Miracle-dz/UniFAR-FTUs huggingface dataset:
Sustainability of Healthcare Information Exchanges: A Game-Theoretic Approach

Based on our interactions with the key personnel of three different healthcare information exchange (HIE) providers in Texas, we develop models to study the sustainability of HIEs and participation levels in these networks. We first examine how heterogeneity among healthcare practitioners (HPs) (in terms of their expected benefit from the HIE membership) affects participation of HPs in HIEs. We find that, under certain conditions, low-gain HPs choose not to join HIEs. Hence, we explore several measures that can encourage more participation in these networks and find that it might be beneficial to (i) establish a second HIE in the region, (ii) propose more value to the low-gain HPs, or (iii) offer or incentivize value-added services. We present several other interesting and useful results that are somewhat counterintuitive. For example, increasing the highest benefit the HPs can get from the HIE might decrease the number of HPs that want to join the HIE. Furthermore, since the amount of funds from the government and the other agencies often changes (and will eventually cease), we analyze how the changes in the benefit HPs obtain from the HIE affect (i) participation in the network, (ii) the HIE subscription fee and the fee for value-added service, (iii) the number of HPs that request value-added service, and (iv) the net values of the HIE provider and HPs. We also provide guidelines for policy makers and HIE providers that may help them improve the sustainability of HIEs and increase the participation levels in these networks.
High optoelectronic performance of layer-by-layer assembled carbon nanotube thin films as transparent electrodes for LC cell

Highly transparent and conductive thin films were assembled as a potential indium tin oxide (ITO) replacement using layer-by-layer (LbL) assembly with carbon nanotubes (CNTs), sodium deoxycholate (DOC) as a stabilizer and poly(diallyldimethyl ammonium chloride) [PDDA] as polycations. This LbL adsorption of both species has linear increase independent on number of bilayers deposited. The self-assembly of DOC-stabilized CNTs and PDDA was shown to realize the thinner and smoother films with transparency (<84% T) and electrical conductivity (~300 Ω/sq) with a 23.5 nm thickness. Moreover, nitric acid doping was also treated for higher conductivity (~104 Ω/sq) due to the removal of insulating materials and the charge transfer doping. The optoelectronic performance of 5 BL DWNT LbL film here is much better than most other CNT thin films and capable of the ITO replacement. Then, the CNT LbL thin films were applied to fabricate liquid crystal (LC) cells as transparent electrodes. Voltage-transmittance performance exhibited the availability of a CNT LbL electrode to LC display. Additionally, the bending stability could suggest that these films potentially be used in a variety of electronic applications. Also, the ability to tailor thin film resistance can be potentially useful for anti-static films and EMI shielding.
A numerical study on the aerodynamic performance of building cross-sections using corner modifications

A numerical investigation is performed in this work in order to evaluate the aerodynamic performance of building cross-section configurations by using corner modifications. The CAARC tall building model is utilized here as reference geometry, which is reshaped considering chamfered and recessed corners. The numerical scheme adopted in this work is presented and simulations are carried-out to obtain the wind loads on the building structures by means of aerodynamic coefficients as well as the flow field conditions near the model’s location. The explicit two-step Taylor-Galerkin scheme is employed in the context of the finite element method, where eight-node hexahedral finite elements with one-point quadrature are used for spatial discretization. Turbulence is described using the LES methodology, with a dynamic sub-grid scale model. Predictions obtained here are compared with experimental and numerical investigations performed previously. Results show that the use of corner modifications can reduce significantly the aerodynamic forces on the building structures, improve flow conditions near the building locations and increase the Strouhal number, which may have an important influence on aeroelastic effects.
Learning new articulator trajectories for a speech production model using artificial neural networks

We present a novel method for generating additional pseudo-articulator trajectories suitable for use within the framework of a stochastically trained speech production system. The system is initialised by inverting a codebook of (articulator, spectral vector) pairs, and the target positions for a set of pseudo-articulators and the mapping from these to speech spectral vectors are then jointly optimised using linearised Kalman filtering and an assembly of neural networks. A separate network is then used to hypothesise a new articulator trajectory as a function of the existing articulators and the output error of the system. The techniques used to initialise and train the system are described and preliminary results for the generation of new pseudo-articulatory inputs are presented.
Reinforcement learning for high-level fuzzy Petri nets

The author has developed a reinforcement learning algorithm for the high-level fuzzy Petri net (HLFPN) models in order to perform structure and parameter learning simultaneously. In addition to the HLFPN itself, the difference and similarity among a variety of subclasses concerning Petri nets are also discussed. As compared with the fuzzy adaptive learning control network (FALCON), the HLFPN model preserves the advantages that: 1) it offers more flexible learning capability because it is able to model both IF-THEN and IF-THEN-ELSE rules; 2) it allows multiple heterogeneous outputs to be drawn if they exist; 3) it offers a more compact data structure for fuzzy production rules so as to save information storage; and 4) it is able to learn faster due to its structural reduction. Finally, main results are presented in the form of seven propositions and supported by some experiments.
The NOESY jigsaw: automated protein secondary structure and main-chain assignment from sparse, unassigned NMR data

