Miracle-dz/UniFAR-FTUs huggingface dataset:
Properties of Formal Grammars With Mixed Type of Rules and Their Linguistic Relevance

In this paper, we will study a class of formal grammars with mixed types of rules. The reason for considering such grammars is that no single style (i.e., formal character of rules) of formal grammars is able to represent the various aspects of language structure in a natural way. Various considerations for setting up such grammars have been discussed. Generation schemes which map strings in the language of one mixed grammar into strings in the language of another mixed grammar (both strings being 'well-formed') have been studied. Linguistic relevance of these concepts has also been discussed.
Proactive Eavesdropping via Jamming over HARQ-Based Communications

This paper studies the wireless surveillance of a hybrid automatic repeat request (HARQ) based suspicious communication link over Rayleigh fading channels. We propose a proactive eavesdropping approach, where a half-duplex monitor can opportunistically jam the suspicious link to exploit its potential retransmissions for overhearing more efficiently. In particular, we consider that the suspicious link uses at most two HARQ rounds for transmitting the same data packet, and we focus on two cases without and with HARQ combining at the monitor receiver. In both cases, we aim to maximize the successful eavesdropping probability at the monitor, by adaptively allocating the jamming power in the first HARQ round according to fading channel conditions, subject to an average jamming power constraint. For both cases, we show that the optimal jamming power allocation follows a threshold-based policy, and the monitor jams with constant power when the eavesdropping channel gain is less than the threshold. Numerical results show that the proposed proactive eavesdropping scheme achieves higher successful eavesdropping probability than the conventional passive eavesdropping, and HARQ combining can help further improve the eavesdropping performance.
A Framework for the Numerical Computation and A Posteriori Verification of Invariant Objects of Evolution Equations

We develop a theoretical framework for computer-assisted proofs of the existence of invariant objects in semilinear PDEs. The invariant objects considered in this paper are equilibrium points, traveling waves, periodic orbits and invariant manifolds attached to fixed points or periodic orbits. The core of the study is writing down the invariance condition as a zero of an operator. These operators are in general not continuous, so one needs to smooth them by means of preconditioners before classical fixed point theorems can be applied. We develop in detail all the aspects of how to work with these objects: how to precondition the equations, how to work with the nonlinear terms, which function spaces can be useful, and how to work with them in a computationally rigorous way. In two companion papers, we present two different implementations of the tools developed in this paper to study periodic orbits.
Genetic algorithms for bad data detection at decomposition of state estimation problem

The accuracy of data used to construct calculation models of the network is an important factor that affects the reliability indices of electric power systems (EPS) at their operation. Along with random errors the measurements of EPS state variables contain quite often bad data. The paper addresses the algorithms of bad data detection by the test equation method at decomposition of a state estimation problem. The authors outline the concept of test equations. The decomposition algorithm includes structural and functional decomposition of the state estimation problem. The structural decomposition is carried out by dividing the calculated scheme into subsystems with respect to voltage levels. The functional decomposition is performed in accordance with the problems solved during the state estimation process: bad data detection, state estimation on the basis of the quadratic and robust criteria. The authors present the methods for detecting bad data a priori and solving the state estimation problem on the basis of robust criterion using test equations. Consideration is given to the applicability of genetic algorithms to these problems. The experimental studies have confirmed the efficiency of the suggested approaches.
Neighbor Discovery in Multichannel Wireless Clique Networks: An Epidemic Approach

We investigate the problem of neighbor discovery in multichannel wireless ad hoc and sensor networks with epidemic information dissemination. Previous works have considered neighbor discovery in a single channel where at most one node can be discovered per time instant. To reduce the effect of collisions observed in single channel solutions, we formulate models for multichannel neighbor discovery and allow for epidemic dissemination of information. As a result, nodes can discover all their neighbors faster, either directly or indirectly by hopping between orthogonal channels and exploring the neighbors in each of them. We show analytically, by simulations, and by experimental evaluations that the expected neighbor discovery time is reduced considerably compared to single channel neighbor discovery solutions.
SOA-Based Ultrafast Multifunctional All-Optical Logic Gates With PolSK Modulated Signals

