Miracle-dz/UniFAR-FTUs huggingface dataset:
A novelty Bayesian method for unsupervised learning of finite mixture models

Mixture models have universal applications in probabilistic modeling for multivariate data. This paper proposes a novelty parameters estimation and model selection method based on half Bayesian. In our algorithm, the mixture coefficient is a determinate variable, and the parameters of the components are random variables. Owing to the special prior distribution of the parameters, the parameters don't converge toward a singular estimation at the boundary of the parameter space, and the redundant components can be automatically removed. In a word, our algorithm has very good performances as follows: (1) automatically selects the number of components, (2) avoids converging the boundary of the parameter space, (3) is less sensitive to initialization, (4) can fulfil simultaneously the parameters estimation and model selection in one algorithm, therefore, the computation efficiency is higher. The experimental results show that the algorithm is effective and has above good performances.
- Reified Input/Output logic - a position paper

We propose a new approach to formalize obligations and permissions from existing legislation. Specifically, we propose to combine two frameworks: Input/Output logic and the logic of prof. J.R. Hobbs. The former is a well-known framework in normative reasoning. The latter is a neo-Davidsonian wide-coverage first order logic for Natural Language Semantics. We propose to wrap Input/Ouput logic around Hobbs’s logic, in order to fill the gap between current logical formalizations of legal text, mostly propositional, and the richness of Natural Language Semantics.
Transporting JPEG Images over MIMO Links in Ad-Hoc Networks: an Investigation

This paper reports an investigation into the transport of sensor-generated still images over, ad-hoc, energy- limited networks. MIMO technology was considered as a means of increasing the data rate over network links and thereby the image throughput of the network as a whole. The combining effect of MIMO block size, packet size, and the JPEG compression technique on the distortion of the received images was of particular interest. In addition, we consider two forms of receiving mechanism: maximum likelihood (ML) and the less complex zero-forcing (ZF). We centered our investigation on simulating link behavior starting with a base of MIMO models written in LabView that were previously developed at the University of Texas. Additions and modifications made to this base, verifying calculations written in Matlab, and the utility of Labview and Matlab in the investigation are described. JPEG images were transported over the resulting Labview simulation through Rayleigh fading and AWGN over a range of signal-to- noise ratios, with SISO, 2times2 MIMO, QPSK modulation, ML, and ZF receivers. The resulting bit error rate vs. SNR curves are presented and are in agreement with theory. In addition the resulting JPEG image distortion is described.
Large-scale hydraulic conductivities inferred from three-dimensional groundwater flow and 4He transport modeling in the Carrizo aquifer, Texas

[1] Through a series of groundwater flow and 4He transport simulations, this study illustrates the conceptual and practical gains achieved by expanding a two-dimensional (2-D) model to a true 3-D one through an application in the Carrizo aquifer and surrounding formations in southwestern Texas. The 3-D simulations allow for a more detailed and accurate definition of the heterogeneities of the system by specifically identifying and differentiating processes that directly impact the three-dimensional hydraulic conductivity field. It is shown that while hydraulic conductivity decreases exponentially along the regional groundwater flow direction, such decrease is better described as a function of depth rather than recharge distance. This relationship reflects the combined influences of differential compaction of the media as well as downdip lithological change. The intrinsic permeability derived from this relationship agrees with field information and with previous findings obtained for the continental crust for depths ≤2 km, suggesting that for large scales, decrease rate of permeability with depth is independent of the media. Results also suggest that the solution for groundwater flow simulations based on calibration of hydraulic heads depends on the ratio between hydraulic conductivities of different formations, indicating that an infinite number of solutions are available for calibration of 3-D groundwater flow models. Understanding how geological processes directly affect the 3-D hydraulic conductivity field at the regional scale is essential not only to hydrogeological applications but also at improving our understanding of the Earth's crust and mantle dynamics by allowing for a more accurate quantification of helium and heat fluxes.
The Organizational Culture's Influence on Risks in IT Projects - a Structuration Perspective

A revelatory case study has been conducted to investigate the influence of the organizational culture on risks in IT projects. Following the Grounded Theory Methodology, we interviewed 26 experts from a large financial service provider. We selected a company from the cooperative banking sector, which makes this case unique. With Giddens’ Structuration Theory as a theoretical lens, we show how the structural and cultural conditions of an organization influence risks in IT projects and project portfolio management.
Wrist-Gathered Acceleration Data and their Correlation with Physical Activity in the Elderly

