Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical Medical Fraud Assessment: Exposition to an Emerging Field

Health care expenditures constitute a significant portion of governmental budgets. The percentage of fraud, waste and abuse within that spending has increased over years. This paper introduces the emerging area of statistical medical fraud assessment, which becomes crucial to handle the increasing size and complexity of the medical programmes. An overview of fraud types and detection is followed by the description of medical claims data. The utilisation of sampling, overpayment estimation and data mining methods in medical fraud assessment are presented. Recent unsupervised methods are illustrated with real world data. Finally, the paper introduces potential future research areas such as integrated decision making approaches and Bayesian methods and concludes with an overall discussion. The main goal of this exposition is to increase awareness about this important area among a broader audience of statisticians.
A Variance Bounds Test of the Linear Quadratic Inventory Model

This paper develops and applies a novel test of the Holt et al. linear quadratic inventory model. It is shown that a central property of the model is that a certain weighted sum of variances and covariances of production, sales, and inventories must be nonnegative. The weights are the basic structural parametersof the model. The model may be tested by seeing whether this sum is in fact nonnegative. When the test is applied to some nondurables data aggregated to the two-digit SIC code level, it almost always rejects the model, even though the model does well by traditional criteria.
Time-on-task effect on workload level discriminability through electroencephalography

Passive brain-computer interfaces (BCIs) allow one to monitor cognitive state, such as levels of workload or alertness, using as input an operator's brain waves recorded through electroencephalography (EEG). Previous studies showed that the power of theta and delta bands at frontal sites increases with load, while the power of the alpha band at parietal sites decreases (Schober et al., 1995). When time-on-task increases, the power of low frequency (LF) bands increases as well, while the power of high frequency bands decreases (Klimesch, 1999). However, to this day, no study has evaluated the interaction of those factors at the electrophysiological level. Yet, this interaction might prevent from clearly distinguishing load levels and thus reduce classification accuracy. This study was designed to assess and characterize load and alertness levels, as well as their interaction, by using EEG power spectral density (PSD) measures. Load was manipulated using a modified Sternberg paradigm (Sternberg, 1966) in which 20 participants encoded 2 or 6 digits and responded to a probe. Alertness varied with time-on-task. Participants' reaction times (RTs) and PSD of 7 frequency bands (delta, theta, alpha, low alpha, high alpha, beta and gamma) were evaluated using variance analyses. Preliminary results are in keeping with the literature for each factor's effect. As regards their interaction, in the alert condition, PSD of the delta, alpha, and beta frequency bands at parietal and frontal sites significantly decreased with load, which made the load levels easy to distinguish. While in the low alertness condition, no significant change was observed between load levels, but for a decrement with load in the alpha band at parietal sites. Those results suggest that a low alertness reduces the discriminability of load levels through EEG. Passive BCIs that would take this interaction into account could adjust their parameters accordingly and increase their classification performances.
A bi-level bi-objective mathematical model for cellular manufacturing system applying evolutionary algorithms

The present study aimed to design a bi-objective bi-level mathematical model for multi-dimensional cellular manufacturing system. Minimizing the total number of voids and balancing the assigned workloads to cells are regarded as two objectives of the upper level of the model. However, the lower level attempts to maximize the workers' interest to work together in a special cell. To this aim, two nested bi-level metaheuristics including particle swarm optimization (NBL-PSO) and a population-based simulated annealing algorithm (NBL-PBSA) were implemented to solve the model. In addition, the goal programming approach was utilized in the upper level procedure of these algorithms. Further, nine numerical examples were applied to verify the suggested framework and the TOPSIS method was used to find the better algorithm. Furthermore, the best weights for upper level objectives were tuned by using a weight sensitivity analysis. Based on computational results, all three objectives were different from their ideal goals when decisions about inter and intra-cell layouts, and cell formation to balance the assigned workloads by considering voids and workers' interest were simultaneously madeby considering a wide assumption-made problem closer to the real world. Finally, NBL-PBSA could perform better than NBL-PSO, which confirmed the efficiency of the proposed framework.
Analytical Method Validation: The Importance for Pharmaceutical Analysis

