Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital halftoning: A model‐based perspective

Model‐based halftoning techniques exploit the properties of the display device and the human visual system to maximize the quality of the displayed images. They rely on accurate models of the display and human perception. The focus of this article is on printers (both black and white and color) but similar techniques can be applied to other display devices. We review two model‐based techniques, the modified error diffusion algorithms and the least‐squares model‐based algorithm. Both algorithms produce images with high spatial resolution and visually pleasant textures. We also review the use of printer and eye models in blue‐noise screening techniques, which attempt to approximate the performance of error diffusion with minimal computation. We examine the performance of these halftoning techniques using both model‐based quality metrics and subjective evaluations. © 1996 John Wiley & Sons, Inc.
Usage of Message Queueing Technologies in the ATLAS Distributed Data Management System

The ATLAS Distributed Data Management system is composed of semi-autonomous, heterogeneous, and independently designed subsystems. To achieve successful operation of such a system, the activities of the agents controlling the subsystems have to be coordinated. In addition, external applications can require to synchronize on events relative to data availability. A common way to proceed is to implement polling strategies within the distributed components, which leads to an increase of the load in the overall system. We describe an alternative based on notifications using standard message queuing. The application of this technology in the distributed system has been exercised.
IxTeT: an integrated approach for plan generation and scheduling

In a traditional approach to solve complex planning problems, plan generation and scheduling are addressed at two different steps of the resolution: plan generation answers the question "what to do? " and scheduling, the question "how to do it? ". This paper describes a planning system, called IxTeT, that handles together and in an opportunistic way plan generation and scheduling, on the basis that the two questions are very much interdependent. Thanks to a rich formalism to represent time, resources, state of the world and changes, our planning system can address a large panel of realistic problems. The search relies on the resolution of flaws on the current plan: it is controlled by a least-commitment opportunistic strategy coupled with an original abstraction hierarchy. A heuristic search driven by a near-admissible algorithm ensures at the same time the global efficiency of the system and the good quality of the solution in terms of flexibility.
Supporting adaptive video applications in future IP networks

Certain video applications may be able to tolerate a degree of fluctuation in quality. Therefore it would be useful for the network to offer a class of service that could meet the requirements of adaptive video sources. As part a general study into the problems of providing multiservice in future packet switched networks we have been investigating the requirements for network support of adaptive video applications. We propose that this class of traffic should be provided with explicit congestion feedback from the network nodes, and that the feedback mechanism should be optimised to the needs and characteristics of adaptive video. Whilst certain aspects of feedback control developed for computer data may offer some benefits to adaptive video, we believe that they may be insufficiently responsive, particularly in cases where the round trip time (RTT) is high. The paper outlines the basic components of a reactive congestion control system, and discusses how we propose to optimise a reactive congestion control scheme to the needs of adaptive video applications. (12 pages)
The Fundamentals of Economic Dynamics and Policy Analyses : Learning through Numerical Examples

The adjective “efficient” means that a point on PPF is Pareto efficient. At the point, any one good or service cannot be increased without reducing the other goods and services. The objective of this paper is to present the fundamentals of economic dynamics and policy analyses. Since 1980s', it is widely recognized that there are two main objectives of macroeconomics; (i) to explicitly construct the micro-foundations of macroeconomic models, and (TM) to analyze the statistical properties of macroeconomic variables. Because macroeconomic variables are described as equilibrium time series sequences chosen by optimizing microeconomic agents, mathematical tools for stochastic dynamic optimization are therefore appropriate to accomplish these objectives. The economic policies in this framework must be described as time series sequences of policy variables. The policy analyses in macroeconomic models which lack explicit treatment of time will often yield misleading outcomes. This is pointed out by R. Lucas and other economists who propose the rational expectations hypothesis. In the following, the importance of dynamic analyses in macroeconomics will be explained in section 1 and section 2 through two examples; section 1 discusses economic growth, and section 2 analyzes tax policies in lifecycle models. Section 3 is an exercise for numerical dynamic policy analyses using “Mathematica”, a computer software for numerical calculation.
Connectivity-Based Shape Descriptors

Abstract In this paper, we propose a method for indexing and retrieval of images based on shapes of objects. The concept of connectivity is introduced. 3D models are used to represent 2D images. 2D images are decomposed a priori using connectivity which is followed by 3D model construction. 3D model descriptors are obtained for 3D models and used to represent the underlying 2D shapes. We have used spherical harmonics descriptors as the 3D model descriptors. Difference between two images is computed as the Euclidean distance between their descriptors. Experiments are performed to test the effectiveness of spherical harmonics for retrieval of 2D images. The proposed method is compared with methods based on principal components analysis (PCA) and generic Fourier descriptors (GFD). It is found that the proposed method is effective. Item S8 within the MPEG-7 still images content set is used for performing experiments.
Computing using delayed feedback systems: towards photonics

