Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Agent Systems Implementation and Testing

The need of software tools for designing and testing complex distributed multi-agent systems is considerable. Over the past few years we have been developing a theoretical and practical methodology for designing, implementing and testing multi-agent systems. As part of this effort an agent-oriented CASE tool, which aims at simplifying the multi-agent system designer’s work, and a testing framework, which provides a uniform and automated approach to the testing of multi-agent systems, have been designed and implemented. The aim of this paper is to give an overview of that work with particular attention to the recent results achieved. Therefore the paper mainly focuses on the testing tool and the issues connected with the diagrammatic notations, which should support developers when moving from the design phase towards the implementation phase of multi-agent systems.
Bats adjust their mouth gape to zoom their biosonar field of view

Significance This study shows how an animal actively and functionally controls its sensory field of view by means of changing its emitter aperture. This would be similar to a human ability to zoom in on a visual scene. The study was performed in a natural situation with wild bats. To perform the study, we developed tools to record and analyze bats’ beams and faces simultaneously. We show that bats rapidly and dramatically alter their biosonar field of view to functionally adjust sensory acquisition. They do so by changing their mouth gape. Hence, we show that bats change the shape of their emitter for active sensing. Active sensing, where sensory acquisition is actively modulated, is an inherent component of almost all sensory systems. Echolocating bats are a prime example of active sensing. They can rapidly adjust many of their biosonar parameters to optimize sensory acquisition. They dynamically adjust pulse design, pulse duration, and pulse rate within dozens of milliseconds according to the sensory information that is required for the task that they are performing. The least studied and least understood degree of freedom in echolocation is emission beamforming—the ability to change the shape of the sonar sound beam in a functional way. Such an ability could have a great impact on the bat’s control over its sensory perception. On the one hand, the bat could direct more energy into a narrow sector to zoom its biosonar field of view, and on the other hand, it could widen the beam to increase the space that it senses. We show that freely behaving bats constantly control their biosonar field of view in natural situations by rapidly adjusting their emitter aperture—the mouth gape. The bats dramatically narrowed the beam when entering a confined space, and they dramatically widened it within dozens of milliseconds when flying toward open space. Hence, mouth-emitting bats dynamically adjust their mouth gape to optimize the area that they sense with their echolocation system.
One Order Parameter Tensor Mean Field Theory for Biaxial Liquid Crystals

In this paper, we present a simple one tensor mean eld model of biaxial nematic liquid crystals. The salient feature of our approach is that ma- terial parameters appear explicitly in the order parameter tensor. We construct the free energy from a mean eld potential based on anisotropic dispersion in- teractions, identify the order parameter tensor and its elements, and obtain self-consistent equations, which are then solved numerically. The results are illustrated in a 3D ternary phase diagram. The phase behavior can be sim- ply related to molecular parameters. The results may be useful for designing molecules that show a thermotropic biaxial phase.
Combined Object Categorization and Segmentation With an Implicit Shape Model

We present a method for object categorization in real-world scenes. Following a common consensus in the field, we do not assume that a figure- ground segmentation is available prior to recognition. However, in contrast to most standard approaches for object class recognition, our approach automati- cally segments the object as a result of the categorization. This combination of recognition and segmentation into one process is made pos- sible by our use of an Implicit Shape Model, which integrates both into a common probabilistic framework. In addition to the recognition and segmentation result, it also generates a per-pixel confidence measure specifying the area that supports a hypothesis and how much it can be trusted. We use this confidence to derive a nat- ural extension of the approach to handle multiple objects in a scene and resolve ambiguities between overlapping hypotheses with a novel MDL-based criterion. In addition, we present an extensive evaluation of our method on a standard dataset for car detection and compare its performance to existing methods from the literature. Our results show that the proposed method significantly outper- forms previously published methods while needing one order of magnitude less training examples. Finally, we present results for articulated objects, which show that the proposed method can categorize and segment unfamiliar objects in differ- ent articulations and with widely varying texture patterns, even under significant partial occlusion.
Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control

formulas and tables for determining the sample sizes for inference on the ICC and kappa. The method, promoted appropriately, is based on precision of the desired estimate rather than hypothesis testing considerations. The final chapter comprises nine “workshops” containing datasets, SAS codes, and outputs illustrating the use of various formulas and techniques discussed in the preceding five chapters. The material covered is of current interest, the informal tone is pleasing to the reader, and the author provides several insightful comments. This book is suitable for a short course due to its expository nature. The reading could have been more enjoyable had not the text been marred by numerous typographical errors, many of which appear in the formulas. The flow was also hindered by frequent inconsistencies in notation. The errors are mostly minor, consisting mainly of jumbled up upperand lower-case letters and inconsistent use of “Kappa,” “kappa,” and the letter “k. ” Also, Workshop 2 is introduced on page 10, before Workshop 1, which is featured on page 14. These irritants diminish the utility of the book. For readers of Technometrics, Measures of Interobserver Agreement would seem to be of limited usefulness. As for those applied statisticians engaged in biomedical research, it perhaps would be worthwhile to wait for the revised, corrected edition. At the moment, the coverage of this topic in the general references by Fleiss (1986) for the continuous data and Fleiss, Levin, and Paik (2003) for the nominal data appears adequate.
The Discursive Mind Model