*Corresponding Author: Sibel A. Ozkan, E-mail: Sibel.Ozkan@pharmacy.ankara. edu.tr ©2018 The Authors. This is an open access article and applies the Creative Commons Attribution (CC BY), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers. Analytical Method Validation: The Importance for Pharmaceutical Analysis Sibel A. Ozkan Ankara University, Faculty of Pharmacy, Department of Analytical Chemistry, Ankara, Turkey.
A Norm-based Comparison of Approaches in ARGESIM Benchmark C17R 'Modelling and Simulation of a SIR-type Epidemic with Cellular Automata and Ordinary Differential Equations' using MATLAB

This model comparison quantifies the difference between the results of a population model, using ordinary differential equations, and an inhomogeneous spatial approach, using a lattice gas cellular automaton. Both approaches describe a classical SIR-epidemic. The system definition of the epidemic, model specification and parameter identification were already formulated by Miksch et al [1]. For the sake of quantifiable comparisons a norm was introduced. The tasks from the revised ARGESIM Comparison 17 [1] were completed and the results were analysed and visualised. Every result was interpreted according to the knowledge of the modelling approaches and the setup of the experiment at hand.
Integrity Based Explanations for Fostering Appropriate Trust in AI Agents

Appropriate trust is an important component of the interaction between people and AI systems, in that ‘inappropriate’ trust can cause disuse, misuse or abuse of AI. To foster appropriate trust in AI, we need to understand how AI systems can elicit appropriate levels of trust from their users. Out of the aspects that influence trust, this paper focuses on the effect of showing integrity. In particular, this paper presents a study of how different integrity-based explanations made by an AI agent affect the appropriateness of trust of a human in that agent. To explore this, (1) we provide a formal definition to measure appropriate trust, (2) present a between-subject user study with 160 participants who collaborated with an AI agent in such a task. In the study, the AI agent assisted its human partner in estimating calories on a food plate by expressing its integrity through explanations focusing on either honesty, transparency or fairness. Our results show that (a) an agent who displays its integrity by being explicit about potential biases in data or algorithms achieved appropriate trust more often compared to being honest about capability or transparent about the decision-making process, and (b) subjective trust builds up and recovers better with honesty-like integrity explanations. Our results contribute to the design of agent-based AI systems that guide humans to appropriately trust them, a formal method to measure appropriate trust, and how to support humans in calibrating their trust in AI.
The Impact of Talent Management on Organizational Effectiveness in Healthcare Sector

The current research aim is to investigate the direct effect of talent management on organizational effectiveness in the health care sector. The study population consisted of all working employees at all levels, from the medical and the managerial domains with a total of 3512 employees, a quantitative research design and regression analysis were used to a convenience sample on a total of 251 valid returns that were gained in a questionnaire based survey, applied among workers from Joint Commission International (JCI) accredited Jordanian private hospitals. The findings showed that there is a strong positive correlation between the study variables; talent management and organizational effectiveness; talent management with its dimensions; attract talent, maintain talent, and develop talent, have a significant effect on organizational effectiveness. In addition the organizational effectiveness dimensions, namely job satisfaction, and organizational involvement were positively and significantly related to each other. This study implies that Jordanian hospitals should try their best to adopt and facilitate talent management strategies implementation to keep its talented employees in nurture tone and more sustained, which will eventually yield favorable results for those hospitals in regard with its effectiveness.
Inclusive Fitness and the Problem of Honest Communication

