Miracle-dz/UniFAR-FTUs huggingface dataset:
Biomimetic Eye-Neck Coordination

We describe a method for coordinating eye and neck motions in the control of a humanoid robotic head. Based on the characteristics of human oculomotor behavior, we formulate the target tracking problem in a state-space control framework and show that suitable controller gains can be either manually tuned with optimal control techniques or learned from bio-mechanical data recorded from newborns subjects. The basic controller relies on eye-neck proprioceptive feedback. In biological systems, vestibular signals and target prediction compensate for external motions and allow target tracking with low lag. We provide ways to integrate inertial and prediction signals in the basic control architecture, whenever these are available. We demonstrate the ability of the method in replicating the behavior of subjects with different ages and show results obtained through a real-time implementation in a humanoid platform.
Learning motion controllers with adaptive depth perception

We present a novel approach to real-time character animation that allows a character to move autonomously based on vision input. By allowing the character to "see" the environment directly using depth perception, we can skip the manual design phase of parameterizing the state space in a reinforcement learning framework. In previous work, this is done manually since finding a minimal set of parameters for describing a character's environment is crucial for efficient learning. Learning from raw vision input, however, suffers from the "curse of dimensionality", which we avoid by introducing a hierarchical state model and a novel regression algorithm. We demonstrate that our controllers allow a character to navigate and survive in environments containing arbitrarily shaped obstacles, which is hard to achieve with existing reinforcement learning frameworks.
The psychometric properties of the Quality of Life Inventory in an Australian community sample

Abstract The Quality of Life Inventory (QOLI, Frisch) is an importance‐weighted measure of life satisfaction that has been found to possess adequate psychometric properties in US and clinically‐based samples. The current study aimed to investigate the normative distribution, factor structure and key psychometric properties of the QOLI in an Australian general community sample. Results indicated that the Australian sample (n = 259) reported slightly better quality of life than the US‐based standardisation sample. Confirmatory factor analyses showed that a three‐factor model provided the best fit for the data. Psychometric properties were all generally found to be adequate and close to Frisch's reported levels. In conclusion, this study indicated that the QOLI may be used in the Australian context with confidence. Future research could examine the clinical utility of the three‐factor model.
An energy-efficient deep learning processor with heterogeneous multi-core architecture for convolutional neural networks and recurrent neural networks

An energy-efficient deep learning processor is proposed for convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in mobile platforms. The 16mm2 chip is fabricated using 65nm technology with 3 key features, 1) Reconfigurable heterogeneous architecture to support both CNNs and RNNs, 2) LUT-based reconfigurable multiplier optimized for dynamic fixed-point with the on-line adaptation, 3) Quantization table-based matrix multiplication to reduce off-chip memory access and remove duplicated multiplications. As a result, compared to the [2] and [3], this work shows 20× and 4.5× higher energy efficiency, respectively. Also, DNPU shows 6.5× higher energy efficiency compared to the [5].
Energy Efficient Fractional Coverage Schemes for Low Cost Wireless Sensor Networks

An effective approach for energy conservation in wireless sensor networks is scheduling sleep intervals for extraneous nodes, while the remaining nodes stay active to provide continuous service. Depending on different types of applications, the network lifetime may be much more critical than covering the entire monitored area at every data reporting round. This paper presents a competition based distributed scheme called FCS to address the fractional coverage problem in wireless sensor networks with tiny, lowcost sensors. Through localized, energy-aware competition, the proposed scheme achieves the desired fractional coverage with a minimum number of active sensors. By taking account of both residual battery energy and recent reporting latency, an enhanced version of FCS which uses a novel competition metric to constrain the maximum reporting latency throughout the network is also proposed. These two schemes also contain the desirable property that it can be extended easily to handle the more general k-coverage problem.
Reputation-Based Resource Allocation in P2P Systems: A Game Theoretic Perspective

Free-riding is a severe problem in peer-to-peer systems. It can be controlled by adopting an effective resource allocation mechanism. This paper game theoretically analyzes a resource allocation mechanism, through two non-cooperative games viz., demand game and reputation game. This mechanism ensures that demands made by peers are commensurate to their shared capacity. We show that Nash equilibrium behavior of agents in our model is cooperative, which leads to fair and efficient allocation of network resource. Our findings show that through this mechanism, the system reaches to equilibrium where peers interact in groups formed on the basis of their capacities.
Cryptic speciation in allotetraploids: Lessons from the Botrychium matricariifolium complex.