In this paper, we propose a novel scheme for ultrafast multifunctional all-optical logic gates, which can achieve not only simple logic gates including AND, NOR, Smacr 1Smacr2,Smacr1Smacr2,XNOR, and XOR but also complex logic gates including half adder, half subtracter, decoder, and comparer based on four-wave mixing in semiconductor optical amplifiers (SOAs) with polarization-shift-keying (PolSK) modulated signals. A comprehensive polarization-dependent broadband dynamic model of this kind of ultrafast multifunctional all-optical logic gates is presented. By numerical simulation, the multifunctional all-optical logic gates are theoretically realized at 40 Gbit/s. The effects of two input signal powers, injected current, frequency detuning, and polarization dependence of SOA on the output performance of the multifunctional all-optical logic gates are theoretically investigated in detail. The results indicate that this scheme is free of pattern effect due to using the PolSK modulation format. Moreover, the nice eye opening of the logic gates indicates the good performance of the proposed ultrafast multifunctional all-optical logic gates. This scheme is potential for applications in future high bit rate optical networks.
Detection Of Agreement vs. Disagreement In Meetings: Training With Unlabeled Data

To support summarization of automatically transcribed meetings, we introduce a classifier to recognize agreement or disagreement utterances, utilizing both word-based and prosodic cues. We show that hand-labeling efforts can be minimized by using unsupervised training on a large unlabeled data set combined with supervised training on a small amount of data. For ASR transcripts with over 45% WER, the system recovers nearly 80% of agree/disagree utterances with a confusion rate of only 3%.
FPART: A Multi-way FPGA Partitioning Procedure Based on the Improved FM Algorithm

| In this paper, a multi-way FPGA partitioning procedure FPART is introduced. The objective function of this procedure is to reduce the number of FPGA devices and the IOB utilization. An improved min-span FM bi-partitioning algorithm on the basis of an advanced gain model is adopted as the fundamental method, and three modules: init-part, optimize, and merge are combined in FPART to approach better results. After initial partitioning, the procedure optimizes the subsets to reduce the total span of cutset and then merges some subsets by removing the cells in them. Experimental results with MCNC'93 benchmarks show that FPART is fast and e cient.
Floating gate analog memory for parameter and variable storage in a learning silicon neuron

Retention of parameters and learnt synaptic weights is a central problem in the construction of neural networks. We have applied analog floating gate technology to solve these problems in the context of biologically realistic 'silicon neurons'. Parameters are stored on a novel floating gate array, and synaptic weights are retained by a floating gate learning synapse, that performs on-chip learning. The latter can emulate a form of long term potentiation (LTP) and long term depression (LTD) as observed in biological neurons.
Simple and Fast Convex Relaxation Method for Cooperative Localization in Sensor Networks Using Range Measurements

We address the sensor network localization problem given noisy range measurements between pairs of nodes. We approach the nonconvex maximum-likelihood formulation via a known simple convex relaxation. We exploit its favorable optimization properties to the full to obtain an approach that is completely distributed, has a simple implementation at each node, and capitalizes on an optimal gradient method to attain fast convergence. We offer a parallel but also an asynchronous flavor, both with theoretical convergence guarantees and iteration complexity analysis. Experimental results establish leading performance. Our algorithms top the accuracy of a comparable state-of-the-art method by one order of magnitude, using one order of magnitude fewer communications.
FORCE DETERMINATION DURING ORTHOGONAL MACHINING OF TITANIUM ALLOY USING ABAQUSexplicit

The aim of this paper is to create a Finite Element Model and simulation of orthogonal machining of titanium alloy grade-5 using tungsten carbide tool to determine the forces. Modeling of machining is done by using Arbitrary Lagrangian Eulerian (ALE) formulation for refinement and smoothing of the mesh, ductile damage initiation creation and Johnson-Cook work material constitutive model is used, which describes the flow stress of the material with strain, strain rate and temperature effect. A FE-Analysis of the orthogonal machining is carried out using a commercial software ABAQUS/Explicit. The simulation results include prediction of chip formation as well as forces at different test condition. KeywordsOrthogonal cutting, FE-Modelling, ALE formulation, forces.
Co-evaporated perovskite solar cells

ii ACKNOWLEDGEMENTS iii CHAPTER 1: INTRODUCTION 1 Solar Cells 1 Advancements in Perovskites 2 CHAPTER 2: FABRICATION 6 Introduction 6 Cleaning Steps 7 Hole Transport Layers 7 Co-evaporation 8 Electron Transport 10 Contact Process 11 CHAPTER 3: CHARACTERIZATION 12 Photocurrent Measurements 12 Quantum Efficiency (QE) Measurements 15 CHAPTER 4: DATA AND RESULTS 19 CHAPTER 5: CONCLUSIONS 26 REFERENCES 27
Dual omental flap in obliterating post-pneumonectomy bronchopleural fistulae

