Showing papers on "Character (mathematics) published in 2020"

Local motion phases for learning multi-contact character movements

Abstract: Training a bipedal character to play basketball and interact with objects, or a quadruped character to move in various locomotion modes, are difficult tasks due to the fast and complex contacts happening during the motion. In this paper, we propose a novel framework to learn fast and dynamic character interactions that involve multiple contacts between the body and an object, another character and the environment, from a rich, unstructured motion capture database. We use one-on-one basketball play and character interactions with the environment as examples. To achieve this task, we propose a novel feature called local motion phase, that can help neural networks to learn asynchronous movements of each bone and its interaction with external objects such as a ball or an environment. We also propose a novel generative scheme to reproduce a wide variation of movements from abstract control signals given by a gamepad, which can be useful for changing the style of the motion under the same context. Our scheme is useful for animating contact-rich, complex interactions for real-time applications such as computer games.

A proposal for reconciling diverse experiments on the superconducting state in Sr2RuO4

Abstract: A variety of precise experiments have been carried out to establish the character of the superconducting state in Sr2RuO4. Many of these appear to imply contradictory conclusions concerning the symmetries of this state. Here we propose that these results can be reconciled if we assume that there is a near-degeneracy between a $${d}_{{x}^{2}-{y}^{2}}$$ (B1g in group theory nomenclature) and a $${g}_{xy({x}^{2}-{y}^{2})}$$ (A2g) superconducting state. From a weak-coupling perspective, such an accidental degeneracy can occur at a point at which a balance between the on-site and nearest-neighbor repulsions triggers a d-wave to g-wave transition.

Uncovering the complex genetics of human character

Abstract: Human personality is 30–60% heritable according to twin and adoption studies. Hundreds of genetic variants are expected to influence its complex development, but few have been identified. We used a machine learning method for genome-wide association studies (GWAS) to uncover complex genotypic–phenotypic networks and environmental interactions. The Temperament and Character Inventory (TCI) measured the self-regulatory components of personality critical for health (i.e., the character traits of self-directedness, cooperativeness, and self-transcendence). In a discovery sample of 2149 healthy Finns, we identified sets of single-nucleotide polymorphisms (SNPs) that cluster within particular individuals (i.e., SNP sets) regardless of phenotype. Second, we identified five clusters of people with distinct profiles of character traits regardless of genotype. Third, we found 42 SNP sets that identified 727 gene loci and were significantly associated with one or more of the character profiles. Each character profile was related to different SNP sets with distinct molecular processes and neuronal functions. Environmental influences measured in childhood and adulthood had small but significant effects. We confirmed the replicability of 95% of the 42 SNP sets in healthy Korean and German samples, as well as their associations with character. The identified SNPs explained nearly all the heritability expected for character in each sample (50 to 58%). We conclude that self-regulatory personality traits are strongly influenced by organized interactions among more than 700 genes despite variable cultures and environments. These gene sets modulate specific molecular processes in brain for intentional goal-setting, self-reflection, empathy, and episodic learning and memory.

Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data

Abstract: We present a probabilistic framework for studying adversarial attacks on discrete data. Based on this framework, we derive a perturbation-based method, Greedy Attack, and a scalable learning-based method, Gumbel Attack, that illustrate various tradeoffs in the design of attacks. We demonstrate the effectiveness of these methods using both quantitative metrics and human evaluation on various state-of-the-art models for text classification, including a word-based CNN, a character-based CNN and an LSTM. As as example of our results, we show that the accuracy of character-based convolutional networks drops to the level of random selection by modifying only five characters through Greedy Attack.

Six Functions of Character Strengths for Thriving at Times of Adversity and Opportunity: a Theoretical Perspective

Abstract: Life is a collection of moments, some light and pleasant, some dark and unpleasant, some neutral. Character strengths contribute to the full range of human experiences, influencing and creating positive opportunities while also helping us to endure the mundane and embrace and navigate the struggles. Some researchers have argued that thriving, which casts a wider net on the human experience than constructs such as flourishing or resilience, constitutes strong well-being and performance at times of both adversity and opportunity (Brown et al. 2017). With this and the many findings in the science of character in mind, six character strengths functions are theorized and then applied across time orientations, making the case for the integral role of character strengths in these matters of thriving. Three opportunity functions are offered, including priming in which character strengths prompt and prepare for strengths awareness and use; mindfulness in which character strengths serve in synergy with mindful awareness of the present reality; and appreciation in which character strengths use expresses value for what has occurred. The three adversity functions include: buffering – character strengths use prevents problems; reappraisal – character strengths explain or reinterpret problems; and resilience – character strengths support the bounce-back from life setbacks. Several applications of these six functions for vocational and educational settings are explored.