Estimating physical activity in the elderly from wrist-gathered acceleration data was studied. Thirty individuals (65+ years) were video-recorded while wearing a wrist device and going about their normal activities within their regular living environment for four hours each. Acceleration data were summarized into an activity value [via the “differential signal magnitude” (DSM) method] and compared to metabolic equivalent of task (MET) values determined by video analysis for each time period (“epoch”). Different sampling rates and epoch sizes were evaluated. Sampling at 4 Hz and using 60-second epochs provided the best results, with a moderate Pearson’s correlation coefficient of 0.58 between DSM activity values and MET values. The area under the receiver operating characteristic curve (AUC) for classifying each minute of data as active (MET >= 2.0) versus moderately active (MET > 1.2 and < 2.0) was 0.87 (sensitivity 80%, specificity 79%). DSM activity values (sampling at 4 Hz) were compared to the widely known signal magnitude area (SMA) values (requiring low-pass filtering and sampling at 40 Hz), with an excellent correlation of 0.994.
Spoofing detection games in underwater sensor networks

Spoofing detection is essential in underwater sensor networks (UWSNs), especially for safety-critical applications. Because of the high bit error rates, low bandwidth and large delay of the acoustic channels, it is challenging to detect spoofing attacks in UWSNs. In this paper, we propose spoofing detection strategies based on reinforcement learning. The interactions between a surface station and an underwater spoofer are formulated as a zero-sum game. The Nash equilibrium (NE) of the spoofing detection game in UWSNs is derived and the existing condition for a unique NE is provided. A spoofing detection method based on Q-learning is proposed for dynamic underwater sensor networks. Simulation results have validated the efficacy of the proposed spoofing detection scheme in UWSNs.
Precariedad laboral y heterogeneidad ocupacional: una propuesta teórico-metodológica

Este articulo aborda la precariedad laboral como parte de la discusion sobre las nuevas y diversas formas de trabajo, que se caracterizan por su condicion inestable e insegura. Para profundizar en el caso mexicano, formulamos una propuesta analitica que destaca las diversas condiciones sociales objetivas de la precariedad y los factores subjetivos de la perdida de seguridad desde la experiencia de los sujetos sociales. Esta discusion teorica se ilustra con tres ocupaciones contrastantes: la de la industria de la confeccion, la de los call centers y la de los musicos profesionales.
On feedback and the classical capacity of a noisy quantum channel

In Shannon information theory the capacity of a memoryless communication channel cannot be increased by the use of feedback. The use of classical feedback is shown to provide no increase in the classical capacity of a memoryless quantum channel when feedback is used across nonentangled input states, or when the channel is an entanglement-breaking channel
DETECT2000: an improved Monte-Carlo simulator for the computer aided design of photon sensing devices

We introduce a new version of DETECT. DETECT is a Monte-Carlo simulator developed for the Computer Aided Design (CAD) of optical photon sensing devices. The simulator generates individual emission photons in specified locations of a photon-emitting device and tracks their passage and interactions in active and passive components of the system. Extensive options are available in the simulator to model the geometry of the photon sensing device, to account for the time and wavelength distribution of emission photons, to track their interactions with surfaces, to account for their possible absorption and re-emission by a wave-shifting components and to model their detection by pixelated photomultipliers or photodiodes. DETECT2000 is a very significant upgrade of DETECT97, which has long been established in the nuclear medicine instrumentation community for its accuracy to model the performances of high resolution energy and position sensitive gamma-ray detectors. The 2000 version of DETECT offers an accelerated version of the simulator which has been redesigned in the object-oriented C++ language. New features such as the tracking of the time and wavelength history of individual optical photons have been added.
Calculation of signal outage due to polarization mode dispersion

A method to calculate signal outage due to polarization mode dispersion (PMD), i.e., PMD outage, when differential group delay, as well as principal state of polarization are varying is presented. The most common way to analyze this problem has been based on assuming worst case polarization, which will give too high a value for PMD outage.
Movement of deep-sea coral populations on climatic timescales

During the past 40,000 years, global climate has moved into and out of a full glacial period, with the deglaciation marked by several millennial-scale rapid climate change events. Here we investigate the ecological response of deep-sea coral communities to both glaciation and these rapid climate change events. We find that the deep-sea coral populations of Desmophyllum dianthus in both the North Atlantic and the Tasmanian seamounts expand at times of rapid climate change. However, during the more stable Last Glacial Maximum, the coral population globally retreats to a more restricted depth range. Holocene populations show regional patterns that provide some insight into what causes these dramatic changes in population structure. The most important factors are likely responses to climatically driven changes in productivity, [O_2] and [CO_3^(2–)].
Endogenous Growth in a Model with Heterogeneous Agents and Voting on Public Goods