PREMISE OF THE STUDY Cryptic species are a challenge for botanists and taxonomists. To improve species delineation in the genus Botrychium (Ophioglossaceae), which includes multiple instances of allotetraploid speciation, we examined a cryptic species complex using genetics and morphology. METHODS We sampled species in the B. matricariifolium complex, concentrating on the Upper Peninsula of Michigan and including multiple proposed morphospecies. We analyzed over 1500 samples using 10 enzyme systems, measured 42 quantitative and qualitative morphological characters for over 650 individuals, and analyzed 145 samples using AFLPs. We tested for diagnostic enzymes in the morphospecies and calculated the correlation between morphological and genetic distances to determine whether putatively distinct morphotypes warrant taxonomic recognition. KEY RESULTS Allozyme allelic variation corresponded loosely to some morphotypes of B. matricariifolium, but with lower genetic distinction among them than found between B. matricariifolium and B. michiganense. Botrychium michiganense contains unique alleles, indicating a different hybrid origin from that of B. matricariifolium and supporting its status as a genetically distinct species. CONCLUSIONS We showed that B. acuminatum morphology and genetics are accommodated taxonomically within B. matricariifolium; B. matricariifolium and B. michiganense likely represent hybridization events between related species; and morphotypes within B. matricariifolium likely represent repeated hybridization events between the same two parental species. These hybridizations have resulted in the array of morphotypes observed by field botanists. By helping to identify diagnostic morphological characters, genetic analyses also help us understand and resolve morphological variation observed in the field.
Extended criteria donors in liver transplant candidates with hepatorenal syndrome

Briceño J, Ciria R, de la Mata M, Montero JL, Rufián S, López‐Cillero P. Extended criteria donors in liver transplant candidates with hepatorenal syndrome. Clin Transplant 2011: 25: E257–E263. © 2011 John Wiley & Sons A/S.
Nitric Oxide and Protein S-Nitrosylation Are Integral to Hydrogen Peroxide-Induced Leaf Cell Death in Rice 1 [ W ] [ OA ]

State Key Laboratory of Plant Genomics and National Center for Plant Gene Research (Beijing), Institute of Genetics and Developmental Biology (A.L., Y.W., J.T., C.L., L.L., B.H., C.C.), and Proteomics Platform, Institute of Biophysics (P.X., F.Y.), Chinese Academy of Sciences, Beijing 100101, China; Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, United Kingdom (G.J.L.); and Graduate School of the Chinese Academy of Sciences, Beijing 100049, China (A.L., L.L., B.H.)
The Actor-Observer Effect and Perceptions of Agency: The Options of Obedience and Pro-social Behavior

The Actor-Observer Effect and Perceptions of Agency: The Options of Obedience and Pro-Social Behavior Samuel D. Downs Department of Psychology, BYU Doctor of Philosophy The actor-observer effect suggests that actors attribute to the situation while observers attribute to the actor’s disposition. This effect has come under scrutiny because of an alternative perspective that accounts for anomalous finding. This alternative, called the contextual perspective, suggests that actors and observers foreground different aspects of the context because of a relationship with the context, and has roots in Gestalt psychology and phenomenology. I manipulated a researcher’s prompt and the presence of a distressed confederate as the context for attributions, and hypothesized that actors and observers would differ on attributions to choice, situation, and disposition because of presence of a distressed confederate. Actors were presented with either a distressed or non-distressed confederate and either a prompt to leave, a prompt to stay, or no prompt. For example, some actors experienced a distressed confederate and were asked to leave while others experienced a non-distressed confederate and were asked to stay. Actors then made a decision to either stay and help the confederate or leave. Observers watched one of ten videos, each of one actor condition in which the actor either stayed or left (five actor conditions by 2 options of stay or leave). Actors’ and observers’ choice, situational, and dispositional attributions were analyzed using factorial MANOVAs. Actors and observers foregrounded the distressed confederate when making attributions to choice, situation, and disposition. Furthermore, observers’ attributions to choice were also influenced by the actor’s behavior. These findings support the contextual perspective since context does influence actors’ and observers’ attributions.
An object-oriented approach to formal specification

