Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal resource allocation techniques for MIMO-OFDMA based cognitive radio networks using integer linear programming

We propose an adaptive resource allocation algorithm for a multiple-input multiple-output (MIMO) orthogonal frequency division multiple access (OFDMA) based downlink cognitive radio network (CRN). The CRN has multiple secondary users (SUs) coexisting with multiple primary users (PUs). The aim is to maximize the total data throughput of SUs while ensuring interference leakage to PUs is below a threshold and quality of services (QoS) for SUs such as data rates and bit error rate (BER) are met. The algorithm optimally allocates radio resources such as subcarrier, spatial beams, powers and bits to each user using an integer linear programming (ILP) framework. The performance of the algorithm has been evaluated using simulation results.
Coverage control for mobile networks with limited-range anisotropic sensors

In this paper the coverage control for mobile sensor networks is studied. The novelty is to consider an anisotropic sensor model where the performance of the sensor depends not only on the distance but also on the orientation from the sensor to the target. Moreover we consider sensors with limited-range sensing defined by a probabilistic model and we assume that each robot is equipped with omni-directional communication capability. A gradient-based distributed algorithm is designed to maximize the joint detection probabilities of the events in the region of interest by the sensors. Simulations illustrate the results.
Toolkits for User Innovation and Design: an Exploration of User Interaction and Value Creation in the Watch Market

This study is the first in-depth empirical analysis of design process and outcome of socalled “toolkits for user innovation and design”, a very recent method for integrating customers in new product development and product design. Evidence from four experiments conducted in the watch market shows that user-designers engage in learning-by-doing behavior and yield highly heterogeneous new products. Their willingness to pay for their self-designed products exceeds that for the bestselling standard watches of the same technical quality up to 200%.
Genetic network programming with sarsa learning and its application to creating stock trading rules

In this paper, trading rules on stock market using the genetic network programming (GNP) with Sarsa learning is described. GNP is an evolutionary computation, which represents its solutions using graph structures and has some useful features inherently. It has been clarified that GNP works well especially in dynamic environments since GNP can create quite compact programs and has an implicit memory function. In this paper, GNP is applied to creating a stock trading model. There are three important points: The first important point is to combine GNP with Sarsa learning which is one of the reinforcement learning algorithms. Evolution-based methods evolve their programs after task execution because they must calculate fitness values, while reinforcement learning can change programs during task execution, therefore the programs can be created efficiently. The second important point is that GNP uses candlestick chart and selects appropriate technical indices to judge the timing of the buying and selling stocks. The third important point is that sub-nodes are used in each node to determine appropriate actions (buying/selling) and to select appropriate stock price information depending on the situation. In the simulations, the trading model is trained using the stock prices of 16 brands in 2001, 2002 and 2003. Then the generalization ability is tested using the stock prices in 2004. From the simulation results, it is clarified that the trading rules of the proposed method obtain much higher profits than Buy&Hold method and its effectiveness has been confirmed.
Using clinical preferences in argumentation about evidence from clinical trials

Medical practice is increasingly based on the best available evidence, but the volume of information requires many clinicians to rely on systematic reviews rather than the primary evidence. However, these reviews are difficult to maintain, and often do not appear transparent to clinicians reading them. In a previous paper, we have proposed a general language for representing knowledge from clinical trials and a framework that allows reasoning with that knowledge in order to construct and evaluate arguments and counterarguments that aggregate that knowledge. However, clinicians need to feel that such a framework is responsive to their assessment of the strengths and weaknesses of different types of evidence. In this paper, we use a specific version of this existing framework to show how we can capture clinical preferences over types of evidence, and we evaluate this in a pilot study, comparing our system against the choices made by clinicians. This pilot study shows how individual clinicians aggregate evidence based on their preferences over the relative significance of the items of evidence, and it shows how our argumentation system can replicate this behaviour.
Diverse clocking strategy in MQCA

Nano-magnets are the basic building blocks of Magnetic QCA. Initially CMOS circuits were used to construct clocking circuit for MQCA logic device. CMOS circuits dissipate high energy; as a result huge amount of power-loss occurs. In this paper we have presented the various clocking strategy built with nano-magnets which reduces the power consumption to a large extent. The advantage of Three-phase clocking architecture with and without phase overlapping is also demonstrated.
Calculation of Sensitivity Coefficients for Individual Airport Emissions in the Continental U.S. using CMAQ-DDM/PM

