Miracle-dz/UniFAR-FTUs huggingface dataset:
Reaching to multiple potential targets : An optimal control perspective

Living in a dynamic environment, we must be able to make flexible plans that can handle ambiguity and changes in goals while acting. Recent studies suggest that brain builds multiple competing plans related to potential goals and use perceptual information to drive this competition, until a single policy is selected. We propose an extended optimal control framework to model human behavior in tasks with multiple goals and show that goal competition is a natural by-product of handling goal uncertainty. We show how an agent’s optimal policy in the presence of goal ambiguity, can be expressed as a weighted mixture of multiple control policies, each of which produces a sequence of actions associated with a specific target. At any instant, weighting factor is an inference of which goal’s policy is best to follow, starting from the current state. Simulations of our multiple-goal optimal control model replicated reaching strategies observed in several human studies. Finally, we made novel predictions about the effects of the spatial probability distributions of the candidate targets and their expected pay-off values on the optimal policy.
Investigating the factors influencing users' continuance intentions towards online reservation

Online reservation has increased significantly in recent years, yet research surrounding the intention to continue using this technology remains insufficient. This research examines the roles of hedonic value, satisfaction, utilitarian value, and subjective norms in motivating continuance intentions towards using online reservation in the United Arab Emirates. The results show that all the relationships proposed in this study are significant, with the exception of the relationship between hedonic value and satisfaction.
Enhanced Signal and Quantitative Detection of Anti-Interferon-Gamma Antibody by Using a Nanometer Biolinker

For rapid screening and quantification of an antisera antibody, a nanometer bithiophene-based conductive biolinker can enhanced signal performance and can be used to verify the interaction of an anti-IFN-γ antibody with an IFN-γ protein. The experimental measurements take a generic approach which takes advantage of the functionality of thiophene-based linkers for biosensors. Effects associated with using bithiophene as a biolinker for surface plasmon resonance (SPR) spectroscopy are examined in this paper. By using an atomic force microscope (AFM), it was observed that the morphology of the bithiophene modified gold sensor surface became smoother than the original gold surface. We compared the response and concentration of the anti-IFN-γ antibody on a bithiophene-coated and dextran-coated biochip as well as on different thickness-modified surfaces under SPR relevant conditions. The results indicate that a response to IFN-γ molecules immobilized on a sensor using a bithiophene biolinker improved more than 8-fold when compared to that of a sensor using a dextran biolinker. Furthermore, the regeneration ability of the sensor surface shows good repeatability as only less than a 1% decrease was found after repeating the experimental work over 6 cycles. The characteristics provided us with a good platform for rapid screening, real-time monitoring and quantitative concentration of the autoimmune antibody activities.
Dynamic Key Distribution System in Ad Hoc Networks

The problem of key management in ad hoc networks was analyzed in this paper. Key management, however, can reveal information about the dynamics of ad hoc network membership, such as the group size and the number of joining and leaving users. Thus, only legitimate members have access to the messages. Once any member in the ad hoc network was going to join or leave the group, the group key would be changed to ensure environment security. The construction of a secure dynamic key distribution system based on elliptic curve Paillier scheme and group filter function was proposed. In this scheme, all members under the same cluster share a group key. Therefore, all broadcast messages can be enciphered and deciphered using this key. The inheriting merits of elliptic curve Paillier scheme make our new scheme result in bandwidth saving, smaller system parameters, lower communication cost and suit to the wireless and mobile communications specially.
Application Value of Mass Spectrometry in the Differentiation of Benign and Malignant Liver Tumors

Background Differentiation of malignant from benign liver tumors remains a challenging problem. In recent years, mass spectrometry (MS) technique has emerged as a promising strategy to diagnose a wide range of malignant tumors. The purpose of this study was to establish classification models to distinguish benign and malignant liver tumors and identify the liver cancer-specific peptides by mass spectrometry. Material/Methods In our study, serum samples from 43 patients with malignant liver tumors and 52 patients with benign liver tumors were treated with weak cation-exchange chromatography Magnetic Beads (MB-WCX) kits and analyzed by the Matrix-Assisted Laser Desorption Time of Flight Mass Spectrometry (MALDI-TOF-MS). Then we established genetic algorithm (GA), supervised neural networks (SNN), and quick classifier (QC) models to distinguish malignant from benign liver tumors. To confirm the clinical applicability of the established models, the blinded validation test was performed in 50 clinical serum samples. Discriminatory peaks associated with malignant liver tumors were subsequently identified by a qTOF Synapt G2-S system. Results A total of 27 discriminant peaks (p<0.05) in mass spectra of serum samples were found by ClinPro Tools software. Recognition capabilities of the established models were 100% (GA), 89.38% (SNN), and 80.84% (QC); cross-validation rates were 81.67% (GA), 81.11% (SNN), and 86.11% (QC). The accuracy rates of the blinded validation test were 78% (GA), 84% (SNN), and 84% (QC). From the 27 discriminatory peptide peaks analyzed, 3 peaks of m/z 2860.34, 2881.54, and 3155.67 were identified as a fragment of fibrinogen alpha chain, fibrinogen beta chain, and inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), respectively. Conclusions Our results demonstrated that MS technique can be helpful in differentiation of benign and malignant liver tumors. Fibrinogen and ITIH4 might be used as biomarkers for the diagnosis of malignant liver tumors.
Random effects probit and logistic regression models for three-level data.