Development of Interactive Multimedia Learning Courseware to Strengthen Students' Character.

Abstract: The development of information technology rapidly has an impact on the changing paradigm of education. On the other hand, education holds an important responsibility to create students who have a good and strong character. This research aims to: 1) describe the concept and framework of interactive multimedia courseware; 2) test the feasibility of interactive multimedia courseware by experts and practitioners; 3) test student responses to the use of interactive multimedia courseware. This research and development involved experts, teachers, and students. The data were collected using expert validation sheets, teacher questionnaires, and student questionnaires. They were then analyzed using the descriptive statistics analysis based on mean and percentage. This research yielded interactive multimedia courseware called IMONEC (Interactive Multimedia courseware integrated with Bandura’s Observational learning model and National historical Event to strengthen students' Character) that integrates three important components: the principles of interactive multimedia learning; Bandura's observational learning model; and the noble values and messages of national historical events to strengthen students' characters. The framework of the interactive multimedia courseware consists of the title, user instruction, home, core competency and basic competency, concept map of material, learning material, and quizzes. The results of the expert validation, teacher questionnaire, and student questionnaire showed that the interactive multimedia courseware is feasible for use in learning and effective in strengthening students’ characters.

Character Region Attention for Text Spotting

Abstract: A scene text spotter is composed of text detection and recognition modules. Many studies have been conducted to unify these modules into an end-to-end trainable model to achieve better performance. A typical architecture places detection and recognition modules into separate branches, and a RoI pooling is commonly used to let the branches share a visual feature. However, there still exists a chance of establishing a more complimentary connection between the modules when adopting recognizer that uses attention-based decoder and detector that represents spatial information of the character regions. This is possible since the two modules share a common sub-task which is to find the location of the character regions. Based on the insight, we construct a tightly coupled single pipeline model. This architecture is formed by utilizing detection outputs in the recognizer and propagating the recognition loss through the detection stage. The use of character score map helps the recognizer attend better to the character center points, and the recognition loss propagation to the detector module enhances the localization of the character regions. Also, a strengthened sharing stage allows feature rectification and boundary localization of arbitrary-shaped text regions. Extensive experiments demonstrate state-of-the-art performance in publicly available straight and curved benchmark dataset.

Character Strengths – Stability, Change, and Relationships with Well-Being Changes

Abstract: Character strengths are positively valued personality traits that are assumed to be stable across time and situations, but also malleable due to cultivation or deliberate intervention. Also, studies showed that character strengths are robustly related to well-being. Consequently, character strengths have often been used in interventions aimed at increasing well-being. However, the stability of character strengths and the longitudinal relationships with well-being are widely unexplored: First, previous reports on the stability of character strengths have mainly focused on one assessment instrument only and second, they did not consider other indicators of stability (and malleability) besides rank-order stability, (i.e., mean-level stability). In this longitudinal study, we assessed character strengths and well-being at two time points and examined the stability and malleability of character strengths and the convergence of changes in character strengths and well-being by means of correlation analyses. Two samples (n1 = 601, n2 = 1162) completed different measures of character strengths and instruments for the assessment of well-being, ill-being, and health within up to three and a half years. Results showed that character strengths are stable over longer time periods (test-retest reliabilities ranging from rtt = .60–.83) and that relationships between changes in strengths and well-being are highly parallel to what has been reported in cross-sectional studies (strongest relationships for zest, hope, curiosity, and love). Furthermore, results suggest that some strengths, most predominantly humor, but also spirituality and prudence might be more amenable for change than others. These results might bear important information for selecting character strengths in interventions.