Abstract The paper proposes the model of discursive mind and describes the cognitive architecture of the dialogically structured mind. The model draws on Hermans’ (1999) theory of the dialogical self (DS) and Wertsch’s (1991) vision of mind as a “tool kit” with socio-cultural instruments, and also on the socio-cognitive approach to personality in experimental psychology. An I-position is understood here as an active totality of experience, shaped in a particular social context and represented in a separate representation module. Th ere are many modules in the mind because in the course of socialization, the individual comes across many different social contexts. Th e described model and its preliminary empirical verification not only gives support to the DS theory, but can also be a leverage of its contribution to general theories of mind stemming from other theoretical traditions
Oil Price Shocks , Monetary Policy and

One of the central questions in recent macroeconomic history is to what extent monetary policy, as opposed to oil price shocks, contributed to the stagflation of the 1970s. Understanding what went wrong in the 1970s is the key to learning from the past. One explanation explored in Barsky and Kilian (2002) is that worldwide shifts in monetary policy regimes not related to the oil market played a major role in causing both the subsequent oil price increases and stagflation in many economies. A competing view exemplified by Bernanke, Gertler and Watson (1997) is that the oil price shocks of the 1970s arose exogenously with respect to global macroeconomic conditions, but were propagated by the reaction of monetary policy-makers, causing stagflation in the process. The argument is that policy-makers responded to the inflationary pressures caused by oil price shocks by raising interest rates, thereby causing a deep recession that would not have occurred without the central bank’s intervention. If policy-makers are only partially successful in controlling inflation, stagflation will ensue.
A GLCM-Feature-Based Approach for Reversible Image Transformation

: Recently, a reversible image transformation (RIT) technology that transforms a secret image to a freely-selected target image is proposed. It not only can generate a stego-image that looks similar to the target image, but also can recover the secret image without any loss. It also has been proved to be very useful in image content protection and reversible data hiding in encrypted images. However, the standard deviation (SD) is selected as the only feature during the matching of the secret and target image blocks in RIT methods, the matching result is not so good and needs to be further improved since the distributions of SDs of the two images may be not very similar. Therefore, this paper proposes a Gray level co-occurrence matrix (GLCM) based approach for reversible image transformation, in which, an effective feature extraction algorithm is utilized to increase the accuracy of blocks matching for improving the visual quality of transformed image, while the auxiliary information, which is utilized to record the transformation parameters, is not increased. Thus, the visual quality of the stego-image should be improved. Experimental results also show that the root mean square of stego-image can be reduced by 4.24% compared with the previous method.
Associations of smoking prevalence with individual and area level social cohesion

Study objective: To discover if area level social cohesion, neighbourhood safety, and home safety are associated with current cigarette smoking among adults after adjustment for concentrations of poverty and low education. Design: Cross sectional survey of a random sample of adults, stratified by 19 geographical areas. Setting: SHAPE, Survey of the Health of Adults, the Population, and the Environment—conducted in 1998 by the Hennepin County Community Health Department and the Minneapolis Department of Health and Family Support in Minnesota. Participants: 5256 men and 4806 women, 18 years and older, randomly selected from 19 geographical areas in an urban county. Main results: Overall, 21.2% of survey respondents reported current cigarette smoking. Both higher area level social cohesion (OR = 0.85, 95% CI = 0.74 to 0.98) and higher individual social cohesion (OR = 0.96, 95% CI = 0.92 to 0.99) were associated with lower likelihoods of smoking. Similar models were obtained for neighbourhood safety and home safety. Conclusions: These findings contribute to the growing literature on the important role of social cohesion and other area level characteristics on smoking behaviour among adults.
Linking Policies to the Spatial Environment

Security policy specification can be difficult to get right. Electronic systems often fail to provide an easy route to encode requirements that would be simple to enforce through controlling how physical principals interact. This paper presents a means to ameliorate potential policy mismatches through the use of location awareness systems allowing changes in the physical world to be mapped to electronic policy state. These changes are represented formally using the event calculus. Patterns over this state are used to track compliance with policy and to detect the fulfilment of obligations. A number of example scenarios are provided to illustrate the utility of the coupling between spatial and policy concerns, and to demonstrate the notation used.
Entropy concentration and the empirical coding game