Background : Post-pneumonectomy bronchopleural fistulae is associated with high mortality and morbidity. The omental flap has been widely used to manage this condition either through laparoscopic or open surgery with varied degrees of success. We present a modification of the omental flap by using two flaps of the omentum in obliterating the bronchopleural fistulae. Objective : To evaluate the effectiveness of dual omental flap in obliteration of postpneumonectomy bronchopleural fistulae D esign: A prospective clinical study. Setting : The cardiothoracic unit at the Kenyatta National Hospital between January 2010 and December 2013 . Subjects : Five patients with post-pneumonectomy bronchopleural fistulae managed in a period of four years. Results : Pneumonectomy were due to cancer of the lungs in two patients and tuberculosis in three patients. All the patients had previously attempted repair with a pleural patch without success. Dual omental flap was utilised to repair the fistulae. All patients had successful repair of the fistula and no recurrence was noted at one year of follow up. Conclusion : Dual omental flap offers an alternative technique in obliterating postpneumonectomy fistula. The two flap technique allows for one flap to completely seal the fistula while the second flap provides an additional airtight closure over the first flap and hence prevents any leakages. This ensures higher success rate with few recurrences.
A Quantitative Investigation of the Acceptable Risk Levels of Object-Oriented Metrics in Open-Source Systems

Object-oriented metrics have been validated empirically as measures of design complexity. These metrics can be used to mitigate potential problems in the software complexity. However, there are few studies that were conducted to formulate the guidelines, represented as threshold values, to interpret the complexity of the software design using metrics. Classes can be clustered into low and high risk levels using threshold values. In this paper, we use a statistical model, derived from the logistic regression, to identify threshold values for the Chidamber and Kemerer (CK) metrics. The methodology is validated empirically on a large open-source system-the Eclipse project. The empirical results indicate that the CK metrics have threshold effects at various risk levels. We have validated the use of these thresholds on the next release of the Eclipse project-Version 2.1-using decision trees. In addition, the selected threshold values were more accurate than those were selected based on either intuitive perspectives or on data distribution parameters. Furthermore, the proposed model can be exploited to find the risk level for an arbitrary threshold value. These findings suggest that there is a relationship between risk levels and object-oriented metrics and that risk levels can be used to identify threshold effects.
A DSL for continuous-time agent-based modeling and simulation

Most state-of-the-art agent-based modeling and simulation (ABMS) frameworks offer a way to describe agent behavior in a programming language. Whereas these frameworks support easy development of time-stepped models, continuous-time models can only be implemented by manually scheduling and retracting events as part of the agent behavior. To facilitate a separation of concerns into model- and simulation-specific code for continuous-time ABMS, we propose an embedded domain-specific language, which allows describing agent behavior concisely, and corresponding simulation algorithms, which allow executing continuous-time models. The language style and the algorithms are adapted from rule-based modeling languages for Continuous-Time Markov Chains and Stochastic Simulation Algorithm variants. We implemented prototypes of the modeling language and simulation algorithms based on Repast Simphony.
Osteoclasts from medullary bone of egg‐laying Japanese quail do not express the putative calcium ‘receptor’

The present study reports the contrasting effects of extracellular calcium ([Ca2+]e) elevation on cytosolic free calcium levels ([Ca2+]i) of osteoclasts, freshly isolated either from medullary bone of the egg‐laying Japanese quail or from rat cortical bone. [Ca2+]i was measured in single osteoclasts using the Ca(2+)‐sensitive fluorochrome, Indo‐1. We found that elevation of [Ca2+]e failed to induce a rise of [Ca2+]i in quail osteoclasts, whilst causing an elevation of [Ca2+]i in rat osteoclasts. The calcium ionophore, ionomycin, led to a sustained elevation of [Ca2+]i in both cell types. These findings suggest that osteoclasts isolated from egg‐laying quail do not possess the calcium sensor or 'receptor' that appears to be vital for the survival and function of rat osteoclasts.
Maps of the adult human hypothalamus

The human hypothalamus is a small deeply located region placed at the crossroad of neurovegetative, neuroendocrine, limbic, and optic systems. Although deep brain stimulation techniques have proven that it could be feasible to modulate these systems, targeting the hypothalamus and in particular specific nuclei and white bundles, is still challenging. Our goal was to make a synthesis of relevant topographical data of the human hypothalamus, under the form of magnetic resonance imaging maps useful for mastering its elaborated structure as well as its neighborhood. As from 1.5 Tesla, Inversion-Recovery sequence allows locating the hypothalamus and most of its components. Spotting hypothalamic compartments is possible according to specific landmarks: the anterior commissure, the mammillary bodies, the preoptic recess, the infundibular recess, the crest between the preoptic and the infundibular recesses, the optical tract, the fornix, and the mammillo-thalamic bundle. The identification of hypothalamus and most of its components could be useful to allow the quantification of local pathological processes and to target specific circuitry to alleviate severe symptoms, using physical or biological agents.
RobotShare : a Framework for Robot Knowledge Sharing

