Miracle-dz/UniFAR-FTUs huggingface dataset:
Model-based brightness constraints: on direct estimation of structure and motion

We describe a new direct method for estimating structure and motion from image intensities of multiple views. We extend the direct methods of B.K. P. Horn and E.J. Weldon (1988) to three views. Adding the third view enables us to solve for motion, and compute a dense depth map of the scene, directly from image spatio-temporal derivatives in a linear manner without first having to find point correspondences or complete optical flow. We describe the advantages and limitations of this method which are then verified with experiments using real images.
Using gaze patterns to predict task intent in collaboration

In everyday interactions, humans naturally exhibit behavioral cues, such as gaze and head movements, that signal their intentions while interpreting the behavioral cues of others to predict their intentions. Such intention prediction enables each partner to adapt their behaviors to the intent of others, serving a critical role in joint action where parties work together to achieve a common goal. Among behavioral cues, eye gaze is particularly important in understanding a person's attention and intention. In this work, we seek to quantify how gaze patterns may indicate a person's intention. Our investigation was contextualized in a dyadic sandwich-making scenario in which a “worker” prepared a sandwich by adding ingredients requested by a “customer. ” In this context, we investigated the extent to which the customers' gaze cues serve as predictors of which ingredients they intend to request. Predictive features were derived to represent characteristics of the customers' gaze patterns. We developed a support vector machine-based (SVM-based) model that achieved 76% accuracy in predicting the customers' intended requests based solely on gaze features. Moreover, the predictor made correct predictions approximately 1.8 s before the spoken request from the customer. We further analyzed several episodes of interactions from our data to develop a deeper understanding of the scenarios where our predictor succeeded and failed in making correct predictions. These analyses revealed additional gaze patterns that may be leveraged to improve intention prediction. This work highlights gaze cues as a significant resource for understanding human intentions and informs the design of real-time recognizers of user intention for intelligent systems, such as assistive robots and ubiquitous devices, that may enable more complex capabilities and improved user experience.
A random Matrix Approach to collective Trends of falling and rising Stock Markets

An inverse statistics analysis of one minute stock quotes from 492 large European companies has revealed the existence of a gain-loss asymmetry in the following index. The gain-loss asymmetry differs from that observed for daily closure prices of the Dow Jones Industrial Average [38], as the probability of the optimal investment horizon for a gain is higher than that of a loss. For individual stocks, the gain-loss asymmetry was observed to only appear for significantly larger return-levels. To the best of our knowledge, this is the first time such an analysis has been performed on high-frequency data. A principal component analysis was done by performing an eigenvalue decomposition of the correlation matrix from a sliding time-window. The first principal component was observed to describe the market excellently. Its corresponding eigenvalue was observed to be significantly larger than theoretical predictions from random matrix theory, implying that the eigenvalue carries information common to all stocks. Using this eigenvalue as an index measuring the collectivity in the market has revealed the existence of collective trends that appear to be stronger during falling than rising markets. This has been observed for two different datasets, the above described one minute stock quotes and daily closure prices from 29 stocks composing the DJIA late February 2008. The observation is in accordance with results of Balogh et al. [40], and provides further support to the speculation of Johansen et al. [37] that a difference in collective trends is the reason behind the gain-loss asymmetry observed in indexes and not for individual stocks for the same return-level. The key idea behind the fear factor model of Donangelo et al. [42] has been strongly supported by the observation that collective trends appear to be stronger during sharp index drops. As the collectivity increment has been observed to be dependent on the size of the index drop, it is suggested that the model should incorporate also individual fear factors for economic sectors, in addition to the global fear factor governing the market as a whole. Periods exhibiting a rising index positively correlated to the strength of collectivity has indicated the presence of an optimism factor that also should be incorporated in the fear factor model [42], forcing stocks to rise synchronously.
Obama, race, and the Republican landslide in 2010

ABSTRACT In the 2010 midterm elections Republicans won in a landslide, making major gains in the Senate and winning the majority in the House. In this article, I assess one factor that contributed to the Republican Party's landslide in 2010: the political salience of racial animosity in the Obama era. In a historical analysis of voting in midterm elections, I find that racial animosity had a substantially larger effect on vote choice in 2010 than in past midterm elections. This effect persists in the face of many short-term factors and regardless of the race of the candidates themselves. I also show that racial animosity affected voter turnout in 2010, stimulating turnout among Republicans and diminishing turnout among Democrats. Consistent with research on presidential voting and opinion formation in the Obama era, these results suggest that one consequence of Obama's presidency was to increase the relevance of racial animosity in American politics.
Listening to the Shepard-Risset Glissando: the Relationship between Emotional Response, Disruption of Equilibrium, and Personality