High-throughput, data-directed computational protocols for Structural Genomics (or Proteomics) are required in order to evaluate the protein products of genes for structure and function at rates comparable to current gene-sequencing technology. This paper presents the JIGSAW algorithm, a novel high-throughput, automated approach to protein structure characterization with nuclear magnetic resonance (NMR). JIGSAW applies graph algorithms and probabilistic reasoning techniques, enforcing first-principles consistency rules in order to overcome a 5-10% signal-to-noise ratio. It consists of two main components: (1) graph-based secondary structure pattern identification in unassigned heteronuclear NMR data, and (2) assignment of spectral peaks by probabilistic alignment of identified secondary structure elements against the primary sequence. JIGSAW's deferment of assignment until after secondary structure identification differs greatly from traditional approaches, which begin by correlating peaks among dozens of experiments. By deferring assignment, JIGSAW not only eliminates this bottleneck, it also allows the number of experiments to be reduced from dozens to four, none of which requires 13 C-labeled protein. This in turn dramatically reduces the amount and expense of wet lab molecular biology for protein expression and purification, as well as the total spectrometer time to collect data. Our results for three test proteins demonstrate that we are able to identify and align approximately 80 percent of α-helical and 60 percent of β-sheet structure. JIGSAW is very fast, running in minutes on a Pentium-class Linux workstation. This approach yields quick and reasonably accurate (as opposed to the traditional slow and extremely accurate) structure calculations, utilizing a suite of graph analysis algorithms to compensate for the data sparseness. JIGSAW could be used for quick structural assays to speed data to the biologist early in the process of investigation, and could in principle be applied in an automation-like fashion to a large fraction of the proteome.
COFFEE-CUP: A cost-efficient routing strategy for delay tolerant networks using time-varying community partitioning

Delay Tolerant Networks are a special kind of ad hoc networks that consist of a number of mobile devices. In DTNs, network topology frequently changes and source-to-destination communication paths can hardly be sustained. However, some social features have been observed in DTNs by analyzing the real mobile trajectories of DTNs. To utilize these social features to facilitate data routing, we present a community-based routing protocol that efficiently detects the community structure by using proposed time-varying community partitioning algorithm. For intracommunity routing, according to the remaining time-to-live of the message, the minimum number of message copies necessary to achieve a given delivery probability is calculated, which is the number of message copies utilized to spray in the current community. To facilitate intercommunity routing, we choose the gateway node that connects the next community frequently as relay. The evaluation of our strategy is conducted by extensive traced-driven simulations. The results of simulations confirm the effectiveness of our routing strategy.
Numerical Methods for Modeling Atomistic Trajectories with Diffusion SDEs

The stochastic dynamics of small scale systems are often not known from a priori physical considerations. We present data-driven numerical methods which can be used to approximate the nonlinear stochastic dynamics associated with time series of system observables. Given a single time series coming from a simulation or experiment, our approach uses maximum likelihood type estimates to obtain a sequence of local stochastic differential equations. The local models coming from one times series are then patched together using a penalized spline procedure. We provide an efficient algorithm for achieving this which utilizes estimates of the local parameter covariance. We also use goodness-of-fit tests to quantitatively determine when an overdamped Langevin approximation can be used to describe the data. For situations where the overdamped approximation fails, we show that other diffusive models can still be used to approximate the dynamics. In addition, we also briefly discuss how variation observed in different curves, calibrated from different time series, can provide information about “hidden” conformational degrees of freedom not explicitly included in the model and how clustering these curves can help one in learning about the effective underlying free energy surface governing the dynamics of the atomistic system. The methods presented are applied to simulations modeling forced time-dependent transport of potassium transport through a gramicidin A channel, but have applicability to other forced (and unforced) systems.
A Named Entity Recognition Method based on Decomposition and Concatenation of Word Chunks

We propose a named entity (NE) recognition method in which word chunks are repeatedly decomposed and concatenated. Our method identifies word chunks with a base chunker, such as a noun phrase chunker, and then recognizes NEs from the recognized word chunk sequences. By using word chunks, we can obtain features that cannot be obtained in word-sequence-based recognition methods, such as the first word of a word chunk, the last word of a word chunk, and so on. However, each word chunk may include a part of an NE or multiple NEs. To solve this problem, we use the following operators: SHIFT for separating the first word from a word chunk, POP for separating the last word from a word chunk, JOIN for concatenating two word chunks, and REDUCE for assigning an NE label to a word chunk. We evaluate our method on a Japanese NE recognition dataset that includes about 200,000 annotations of 191 types of NEs from over 8,500 news articles. The experimental results show that the training and processing speeds of our method are faster than those of a linear-chain structured perceptron and a semi-Markov perceptron, while maintaining high accuracy.
Model-checking Distributed Applications with GRAS