In analysis of binary data from clustered and longitudinal studies, random effect models have been recently developed to accommodate two-level problems such as subjects nested within clusters or repeated classifications within subjects. Unfortunately, these models cannot be applied to three-level problems that occur frequently in practice. For example, multicenter longitudinal clinical trials involve repeated assessments within individuals and individuals are nested within study centers. This combination of clustered and longitudinal data represents the classic three-level problem in biometry. Similarly, in prevention studies, various educational programs designed to minimize risk taking behavior (e.g., smoking prevention and cessation) may be compared where randomization to various design conditions is at the level of the school and the intervention is performed at the level of the classroom. Previous statistical approaches to the three-level problem for binary response data have either ignored one level of nesting, treated it as a fixed effect, or used first- and second-order Taylor series expansions of the logarithm of the conditional likelihood to linearize these models and estimate model parameters using more conventional procedures for measurement data. Recent studies indicate that these approximate solutions exhibit considerable bias and provide little advantage over use of traditional logistic regression analysis ignoring the hierarchical structure. In this paper, we generalize earlier results for two-level random effects probit and logistic regression models to the three-level case. Parameter estimation is based on full-information maximum marginal likelihood estimation (MMLE) using numerical quadrature to approximate the multiple random effects. The model is illustrated using data from 135 classrooms from 28 schools on the effects of two smoking cessation interventions.
Pheromonal Communication in Nereids and the Likely Intervention by Petroleum Derived Pollutants1

Abstract Nereis succinea and Platynereis dumerilii (Annelida, Polychaeta) are broadcast spawners and reproduce semelparously. The final events in reproduction, swarming and spawning are co-ordinated by sex pheromones. A water-soluble fraction of crude oil, the volatile fraction (C9—C16) of EKO FISK crude oil was found to induce release of gametes in male nereids at levels <0.3 ppm. Using vacuum distillation, column chromatography, preparative GC and GC-MS analysis we showed that C5-alkylated benzenes were most potent in inducing sperm release, of those n-butyl-4-methylbenzene and 1,4-diethyl-2-methylbenzene were found to induce release of gametes at concentrations ≥4 nM. This threshold is lower than those reported for natural pheromones (nereithione: 60 nM, uric acid: 600 nM) but higher than background levels of aromatic compounds of 0.05 nM and below. Other oil fractions showed additional effects, blocking pheromone reception or narcotising and intoxicating animals. Part of these effects could be assigned to naphthalenes at levels down to approx. 320 nM. In the original mixtures, their action was modified or compensated by the presence of gamete release inducing alkylated benzenes. Other highly paralysing substances remained elusive.
The Effects of Assisted Ergometer Training With a Functional Electrical Stimulation on Exercise Capacity and Functional Ability in Subacute Stroke Patients

Objective To determine if assistive ergometer training can improve the functional ability and aerobic capacity of subacute stroke patients and if functional electrical stimulation (FES) of the paretic leg during ergometer cycling has additional effects. Methods Sixteen subacute stroke patents were randomly assigned to the FES group (n=8) or the control group (n=8). All patients underwent assistive ergometer training for 30 minutes (five times per week for 4 weeks). The electrical stimulation group received FES of the paretic lower limb muscles during assistive ergometer training. The six-minute walk test (6MWT), Berg Balance Scale (BBS), and the Korean version of Modified Barthel Index (K-MBI) were evaluated at the beginning and end of treatment. Peak oxygen consumption (Vo2peak), metabolic equivalent (MET), resting and maximal heart rate, resting and maximal blood pressure, maximal rate pressure product, submaximal rate pressure product, submaximal rate of perceived exertion, exercise duration, respiratory exchange ratio, and estimated anaerobic threshold (AT) were determined with the exercise tolerance test before and after treatment. Results At 4 weeks after treatment, the FES assistive ergometer training group showed significant improvements in 6MWT (p=0.01), BBS (p=0.01), K-MBI (p=0.01), Vo2peak (p=0.02), MET (p=0.02), and estimated AT (p=0.02). The control group showed improvements in only BBS (p=0.01) and K-MBI (p=0.02). However, there was no significant difference in exercise capacity and functional ability between the two groups. Conclusion This study demonstrated that ergometer training for 4 weeks improved the functional ability of subacute stroke patients. In addition, aerobic capacity was improved after assisted ergometer training with a FES only.
Statistical parametric mapping of FDG-PET in Alzheimer disease