Fine particulate matter (PM2.5) is a federally-regulated air pollutant with well-known impacts on human health. The FAA's Destination 2025 program seeks to decrease aviation-related health impacts across the U.S. by 50% by the year 2018. Atmospheric models, such as the Community Multiscale Air Quality model (CMAQ), are used to estimate the atmospheric concentration of pollutants such as PM2.5. Sensitivity analysis of these models has long been limited to finite difference and regression-based methods, both of which require many computationally intensive model simulations to link changes in output with perturbations in input. Further, they are unable to offer detailed or ad hoc analysis for changes within a domain, such as changes in emissions on an airport-by-airport basis. In order to calculate the sensitivity of PM2.5 concentrations to emissions from individual airports, we utilize the Decoupled Direct Method in three dimensions (DDM-3D), an advanced sensitivity analysis tool recently implemented in CMAQ. DDM-3D allows calculation of sensitivity coefficients within a single simulation, eliminating the need for multiple model runs. However, while the output provides results for a variety of input perturbations in a single simulation, the processing time for each run is dramatically increased compared to simulations conducted without the DDM-3D module. Use of the XSEDE Stampede computing cluster allows us to calculate sensitivity coefficients for a large number of input parameters. This allows for a much wider variety of ad hoc aviation policy scenarios to be generated and evaluated than would be possible using other sensitivity analysis methods or smaller-scaled computing systems. We present a design of experiments to compute individual sensitivity coefficients for 139 major airports in the US, due to six different precursor emissions that form PM2.5 in the atmosphere. Simulations based on this design are currently in progress, with full results to be published at a later date.
MATHEMATICAL ENGINEERING TECHNICAL REPORTS Generalized SSA and Its Applications to Watermarking 3D Polygonal Meshes

This paper presents a generalization of the singular spectrum analysis (SSA) and applies it to the construction of a new robust watermarking method that adds a watermark to a 3D polygonal mesh in the spectral domain. The SSA is originally designed as a tool for analyzing one-dimensional sequence such as time series, and hence it is not suitable for multi-dimensional data. In order to overcome this difficulty, we generalize the basic SSA in such a way that it can be used for the 3D polygonal meshes. Watermarks embedded by this method are resistant to similarity transformations and random noises.
Scalable video transmission over multiuser MIMO-OFDM systems

With the proliferation of wireless services, multimedia interactivities are quickly becoming ubiquitous. As multimedia traffics generally have large packets volume, high data rate requirements in wireless transmission are critical. Next generation wireless systems, e.g., multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM), provide high throughput and support flexible resource management strategies for multimedia services. By fully exploiting spatial, temporal, and frequency diversities of MIMO-OFDM systems, intelligent resource allocation schemes can increase the overall performance of the multimedia streaming system significantly. In this paper, we describe a general framework of cross-layer resource allocation design for scalable video transmission over multiuser MIMO-OFDM systems. Scalable video coding provides an efficient solution for video adaptation to satisfy diverse requirements from heterogeneous mobile clients according to their system specifications and channel conditions. Generally, the base layer of a scalable video bitstream is more important than the enhancement layer as the enhancement layer can only be used for decoding if the base layer is available. Scalable video packets are entitled with different priorities in video reconstructions. Adaptation can be achieved by discarding some enhancement layer packets when network is congested. Based on the characteristics of scalable video, our objective is to optimize the overall system performance for multiple scalable video downlink over the Space Division Multiple Access (SDMA)-OFDM system from a multiple-antenna base station. Our cross-layer optimization is achieved by jointly linking packet prioritization from application layer and radio resource allocation at the physical layer. Based on packet priority of scalable video, time-frequency resource, power and modulation schemes are adaptively selected based on a prioritized water filling algorithm to maximize the overall system performance and to ensure fairness among different users. The performance of the proposed strategy is demonstrated by experimental comparisons with conventional radio resource allocation schemes.
Design of a variable stiffness flexible manipulator with composite granular jamming and membrane coupling

Robotic manipulators for minimally invasive surgeries have traditionally been rigid, with a steerable end effector. While the rigidity of manipulators improve precision and controllability, it limits reachability and dexterity in constrained environments. Soft manipulators with controllable stiffness on the other hand, can be deployed in single port or natural orifice surgical applications to reach a wide range of areas inside the body, while being able to passively adapt to uncertain external forces, adapt the stiffness distribution to suit the kinematic and dynamic requirements of the task, and provide flexibility for configuration control. Here, we present the design of a snake-like laboratory made soft robot manipulator of 20 mm in average diameter, which can actuate, soften, or stiffen joints independently along the length of the manipulator by combining granular jamming with McKibben actuators. It presents a comprehensive study on the relative contributions of the granule size, material type, and membrane coupling on the range, profile, and variability of stiffness.
Connectionist Speech Recognition: Status and Prospects