The endless scale illusion, obtained by cyclically repeating a chromatic scale made up of Shepard tones, has been used in a variety of musical works. Music psychology and neuroscience has been interested in this particular psychoacoustic phenomenon mainly for studying the cognitive processes of pitch perception involved. In the present study, we investigated the emotional states induced by the Shepard-Risset glissando, a variant of the Shepard scale. For this purpose we chose three musical stimuli: a Matlab-generated Shepard Risset glissando, Jean-Claude Risset's Computer Suite from Little Boy, which presents a Shepard-Risset glissando integrated in the aesthetic context of a composition, and an ordinary orchestral glissando taken from the opening of Iannis Xenakis's Metastasis. Seventy-three volunteers completed a listening experiment during which they rated their emotional response to these stimuli on a seven-point Likert scale and indicated whether they had experienced a disruption of equilibrium. Personality was also measured with the Five-Factor Model of personality traits. The results show that negative emotions were most strongly evoked during listening to each of the stimuli. We also found that the Shepard-Risset glissando illusion, both within the aesthetic context of a musical composition and on its own, was capable of evoking disruption of equilibrium, frequently leading to the associated feeling of falling. Moreover, generally for the Shepard-Risset glissando illusion, higher negative emotional ratings were given by individuals who had experienced a feeling of disturbance of equilibrium relative to those who had not had this experience. Finally, we found a complex pattern of relationships between personality and the subjective experience of the glissando. Openness to experience correlated positively with positive emotion ratings for the Computer Suite, while agreeableness correlated negatively with positive emotion ratings for the Matlab stimulus. Moreover, results indicated higher (Bonferroni-uncorrected) neuroticism for those who experienced an equilibrium disturbance relative to subjects who did not have this experience during listening to the Computer Suite. These findings suggest that musical paradoxes may be of interest not only for the insights they provide on our perceptual system, but also for the richness of the emotional experience elicited during listening.
The Research of Image Detail Enhancement Algorithm with Laplacian Pyramid

Image enhancement is a low-level image processing and have many methods of image enhancement processing. Which based on Laplacian pyramid of the image processing algorithm for image enhancement is an essential field of image processing and crucial technologies. The Laplacian pyramid is ubiquitous for decomposing images into multiple scales and is widely used for image analysis. In this paper, we describes the basic tenets of the Laplacian pyramid decomposition, and analysis using user-defined threshold values to distinguish between the image detail and edges of the disadvantages, and propose to use the global information directly to obtain the threshold value method. In the basis of the obtained good results, the limit remapping layers that not only able to reduce time and cost, and can reduce the cost of unnecessary calculations. As we demonstrate, our method produces consistently high-quality results in the process of image detail enhancement.
A Review on Modeling of Hybrid Solid Oxide Fuel Cell Systems

Over the past 2 decades, there has been tremendous progress on numerical and computational tools for fuel cells and energy systems based on them. The purpose of this work is to summarize the current status of hybrid solid oxide fuel cell (SOFC) cycles and identify areas that require further studies. In this review paper, a comprehensive literature survey on different types of SOFC hybrid systems modeling is presented. The paper has three parts. First, it describes the importance of the fuel cells modeling especially in SOFC hybrid cycles. Key features of the fuel cell models are highlighted and model selection criteria are explained. In the second part, the models in the open literature are categorized and discussed. It includes discussion on a detail example of SOFC-gas turbine cycle model, description of early models, models with different objectives such as parametric analysis, comparison of configurations, exergy analysis, optimization, non-stationary power generation applications, transient and off-design analysis, thermoeconomic analysis and so on. Finally, in the last section, key features of selected models are summarized and suggestions for areas that require further studies are presented. In this paper, a hybrid cycle can be any combination of SOFC and gas turbine, steam turbine, coal integrated gasification, and application in combined heat and power cycle.
Rate-Distortion Analysis and Modeling of Dead-Zone Plus Uniform Threshold Scalar Quantization for Generalized Gaussian Random Variables

This paper provides a systematical rate-distortion (R-D) analysis and modeling of generalized Gaussian distribution (GGD) under the dead-zone plus uniform threshold scalar quantization (DZ+UTSQ) and nearly-uniform reconstruction quantization (NURQ). In R-D analysis, we clearly explain the property of GGD source under efficient DZ+UTSQ/NURQ. On this basis, in R-D modeling, the heuristic D-R model is proposed.
Automatic analysis and synthesis of fujisaki's intonation model for TTS

This paper deals with the automatic analysis and synthesis of intonation using Fujisaki’s model. Both the accent commands and the phrase commands are related to the accent group. We propose an analysis method which imposes strong linguistic constraints. This method gives good results when compared to other current methods and is the best option for synthesis (at least when using accent groups). For synthesis, several prediction algorithms are evaluated. The results show that VCART (an extension of CART to predict vector values) gives the best performance when compared with standard CART or with Neural Networks. The paper also analyzes which features are more relevant to predict the parameters of Fujisaki’s model.
Multiple Cluster Comparison:The Identification of Stable Objects