We consider a Barro-type endogenous growth model in which the government's purchases of goods and services enter into the production function. The provision of government services is financed by flat-rate (linear) income or lump-sum taxes. It is assumed that individuals differing in their discount factors vote on the tax rates. We propose a concept of voting equilibrium leading to some versions of the median voter theorem for steady-state equilibria, fully characterize steady-state equilibria and show that if the median voter discount factor is sufficiently low, the long-run rate of growth in the case of flat-rate income taxation is higher than that in the case of lump-sum taxation.
Optimal Control of Towing Kites

In this paper we present a challenging application of periodic optimal control. A kite that is towing a ship into a given target direction should fly optimal loops. We show how to find the maximum average tractive force by controlling the roll angle of the towing kite taking into account that the wind is increasing with the altitude over the sea. The optimal control problem for this highly nonlinear and unstable system has periodicity constraints, free initial values, and a free cycle duration. For its solution, we use MUSCOD-II, an optimal control package based on the direct multiple shooting method. Finally, we discuss the influence of an important design parameter, the effective glide ratio of the kite
Wideband & wide angle scanning array with parasitic superstrate

We present a wideband Tightly Coupled Dipole Array (TCDA) with integrated balun and novel parasitic superstrate capable of wide angle scanning. Although previous TCDAs have been shown to have good scanning performances, use of dielectric superstrates are required, implying additional costs and weight. In this paper, we replace the solid dielectric with parasitic periodic elements for wide angle and wideband impedance matching. The proposed design achieves unprecedented 6.1:1 bandwidth with VSWR<;3.2 when scanning ±60° in H-plane and ±75° in E-plane. The lossless parasitic elements are printed on the same PCB as the radiating elements and the integrated balun, resulting in a low-cost and light-weight design as compared to conventional TCDAs with dielectric loading.
The novel hybrid Modified Particle Swarm Optimization – Neural Network ( MPSO-NN ) Algorithm for classifying the Diabetes

Diabetes Mellitus (DM) is a chronic, lifelong metabolism disorder. It affects the ability of the body system to use the energy found in food. The improper management of the disease will lead to Heart disease, kidney disease, eye disease, nerve disease and pregnancy complications. Classification model helps physicians to improve their prognosis, diagnosis or treatment planning procedures. It is most widely utilized in medical domain to explore patient’s data and extract the knowledge from it. This paper aims in developing a novel hybrid classification algorithm for diabetes classification. The Particle Swarm Optimization algorithm is modified and combined with Multi Layer Perceptron Network with back propagation learning and is named as novel Modified Particle Swarm Optimization-Neural Network (MPSO-NN) Algorithm to classify the diabetic patients as positive as binary 0 and negative as binary 1. The metrics like Sensitivity, Specificity, False Positive Rate, Accuracy, Mean Squared Error, Regression, and Percentage of Error is used to evaluate the performance of the algorithm. It is found that the proposed novel hybrid algorithm is better in overall performance in classifying the data set.
Aspect Extraction and Sentiment Classification of Mobile Apps using App-Store Reviews

Understanding of customer sentiment can be useful for product development. On top of that if the priorities for the development order can be known, then development procedure become simpler. This work has tried to address this issue in the mobile app domain. Along with aspect and opinion extraction this work has also categorized the extracted aspects ac-cording to their importance. This can help developers to focus their time and energy at the right place.
Faster Detection of Step Initiation for the Lower Limb Exoskeleton with Vertical GRF Events

We propose a new approach called as a peak time approach for faster detection of step initiation for the lower limb exoskeleton. As faster detection of step initiation is an important criterion in evaluating the lower limb exoskeleton, many studies have investigated approaches to detect step initiation faster, including using electromyography, the center of pressure, the heel-off time and the toe-off time. In this study, we will utilize vertical ground reaction force events to detect step initiation, and compare our approach with prior approaches. Additionally, we will predict the first step's heel strike time with vertical ground reaction force events from multiple regression equations to support our approach. The lower limb exoskeleton should assist the operator's movement much faster and more reliably with our approach.
Selection of weak VARMA models by modified Akaike's information criteria

This article considers the problem of order selection of the vector autoregressive moving‐average (VARMA) models under the assumption that the errors are uncorrelated but not necessarily independent. These models are called weak VARMA by opposition to the standard VARMA models, also called strong VARMA models, in which the error terms are supposed to be i.i. d. We relax the standard independence assumption to extend the range of application of the VARMA models, allowing us to treat linear representations of general nonlinear processes. We propose a modified version of the Akaike information criterion for identifying the orders of weak VARMA models.
Physician Knowledge, Assessment, and Reporting of Older Driver Fitness