In this paper, we present our work to add a model-checker to the GRAS framework. This tool provides a development environment allowing debug real applications within the simulator before deploying them on real platforms. We discuss the main difficulties to integrate such a model-checker, and present our approach to solve them, as well as a prototype implementation.
In Vivo Stem Cell Imaging

In recent years, the emerging and advances of non-invasive in vivo stem cell imaging has significantly contributed to the real-time tracking of transplanted stem cells as well as monitoring their proliferation, migration and persistence in live animals and ultimately possibly in humans. This review summarized the different in vivo imaging modalities for imaging stem cell, especially for its monitoring viability, death and proliferation; and discussed the strategies of combined multimodality approaches for monitoring of the fate of transplanted stem cell by offering the opportunity to distinguish different biological and biochemical processes.
Wide Range Multiscale Entropy Changes through Development

How variability in the brain's neurophysiologic signals evolves during development is important for a global, system-level understanding of brain maturation and its disturbance in neurodevelopmental disorders. In the current study, we use multiscale entropy (MSE), a measure that has been related to signal complexity, to investigate how this variability evolves during development across a broad range of temporal scales. We computed MSE, standard deviation (STD) and standard spectral analyses on resting EEG from 188 healthy individuals aged 8-22 years old. We found age-related increases in entropy at lower scales (<~20 ms) and decreases in entropy at higher scales (~60-80 ms). Decreases in the overall signal STD were anticorrelated with entropy, especially in the lower scales, where regression analyses showed substantial covariation of observed changes. Our findings document for the first time the scale dependency of developmental changes from childhood to early adulthood, challenging a parsimonious MSE-based account of brain maturation along a unidimensional, complexity measure. At the level of analysis permitted by electroencephalography (EEG), MSE could capture critical spatiotemporal variations in the role of noise in the brain. However, interpretations critically rely on defining how signal STD affects MSE properties.
New Energy Efficiency Metric With Imperfect Channel Considerations for OFDMA Systems

Considering recent important efforts on green radio technologies, this letter deliberates on imperfect channel realizations to propose a new effective-energy-efficiency metric for downlink transmissions in multi-user multi-carrier orthogonal frequency-division multiple access (OFDMA) systems. To comply with the traditional energy efficiency formula under the assumption of perfect channel knowledge available, this new metric is designed on the fractional relation between the effective throughput and the total power consumed at the transmitter. Mathematical proofs are presented to confirm such compliance in terms of the impact of the channel coefficients on the energy efficiency performance. In order to prove the property, this letter also presents profound insight into the monotonicity of the non-centralized chi-squared distribution.
Digestive tract hemorrhage due to complications with gastrointestinal stromal tumor treated with sunitinib: A case report

Gastrointestinal stromal tumors (GISTs) are rare, and account for 1% of all gastrointestinal neoplasms. GISTs are the most frequent mesenchymal tumors of the gastrointestinal tract. However, the clinical and pathological characteristics of these neoplasms are not adequately understood. The best treatment approach for GISTs remains unclear. In the present study, we report a case of a GIST originating from the stomach. A digestive tract hemorrhage occurred as a complication of sunitinib treatment. This is the first report of a digestive tract hemorrhage due to sunitinib treatment.
The Sample Type used Affects the Levels of Gelatinases (MMP-2 and -9) and their Inhibitors (TIMP-1 and -2) in Circulating Blood of Healthy Controls and Breast Cancer Patients

New potential tumor markers such as matrix metalloproteinases and their inhibitors have been extensively studied during the last decades. The aim is to find prognostic markers that are measurable in easily available samples, such as serum or plasma. The proper sample type to use when measuring the levels of gelatinases and their inhibitors from blood samples is currently under critical evaluation. In this study, the effect of sample type is studied in 26 healthy controls, and the result is confirmed in a series of 80 breast carcinoma patients. Sample type had the most evident effect on the levels of TIMP-1 and MMP-9. Serum samples gave about two-fold levels of TIMP-1 compared to plasma samples (p < 0.001), with a strong linear correlation between these two (r = 0.79). Pro-MMP-9 levels were significantly affected by the presence of a blood coagulation activator in the serum sample, or a different anticoagulant in the plasma sample. The serum and plasma values had only a weak correlation (r = 0.37). In conclusion, sample type should be carefully considered, especially when measuring proMMP-9, and plasma should be preferred for this measurement. For TIMP-1 the correlation of serum and plasma values is good; the use of serum samples can therefore be justified as long as the generally higher levels in the serum are acknowledged.
Twitter Stance Detection — A Subjectivity and Sentiment Polarity Inspired Two-Phase Approach

