment? ’, was used to initiate this research and create the necessary channels for data collection. The research began by focusing on semi-structured interview questions pertaining to the relationship between the concepts of an informal environment and decision-making. Using the notion of informality as the research’s contextual background and research setting, the goal was to allow implicit themes and concepts to emerge during the collective data collection and analysis stages. Unlike traditional research that starts with a predetermined set of research questions to guide the research (Grover & Vriens, 2006), grounded theory uses abstract wonderment to allow emergent data through continuous analysis to direct the research as appropriate. In keeping with such principles, further, narrower, research questions for this research emerged after the first stage of interviews, and were continuously modified and altered based on the discussions within the interviews and theoretical memoing. Figure 4.1 highlights how abstract wonderment was used to initiate this thesis research and how data patterns informed the emerging research questions.
How a Systematic Approach to Uncertainty Quantification Renders Molecular Simulation a Quantitative Tool in Predicting the Critical Constants for Large n -Alkanes

How a Systematic Approach to Uncertainty Quantification Renders Molecular Simulation a Quantitative Tool in Predicting the Critical Constants for Large n-Alkanes Richard Alma Messerly Department of Chemical Engineering, BYU Doctor of Philosophy Accurate thermophysical property data are crucial for designing efficient chemical processes. For this reason, the Design Institute for Physical Properties (DIPPR 801) provides evaluated experimental data and prediction of various thermophysical properties. The critical temperature (Tc), critical density (ρc), critical pressure (Pc), critical compressibility factor (Zc), and normal boiling point (Tb) are important constants to check for thermodynamic consistency and to estimate other properties. The n-alkane family is of primary interest because it is generally assumed that other families of compounds behave similarly to the n-alkane family with increasing chain-length. Unfortunately, due to thermal decomposition, experimental measurements of Tc, ρc, and Pc for large n-alkanes are scarce and potentially unreliable. For this reason, molecular simulation is an attractive alternative for estimating the critical constants. However, molecular simulation has often been viewed as a tool that is limited to providing qualitative insight. One key reason for this perceived weakness is the difficulty in quantifying the uncertainty of the simulation results. This research focuses on a systematic top-down approach to quantifying the uncertainty in Gibbs Ensemble Monte Carlo (GEMC) simulations for large n-alkanes. We implemented four different methods in order to obtain quantitatively reliable molecular simulation results. First, we followed a rigorous statistical analysis to assign the uncertainty of the critical constants when obtained from GEMC. Second, we developed an improved method for predicting Pc with the standard force field models in the literature. Third, we implemented an experimental design to reduce the uncertainty associated with Tc, ρc, Pc, and Zc. Finally, we quantified the uncertainty associated with the Lennard-Jones 12-6 potential parameters. This research demonstrates how uncertainty quantification renders molecular simulation a quantitative tool for thermophysical property evaluation. Specifically, by quantifying and reducing the uncertainty associated with molecular simulation results, we were able to discern between different experimental data sets and prediction models for the critical constants. In this regard, our results enabled the development of improved prediction models for Tc, ρc, Pc, and Zc for large n-alkanes. In addition, we developed a new Tb prediction model in order to ensure thermodynamic consistency between Tc, Pc, and Tb.
Desis: Efficient Window Aggregation in Decentralized Networks

Stream processing is widely applied in industry as well as in research to process unbounded data streams. In many use cases, specific data streams are processed by multiple continuous queries. Current systems group events of an unbounded data stream into bounded windows to produce results of individual queries in a timely fashion. For multiple concurrent queries, multiple concurrent and usually overlapping windows are generated. To reduce redundant computations and share partial results, state-of-the-art solutions divide windows into slices and then share the results of those slices. However, this is only applicable for queries with the same aggregation function and window measure, as in the case of overlaps for sliding windows. For multiple queries on the same stream with different aggregation functions and window measures, partial results cannot be shared. Furthermore, data streams are produced from devices that are distributed in large decentralized networks. Current systems cannot process queries on decentralized data streams efficiently. All queries in a decentralized network are either computed centrally or processed individually without exploiting partial results across queries. We present Desis, a stream processing system that can efficiently process multiple stream aggregation queries. We propose an aggregation engine that can share partial results between multiple queries with different window types, measures, and aggregation functions. In decentralized networks, Desis moves computation to data sources and shares overlapping computation as early as possible between queries. Desis outperforms existing solutions by orders of magnitude in throughput when processing multiple queries and can scale to millions of queries. In a decentralized setup, Desis can save up to 99% of network traffic and scale performance linearly.
Self‐defining tree‐like classifiers for interpretation of Raman spectroscopic experiments