We give a characterization of maximum entropy/minimum relative entropy inference by providing two ‘strong entropy concentration’ theorems. These theorems unify and generalize Jaynes’‘concentration phenomenon’ and Van Campenhout and Cover's ‘conditional limit theorem’. The theorems characterize exactly in what sense a prior distribution Q conditioned on a given constraint and the distribution minimizing D(P ‖ Q) over all P satisfying the constraint are ‘close’ to each other. We then apply our theorems to establish the relationship between entropy concentration and a game‐theoretic characterization of maximum entropy inference of Topsøe and others.
The specification of visual language syntax

An approach for building a language-independent visual programming environment based on syntax specifications is described. A model for specifying the syntax of visual languages, picture layout grammars, is described, and an example is given of how these grammars are used to define the syntax of a visual language. A spatial parser is an algorithm for recovering the underlying structure of a visual program from the picture. A spatial parser for visual languages whose syntax is specified by a picture layout grammar was implemented. This parser forms the basis of a visual programming environment. <<ETX>>
Materials of interaction : responsive materials in the design of transformable interactive surfaces

Materials that embody computational properties are reshaping the ways in which we design, interact and communicate. This thesis looks at the topic of form transformation and how to bring the programmability and versatility of digital forms into the physical world. The focus is placed on the relationship between materials, form and interaction, in particular how the behavior and properties of shape-changing materials can support the design of transformable interactive surfaces. Three design implementations are presented, each addressing a distinct subject area in the design of form transformation, namely topology, texture and permeability. Surflex is a composite that uses active and passive shape-changing materials to undergo large surface deformations. Sprout I/O implements small shape deformations and co-located input/output at a surface boundary to create a dynamic texture for communication. Shutters uses shape change to regulate a surface’s permeability and control environmental exchanges between two distinct spaces. Drawing lessons from these projects, a soft mechanical alphabet and language for form transformation are derived, providing new formal possibilities for enriching human-computer interactions. Thesis Supervisor Pattie Maes Associate Professor of Media Technology Program in Media Arts and Sciences Materials of Interaction Responsive Materials in the Design of Transformable Interactive Surfaces
Context-Aware Access Control Model for Privacy Support in Mobile-Based Assisted Living

Abstract The average lifetime of people in advanced countries is significantly increased in the XXI century. The number of old and dependent people is rising due many innovations in health care: new technologies, medicine, and a lot of innovative devices that allow people to monitor their health and consult with a doctor in case of problems. In the last years, a number of information systems have been developed in the health-care area to assist people in living. Modern systems increase their mobility by using personal mobile devices that allows them to interact with the system at any point of time. Smart space is a technology that allows developing such systems. It is an aggregation of devices that can share their resources (information and services) and cooperate with each other. Sharing personal information between different devices requires ensuring privacy support. For these purposes, a dynamic access control support for information that is shared by devices is needed. In particular, a new access control model for accessing resources is needed. The model should describe the current situation via a context. This paper proposes a model of the context-aware access control for smart systems based on smart space technology.
Long short-term memory recurrent neural network-based acoustic model using connectionist temporal classification on a large-scale training corpus

A Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model (GMM). However, these models based on a hybrid method require a forced aligned Hidden Markov Model (HMM) state sequence obtained from the GMM-based acoustic model. Therefore, it requires a long computation time for training both the GMM-based acoustic model and a deep learning-based acoustic model. In order to solve this problem, an acoustic model using CTC algorithm is proposed. CTC algorithm does not require the GMM-based acoustic model because it does not use the forced aligned HMM state sequence. However, previous works on a LSTM RNN-based acoustic model using CTC used a small-scale training corpus. In this paper, the LSTM RNN-based acoustic model using CTC is trained on a large-scale training corpus and its performance is evaluated. The implemented acoustic model has a performance of 6.18% and 15.01% in terms of Word Error Rate (WER) for clean speech and noisy speech, respectively. This is similar to a performance of the acoustic model based on the hybrid method.
ImageAdmixture: Putting Together Dissimilar Objects from Groups

We present a semiautomatic image editing framework dedicated to individual structured object replacement from groups. The major technical difficulty is element separation with irregular spatial distribution, hampering previous texture, and image synthesis methods from easily producing visually compelling results. Our method uses the object-level operations and finds grouped elements based on appearance similarity and curvilinear features. This framework enables a number of image editing applications, including natural image mixing, structure preserving appearance transfer, and texture mixing.
ICA analysis of fMRI with real-time constraints: an evaluation of fast detection performance as function of algorithms, parameters and a priori conditions