We report on recent advances in the ICSI connectionist speech recognition project. Highlights include: • Experimental results showing that connectionist methods can improve the performance of a context independent maximum likelihood trained HMM system, resulting in a performance close to that achieved using state of the art context dependent HMM systems of much higher complexity. • Mixing (context independent) connectionist probability estimates with maximum likelihood trained context dependent models to improve the performance of a state of the art system • The development of a network decomposition method that allows connectionist modelling of context dependent phones eeciently and parsimoniously, with no statistical independence assumptions.
The histological characteristics and clinical outcomes of lung cancer in patients with combined pulmonary fibrosis and emphysema

Combined pulmonary fibrosis and emphysema (CPFE) is an important risk factor for lung cancer (LC), because most patients with CPFE are smokers. However, the histological characteristics of LC in patients with CPFE (LC‐CPFE) remain unclear. We conducted this study to explore the clinicopathological characteristics of LC‐CPFE. We retrospectively reviewed data from 985 patients who underwent resection for primary LC, and compared the clinicopathological characteristics of patients with LC‐CPFE and non‐CPFE LC. We identified 72 cases of LC‐CPFE, which were significantly associated with squamous cell carcinoma (SqCC) histology (n = 46, P < 0.001) and higher tumor grade (n = 44, P < 0.001), compared to non‐CPFE LC. Most LC‐CPFE lesions were contiguous with fibrotic areas around the tumor (n = 59, 81.9%), and this association was independent of tumor location. Furthermore, dysplastic epithelium was identified in the fibrotic area for 31 (52.5%) LC‐CPFE lesions. Moreover, compared to patients with pulmonary fibrosis alone in the non‐CPFE group (n = 31), patients with CPFE were predominantly male (P = 0.008) and smokers (P < 0.001), with LC‐CPFE predominantly exhibiting SqCC histology (P = 0.010) and being contiguous with the tumor‐associated fibrotic areas (P < 0.001). Multivariate analysis revealed that CPFE was an independent predictor of overall survival (hazard ratio: 1.734; 95% confidence interval: 1.060–2.791; P = 0.028). Our results indicate that LC‐CPFE has a distinct histological phenotype, can arise from the dysplastic epithelium in the fibrotic area around the tumor, and is associated with poor survival outcomes.
Computerized Medical Imaging and Graphics an Improved Representation of Regional Boundaries on Parcellated Morphological Surfaces

Establishing the correspondences of brain anatomy with function is important for understanding neu-roimaging data. Regional delineations on morphological surfaces define anatomical landmarks and help to visualize and interpret both functional data and morphological measures mapped onto the cortical surface. We present an efficient algorithm that accurately delineates the morphological surface of the cerebral cortex in real time during generation of the surface using information from parcellated 3D data. With this accurate region delineation, we then develop methods for boundary-preserved simplification and smoothing, as well as procedures for the automated correction of small, misclassified regions to improve the quality of the delineated surface. We demonstrate that our delineation algorithm, together with a new method for double-snapshot visualization of cortical regions, can be used to establish a clear correspondence between brain anatomy and mapped quantities, such as morphological measures, across groups of subjects.
Millimeter-wave IC design techniques for beam-forming applications

The paper presents some key circuit design techniques for millimeter-wave ICs, especially for beam-forming applications. Firstly, the world-first CMOS SPDT switches operating beyond 140 GHz are introduced. Two prototypes, at 130-180 GHz and 220-285 GHz respectively, demonstrate the magnetic switchable artificial resonator concept and achieve size reduction over 90% (in terms of wavelength). Secondly, two 60-GHz fine-resolution phase shifters are presented, one with 3-bit 90° phase control capability while the other one featured with 5-bit 360°. Lastly, a 60-GHz bidirectional switchless LNAPA is demonstrated. All the works are measured and achieve excellent performance.
Light hadron spectroscopy using domain wall valence quarks on an asqtad sea

We present a calculation of the light-hadron spectrum in full QCD using three-flavor Asqtad sea quarks and domain wall valence quarks. Calculations of meson masses, meson and baryon masses are performed on a lattice of spatial size $$L\simeq 2.5$$~fm, and a lattice spacing of $$a \simeq 0.124$$~fm, for pion masses as light as $$m_\pi \sim 300$$~MeV. Extrapolation of baryon masses to the physical pion mass is studied using continuum and mixed action heavy baryon chiral perturbation theory for two and three flavors. The success and limitations of chiral perturbation theory with these extrapolations is discussed.
An open-source tool for negation detection: a maximum-margin approach