Neural Graph Matching Networks for Chinese Short Text Matching

Abstract: Chinese short text matching usually employs word sequences rather than character sequences to get better performance. However, Chinese word segmentation can be erroneous, ambiguous or inconsistent, which consequently hurts the final matching performance. To address this problem, we propose neural graph matching networks, a novel sentence matching framework capable of dealing with multi-granular input information. Instead of a character sequence or a single word sequence, paired word lattices formed from multiple word segmentation hypotheses are used as input and the model learns a graph representation according to an attentive graph matching mechanism. Experiments on two Chinese datasets show that our models outperform the state-of-the-art short text matching models.

Cyberbullying detection in social media text based on character‐level convolutional neural network with shortcuts

Abstract: As people spend increasingly more time on social networks, cyberbullying has become a social problem that needs to be solved by machine learning methods. Our research focuses on textual cyberbullying detection because text is the most common form of social media. However, the content information in social media is short, noisy, and unstructured with incorrect spellings and symbols, and this impacts the performance of some traditional machine learning methods based on vocabulary knowledge. For this reason, we propose a Char‐CNNS (Character‐level Convolutional Neural Network with Shortcuts) model to identify whether the text in social media contains cyberbullying. We use characters as the smallest unit of learning, enabling the model to overcome spelling errors and intentional obfuscation in real‐world corpora. Shortcuts are utilized to stitch different levels of features to learn more granular bullying signals, and a focal loss function is adopted to overcome the class imbalance problem. We also provide a new Chinese Weibo comment dataset specifically for cyberbullying detection, and experiments are performed on both the Chinese Weibo dataset and the English Tweet dataset. The experimental results show that our approach is competitive with state‐of‐the‐art techniques on cyberbullying detection task.

A Character-Centric Neural Model for Automated Story Generation

Abstract: Automated story generation is a challenging task which aims to automatically generate convincing stories composed of successive plots correlated with consistent characters. Most recent generation models are built upon advanced neural networks, e.g., variational autoencoder, generative adversarial network, convolutional sequence to sequence model. Although these models have achieved prompting results on learning linguistic patterns, very few methods consider the attributes and prior knowledge of the story genre, especially from the perspectives of explainability and consistency. To fill this gap, we propose a character-centric neural storytelling model, where a story is created encircling the given character, i.e., each part of a story is conditioned on a given character and corresponded context environment. In this way, we explicitly capture the character information and the relations between plots and characters to improve explainability and consistency. Experimental results on open dataset indicate that our model yields meaningful improvements over several strong baselines on both human and automatic evaluations.

Bangla handwritten character recognition using MobileNet V1 architecture

Abstract: Handwritten character recognition is a very tough task in case of complex shaped alphabet set like Bangla script. As optical character recognition (OCR) has a huge application in mobile devices, model needs to be suitable for mobile applications. Many researches have been performed in this arena but none of them achieved satisfactory accuracy or could not detect more than 200 characters. MobileNet is a state of art (convolutional neural network) CNN architecture which is designed for mobile devices as it requires less computing power. In this paper, we used MobileNet for handwritten character recognition. It has achieved 96.46% accuracy in recognizing 231 classes (171 compound, 50 basic and 10 numerals), 96.17% accuracy in 171 compound character classes, 98.37% accuracy in 50 basic character classes and 99.56% accuracy in 10 numeral character classes.

An Isolable Tetrasilicon Analogue of a Planar Bicyclo[1.1.0]butane with π-Type Single-Bonding Character.

Abstract: Isolable compounds that contain a π-bonding without an underlying σ-bond framework (π-type single bonding) remain extremely scarce. Herein, we report the synthesis and properties of 1,3-diiodotetra...

Numerical Simulation of Mountain Waves over the Southern Andes. Part I: Mountain Wave and Secondary Wave Character, Evolutions, and Breaking