The goal of our studies provides the possibility to early diagnose the Alzheimer's disease. This purpose is achieved combining 3 Tesla Magnetic Resonance Imaging with the Positron Emission Tomography (PET) and the Computer Tomography. In this work we illustrate the first step of this program showing the very preliminary results of the PET functional analysis based on the Statistical Parametric Mapping.
The Pricing of Options with Jump Diffusion and Stochastic Volatility

The Black-Scholes model has been widely used in option pricing for roughly four decades. However, there are two puzzles that have turned out to be difficult to explain with the Black-Scholes model: the leptokurtic feature and the volatility smile. In this study, the two puzzles will be investigated. An improved model – a double exponential jump diffusion model (referred to as the Kou model) will be introduced. The Monte Carlo method will be used to simulate the Black-Scholes model and the double exponential jump diffusion model to price the IBM call option. The call option prices estimated by both models will be compared to the market call prices. The results show that the call option prices estimated by the double exponential jump diffusion model fit the IBM market call option prices better than the call option prices estimated by the Black-Scholes model do. The volatility in both the Black-Scholes model and the double exponential jump diffusion model is assumed to be a constant. However, it is stochastic in reality. Two new models based on the Black-Scholes model and the double exponential jump diffusion model with a stochastic volatility will be developed. The stochastic volatility is determined by a GARCH(1,1) model. The advantage of GARCH model is that GARCH model captures some features associated with financial time series, such as, fat tails, volatility clustering, and leverage effects. First, the structure of
Efficient representation of nonreflecting boundary conditions for the time‐dependent Schrödinger equation in two dimensions

We present a fast algorithm for the evaluation of exact, nonreflecting boundary conditions for the time‐dependent Schrödinger equation in two dimensions on the unit circle. After separation of variables, the exact outgoing condition for each Fourier mode contains a nonlocal term that is a convolution integral in time. The kernel for that convolution is the inverse Laplace transform of the logarithmic derivative of a modified Bessel function, and the convolution integral can be split into two parts: a local part and a history part, which can be treated separately. The local part is easily handled by an appropriate quadrature. For the history part, we show that the convolution kernel can be well approximated by a sum of exponentials. Once such a representation is available, the convolution integrals can be evaluated recursively, reducing the cost from O(N2) work to O(N), where N is the number of time steps. The main technical development lies in the uniform rational approximation of the logarithmic derivative of the modified Bessel function Kν(√is). © 2007 Wiley Periodicals, Inc.
Designing a Web-based Distance Learning Environment based on an Intelligent Tutoring System

This paper describes a distributed approach toward designing an Intelligent Tutoring System for the World Wide Web (WWW) at the University "Politehnica" of Bucharest (Romania). This approach was implemented in the EDIT Learning Environment and is defined by the following features: a www dedicated architecture, a distributed way of working both in the design and exploitation phases; reusability; a heuristic method to gather the needed information from all people involved in the project rather than from a limited number of experts; an integrated communication system; and cost effectiveness. Inter-human communication receives a great deal of attention, as it is an important speed up factor for the learning process. (MES) Reproductions supplied by EDRS are the best that can be made from the ori inal document.
Learning to Recommend Point-of-Interest with the Weighted Bayesian Personalized Ranking Method in LBSNs

Point-of-interest (POI) recommendation has been well studied in recent years. However, most of the existing methods focus on the recommendation scenarios where users can provide explicit feedback. In most cases, however, the feedback is not explicit, but implicit. For example, we can only get a user’s check-in behaviors from the history of what POIs she/he has visited, but never know how much she/he likes and why she/he does not like them. Recently, some researchers have noticed this problem and began to learn the user preferences from the partial order of POIs. However, these works give equal weight to each POI pair and cannot distinguish the contributions from different POI pairs. Intuitively, for the two POIs in a POI pair, the larger the frequency difference of being visited and the farther the geographical distance between them, the higher the contribution of this POI pair to the ranking function. Based on the above observations, we propose a weighted ranking method for POI recommendation. Specifically, we first introduce a Bayesian personalized ranking criterion designed for implicit feedback to POI recommendation. To fully utilize the partial order of POIs, we then treat the cost function in a weighted way, that is give each POI pair a different weight according to their frequency of being visited and the geographical distance between them. Data analysis and experimental results on two real-world datasets demonstrate the existence of user preference on different POI pairs and the effectiveness of our weighted ranking method.
AtomsMasher: Personalised Context-Sensitive Automation for the Web