In this contribution, a technique is proposed to create a data‐driven interpretation of a given chemometric analysis of a Raman dataset. In real‐world applications, the chemometric analysis is fixed by some external measurement, for example, a legal standard, or a set of fixed goals. Thus, the exact chemometric work flow is fixed because of those goals. However, a further optimization, for example, of the measurement itself relies on an interpretation of the resulting chemometric analysis. For this purpose, a data‐driven analysis of the chemometric analysis itself has to be carried out. This contribution tries to achieve that goal by combining two methods. The first proposed technique is the calculation of the so‐called importance map, which allows the computation of the importance of every channel for a given model and a given dataset. This importance map is constructed after the complete result of an out‐of‐bag (OOB) validation and the decrease of accuracy by randomized channels. The second technique is the growing of the optimal decision tree based on the action of the model used for chemometric analysis. By this way, a clustering is achieved on which by binary classifiers, the optimal decision tree is grown. This tree can be interpreted as dividing the whole dataset into meta clusters. Combining these techniques, a new way of interpreting datasets based on the chosen model is proposed. This combination closes the gap between chemometric analysis and the need for interpretation. Copyright © 2016 John Wiley & Sons, Ltd.
SAM-Deblur: Let Segment Anything Boost Image Deblurring

Image deblurring is a critical task in the field of image restoration, aiming to eliminate blurring artifacts. However, the challenge of addressing non-uniform blurring leads to an ill-posed problem, which limits the generalization performance of existing deblurring models. To solve the problem, we propose a framework SAM-Deblur, integrating prior knowledge from the Segment Anything Model (SAM) into the deblurring task for the first time. In particular, SAM-Deblur is divided into three stages. First, we preprocess the blurred images, obtain segment masks via SAM, and propose a mask dropout method for training to enhance model robustness. Then, to fully leverage the structural priors generated by SAM, we propose a Mask Average Pooling (MAP) unit specifically designed to average SAM-generated segmented areas, serving as a plug-and-play component which can be seamlessly integrated into existing deblurring networks. Finally, we feed the fused features generated by the MAP Unit into the deblurring model to obtain a sharp image. Experimental results on the RealBlurJ, ReloBlur, and REDS datasets reveal that incorporating our methods improves GoPro-trained NAFNet's PSNR by 0.05, 0.96, and 7.03, respectively. Project page is available at GitHub \href{https://hplqaq.github. io/projects/sam-deblur}{HPLQAQ/SAM-Deblur}.
Estimation of flying capacitors voltages in multicell converters

Flying Capacitor Multicell (FCM) converters are very interesting alternative for medium voltage and high power applications. The main reason is increase in the number of output voltage levels and its apparent frequency. Balancing of flying capacitors voltages is an important constraint to proper performance of FCM converters. So, observation of these flying capacitors voltages for using in active control is valuable where using voltage sensors results in increasing cost and size of the converter. This paper presents a new strategy to estimate flying capacitors voltages of FCM converters. The proposed strategy is based on discrete time model of converter. So, advantage of the proposed strategy is its simplicity which is effective for real time implementation. The circuit is simulated using PSCAD/EMTDC software and simulation results are presented to validate the effectiveness of the proposed estimation strategy to observe the flying capacitors voltages.
A decomposition based multiobjective evolutionary algorithm with semi-supervised classification