Reservoir computing has recently been introduced as a new paradigm in the eld of machine learning. It is based on the dynamical properties of a network of randomly connected nodes or neurons and shows to be very promising to solve complex classication problems in a computationally ecient way. The key idea is that an input generates nonlinearly transient behavior rendering transient reservoir states suitable for linear classication. Our goal is to study up to which extent systems with delay, and especially photonic systems, can be used as reservoirs. Recently an new architecture has been proposed1 , based on a single nonlinear node with delayed feedback. An electronic1 and an opto-electronic implementation2, 3 have been demonstrated and both have proven to be very successful in terms of performance. This simple conguration, which replaces an entire network of randomly connected nonlinear nodes with one single hardware node and a delay line, is signicantly easier to implement experimentally. It is no longer necessary to construct an entire network of hundreds or even thousands of circuits, each one representing a node. With this approach one node and a delay line suce to construct a computational unit. In this manuscript, we present a further investigation of the properties of delayed feedback congurations used as a reservoir. Instead of quantifying the performance as an error obtained for a certain benchmark, we now investigate a task-independent property, the linear memory of the system.
Reducing Channel Capacity for Scalable Video Coding in a Distributed Network

In recent years, the development of multimedia devices has meant that a wider multimedia streaming service can be supported, and there are now many ways in which TV channels can communicate with different terminals. Generally, scalable video streaming is known to provide more efficient channel capacity than simulcast video streaming. Simulcast video streaming requires a large network bandwidth for all resolutions, but scalable video streaming needs only one flow for all resolutions. In previous research, scalable video streaming has been compared with simulcast video streaming for network channel capacity, in two user simulation environments. The simulation results show that the channel capacity of SVC is 16% to 20% smaller than AVC, but scalable video streaming is not efficient because of the limit of the present network framework. In this paper, we propose a new network framework with an SVC extractor. The proposed network framework shows a channel capacity 50% (maximum) lower than that found in previous research studies.
Near-unity photoluminescence quantum yield in MoS2

Brighter molybdenum layers The confined layers of molybdenum disulphide (MoS2) exhibit photoluminescence that is attractive for optolectronic applications. In practice, efficiencies are low, presumably because defects trap excitons before they can recombine and radiate light. Amani et al. show that treatment of monolayer MoS2 with a nonoxidizing organic superacid, bis(trifluoromethane) sulfonimide, increased luminescence efficiency in excess of 95%. The enhancement mechanism may be related to the shielding of defects, such as sulfur vacancies. Science, this issue p. 1065 Superacid treatment enhances the luminescence efficiency of monolayer molybdenum disulfide from 1% to >95%. Two-dimensional (2D) transition metal dichalcogenides have emerged as a promising material system for optoelectronic applications, but their primary figure of merit, the room-temperature photoluminescence quantum yield (QY), is extremely low. The prototypical 2D material molybdenum disulfide (MoS2) is reported to have a maximum QY of 0.6%, which indicates a considerable defect density. Here we report on an air-stable, solution-based chemical treatment by an organic superacid, which uniformly enhances the photoluminescence and minority carrier lifetime of MoS2 monolayers by more than two orders of magnitude. The treatment eliminates defect-mediated nonradiative recombination, thus resulting in a final QY of more than 95%, with a longest-observed lifetime of 10.8 ± 0.6 nanoseconds. Our ability to obtain optoelectronic monolayers with near-perfect properties opens the door for the development of highly efficient light-emitting diodes, lasers, and solar cells based on 2D materials.
Información sobre internacionalización de empresas de Castilla-León

Management decision to initiate or develop internationalization depends on the ability to take advantage of opportunities afforded by foreign markets. Factors determining whether these opportunities may be profited from are: access to information and the cognitive approach of the decision-making agent, namely the manager. In our research we explore which sources of information provide the stimuli impacting the decision to internationalize and assess the influence of management’s cognitive approach. To achieve this we conducted a survey amongst Spanish firms in the region of Castilla and Leon in two specific areas: “information technologies” and “natural stone”. Informative stimuli are split into three concepts (appealing factors, barriers and support) and we draw on several sources of information (Icex, Excal, Chambers of Commerce and trade associations). We address two cognitive areas: proactivity and level of knowledge. Findings highlight the relative importance of the different sources of information explored in the analysis. Our research also evidences how the information received by managers merges with their own cognitive approaches forming the basis to assess the factors impacting the decision to internationalize.
Model selection for stochastic block models

