Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimistic Synchronization of Cooperative XML Authoring Using Tunable Transaction Boundaries

Design applications, e.g., CAD or media production, often require multiple users to work cooperatively on shared data, e.g., XML documents. Using explicit transactions in such environments is difficult, because designers usually do not want to consider transactions or ACID. However, applying transactions in order to control visibility of changes or specify recovery units, is reasonable, but determining transaction boundaries must be transparent for the designer. For this reason we propose a novel approach for the automatic determination of transaction boundaries which considers the degree of cooperation designers want to achieve. Furthermore, we present an optimistic synchronization model based on the traditional backward oriented concurrency control (BOCC) algorithm, in order to synchronize the determined transactions in multi-user environments. It exploits the semantics of tree operations on XML data and enforces a correctness criterion weaker than serializability. As our evaluation shows, when multiple users work cooperatively on shared data, this model significantly reduces the number of transaction aborts in comparison to the traditional BOCC approach.
The Role of Pitx2 in Maintaining the Phenotype of Myogenic Precursor Cells in the Extraocular Muscles

Many differences exist between extraocular muscles (EOM) and non-cranial skeletal muscles. One striking difference is the sparing of EOM in various muscular dystrophies compared to non-cranial skeletal muscles. EOM undergo continuous myonuclear remodeling in normal, uninjured adults, and distinct transcription factors are required for the early determination, development, and maintenance of EOM compared to limb skeletal muscle. Pitx2, a bicoid-like homeobox transcription factor, is required for the development of EOM and the maintenance of characteristic properties of the adult EOM phenotype, but is not required for the development of limb muscle. We hypothesize that these unique properties of EOM contribute to the constitutive differences between EOM and non-craniofacial skeletal muscles. Using flow cytometry, CD34+/Sca1−/CD45−/CD31− cells (EECD34 cells) were isolated from extraocular and limb skeletal muscle and in vitro, EOM EECD34 cells proliferated faster than limb muscle EECD34 cells. To further define these myogenic precursor cells from EOM and limb skeletal muscle, they were analyzed for their expression of Pitx2. Western blotting and immunohistochemical data demonstrated that EOM express higher levels of Pitx2 than limb muscle, and 80% of the EECD34 cells expressed Pitx2. siRNA knockdown of Pitx2 expression in EECD34 cells in vitro decreased proliferation rates and impaired the ability of EECD34 cells to fuse into multinucleated myotubes. High levels of Pitx2 were retained in dystrophic and aging mouse EOM and the EOM EECD34 cells compared to limb muscle. The differential expression of Pitx2 between EOM and limb skeletal muscle along with the functional changes in response to lower levels of Pitx2 expression in the myogenic precursor cells suggest a role for Pitx2 in the maintenance of constitutive differences between EOM and limb skeletal muscle that may contribute to the sparing of EOM in muscular dystrophies.
Scalable Near-Optimal Recursive Structure from Motion

Rao-BlackWellized particle filters have achieved a breakthrough in the scalability of filters used for Structure from Motion (SFM) and Simultaneous Localization and Mapping (SLAM). The new generations of these filters employ as proposal distribution the optimal \emph{importance function} i.e, the one taking into consideration not only the previous motion of the camera, but also the most recent measurement. However the way they sample from this importance function is not optimal since the locations of 3-d features are updated using a motion predicted only from the previous state. This results in a performance lower than the Extended Kalman Filters (EKF)s. We propose in this paper an approach that bears similarity with the Random Sample Consensus (RANSAC) paradigm and that enables us to sample more efficiently from the optimal importance function. It allows us to update the depth based on a motion updated using information from the most recent image and hence the updated samples would have a higher chance to be in regions corresponding to high posterior probability. This results in a performance equal to the performance of the EKF with much higher scalability. Also, our samples being generated and updated based on random sampling of the features, this provides an improved robustness to outliers.
Actuation requirements for hopping and running of the musculoskeletal robot BioBiped1