With the rapid increase in size and complexity of data, coupled visualization and analysis, or visual analytics, has become a major methodology for data analysis and exploration. We describe a specific integration of analysis and visualization for the evaluation of cluster stability. We partition or decompose a dataset into a family of disjoint subsets. Partitions may refer to the results of clustering, of groupings of categorical dimensions, of binned numerical dimensions, of predetermined class labeling dimensions, or prior knowledge structured in mutually exclusive format (one data item associated with one and only one outcome). Partition or cluster stability analysis identifies near-optimal structure, builds ensembles, or conducts validation. We then use a new visualization tool, CComViz (Cluster Comparison Visualization), for the comparison and evaluation of multiple partitioning of the same dataset. We have added a novel layout algorithm for informatively rearranging the order of the records and dimensions. With these techniques, we are able to visualize data stability, data flow, density distribution and hierarchy, and data correlation at the record, at the group and at the dimension levels within a single graphical interactive display. We provide several example applications of CComViz.
Cutting Edge: Nitric Oxide Inhibits the NLRP3 Inflammasome

Although the NLRP3 inflammasome plays a pivotal role in host defense, its uncontrolled activation is associated with inflammatory disorders, suggesting that regulation of the inflammasome is important to prevent detrimental effects. Type I IFNs and long-term LPS stimulation were shown to negatively regulate NLRP3 activation. In this study, we found that endogenous NO is involved in the regulation of NLRP3 inflammasome activation by either IFN-β pretreatment or long-term LPS stimulation. Furthermore, S-nitroso-N-acetylpenicillamine (SNAP), an NO donor, markedly inhibited NLRP3 inflammasome activation, whereas the AIM2 and NLRC4 inflammasomes were only partially inhibited by SNAP. An increase in mitochondrial reactive oxygen species induced by ATP was only modestly affected by SNAP treatment. Interestingly, S-nitrosylation of NLRP3 was detected in macrophages treated with SNAP, and this modification may account for the NO-mediated mechanism controlling inflammasome activation. Taken together, these results revealed a novel role for NO in regulating the NLRP3 inflammasome.
Parliamentary Group and Individual Voting Behaviour in the Finnish Parliament in Year 2003 : A Group Cohesion and Voting Similarity Analysis

Although group cohesion studies are rather common elsewhere, the last analyses of the Finnish parliament Eduskunta were published in the 1960s. This article provides, firstly, a fresh group cohesion analysis using the Agreement Index, which is a modified version of the classic Rice index. Secondly, two advanced voting similarity analyses, together with a new easy-to-understand way of illustrating the results, are provided. Where the Agreement Index operates at the parliamentary party group level, the voting similarity analyses are able to analyse and illustrate the individual MP level. The article is partly methodological in testing the voting similarity methods, however, it also provides insight into the recent voting behaviour within Eduskunta.
SoundSaber - A Motion Capture Instrument

The paper presents the SoundSaber - a musical instrument based on motion capture technology. We present technical details of the instrument and discuss the design development process. The SoundSaber may be used as an example of how high-delity motion capture equipment can be used for prototyping musical instruments, and we illustrate this with an example of a low-cost implementation of our motion capture instrument.
Fundus Foveal Localization Based on Image Relative Subtraction-IReS Approach

In this paper we present a method for fovea localization which does not use organization information of other retinal structures like optic disk and arcades. The main advantage of this method is that it does not require segmentation/localization of other retinal structures which are required as prior knowledge in existing fovea localization methods. The key idea is to enhance the relative contrast between the fovea and its surrounding such that it is well-separated in a retinal image. The approach was tested on 520 retinal images which include normal and pathological color retinal images. An overall accuracy of 90.57% is reported independent of left eye, right eye, normal or pathological retinal images.
Impact of triangle shapes using high-order discretizations and direct mesh adaptation for output error