In multiobjective evolutionary algorithms, how to select the optimal solutions from the offspring candidate set significantly affects the optimization process. Usually, the selection process is largely based on the real objective values or surrogate model estimating objective values. However, these selection processes are very time consuming sometimes, especially for some real optimization problems. Recently, some researches began to employ supervised classification to assist offspring selection, but these works are difficult to prepare the exact positive and negative samples or time consuming of parameter tuning problems. In order to solve these disadvantages, we propose a decomposition based multiobjective evolutionary algorithm with semi-supervised classification. This approach using random sampling and non-dominated sorting to construct semi supervised classifier. In each generation, a set of candidate solutions are generated for each subproblem and only good solutions are reserved by classifier. If there is more than one good solutions, we calculate each of good solutions by real objective function and choose the best one as the offspring solution. Based on the typical decomposition based multiobjective evolutionary algorithm MOEA/D, we design algorithm framework through integrating the novel offspring selection process based on semi-supervised classification. Experiments show that the proposed algorithm performs best in most test cases and improves the performance of MOEA/D.
Conserving wild bees for crop pollination

A substantial proportion of the worlds crops rely on insect pollination, yet for many we have little or no information as to which pollinators are most effective. Pollinator management has traditionally focussed exclusively on one species, the honeybee, Apis mellifera. Yet this bee is not able to adequately pollinate some crops, and is an unreliable pollinator in cold and wet climates. Natural populations of wild bee species and other insects probably contribute greatly to pollination of many crops. Yet many of these insects have declined greatly in the last 50 years as a result of agricultural intensification. It seems certain that the yield of some crops is now limited by inadequate pollination, and that opportunities for diversification into novel crops may be reduced through a lack of suitable pollinators. Agri-environment schemes provide an opportunity to enhance pollinator populations in farmland, but at present little is known as to which schemes are most suitable. Large-scale field trials are needed to assess how best to encourage and sustain populations of wild pollinators on farmland.
Isolated systolic hypertension in the young: a position paper endorsed by the European Society of Hypertension

&NA; Whether isolated systolic hypertension in the young (ISHY) implies a worse outcome and needs antihypertensive treatment is still a matter for dispute. ISHY is thought to have different mechanisms than systolic hypertension in the elderly. However, findings from previous studies have provided inconsistent results. From the analysis of the literature, two main lines of research and conceptualization have emerged. Simultaneous assessment of peripheral and central blood pressure led to the identification of a condition called pseudo or spurious hypertension, which was considered an innocent condition. However, an increase in pulse wave velocity has been found by some authors in about 20% of the individuals with ISHY. In addition, obesity and metabolic disturbances have often been documented to be associated with ISHY both in children and young adults. The first aspect to consider whenever evaluating a person with ISHY is the possible presence of white-coat hypertension, which has been frequently found in this condition. In addition, assessment of central blood pressure is useful for identifying ISHY patients whose central blood pressure is normal. ISHY is infrequently mentioned in the guidelines on diagnosis and treatment of hypertension. According to the 2013 European Guidelines on the management of hypertension, people with ISHY should be followed carefully, modifying risk factors by lifestyle changes and avoiding antihypertensive drugs. Only future clinical trials will elucidate if a benefit can be achieved with pharmacological treatment in some subgroups of ISHY patients with associated risk factors and/or high central blood pressure.
The Psychopath: Emotion and the Brain

List of Figures and Tables. Acknowledgments. 1. What is Psychopathy? 2. The Background Facts. 3. What is the Ultimate Cause of Psychopathy? 4. The Psychopathic Individual: The Functional Impairment. 5. Cognitive Accounts of Psychopathy. 6. Neural Accounts of Psychopathy. 7. A Neuro-Cognitive Account of Reactive Aggression. 8. A Neuro-Cognitive Account of Psychopathy. 9. Conundrums and Conclusions. References. Subject Index. Author Index.
Comparison of two ways of defining phonological words for assessing stuttering pattern changes with age in Spanish speakers who stutter