As a flexible representation for complex systems, networks (graphs) model entities and their interactions as nodes and edges. In many real-world networks, nodes divide naturally into functional communities, where nodes in the same group connect to the rest of the network in similar ways. Discovering such communities is an important part of modeling networks, as community structure offers clues to the processes which generated the graph. The stochastic block model is a popular network model based on community structures. It splits nodes into blocks, within which all nodes are stochastically equivalent in terms of how they connect to the rest of the network. As a generative model, it has a well-defined likelihood function with consistent parameter estimates. It is also highly flexible, capable of modeling a wide variety of community structures, including degree specific and overlapping communities. Performance of different block models vary under different scenarios. Picking the right model is crucial for successful network modeling. A good model choice should balance the trade-off between complexity and fit. The task of model selection is to automatically choose such a model given the data and the inference task. As a problem of wide interest, numerous statistical model selection techniques have been developed for classic independent data. Unfortunately, it has been a common mistake to use these techniques in block models without rigorous examinations of their derivations, ignoring the fact that some of the fundamental assumptions has been violated by moving into the domain of relational data sets such as networks. In this dissertation, I thoroughly exam the literature of statistical model selection techniques, including both Frequentist and Bayesian approaches. My goal is to develop principled statistical model selection criteria for block models by adapting classic methods for network data. I do this by running bootstrapping simulations with an efficient algorithm, and correcting classic model selection theories for block models based on the simulation data. The new model selection methods are verified by both synthetic and real world data sets.
Hand gesture recognition based on motion history images for a simple human-computer interaction system

A human-computer interaction can be developed using several kind of tools. One choice is using images captured using a camera. This paper proposed a simple human-computer interaction system based on hand movement captured by a web camera. The system aims to classify the captured movement into one of three classes. The first two classes contain hand movements to the left and right, respectively. The third class contains non-hand movements or hand movements to other directions. The method used in this paper is based on Motion History Images (MHIs) and nearest neighbor classifier. The resulting MHIs are processed in two manners, namely by summing the pixel values along the vertical axis and reshaping into vectors. We also use two distance criteria in this paper, respectively the Euclidian distance and cross correlation. This paper compared the performance of the combinations of different MHI data processing and distance criteria using 10 runs of 2-fold cross validation. Our experiments show that reshaping the MHI data into vectors combined with a Euclidean distance criterion gives the highest average accuracy, namely 55.67%.
Pricing radio resources for utility maximization in the context of channel capacity region per unit cost

Distributed rate-and-power control are considered for a wireless data network where users maximize their utilities (QoS) measured in bits per Joule. The outcome of the distributed algorithm (a non-cooperative game) is a Nash equilibrium which is Pareto inefficient. Pricing of resources (rate and power) are used to effect Pareto improvements in the system. Numerical results show that pricing improves the utilities of all users while reducing their transmit powers. Specifically, we present these results in the context of Verdu's (1990) capacity region per unit cost where we observe that pricing of resources results in moving the vector of users' utilities closer to the boundary of the capacity region per unit cost-which is also the ultimate boundary for utility vectors measured in bits per Joule. This connects the concept of utility (measured in bits per Joule) and that of capacity per unit cost. In the absence of reliable decoding, pricing under heavy system loads moves the Nash equilibrium very close to the Pareto frontier, which is the practical boundary of utility possibilities.
Adaptive-sized hybrid neural network for segmentation of breast cancer cells in pathology images

In this report, we describe a novel method to automatically segment several kinds of cells in breast cancer pathology images. The information on the number of cells is expected to assist pathologists in consistent diagnosis of breast cancer. Currently, most pathologists make a diagnosis based on a rough estimation of the number of cells on an image. Because of the rough estimation, the diagnosis is not objective. To assist pathologists to make a consistent, objective and fast diagnosis, it is necessary to develop a computer system to automatically recognize and count several kinds of cells. As the first step for this purpose, we propose a novel neural network model, called an adaptive-sized hybrid neural network (ASH-NN), and develop a method based on this network model to segment cells from breast cancer pathology images. The proposed neural network consists of three layers and the connection weights between the first and second layers are updated by self-organization, and the weights between the second and third layers are determined based on supervised learning. The ASH-NN has the capability of (1) automatic adjustment of the number of hidden units and (2) quick learning.
Applying pattern recognition techniques based on hidden Markov models for vehicular position location in cellular networks