This paper presents an open-source toolkit for negation detection. It identifies negation cues and their corresponding scope in either raw or parsed text using maximum-margin classification. The system design draws on best practice from the existing literature on negation detection, aiming for a simple and portable system that still achieves competitive performance. Pre-trained models and experimental results are provided for English.
The Validity and Sensitivity of Debt Signaling Hypothesis in Transitional Market: Perspectives from Egypt

This paper examines the signaling hypothesis of firm's debt on its market value in a transitional market. The methodology addresses at the first stage the determination of the relevant determinants of capital structure in a transitional economy. Next, the potential signaling effects of the relevant determinants of capital structure are examined. The robustness of the signaling effect is examined using the 'Extreme Bound Analysis'. The overall results show significant consistency which indicates that financial flexibility and interest rates are robust and significant determinants of capital structure and have signaling effects on market value. The results are consistent with other research in determinants of capital structure in other developing countries and with other related research methodology. This offers a source of validity to the relevant determinants of capital structure and their signaling effect in Egypt as a transitional market.
PDE and Monte Carlo approaches to solving the master equation applied to gene regulation

Stochastic models of biochemical reaction networks are used for understanding the properties of molecular regulatory circuits in living cells. The state of the cell is defined by the number of copies of each molecular species in the model. The chemical master equation (CME) governs the time evolution of the the probability density function of the often high-dimensional state space. The CME is approximated by a partial differential equation (PDE), the Fokker-Planck equation and solved numerically. Direct solution of the CME rapidly becomes computationally expensive for increasingly complex biological models, since the state space grows exponentially with the number of dimensions. Adaptive numerical methods can be applied in time and space in the PDE framework, and error estimates of the approximate solutions are derived. A method for splitting the CME operator in order to apply the PDE approximation in a subspace of the state space is also developed. The performance is compared to the most widely spread alternative computational method.
Towards a Learning Analytics Support for Intelligent Tutoring Systems on MOOC Platforms

Nowadays, many MOOC platforms have arisen to provide free knowledge. These platforms have a large catalog of courses for different specializations that progressively demand more specific learning resources and assessment methods to evaluate the progression of students. Current MOOC platforms are gradually giving support to these new requirements but with a limited assistance. This paper presents the state of art of the analytical system for three relevant MOOC platforms, one of the main pillars for analyzing the progression of courses. Other initiatives are also reviewed to show that current MOOC analytical systems are not ready to support custom MOOC-aware intelligent tutoring systems (ITSs). Thus, the design of a learning analytics system to assist these tools for MOOC platforms is presented.
Statistical modelling of fishing activities in the North Atlantic

This paper deals with the issue of modeling daily catches of fishing boats in the Grand Bank fishing grounds. We have data on catches per species for a number of vessels collected by the European Union in the context of the North Atlantic Fisheries Organization. Many variables can be thought to influence the amount caught: a number of ship characteristics (such as the size of the ship, the fishing technique used, the mesh size of the nets, etc.), are obvious candidates, but one can also consider the season or the actual location of the catch. In all, our database leads to 23 possible regressors, resulting in a set of 8:4£106 possible linear regression models. Prediction of future catches and posterior inference will be based on Bayesian model averaging, using a Markov Chain Monte Carlo Model Composition (MC3) approach. Particular attention is paid to the elicitation of the prior and the prediction of catch for single and aggregated observations.
What types of countries do people want aid money to go to ?

This paper analyses which recipient-country characteristics are the most important to people considering giving aid money to an NGO to spend in a developing country. For example, do most people prefer to give money to a country where average incomes are low, or to one with high child mortality rates? Do they prefer to support a country whose people are starving or a country with close geographic ties to their own? We investigate the relative importance of country characteristics to potential private donors using a method that combines experimental economics with conjoint analysis. We focus on five country characteristics: average income per person, ties to the donor country, the level of hunger and malnutrition, the quality of infrastructure and the child mortality rate. Our results suggest that hunger and malnutrition is the most important attribute, with ties to the donor country the least important attribute.
Temperature-Aware Routing for Telemedicine Applications in Embedded Biomedical Sensor Networks

Biomedical sensors, called invivo sensors, are implanted in human bodies, and cause some harmful effects on surrounding body tissues. Particularly, temperature rise of the invivo sensors is dangerous for surrounding tissues, and a high temperature may damage them from a long term monitoring. In this paper, we propose a thermal-aware routing algorithm, called least total-route-temperature (LTRT) protocol, in which nodes temperatures are converted into graph weights, and minimum temperature routes are obtained. Furthermore, we provide an extensive simulation evaluation for comparing several other related schemes. Simulation results show the advantages of the proposed scheme.