Phonological words (PWs) are defined as having a single word that acts as a nucleus and an optional number of function words preceding and following that act as satellites. Content and function words are one way of specifying the nucleus and satellites of PW. PW, defined in this way, have been found useful in the characterization of patterns of disfluency over ages for both English and Spanish speakers who stutter. Since content words carry stress in English, PWs segmented using content words as the nucleus would correspond to a large extent with PWs segmented that use a stressed word as the nucleus. This correlation between word type and stress does not apply to the same extent in Spanish. Samples of Spanish from speakers of different ages were segmented into PWs using a stressed, rather than a content, word as the nucleus and unstressed, rather than function, words as satellites. PWs were partitioned into those that were common to the two segmentation methods (common set) and those that differed (different set). There were two separate segmentations when PWs differed, those appropriate to content word nuclei, and those appropriate to stressed word nuclei. The two types of segmentation on the different set were analyzed separately to see whether one, both or neither method led to similar patterns of disfluency to those reported when content words were used as nuclei in English and Spanish. Generally speaking, the patterns of stuttering in PW found in English applied to all three analyses (common and the two on the different set) in Spanish. Thus, neither segmentation method showed a marked superiority in predicting the patterns of disfluency over age groups for the different set of Spanish data. It is argued that stressed or content word status can lead to a word being a nucleus and that there may be other factors (e.g. speech rate) that underlie stressed words and content words that affect the words around these PW nuclei in a similar way.
Trends in Psychostimulant and Antidepressant Use by Children in 2 Canadian Provinces

Objective: We used population-based administrative prescription medication data to examine regional differences in psychostimulant and antidepressant (AD) use among children from 2 Canadian provinces: British Columbia (BC) and Manitoba (MB). Method: Using 1997 to 2003 prescription data, annual rates of psychostimulant and AD use were determined for children aged 19 years and under in both provinces. Further comparisons of rates were made according to sex, age group, and specific classes of dispensed medications. Results: During 1997 to 2003, psychostimulant use rose by 44.9% in MB and 13.3% in BC. Among male children, psychostimulant use increased by 40.2% in MB, compared with an increase of only 8.6% in BC. AD utilization was similar between provinces, with increases of 80% and 75% in MB and BC, respectively. In both provinces, AD use was highest among older children. Conclusions: Our observations of regional variation in psychotropic medication use potentially reflect provincial differences in drug benefit policies, disease prevalence, and (or) physician diagnosis and treatment.
Normalization of Feature Distributions for Linear-Quadratic Fusion in Landmine Detection Using GPR

Successful detection of antipersonnel landmines often requires decision making on the basis of more than one decisive feature. The fusion of the available features should be done in a statistically optimal way. Theoretically this might be achieved by Bayesian-based criteria. However application of Bayes' criteria in general form is computationally difficult and may lead to overtraining of classification algorithms. To avoid this situation linear and quadratic fusion rules are often used in practice. However these rules produce optimal results only when the decisive features are normally distributed, which is not always guaranteed in practice. To improve the performance of the linear and quadratic detectors we suggest a normalization of the decisive features by means of Johnson's transform prior to the fusion. We describe the normalization algorithm and demonstrate that it improves performance of the linear and quadratic classifiers. The performance is judged in terms of the ROC curves
New Optimisation Methods for Machine Learning

A thesis submitted for the degree of Doctor of Philosophy of The Australian National University. In this work we introduce several new optimisation methods for problems in machine learning. Our algorithms broadly fall into two categories: optimisation of finite sums and of graph structured objectives. The finite sum problem is simply the minimisation of objective functions that are naturally expressed as a summation over a large number of terms, where each term has a similar or identical weight. Such objectives most often appear in machine learning in the empirical risk minimisation framework in the non-online learning setting. The second category, that of graph structured objectives, consists of objectives that result from applying maximum likelihood to Markov random field models. Unlike the finite sum case, all the non-linearity is contained within a partition function term, which does not readily decompose into a summation. For the finite sum problem, we introduce the Finito and SAGA algorithms, as well as variants of each. For graph-structured problems, we take three complementary approaches. We look at learning the parameters for a fixed structure, learning the structure independently, and learning both simultaneously. Specifically, for the combined approach, we introduce a new method for encouraging graph structures with the "scale-free" property. For the structure learning problem, we establish SHORTCUT, a O(n^{2.5}) expected time approximate structure learning method for Gaussian graphical models. For problems where the structure is known but the parameters unknown, we introduce an approximate maximum likelihood learning algorithm that is capable of learning a useful subclass of Gaussian graphical models.