This paper introduces AtomsMasher, an environment for creating reactive scripts that can draw upon widely heterogeneous information to automate common information-intensive tasks. AtomsMasher is enabled by the wealth of user-contributed personal, social and contextual information that has arisen from Web2.0 social networking content sharing and micro-blogging sites. Starting with existing web mashup tools and end-user automation, we describe new challenges in achieving reactive behaviours: deriving a consistent representation that can be used to predictably drive discrete action from a multitude of noisy, incomplete and inconsistent data sources. Our solution employs a mix of automatic and user-assisted approaches to build a common internal representation in RDF, which is used to provide a simplified programming model that lets Web2.0 programmers succinctly specify behaviours in terms of high level relationships between entities and their current contextual state. We highlight the advantages and limitations of this architecture, and conclude with ongoing work towards making the system more predictable and understandable, and accessible to non-programmers.
Is Your Story Going to Spread Like a Virus ? Machine Learning Methods for News Popularity Prediction

Online website has been the majority source for news to spread. An interesting news is going to be shared thousands of times through the Internet. In this project, we tried different methods to predict the popularity before its publication. Using over 39000 sample articles from Mashable.com, we tried to address the problem both as a numerical and a multinomial classification problem. Specifically, we applied linear regression, polynomial regression, GAM with smoothing splines and Lasso to predict the exact shares. The best CV error result is 0.7649 acquired by GAM with smoothing splines. And then we used SVM, Random Forest and Bagging to predict popularity, which is divided into four categories, for each article. In this case, Random Forest gives us the best result, which achieves 50.4% prediction accuracy.
Towards efficient and reliable context data distribution in disaster area scenarios

Disaster areas typical to deal with unexpected and sudden disasters, such as earthquakes and terrorist attacks, can take advantage of context-awareness, namely the capability of providing applications with full awareness of current execution context, to increase the possibility of saving human lives. Unfortunately, context-aware services in disaster areas require efficient and reliable context data distribution. Towards this direction, we present our quality-based context data distribution infrastructure, and we introduce two optimizations useful to improve both distribution efficiency and reliability. Experimental results, obtained by means of simulations, support our solutions.
Scheduling Real-Time Jobs in Distributed Systems-Simulation and Performance Analysis

One of the major challenges in ultrascale systems is the effective scheduling of complex jobs within strict timing constraints. The distributed and heterogeneous system resources constitute another critical issue that must be addressed by the employed scheduling strategy. In this paper, we investigate by simulation the performance of various policies for the scheduling of real-time directed acyclic graphs in a heterogeneous distributed environment. We apply bin packing techniques during the processor selection phase of the scheduling process, in order to utilize schedule gaps and thus enhance existing list scheduling methods. The simulation results show that the proposed policies outperform all of the other examined algorithms.
The Combination of TRAIL Treatment and Cancer Cell Selective Expression of TRAIL-Death Receptor DR4 Induces Cell Death in TRAIL-Resistant Cancer Cells

The human telomerase reverse transcriptase (hTERT) promoter can be used for the tumor-specific expression of transgenes in order to induce selective cancer cell death. The hTERT core promoter is active in cancer cells but not in normal cells. To examine whether the combination of TNF-related apoptosis inducing ligand (TRAIL) treatment and cancer cell-selective expression of the TRAIL-death receptor could induce cell death in TRAIL-resistant cancer cells, we generated a death receptor-4 (DR4)-expressing adenovirus (Ad-hTERT-DR4), in which the expression of DR4 is driven by the hTERT promoter. Upon infection, DR4 expression was slightly increased in cancer cell lines, and cell death was observed in TRAIL-resistant cancer cell lines but not in normal human cells when DR4 infection was combined with TRAIL treatment. We also generated an adenovirus that expresses a secretable isoleucine zipper (ILZ)-fused, extracellular portion of TRAIL (Ad-ILZ-TRAIL). In cells infected with Ad-ILZ-TRAIL, TRAIL was expressed, secreted, oligomerized and biologically active in the induction of apoptosis in TRAIL-sensitive cancer cells. When Ad-hTERT-DR4 infected TRAIL-resistant HCE4 cells and Ad-ILZ-TRAIL infected TRAIL-resistant HCE7 cells were co-cultured, cell deaths were evident 24 h after co-culture. Taken together, our results reveal that the combination of TRAIL and cancer cell-specific expression of DR4 has the potential to overcome the resistance of cancer cells to TRAIL without inducing significant cell death in normal cells.