Actuation with variable elasticity is considered a key property for the realization of human-like bipedal locomotion. Also, an intelligent and self-stable mechanical system is indispensable. While much effort of current research has been devoted to the development of variable impedance joint actuators, this paper deals with the important question of how to determine the actuation requirements of a compliant, musculoskeletal robot that is targeted at fast dynamic motions. In a step-by-step approach, design decisions for the elastic humanoid robot BioBiped1 are presented. Using multibody system dynamics models and simulations, incorporating bidirectional series elastic actuator models and a realistic ground contact model, we analyze the actuation requirements of the employed electrical motors for computer generated hopping and human data based running motions. The numerical simulation results are accompanied by videos of the dynamics simulations. Recent experiments on the real hardware have indicated that the selected motor-gear units and elastic transmissions support the desired dynamic motion goals.
Semi-Supervised Learning for Blog Classification

Blog classification (e.g., identifying bloggers' gender or age) is one of the most interesting current problems in blog analysis. Although this problem is usually solved by applying supervised learning techniques, the large labeled dataset required for training is not always available. In contrast, unlabeled blogs can easily be collected from the web. Therefore, a semi-supervised learning method for blog classification, effectively using unlabeled data, is proposed. In this method, entries from the same blog are assumed to have the same characteristics. With this assumption, the proposed method captures the characteristics of each blog, such as writing style and topic, and uses these characteristics to improve the classification accuracy.
Least squares filters in canonical coordinates for transform coding, filtering, and quantizing

Canonical correlations are used to decompose the Wiener filter into a whitening transform coder, a canonical filter, and a coloring transform decoder. The outputs of the whitening transform coder are called canonical coordinates, and these are the coordinates that are reduced in rank and coded in a quantized version of the Gauss-Markov theorem. Canonical correlations produce new formulas for error covariance, spectral flatness, and entropy and lead to plausible definitions of angles between random vectors in a stochastic setting. Adaptive canonical coordinates suggest an approach to channel equalization.
Understanding Gentzen and Frege Systems for QBF

Recently Beyersdorff, Bonacina, and Chew [10] introduced a natural class of Frege systems for quantified Boolean formulas (QBF) and showed strong lower bounds for restricted versions of these systems. Here we provide a comprehensive analysis of the new extended Frege system from [10], denoted EF + ∀red, which is a natural extension of classical extended Frege EF.Our main results are the following: Firstly, we prove that the standard Gentzen-style system ${\text{G}}_1^{\ast}$ p-simulates EF + ∀red and that ${\text{G}}_1^{\ast}$ is strictly stronger under standard complexity-theoretic hardness assumptions.Secondly, we show a correspondence of EF + ∀red to bounded arithmetic: EF + ∀red can be seen as the non-uniform propositional version of intuitionistic $S_2^1$. Specifically, intuitionistic $S_2^1$ proofs of arbitrary statements in prenex form translate to polynomial-size EF + ∀red proofs, and EF + ∀red is in a sense the weakest system with this property.Finally, we show that unconditional lower bounds for EF + ∀red would imply either a major breakthrough in circuit complexity or in classical proof complexity, and in fact the converse implications hold as well. Therefore, the system EF + ∀red naturally unites the central problems from circuit and proof complexity.Technically, our results rest on a formalised strategy extraction theorem for EF + ∀red akin to witnessing in intuitionistic $S_2^1$ and a normal form for EF + ∀red proofs.
Correlations between Tobacco Smoking, Craving, and Cognition in Smokers with and without Schizophrenia