Independent component analysis (ICA) techniques offer a data-driven possibility to analyze brain functional MRI data in real-time. Typical ICA methods used in functional magnetic resonance imaging (fMRI), however, have been until now mostly developed and optimized for the off-line case in which all data is available. Real-time experiments are ill-posed for ICA in that several constraints are added: limited data, limited analysis time and dynamic changes in the data and computational speed. Previous studies have shown that particular choices of ICA parameters can be used to monitor real-time fMRI (rt-fMRI) brain activation, but it is unknown how other choices would perform. In this rt-fMRI simulation study we investigate and compare the performance of 14 different publicly available ICA algorithms systematically sampling different growing window lengths (WLs), model order (MO) as well as a priori conditions (none, spatial or temporal). Performance is evaluated by computing the spatial and temporal correlation to a target component as well as computation time. Four algorithms are identified as best performing (constrained ICA, fastICA, amuse, and evd), with their corresponding parameter choices. Both spatial and temporal priors are found to provide equal or improved performances in similarity to the target compared with their off-line counterpart, with greatly reduced computation costs. This study suggests parameter choices that can be further investigated in a sliding-window approach for a rt-fMRI experiment.
Structural Analysis of a Glycoside Hydrolase Family 11 Xylanase from Neocallimastix patriciarum

Background: Thermophilic xylanases are valuable in many industrial applications. Results: The structures of a xylanase XynCDBFV and its complex with xylooligosaccharides were determined, and its N-terminal region (NTR) contributes to thermostability. Conclusion: NTR may stabilize the overall protein folding of XynCDBFV. Significance: The structural and functional investigation of unprecedented NTR of XynCDBFV provides a new insight into the molecular basis of thermophilic xylanases. The catalytic domain of XynCDBFV, a glycoside hydrolase family 11 (GH11) xylanase from ruminal fungus Neocallimastix patriciarum previously engineered to exhibit higher specific activity and broader pH adaptability, holds great potential in commercial applications. Here, the crystal structures of XynCDBFV and its complex with substrate were determined to 1.27–1.43 Å resolution. These structures revealed a typical GH11 β-jelly-roll fold and detailed interaction networks between the enzyme and ligands. Notably, an extended N-terminal region (NTR) consisting of 11 amino acids was identified in the XynCDBFV structure, which is found unique among GH11 xylanases. The NTR is attached to the catalytic core by hydrogen bonds and stacking forces along with a disulfide bond between Cys-4 and Cys-172. Interestingly, the NTR deletion mutant retained 61.5% and 19.5% enzymatic activity at 55 °C and 75 °C, respectively, compared with the wild-type enzyme, whereas the C4A/C172A mutant showed 86.8% and 23.3% activity. These results suggest that NTR plays a role in XynCDBFV thermostability, and the Cys-4/Cys-172 disulfide bond is critical to the NTR-mediated interactions. Furthermore, we also demonstrated that Pichia pastoris produces XynCDBFV with higher catalytic activity at higher temperature than Escherichia coli, in which incorrect NTR folding and inefficient disulfide bond formation might have occurred. In conclusion, these structural and functional analyses of the industrially favored XynCDBFV provide a molecular basis of NTR contribution to its thermostability.
Centriolar Satellites

We identified Xenopus pericentriolar material-1 (PCM-1), which had been reported to constitute pericentriolar material, cloned its cDNA, and generated a specific pAb against this molecule. Immunolabeling revealed that PCM-1 was not a pericentriolar material protein, but a specific component of centriolar satellites, morphologically characterized as electron-dense granules, ∼70–100 nm in diameter, scattered around centrosomes. Using a GFP fusion protein with PCM-1, we found that PCM-1–containing centriolar satellites moved along microtubules toward their minus ends, i.e., toward centrosomes, in live cells, as well as in vitro reconstituted asters. These findings defined centriolar satellites at the molecular level, and explained their pericentriolar localization. Next, to understand the relationship between centriolar satellites and centriolar replication, we examined the expression and subcellular localization of PCM-1 in ciliated epithelial cells during ciliogenesis. When ciliogenesis was induced in mouse nasal respiratory epithelial cells, PCM-1 immunofluorescence was markedly elevated at the apical cytoplasm. At the electron microscopic level, anti–PCM-1 pAb exclusively labeled fibrous granules, but not deuterosomes, both of which have been suggested to play central roles in centriolar replication in ciliogenesis. These findings suggested that centriolar satellites and fibrous granules are identical novel nonmembranous organelles containing PCM-1, which may play some important role(s) in centriolar replication.