Abstract: This paper addresses the compressible nonlinear dynamics accompanying increasing mountain wave (MW) forcing over the southern Andes and propagation into the mesosphere and lower thermosphere (MLT) under winter conditions. A stretched grid provides very high resolution of the MW dynamics in a large computational domain. A slow increase of cross-mountain winds enables MWs to initially break in the mesosphere and extend to lower and higher altitudes thereafter. MW structure and breaking is strongly modulated by static mean and semidiurnal tide fields exhibiting a critical level at ~114 km for zonal MW propagation. Varying vertical group velocities for different zonal wavelengths λx yield initial breaking in the lee of the major Andes peaks for λx ~ 50 km, and extending significantly upstream for larger λx approaching the critical level at later times. The localized extent of the Andes terrain in latitude leads to “ship wave” responses above the individual peaks at earlier times, and a much larger ship-wave response at 100 km and above as the larger-scale MWs achieve large amplitudes. Other responses above regions of MW breaking include large-scale secondary gravity waves and acoustic waves that achieve very large amplitudes extending well into the thermosphere. MW breaking also causes momentum deposition that yields local decelerations initially, which merge and extend horizontally thereafter and persist throughout the event. Companion papers examine the associated momentum fluxes, mean-flow evolution, gravity wave–tidal interactions, and the MW instability dynamics and sources of secondary gravity waves and acoustic waves.

Critiquing – and Rescuing – ‘Character’:

Abstract: The article looks at how sociology might regard the concept of ‘character’, both in terms of the way it is used in public discourse and in its own accounts of social life. In the former, the concep...

A Bayesian Approach for Inferring the Impact of a Discrete Character on Rates of Continuous-Character Evolution in the Presence of Background-Rate Variation.

Abstract: Understanding how and why rates of character evolution vary across the Tree of Life is central to many evolutionary questions; for example, does the trophic apparatus (a set of continuous characters) evolve at a higher rate in fish lineages that dwell in reef versus nonreef habitats (a discrete character)? Existing approaches for inferring the relationship between a discrete character and rates of continuous-character evolution rely on comparing a null model (in which rates of continuous-character evolution are constant across lineages) to an alternative model (in which rates of continuous-character evolution depend on the state of the discrete character under consideration). However, these approaches are susceptible to a "straw-man" effect: the influence of the discrete character is inflated because the null model is extremely unrealistic. Here, we describe MuSSCRat, a Bayesian approach for inferring the impact of a discrete trait on rates of continuous-character evolution in the presence of alternative sources of rate variation ("background-rate variation"). We demonstrate by simulation that our method is able to reliably infer the degree of state-dependent rate variation, and show that ignoring background-rate variation leads to biased inferences regarding the degree of state-dependent rate variation in grunts (the fish group Haemulidae). [Bayesian phylogenetic comparative methods; continuous-character evolution; data augmentation; discrete-character evolution.].

Young Children's Mathematical Learning From Intelligent Characters.

Abstract: Children's math learning (N = 217; Mage = 4.87 years; 63% European American, 96% college-educated families) from an intelligent character game was examined via social meaningfulness (parasocial relationships [PSRs]) and social contingency (parasocial interactions, e.g., math talk). In three studies (data collected in the DC area: 12/2015-10/2017), children's parasocial relationships and math talk with the intelligent character predicted quicker, more accurate math responses during virtual game play. Children performed better on a math transfer task with physical objects when exposed to an embodied character (Study 2), and when the character used socially contingent replies, which was mediated by math talk (Study 3). Results suggest that children's parasocial relationships and parasocial interactions with intelligent characters provide new frontiers for 21st century learning.

ARAnimator: in-situ character animation in mobile AR with user-defined motion gestures

Abstract: Creating animated virtual AR characters closely interacting with real environments is interesting but difficult. Existing systems adopt video see-through approaches to indirectly control a virtual character in mobile AR, making close interaction with real environments not intuitive. In this work we use an AR-enabled mobile device to directly control the position and motion of a virtual character situated in a real environment. We conduct two guessability studies to elicit user-defined motions of a virtual character interacting with real environments, and a set of user-defined motion gestures describing specific character motions. We found that an SVM-based learning approach achieves reasonably high accuracy for gesture classification from the motion data of a mobile device. We present ARAnimator, which allows novice and casual animation users to directly represent a virtual character by an AR-enabled mobile phone and control its animation in AR scenes using motion gestures of the device, followed by animation preview and interactive editing through a video see-through interface. Our experimental results show that with ARAnimator, users are able to easily create in-situ character animations closely interacting with different real environments.

On the nature and origins of cognition as a form of motivated activity

Abstract: A fundamental challenge for enactive theory and other radical varieties of non-representational “E cognition” is to reconceive the end-directed character of cognitive activity in naturally emergent...