Background: There is a considerably higher prevalence of smoking among schizophrenia patients compared to the general population. Aim: To examine relationships amongst smoking reinforcement indices, cigarette craving, and cognition in patients with schizophrenia and non-psychiatric controls. Methodology: Smoking topography, craving, and cognition was assessed in daily cigarette smokers with (SCZ, n = 15) and without schizophrenia (CON, n = 16). The relationship between these variables was also explored. Results: Smoking behavior did not differ between groups. However, SCZ had higher levels of plasma nicotine and cotinine than CON. Further, relationships between depressive symptoms and nicotine dependence severity were observed in SCZ and not in CON. Conclusions: While topography and craving differences were not observed, SCZ showed biochemical verification of heavier smoking than CON. SCZ and CON also had different motivators for smoking. These findings may have implications for the development of novel smoking cessation strategies for those with schizophrenia.
The Galvactivator: A glove that senses and communicates skin conductivity

The galvactivator is a glove-like wearable device that senses the wearer's skin conductivity and maps its values to a bright LED display, making the skin conductivity level visible. Increases in skin conductivity tend to be good indicators of physiological arousal --causing the galvactivator display to glow brightly. The new form factor of this sensor frees the wearer from the traditional requirement of being tethered to a rack of equipment; thus, the device facilitates study of the skin conductivity response in everyday settings. We recently built and distributed over 1000 galvactivators to audience members at a daylong symposium. To explore the communication potential of this device, we collected and analyzed the aggregate brightness levels emitted by the devices using a video camera focused on the audience. We found that the brightness tended to be higher at the beginning of presentations and during interactive sessions, and lower during segments when a speaker spoke for long periods of time. We also collected anecdotes from participants about their interpersonal uses of the device. This paper describes the construction of the galvactivator, our experiments with the large audience, and several other potentially useful applications ranging from facilitation of conversation between two people, to new ways of aiding autistic children.
GutCyc: a Multi-Study Collection of Human Gut Microbiome Metabolic Models

Advances in high-throughput sequencing are reshaping how we perceive microbial communities inhabiting the human body, with implications for therapeutic interventions. Several large-scale datasets derived from hundreds of human microbiome samples sourced from multiple studies are now publicly available. However, idiosyncratic data processing methods between studies introduce systematic differences that confound comparative analyses. To overcome these challenges, we developed GUTCYC, a compendium of environmental pathway genome databases constructed from 418 assembled human microbiome datasets using METAPATHWAYS, enabling reproducible functional metagenomic annotation. We also generated metabolic network reconstructions for each metagenome using the PATHWAY TOOLS software, empowering researchers and clinicians interested in visualizing and interpreting metabolic pathways encoded by the human gut microbiome. For the first time, GUTCYC provides consistent annotations and metabolic pathway predictions, making possible comparative community analyses between health and disease states in inflammatory bowel disease, Crohn’s disease, and type 2 diabetes. GUTCYC data products are searchable online, or may be downloaded and explored locally using METAPATHWAYS and PATHWAY TOOLS.
Naturally Supervised Learning in Manipulable Technologies

The relationship between physiological systems and modern electromechanical technologies is fast becoming intimate with high degrees of complex interaction. It can be argued that muscular function, limb movements, and touch perception serve supervisory functions for movement control in motion and touch-based (e.g. manipulable) devices/interfaces and human-machine interfaces in general. To get at this hypothesis requires the use of novel techniques and analyses which demonstrate the multifaceted and regulatory role of adaptive physiological processes in these interactions. Neuromechanics is an approach that unifies the role of physiological function, motor performance, and environmental effects in determining human performance. A neuromechanical perspective will be used to explain the effect of environmental fluctuations on supervisory mechanisms, which leads to adaptive physiological responses. Three experiments are presented using two different types of virtual environment that allowed for selective switching between two sets of environmental forces. This switching was done in various ways to maximize the variety of results. Electromyography (EMG) and kinematic information contributed to the development of human performance-related measures. Both descriptive and specialized analyses were conducted: peak amplitude analysis, loop trace analysis, and the analysis of unmatched muscle power. Results presented here provide a window into performance under a range of conditions. These analyses also demonstrated myriad consequences for force-related fluctuations on dynamic physiological regulation. The findings presented here could be applied to the dynamic control of touch-based and movement-sensitive human-machine systems. In particular, the design of systems such as human-robotic systems, touch screen devices, and rehabilitative technologies could benefit from this research.
Entrepreneurial Inclination and Entrepreneurial Characteristics of University Students: With Special Reference to University of Kelaniya, Sri Lanka