Formal methods for software development are becoming increasingly necessary as software becomes an important part of everyday life. To handle the complexities inherent in largescale software systems these methods need to be combined with a sound development methodology which supports modularity and reusability. Object orientation, based on the concept that systems are composed of collections of interacting objects whose behaviours are speci ed by classes, is such a methodology. This thesis presents the formal speci cation language Object-Z which is an extension of the formal speci cation language Z to facilitate speci cation in an object-oriented style. The major extension in Object-Z is the introduction of the class schema which captures the object-oriented notion of a class by encapsulating a single state schema with all the operation schemas which may a ect its variables. The class schema is not simply a syntactic extension but also de nes a type whose instances are objects. Object-Z also supports single and multiple inheritance allowing classes to be reused in the de nition of other classes and polymorphism allowing a variable to be assigned to objects of more than one class. The thesis also presents a set-theoretic model of classes in Object-Z which could form the basis of a full formal semantics. The model, based on the histories of a class, i.e. the sequences of states and operations which an object of the class can undergo, facilitates the speci cation of liveness properties using a temporal logic notation. A fully-abstract model of classes in Object-Z, derived from the history model, is also presented. This model is used to formally de ne a notion of behavioural compatibility in Object-Z which could form the basis of a theory of class re nement. Acknowledgements I would like to express my sincere thanks to my supervisor, Dr Roger Duke, for his comments and guidance as the ideas in this thesis evolved, and for his unremitting encouragement and help when they weren't evolving so well. I would also like to thank the following people without whose contributions to the development of Object-Z, this thesis would not have been possible { Gordon Rose, Paul King, David Duke and David Carrington. Thanks go also to Ian Hayes, Cecily Bailes and Anthony Lee for their invaluable comments and suggestions. I would like to acknowledge the generous nancial support provided by the Overseas Telecommunications Corporation (OTC) of Australia and the Commonwealth Government of Australia in the form of postgraduate awards. I would also like to acknowledge the Department of Computer Science for support in the form of a research scholarship and tutorship, and for nancing attendance at several conferences both in Australia and overseas. Finally, I would like to acknowledge the support of my family and friends. To my parents, I express my love and gratitude for their encouragement and nancial support in my earlier years of study. I also, especially, thank Kim for her continuing love, patience and understanding.
Keyword-detection approach to automatic image annotation

In this paper we consider the problem of automatically annotating images with keywords. We first discuss performance measures for the problem in some length. We propose a new information-theory based measure de-symmetrised mutual information (DTMI). We then describe a straightforward solution to the annotation problem. We first train a set of classifiers to detect the presence of each individual keyword in the set of training images. For this we use the PicSOM image analysis framework. We then describe a method of converting the classifier outputs back into keyword annotations for the test set. We compare the performance of the proposed method experimentally to that of other methods presented in the literature. For the experiments we use data from the Corel database. The result of the comparison is favourable to the proposed method.
Detecting Communities of Interests in Social Media Platforms using Genetic Algorithms

individuals that use them. The bulk of research in community detection is directed at network analysis of individuals and their interaction with other members within the network. However, connections outside the network can also be useful for community detection, as in the following of elite Twitter users by regular users. This research develops a mechanism for clustering elite Twitter users on the basis of connections and interactions within their followers. Since clustering is sensitive to initial configurations, the approach is modified using genetic algorithms to traverse multiple regions of the solution space. Application of this approach to a set of 25,000 Twitter users demonstrates that it forms coherent communities within a few iterations, outperforming other clustering approaches for community detection.
Syllable-level Neural Language Model for Agglutinative Language

We introduce a novel method to diminish the problem of out of vocabulary words by introducing an embedding method which leverages the agglutinative property of language. We propose additional embedding derived from syllables and morphemes for the words to improve the performance of language model. We apply the above method to input prediction tasks and achieve state of the art performance in terms of Key Stroke Saving (KSS) w.r. t. to existing device input prediction methods.
Community: Culture and Function in the Seventh-day Adventist Church Community

Thi s study looks specifically a t the purpos e and function of religious communities in order to analyze the Seventh-day Adventist community as a system and communitarian organism that creates and sustains moral codes and social controls, which thereby is symbiotic with certain identity expressions/formations. My discussion takes place in a rapidly changing broader society of high mobility, internet connectivity, and cultural change. Within the larger global community exists an American community, and within, but also overreaching that community exists a Seventh-day Adventist community that is global. These communities interact with each other, crossing membership boundaries. Within the Seventh-day Adventist world-church, smaller subgroups emerge with unique social
Direction-of-arrival estimation via Khatri-Rao subspace using compressed sensing

Direction-of-arrival (DOA) estimation via Khatri-Rao (KR) subspace with multiple signal classification (MUSIC) algorithm can cope with a higher number of incoming waves than that of sensors, while it requires the signals to be quasi-stationary and needs a larger number of “frames” than that of incoming waves. On the other hand, a hybrid approach of compressed sensing and MUSIC algorithm can estimate DOAs with snapshots less than the number of sources for noiseless observation, although the number of incoming waves must be less than that of sensors. Exploiting the fact that the frame in MUSIC via KR subspace corresponds to the snapshot in conventional MUSIC without observation noise, we propose a DOA estimation scheme using KR product array processing and compressed sensing, which can cope with a greater number of incoming waves than both that of sensors and that of frames. The validity of the proposed method is shown via numerical experiments.
Detecting the Earliest Galaxies through Two New Sources of 21 Centimeter Fluctuations