ABSTRACT Physicians have the potential to serve as an important portal for information gathering, assessment, counseling, and reporting older driver fitness, as almost all older adults require medical care and have a primary care physician. However, there are few studies that have evaluated physician knowledge about, attitudes toward, and performance of older driver fitness assessment. Two pilot studies were conducted to assess physician knowledge and attitudes and aid understanding of physician knowledge of legal reporting requirements regarding older driver medical fitness. Results suggest that although physicians believe that patients should be evaluated for safe driving, many physicians do not routinely assess fitness to drive and few feel qualified to do so. It also appears that physicians may not be adequately knowledgeable about laws about reporting unsafe drivers. Thus, occupational therapy practitioners have an opportunity to educate about driving as a complex instrumental activity of daily living.
MALDI-MSI : Biomarker Discovery for Radiation Exposures

Once outside of the Earth’s atmosphere astronauts are exposed to two main types of radiation, galactic cosmic rays (GCR) and solar energetic particles. GCRs consist of high energy, charged nuclei of elements ranging from H (hydrogen) to Fe (iron). High energy, charged nuclei of H (protons) are the major component of GCRs whereas approximately 1% of GCRs are larger ions with charges greater than 2+, which are referred to as ions of high (H) atomic number (Z) and energy (E) or HZE ions. Solar energetic particles which occur during solar particle events (SPE) are dominantly protons. Due to the differences in radiation type, dose-rate and energy that astronauts experience compared to radiation encountered on Earth, relatively little is known about the biological effects of exposure during manned space missions. Additionally, much of the current models used to calculate exposure risk are based on extrapolated data obtained on Earth. Increased risk of cancer, CNS disease and cataracts are real-life issues faced by astronauts. Radioactive material has also been used for decades in industry, medicine and weapons of mass destruction. Radiation exposure (accidental or deliberate) as well as radiation therapy used to treat lung cancers often results in radiation-induced lung injury (pneumonitis and fibrosis), which can be fatal . The need for medical countermeasures (MCM) for treatment or mitigation in the event of accidental or intentional radiation exposures is evident. In order to develop MCMs, a clearer understanding of the mechanisms of action by which radiation induces tissue/organ damage is needed, as currently these mechanisms remain incompletely understood. What is evident through the literature in both space radiation and terrestrial radiation studies is that oxidative stress/damage plays a central role in pathologies, and many of the countermeasures evaluated to date have been antioxidants. The identification of biomarkers for radiation damage/syndromes will not only enable the development and evaluation of MCMs, but also aid in identifying biological readouts that will allow for efficient diagnosis following exposures, which may be vital for survival.
Functionalized micromolded nanoparticles towards gas sensor arrays

Chemiresistive sensors show promise for low-cost gas monitoring applications, but their low selectivity raises concerns about false positive response. We present a patterning process with self-aligned functionalization mask for gas-sensitive nanoparticles in multiplexed arrays. The nanoparticle patterning features are laser micromachined alongside mask features in a polymer template during the same process step. Nanoparticle ink is introduced to the fabricated template, creating cohesive features without a residual layer. The template is selectively removed to create a mask to expose, passivate, or functionalize the templated nanoparticles. We fabricate an Ag-on-ZnO chemiresistor and demonstrate device response to a CO/Ar mixture gas.
Ex Ante Scale Dynamics Analysis in the Policy Debate on Sustainable Biofuels in Mozambique

In this paper, we explore how ex ante scale dynamics analysis can contribute to better understanding of interactions between scales and levels, and how these interactions influence solution space in policy processes. In so doing, we address opportunities and challenges of conducting ex ante scale dynamics analysis as part of an action-oriented social science research approach that seeks to enhance its contribution to more scale-sensitive policy development. The policy debate on sustainable biofuels in Mozambique provides the empirical context in which we analyze interactions across administrative, institutional, and economic scales and levels, and how these interactions influence the space in which policy solutions can be explored and designed. On the basis of the analysis, we conclude that ex ante scale dynamics analysis can contribute to: (1) increasing awareness of interactions between scales and levels, and their implications for policy, (2) identifying immediate and potential matches and mismatches between scales and levels, and developing (adaptive) capacity to address them, and (3) identifying stakeholders and their scale- and level-related interests that can provide the basis for collaborative multi-stakeholder learning. Consequently, ex ante scale dynamics analysis can provide an important contribution to balancing and harmonizing interactions across different scales and levels, from which innovative and scale-sensitive policy responses can emerge. As part of an action-oriented, social science research approach, careful attention needs to be paid to processes of scale and level inclusion and exclusion when conducting scale dynamics analysis.