The purpose of this research paper is to explore the entrepreneurship profile of students of University of Kelaniya and to investigate the relationship between entrepreneurial inclination and entrepreneurial traits. In this study, six traits were identified according to the past literature. Namely, need for achievement, locus of control, risk taking propensity, tolerance of ambiguity self-confidence and innovativeness. And it was tested that, how this relationship varies with moderator variable, namely, gender. Both primary and secondary data was used in the study. Data were collected from 240 undergraduates at the University of Kelaniya. Mainly mixed method approach was used to analyse data. The results revealed that need for achievement, locus of control, risk taking propensity, self-conﬁdence, and innovativeness positively affect entrepreneurial intention. But, tolerance of ambiguity did not positively affect entrepreneurial inclination. At the same time, they show that, the relationship between inclination and traits weaken by gender. These ﬁndings were supported by qualitative study also. This study was provided insight into entrepreneurship education, as to which entrepreneurial characteristics can be developed to raise good entrepreneurs. Educational systems need to be oriented to emphasize and value entrepreneurship in order to promote an enterprise culture. Methods to teach entrepreneurship should also be explored further. This study will help to fill the gap in the existing body of knowledge and provide new knowledge in to the Sri Lankan context.
Surface Modified Nanoporous Materials for Hydrogen Sensing

Nanoporous silicon and nanoporous ZnO were prepared by the electrochemical anodization of crystalline Si and Zn substrate. To passivate the defect states (arise due to the nano structure of the PS and ZnO thin film and introduce a barrier for the current conduction) and to improve the gas sensitivity the porous silicon and nano crystalline ZnO surfaces were modified by PdCl2 solution. The Pd modified sensors having Pd-Ag (26%)/PS/Si/Al (MIS) and Pd-Ag (26%)/ZnO/Zn (MIM) device structures were investigated in different hydrogen concentrations (0.01, 0.05, 0.1, 0.5 and 1.0%) regarding optimum biasing voltage and temperature. Both the sensor showed superior performance in terms of operating temperature, response magnitude, response time and recovery time.
After Ant: Complexity, Naming and Topology

What is a theory? Or, more broadly, what is a good way of addressing intellectual problems? This paper explores the tension central to the notion of an ‘actor’ - ‘network’ which is an intentionally oxymoronic term that combines—and elides the distinction between—structure and agency. It then notes that this tension has been lost as ‘actor-network’ has been converted into a smooth and consistent ‘theory’ that has been (too) simply and easily displaced, criticised or applied. It recalls another term important to the actor-network approach—that of translation— which is another term in tension, since (the play of words works best in the romance languages) to translate is to also betray (traductore, tradition). It is suggested that in social theory simplicity should not displace the complexities of tension. The chapter concludes by exploring a series of metaphors for grappling with tensions rather than wishing these away, and in particular considers the importance of topological complexity, and the notion of fractionality. Today we have naming of parts. Yesterday, We had daily cleaning. And tomorrow morning, We shall have what to do after firing. But today, Today we have naming of parts. Japonica Glistens like coral in all of the neighbouring gardens, And today we have naming of parts. ' (Henry Reed, Lessons of the War: 1)
Scheduling dependent real-time activities