The impact of triangle shapes, including angle sizes and aspect ratios, on accuracy and stiffness is investigated for simulations of highly anisotropic problems. The results indicate that for high-order discretizations, large angles do not have an adverse impact on solution accuracy. However, a correct aspect ratio is critical for accuracy for both linear and high-order discretizations. In addition, large angles are not problematic for the conditioning of the linear systems arising from discretization. They can be overcome through small increases in preconditioning costs. A direct adaptation scheme that controls the output error via mesh operations and mesh smoothing is also developed. The decision of mesh operations is solely based on output error distribution without any a priori assumption on error convergence rate. Anisotropy is introduced by evaluating the error changes due to potential edge split, and thus the anisotropies of both primal and dual solutions are taken into account. This scheme is demonstrated to produce grids with fewer degrees of freedom for a specified error level than the existing metric-based approach. Acknowledgments I would like to express my gratitude to the many people who have made this thesis possible. First and foremost, I would like to thank my advisor, Prof. David Darmofal, for giving me the opportunity to work with him on this interesting yet challenging research project. I am grateful for his insights, guidance, and inspiration throughout this research, and look forward to continuing our work together. I would also like to thank the Project X team (This work would not have been possible without the numerous insightful discussions with them and their tireless effort involved in the Project X codes. Special thanks go to Laslo Diosady, Xun Huan, JM Modisette, and Masa Yano for their patient help during the drafting of this thesis. Many discussions with Bob Haimes have also proven very useful. I would also like to thank everyone at ACDL for making the last two years a lot of fun. Last but not least, I would like to thank my parents for their constant love, support, and encouragement, without which I am sure I would not have gotten this far.
Multimedia Implicit Tagging

Social and behavioral signals carry invaluable information regarding how audiences perceive the multimedia content. Assessing the responses from the audience, we can generate tags, summaries and other forms of metadata for multimedia representation and indexing. Tags are a form of metadata which enables a retrieval system to find and re-find the content of interest (Larson et al. (2011)). Unlike classic tagging schemes where users direct input is needed, Implicit Human-Centered Tagging (IHCT) was proposed (Pantic and Vinciarelli (2009)) to generate tags without any specific input or effort from users. Translating the behavioral responses into tags results in “implicit” tags since there is no need for users’ direct input as reactions to multimedia are displayed spontaneously (Soleymani and Pantic (2012)). User generated explicit tags are not always assigned with the intention of describing the content and might be given to promote the users themselves (Pantic and Vinciarelli (2009)). Implicit tags have the advantage of being detected for a certain goal relevant to a given application. For example, an online radio is interested in the mood of its songs can assess listeners emotions; a marketing company is interested in assessing in the success of its video advertisements. It is also worth mentioning that implicit tags can be a complementary
Turkish Text Categorization Using N-Gram Words

An N-gram is a representation method that consists of a sequence of N-contiguous characters or words. There have been so many studies which use N-gram based representations for the traditional text classification tasks. In contrast to other languages, the studies in Turkish are limited. In this paper, we analyze text classification algorithms on a Turkish dataset by using N-gram words. We have compared several classifiers (Bayesian probabilistic classifiers, nearest neighbor classifiers and decision trees) using different types of features. We applied the classifiers on different data sets that are represented with unigram, bigram and trigram words. In the experiments, a total of 600 text documents that are assigned to six categories were used and the best success rate of 95.83% was achieved by using unigrams.
Learning Time-Varying Concepts

This work extends computational learning theory to situations in which concepts vary over time, e.g., system identification of a time-varying plant. We have extended formal definitions of concepts and learning to provide a framework in which an algorithm can track a concept as it evolves over time. Given this framework and focusing on memory-based algorithms, we have derived some PAC-style sample complexity results that determine, for example, when tracking is feasible. We have also used a similar framework and focused on incremental tracking algorithms for which we have derived some bounds on the mistake or error rates for some specific concept classes.
Characterizing API Elements in Software Documentation with Vector Representation

Software documentation is a very crucial resource for developers in understanding various aspects of software development process. A unique nature of such documentation is the presence of code elements embedded in natural-language texts that explain their purposes, usages and mutual relations with others. Some recent work has explored that nature for different purposes such as discovering code elements in documents and generating summaries for classes and methods with context. However, none of the existing approaches is capable of further capturing semantic relations of code elements with related words and with other relevant APIs at the same time. In this paper, by considering an embedded API element as a word, we characterize this API via a low-dimensional continuous vector that encodes information on many contexts in which that API appears as possible. Our empirical study shows that the vector representation learned from a large corpus of documentation is capable of capturing semantic relations between API elements and words. That is, APIs with similar functionalities have similar embeddings; and APIs and related words are close to each other in the vector space without explicit textual matching. Our experiment also suggests that the proposed representation for embedded API elements has a promising application in API code search.
Variable step-size based-adaptive algorithm for acoustic feedback cancellation during online operation of ANC systems

In active noise control (ANC) system, the injection of white Gaussian noise (WGN) for online feedback path modeling and neutralization (FBPMN) degrades the noise-reduction-performance (NRP). In this paper: (1) a tuning free gain scheduling of WGN is used to improve the NRP of ANC system, and (2) a variable step-size is used to compensate for the decrease in the convergence of FBPMN filter due to gain scheduling. The computer simulations are carried out to demonstrate the performance of the proposed method.