The problem of stance detection from Twitter tweets, has recently gained significant research attention. This paper addresses the problem of detecting the stance of given tweets, with respect to given topics, from user-generated text (tweets). We use the SemEval 2016 stance detection task dataset. The labels comprise of positive, negative and neutral stances, with respect to given topics. We develop a two-phase feature-driven model. First, the tweets are classified as neutral vs. non-neutral. Next, non-neutral tweets are classified as positive vs. negative. The first phase of our work draws inspiration from the subjectivity classification and the second phase from the sentiment classification literature. We propose the use of two novel features, which along with our streamlined approach, plays a key role deriving the strong results that we obtain. We use traditional support vector machine (SVM) based machine learning. Our system (F-score: 74.44 for SemEval 2016 Task A and 61.57 for Task B) significantly outperforms the state of the art (F-score: 68.98 for Task A and 56.28 for Task B). While the performance of the system on Task A shows the effectiveness of our model for targets on which the model was trained upon, the performance of the system on Task B shows the generalization that our model achieves. The stance detection problem in Twitter is applicable for user opinion mining related applications and other social influence and information flow modeling applications, in real life.
Another piece of the puzzle of apoptotic cytochrome c release

Involvement of the mitochondrial permeability transition pore (PTP) in apoptosis and PTP structure are highly controversial. In this issue of Molecular Microbiology, experiments based on yeast genetics analyse the roles of the three proteins commonly considered to form the PTP, i.e. porin, ADP/ATP carrier (ACC) and mitochondrial cyclophilin, on apoptosis‐like cell death. Whereas knocking out cyclophilin had no effect, the porin‐1 knockout yeast showed enhanced apoptosis, suggesting that porin‐1 has an antiapoptotic role. Loss of the ACC proteins afforded protection against some causes of death, but enhanced death induced by H2O2, suggesting a more complex role for the ACC proteins in regulating apoptosis‐like death in yeast.
Human-inspired Scaling in Learning Classifier Systems: Case Study on the n-bit Multiplexer Problem Set

Learning classifier systems (LCSs) originated from artificial cognitive systems research, but migrated such that LCS became powerful classification techniques. Modern LCSs can be used to extract building blocks of knowledge in order to solve more difficult problems in the same or a related domain. The past work showed that the reuse of knowledge through the adoption of code fragments, GP-like sub-trees, into the XCS learning classifier system framework could provide advances in scaling. However, unless the pattern underlying the complete domain can be described by the selected LCS representation of the problem, a limit of scaling will eventually be reached. This is due to LCSs' 'divide and conquer' approach utilizing rule-based solutions, which entails an increasing number of rules (subclauses) to describe a problem as it scales. Inspired by human problem solving abilities, the novel work in this paper seeks to reuse learned knowledge and learned functionality to scale to complex problems by transferring them from simpler problems. Progress is demonstrated on the benchmark Multiplexer (Mux) domain, albeit the developed approach is applicable to other scalable domains. The fundamental axioms necessary for learning are proposed. The methods for transfer learning in LCSs are developed. Also, learning is recast as a decomposition into a series of sub-problems. Results show that from a conventional tabula rasa, with only a vague notion of what subordinate problems might be relevant, it is possible to learn a general solution to any n-bit Mux problem for the first time. This is verified by tests on the 264, 521 and 1034 bit Mux problems.
Cardiovascular Health Literacy and Treatment Adherence in Persons with Serious Mental Illness

Persons with serious mental illnesses have shorter life spans than the general population, and their earlier death is often related to cardiovascular disease. This study examined health literacy and cardiovascular medication adherence in a sample of 98 participants with serious mental illness. Sixty-two percent of the participants had adequate health literacy as measured by the Test of Functional Health Literacy in Adults. The association between health literacy and medication adherence was not significant after controlling for education. There was a significant association between health literacy and education level, health literacy and site, and health literacy and the sodium intake subscale of the Hill-Bone adherence scale.
Modelling cooperation in mobile ad hoc networks: a formal description of selfishness

The advance in wireless technologies makes now viable to start to develop ad hoc networks. However, without a mechanism that prevents misbehaviors, these networks could result easily unreliable. We develop a model, based on game theory, capable of formally explaining characteristics of ad hoc networks (as the nodes' selfishness or the network mobility). It also allows to formally study and analyze strategies for cooperation. As example, we describe a simple strategy that enforces packet forwarding among nodes.