Field trials of subscriber locations in a cellular network are discussed. The vehicular position location applied is a hybrid method based on pattern recognition and time of arrival (TOA) measurements. The pattern recognition is performed by hidden Markov models (HMMs) trained with prediction data to model the strength of the received signals for particular areas. The TOA gives first estimations of where the active mobile is located and which set of HMMs is to be used for the position estimation. To assess the accuracy of the proposed location method, calls have been performed from a car, driving through various streets and timing advance (TA) zones in a single GSM cell. The results are quite optimistic; the solution may fulfil the demand of many subscriber location applications, without requiring any modifications of existing standards, infrastructure or the mobiles.
A research of E-learning based on ontology

E-learning device is widely used in our daily life. However mobile e-learning machine is not satisfied as expected. Most mobile e-learning device is a static learning device, unable to fulfill the requirement of collaboration, long standby time, usage under strong sunlight. To meet these requirements, we developed a learning machine based on electronic paper. This paper will discuss the software consideration of the device. The software is a knowledge navigation system, the navigation system is based on Ontology. Research shows that combine frame Ontology and description logic together can afford a uniform interface to user application.
Deterioration detection system using local stationary binomial models for telecommunication services

This report concerns a system for monitoring the quality of connections in the gateway switches that interconnect telecommunication networks. Although the time series to be monitored is clearly nonstationary, movements that are almost periodic can be seen if a seven-day interval is set as the period. Therefore, this system introduces a new technique, which is called “partition model optimization”, for using the Akaike information criterion (AIC) to determine appropriate groups of local stationary models from a quasi-periodic time series. Also, to perform quality management in real time, a high-speed decision technique called the sequential probability ratio test (SPRT) is introduced. The effectiveness of the partition model optimization technique was systematically investigated in advance by using actual traffic data. Not only was decision parameter maintenance unnecessary, but deterioration could also be detected more accurately than with the conventional system. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 86(6): 73–82, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.10157
Neural network for aerosol retrieval from hyperspectral imagery

Abstract. We retrieve aerosol optical thickness (AOT) independently for brown carbon, dust and sulfate from hyperspectral image data. The model, a neural network, is trained on atmospheric radiative transfer calculations from MODTRAN 6.0 with varying aerosol concentration and type, surface albedo, water vapor, and viewing geometries. From a set of test radiative transfer calculations, we are able to retrieve AOT with a standard error of better than ±0.05. No a priori information on the surface albedo or atmospheric state is necessary for our model. We apply the model to AVIRIS-NG imagery from a recent campaign over India and demonstrate its performance under high and low aerosol loadings and different aerosol types.
Self-timed precharge latch

The use of a self-timed precharge latch (STPL) is proposed for the design of self-timed systems based on the four-cycle and two-cycle handshaking protocols. The techniques allow different data to be stored and computed in consecutive pipeline stages simultaneously without using a conventional data register. The techniques allow the propagation of data across the pipeline stage in a fast domino manner, parallel with the handshaking signals. The variable-length FIFOs designed using these techniques result in a significant reduction in silicon area and fall-through delay compared with those designed based on other techniques. <<ETX>>
Flexible Path Selection and Obstacle Detection for AGV

This paper desbcribes an improvement of a conventional AGV in the following two points: finding a path in complex junctions, and recognition of obstacles on the path. A TV camera is employed to look ahead of the vehicle to find lines in the input image. Centers of the lines are detected by combining dynamic thesholding and differentiation. Center points are detected at several vertical positions, and the arrangement of the lines in the image is classified into several categories: a branch, a crossing, and so on. The second problem is solved by a sort of motion stereo. The motion is obtained from the image of the white lie near the potential obstacle. The height of the obstacle is computed and the decision is made if it is really an obstacle. Some experimental results are shown to verify the proposed methods. The relation to other research themes for realizing AGV for more general environments are discussed.
On-line state estimation and parameter identification for flight

A practical method for real-time flight-state estimation and aerodynamic-coefficient identification for aircraft is introduced. In order to achieve high accuracy, a discrete-time model for flight is presented. This model can offer hierarchical structures by means of which a faster real-time filtering algorithm is achieved. For the identification of aerodynamic coefficients, a recursive maximum likelihood method is presented which is suitable for flight regimes at low and high angles of attack. The results of simulation and computation show that the outputs of real-time flight state estimation are much more accurate than those calculated directly from noisy measurements. <<ETX>>