Cursive Character Recognition in Natural Scene Images Using a Multilevel Convolutional Neural Network Fusion

Abstract: The accuracy of current natural scene text recognition algorithms is limited by the poor performance of character recognition methods for these images. The complex backgrounds, variations in the writing, text size, orientations, low resolution and multi-language text make recognition of text in natural images a complex and challenging task. Conventional machine learning and deep learning-based methods have been developed that have achieved satisfactory results, but character recognition for cursive text such as Arabic and Urdu scripts in natural images is still an open research problem. The characters in the cursive text are connected and are difficult to segment for recognition. Variations in the shape of a character due to its different positions within a word make the recognition task more challenging than non-cursive text. Optical character recognition (OCR) techniques proposed for Arabic and Urdu scanned documents perform very poorly when applied to character recognition in natural images. In this paper, we propose a multi-scale feature aggregation (MSFA) and a multi-level feature fusion (MLFF) network architecture to recognize isolated Urdu characters in natural images. The network first aggregates multi-scale features of the convolutional layers by up-sampling and addition operations and then combines them with the high-level features. Finally, the outputs of the MSFA and MLFF networks are fused together to create more robust and powerful features. A comprehensive dataset of segmented Urdu characters is developed for the evaluation of the proposed network models. Synthetic text on the patches of images with real natural scene backgrounds is generated to increase the samples of infrequently used characters. The proposed model is evaluated on the Chars74K and ICDAR03 datasets. To validate the proposed model on the new Urdu character image dataset, we compare its performance with the histogram of oriented gradients (HoG) method. The experimental results show that the aggregation of multi-scale and multilevel features and their fusion is more effective, and outperforms other methods on the Urdu character image and Chars74K datasets.

Character strengths-based interventions: Open questions and ideas for future research

Abstract: Numerous studies have confirmed the effectiveness of character strengths-based interventions for fostering well-being However, there are still several open questions The present article discusses

Pose2Pose: pose selection and transfer for 2D character animation

Abstract: An artist faces two challenges when creating a 2D animated character to mimic a specific human performance. First, the artist must design and draw a collection of artwork depicting portions of the character in a suitable set of poses, for example arm and hand poses that can be selected and combined to express the range of gestures typical for that person. Next, to depict a specific performance, the artist must select and position the appropriate set of artwork at each moment of the animation. This paper presents a system that addresses these challenges by leveraging video of the target human performer. Our system tracks arm and hand poses in an example video of the target. The UI displays clusters of these poses to help artists select representative poses that capture the actor's style and personality. From this mapping of pose data to character artwork, our system can generate an animation from a new performance video. It relies on a dynamic programming algorithm to optimize for smooth animations that match the poses found in the video. Artists used our system to create four 2D characters and were pleased with the final automatically animated results. We also describe additional applications addressing audio-driven or text-based animations.

Smith-Treumann theory and the linkage principle

Abstract: In this paper we apply Treumann's "Smith theory for sheaves" in the context of the Iwahori-Whittaker model of the Satake category. We deduce two results in the representation theory of reductive algebraic groups over fields of positive characteristic: (a) a geometric proof of the linkage principle; (b) a character formula for tilting modules in terms of the p-canonical basis, valid in all blocks and in all characteristics.

Editorial – Towards FAIR and SQUARE hydrological data

Abstract: Data issues were, are, and will remain a core component of the hydrological sciences. Their character and influence on the way the discipline is practiced may vary through time, but their intrinsic...

Acting Oneself as Another: An Actor’s Empathy for her Character

Abstract: What does it mean for an actor to empathize with the character she is playing? We review different theories of empathy and of acting. We then consider the notion of “twofoldness” (Wollheim), which has been used to characterize the observer or audience perspective on the relation between actor and character (Smith). This same kind of twofoldness or double attunement applies from the perspective of the actor herself who must, at certain points of preparation, distinguish between the character portrayed and her own portrayal effected in her craft. We argue that this concept helps us to understand how the actor can empathize with her character. For the actor who must study and rehearse her character, empathy may begin with higher-order (narrative or imaginative) processes that provide a contextualized understanding of the character. This understanding eventually integrates with more basic empathic processes in her actual performance.

First do no harm.

Abstract: Every social order creates those character forms which it needs for its own preservation … The character structure … is the crystallization of the sociological process of a given epoch. (Wilhelm R...