The first galaxies that formed at a redshift z ~ 20-30 emitted continuum photons with energies between the Lyα and Lyman limit wavelengths of hydrogen, to which the neutral universe was transparent except at the Lyman series resonances. As these photons redshifted or scattered into the Lyα resonance, they coupled the spin temperature of the 21 cm transition of hydrogen to the gas temperature, allowing it to deviate from the microwave background temperature. We show that the fluctuations in the radiation emitted by the first galaxies produced strong fluctuations in the 21 cm flux before the Lyα coupling became saturated. The fluctuations were caused by biased inhomogeneities in the density of galaxies, along with Poisson fluctuations in the number of galaxies. Observing the power spectra of these two sources would probe the number density of the earliest galaxies and the typical mass of their host dark matter halos. The enhanced amplitude of the 21 cm fluctuations from the era of Lyα coupling improves considerably the practical prospects for their detection.
Combating Fraud in Online Social Networks: Detecting Stealthy Facebook Like Farms

As businesses increasingly rely on social networking sites to engage with their customers, it is crucial to understand and counter reputation manipulation activities, including fraudulently boosting the number of Facebook page likes using like farms. To this end, several fraud detection algorithms have been proposed and some deployed by Facebook that use graph co-clustering to distinguish between genuine likes and those generated by farm-controlled profiles. However, as we show in this paper, these tools do not work well with stealthy farms whose users spread likes over longer timespans and like popular pages, aiming to mimic regular users. We present an empirical analysis of the graph-based detection tools used by Facebook and highlight their shortcomings against more sophisticated farms. Next, we focus on characterizing content generated by social networks accounts on their timelines, as an indicator of genuine versus fake social activity. We analyze a wide range of features extracted from timeline posts, which we group into two main classes: lexical and non-lexical. We postulate and verify that like farm accounts tend to often re-share content, use fewer words and poorer vocabulary, and more often generate duplicate comments and likes compared to normal users. We extract relevant lexical and non-lexical features and and use them to build a classifier to detect like farms accounts, achieving significantly higher accuracy, namely, at least 99% precision and 93% recall.
Single-Molecule Analysis Reveals the Kinetics and Physiological Relevance of MutL-ssDNA Binding

DNA binding by MutL homologs (MLH/PMS) during mismatch repair (MMR) has been considered based on biochemical and genetic studies. Bulk studies with MutL and its yeast homologs Mlh1-Pms1 have suggested an integral role for a single-stranded DNA (ssDNA) binding activity during MMR. We have developed single-molecule Förster resonance energy transfer (smFRET) and a single-molecule DNA flow-extension assays to examine MutL interaction with ssDNA in real time. The smFRET assay allowed us to observe MutL-ssDNA association and dissociation. We determined that MutL-ssDNA binding required ATP and was the greatest at ionic strength below 25 mM (KD = 29 nM) while it dramatically decreases above 100 mM (KD>2 µM). Single-molecule DNA flow-extension analysis suggests that multiple MutL proteins may bind ssDNA at low ionic strength but this activity does not enhance stability at elevated ionic strengths. These studies are consistent with the conclusion that a stable MutL-ssDNA interaction is unlikely to occur at physiological salt eliminating a number of MMR models. However, the activity may infer some related dynamic DNA transaction process during MMR.
A model of local‐minima distribution on conformational space and its application to protein structure prediction

A quantitative two‐parameter model is developed to describe local energy minima distribution. On a conformational space measured by least‐square‐fitting root‐mean‐squared distance (RMSD), the number of local minima in a r RMSD region is proposed to be proportional to exp(−1/r). As part of the model derivations, the minimum RMSD of decoys from the largest cluster, the number of decoys in the largest cluster, and the RMSD distribution of the decoys have inner connections with each other. The model is successfully verified on a 49 helix‐packing decoy set and a 30 loop‐prediction decoy set, as well as both knowledge‐based potential (DFIRE) and physical force‐fields (OPLS and CHARMM). One of the model's applications is predicting behaviors of a large amount of decoys (e.g., minimum RMSD of 40,000 decoys) by generating only a small number of decoys (e.g., 500). It may be applied to structure predictions guided by any Lennard‐Jones‐like potential functions and can be extended to other sampling methods guided by simple energy terms. Proteins 2006. © 2006 Wiley‐Liss, Inc.
Computerized Interpretive Approach for the Exner Comprehensive Rorschach System.

This paper provides a description of an online Rorschach interpretation algorithm for the Exner cospretensive system, as well as a study conducted to evaluate the validity of the online interpretive algorithm. The user, systems, and equipment specifications for the algorithm are explained, and the potential advantages of its use to enhance clinical efficiency and clinician skill in decision making are discussed. The validity . )f the online interpretation was tested by asking two expert cliniciars tc compare the computerized output with the structural interpretation used in the text for 10 sets of Rorschach reports, The computer-generated narratives were found to be as useful as clinician reports in four of The six characteristics reported, and possible solutions to the problems with the remaining two dimensions are suggested,. Sixteen references are cited. (HER)