Knowledge representation is a traditional field in artificial intelligence. Researchers have developed various ways to represent and share information among intelligent agents. Agents that share resources, data, information, and knowledge perform better than agents working alone. However, previous research also reveals that sharing knowledge among a large number of entities in an open environment is a problem yet to be solved. Intelligent robots are designed and produced by different manufactures. They have various physical attributes, use different knowledge representations and have different needs. In this research, we pose robot knowledge sharing as an activity to be developed in an open environment the World Wide Web. Just as search engines like Google provide enormous power for information exchange and sharing for humans, we believe a searching mechanism designed for intelligent agents can provide a robust approach for sharing knowledge among robots. We have developed knowledge representation for robots that allows Internet access and a knowledge organization and search indexing engine that performs knowledge retrieval.
Open distributed autonomous multi-agent coordination on the Web

Agent technology is becoming more prevalent as the availability of network access, and the demand for the end uses of agents becomes greater. Intelligent agents for information filtering and retrieval applications and tools are being employed in a variety of ways on the Web. A centralized agent for information discovery has usually limited capabilities for finding diverse and distributed information online. The main thrust of the paper is to present a framework that allows distributed adaptive information KODAMA agents to work together to browse and retrieve distributed information based on the preexisting hyperlink structure on the Web and how this community of agents can automatically extract meta-information and cooperate to retrieve online distributed relevant information. We have developed the software architecture, and a working prototype showing the benefits to the Web of interactive architectures based on the coordination of the hyperlink structure on the network. The authors summarize the current results of the project, and discuss some ideas on future work.
Changes in Smoking Rates Among Pregnant Women and the General Female Population in Australia, Finland, Norway, and Sweden

Abstract Introduction: Smoking rates have dropped substantially in most developed countries in recent decades. This general trend has, however, not always been evident among women—particularly younger women. Smoking habits do, however, often change in connection with pregnancy and the aim of this study is to determine whether smoking during pregnancy follows general trends in smoking rates in the general female population in four countries with active anti-tobacco policies and decreasing population smoking rates. Methods: Changes in rates of persistent smoking, that is, smoking in late pregnancy or daily smoking among all women of childbearing age were described according to age groups. Data were retrieved from the Australian Household Drug Surveys during 2000–2013 and from registries and surveys in Finland, Norway, and Sweden between 1995 and 2014. Results: In general, persistent smoking has decreased and late-pregnancy smoking rates are lower than daily smoking rates among all women. However, younger women are more likely to be persistent smokers regardless of pregnancy status. In Norway and Finland, persistent smoking was most common among young pregnant women and in Sweden there was an increased polarization between age groups. In Australia, a steady decrease in smoking rates appears to have stalled in younger pregnant women. Conclusion: Although smoking has declined substantially in recent decades, there are groups lagging behind this general trend. Young pregnant women are of particular concern in this respect. The possibility that these findings reflect the changing characteristics of younger pregnant women is discussed. Implications: This study puts recent trends in maternal smoking into a broader context by relating developments to changes in smoking rates among women in general. By using similar data from four countries we were able to follow changes in smoking rates “within” groups of women within the four countries without being limited by methodological problems related to cross-country or inter-group comparisons. We were above all able to show that aggregate data disclose the strong age gradient in maternal smoking habits.
A Generalized Pyramid Matching Kernel for Human Action Recognition in Realistic Videos

Human action recognition is an increasingly important research topic in the fields of video sensing, analysis and understanding. Caused by unconstrained sensing conditions, there exist large intra-class variations and inter-class ambiguities in realistic videos, which hinder the improvement of recognition performance for recent vision-based action recognition systems. In this paper, we propose a generalized pyramid matching kernel (GPMK) for recognizing human actions in realistic videos, based on a multi-channel “bag of words” representation constructed from local spatial-temporal features of video clips. As an extension to the spatial-temporal pyramid matching (STPM) kernel, the GPMK leverages heterogeneous visual cues in multiple feature descriptor types and spatial-temporal grid granularity levels, to build a valid similarity metric between two video clips for kernel-based classification. Instead of the predefined and fixed weights used in STPM, we present a simple, yet effective, method to compute adaptive channel weights of GPMK based on the kernel target alignment from training data. It incorporates prior knowledge and the data-driven information of different channels in a principled way. The experimental results on three challenging video datasets (i.e., Hollywood2, Youtube and HMDB51) validate the superiority of our GPMK w.r. t. the traditional STPM kernel for realistic human action recognition and outperform the state-of-the-art results in the literature.