A real-time application is typically composed of a number of cooperating activities that must execute within specific time intervals. Since there are usually more activities to be executed than there are processors on which to execute them, several activities must share a single processor. Necessarily, satisfying the activities' timing constraints is a prime concern in making the scheduling decisions for that processor. Unfortunately, the activities are not independent. Rather, they share data and devices, observe concurrency constraints on code execution, and send signals to one another. These interactions can be modeled as contention for shared resources that must be used by one activity at a time. An activity awaiting access to a resource currently held by another activity is said to depend on that activity, and a dependency relationship is said to exist between them. Dependency relationships may encompass both precedence constraints and resource conflicts. No algorithm solves the problem of scheduling activities with dynamic dependency relationships in a way that is suitable for all real-time systems. This thesis provides an algorithm, called scDASA, that is effective for scheduling the class of real-time systems known as supervisory control systems. Simulation experiments that account for the time required to make scheduling decisions demonstrate that scDASA provides equivalent or superior performance to other scheduling algorithms of interest under a wide range of conditions for parameterized, synthetic workloads. scDASA performs particularly well during overloads, when it is impossible to complete all of the activities. This research makes a number of contributions to the field of computer science, including: a formal model for analyzing scheduling algorithms; the scDASA scheduling algorithm, which integrates resource management with standard scheduling functions; results that demonstrate the efficacy of scDASA in a variety of situations; and a simulator. In addition, this work may improve the current practices employed in designing and constructing supervisory control systems by encouraging the use of modern software engineering methodologies and reducing the amount of tuning that is required to produce systems that meet their real-time constraints--while providing improved scheduling, graceful degradation, and more freedom in modifying the system over time.
The Effects of Glycogen Synthase Kinase-3beta in Serotonin Neurons

Glycogen synthase kinase-3 (GSK3) is a constitutively active protein kinase in brain. Increasing evidence has shown that GSK3 acts as a modulator in the serotonin neurotransmission system, including direct interaction with serotonin 1B (5-HT1B) receptors in a highly selective manner and prominent modulating effect on 5-HT1B receptor activity. In this study, we utilized the serotonin neuron-selective GSK3β knockout (snGSK3β-KO) mice to test if GSK3β in serotonin neurons selectively modulates 5-HT1B autoreceptor activity and function. The snGSK3β-KO mice were generated by crossbreeding GSK3β-floxed mice and ePet1-Cre mice. These mice had normal growth and physiological characteristics, similar numbers of tryptophan hydroxylase-2 (TpH2)-expressing serotonin neurons, and the same brain serotonin content as in littermate wild type mice. However, the expression of GSK3β in snGSK3β-KO mice was diminished in TpH2-expressing serotonin neurons. Compared to littermate wild type mice, snGSK3β-KO mice had a reduced response to the 5-HT1B receptor agonist anpirtoline in the regulation of serotonergic neuron firing, cAMP production, and serotonin release, whereas these animals displayed a normal response to the 5-HT1A receptor agonist 8-OH-DPAT. The effect of anpirtoline on the horizontal, center, and vertical activities in the open field test was differentially affected by GSK3β depletion in serotonin neurons, wherein vertical activity, but not horizontal activity, was significantly altered in snGSK3β-KO mice. In addition, there was an enhanced anti-immobility response to anpirtoline in the tail suspension test in snGSK3β-KO mice. Therefore, results of this study demonstrated a serotonin neuron-targeting function of GSK3β by regulating 5-HT1B autoreceptors, which impacts serotonergic neuron firing, serotonin release, and serotonin-regulated behaviors.
Retrieval and Visualization of Human Motion Data via Stick Figures

We propose 2D stick figures as a unified medium for visualizing and searching for human motion data. The stick figures can express a wide range or human motion, and they are easy to be drawn by people without any professional training. In our interface, the user can browse overall motion by viewing the stick figure images generated from the database and retrieve them directly by using sketched stick figures as an input query. We started with a preliminary survey to observe how people draw stick figures. Based on the rules observed from the user study, we developed an algorithm converting motion data to a sequence of stick figures. The feature‐based comparison method between the stick figures provides an interactive and progressive search for the users. They assist the user's sketching by showing the current retrieval result at each stroke. We demonstrate the utility of the system with a user study, in which the participants retrieved example motion segments from the database with 102 motion files by using our interface.