Inclusive fitness has been under intense scrutiny in recent years, with many critics claiming the framework leads to incorrect predictions. We consider one particularly influential heuristic for estimating inclusive fitness in the context of the very case that motivated reliance on it to begin with: the Sir Philip Sidney signalling game played with relatives. Using a neighbour-modulated fitness model, we show when and why this heuristic is problematic. We argue that reliance on the heuristic rests on a misunderstanding of what it means for two organisms to be related and perpetuates a mischaracterization of the role of the ‘relatedness’ parameter in inclusive fitness. 1 Introduction 2 Heuristic Inclusive Fitness 3 The Sir Philip Sidney Game 4 Model 5 Results 6 Conclusion Appendix 1 Introduction 2 Heuristic Inclusive Fitness 3 The Sir Philip Sidney Game 4 Model 5 Results 6 Conclusion Appendix
Vision-Based Lane Departure Detection Using a Stacked Sparse Autoencoder

This paper presents a lane departure detection approach that utilizes a stacked sparse autoencoder (SSAE) for vehicles driving on motorways or similar roads. Image preprocessing techniques are successfully executed in the initialization procedure to obtain robust region-of-interest extraction parts. Lane detection operations based on Hough transform with a polar angle constraint and a matching algorithm are then implemented for two-lane boundary extraction. The slopes and intercepts of lines are obtained by converting the two lanes from polar to Cartesian space. Lateral offsets are also computed as an important step of feature extraction in the image pixel coordinate without any intrinsic or extrinsic camera parameter. Subsequently, a softmax classifier is designed with the proposed SSAE. The slopes and intercepts of lines and lateral offsets are the feature inputs. A greedy, layer-wise method is employed based on the inputs to pretrain the weights of the entire deep network. Fine-tuning is conducted to determine the global optimal parameters by simultaneously altering all layer parameters. The outputs are three detection labels. Experimental results indicate that the proposed approach can detect lane departure robustly with a high detection rate. The efficiency of the proposed method is demonstrated on several real images.
Third-order optical nonlinearities in bulk and fs-laser inscribed waveguides in strengthened alkali aluminosilcate glass

The development of advanced photonics devices requires materials with large optical nonlinearities, fast response times and high optical transparency, while at the same time allowing for the micro/nano-processing needed for integrated photonics. In this context, glasses have been receiving considerable attention given their relevant optical properties which can be specifically tailored by compositional control. Corning Gorilla® Glass (strengthened alkali aluminosilicate glass) is well-known for its use as a protective screen in mobile devices, and has attracted interest as a potential candidate for optical devices. Therefore, it is crucial not only to expand the knowledge on the fabrication of waveguides in Gorilla Glass under different regimes, but also to determine its nonlinear optical response, both using fs-laser pulses. Thus, this paper reports, for the first time, characterization of the third-order optical nonlinearities of Gorilla Glass, as well as linear and nonlinear characterization of waveguide written with femtosecond pulses under the low repetition rate regime (1 kHz).
Stochastic proximal quasi-Newton methods for non-convex composite optimization

ABSTRACT In this paper, we propose a generic algorithmic framework for stochastic proximal quasi-Newton (SPQN) methods to solve non-convex composite optimization problems. Stochastic second-order information is explored to construct proximal subproblem. Under mild conditions we show the non-asympotic convergence of the proposed algorithm to stationary point of original problems and analyse its computational complexity. Besides, we extend the proximal form of Polyak-Łojasiewicz (PL) inequality to constrained settings and obtain the constrained proximal PL (CP-PL) inequality. Under CP-PL inequality linear convergence rate of the proposed algorithm is achieved. Moreover, we propose a modified self-scaling symmetric rank one incorporated in the framework for SPQN method, which is called stochastic symmetric rank one method. Finally, we report some numerical experiments to reveal the effectiveness of the proposed algorithm.
Are on-chip heralded single photon sources possible by intermodal four wave mixing in silicon waveguides?

On-chip heralded single photon sources are of key importance in the development of chip-scale devices exploiting the quantum properties of light. Single photon states can be produced as single photons heralded from correlated photon pairs generated through spontaneous four wave mixing. On-chip heralded single photon sources based on spontaneous four wave mixing have been already demonstrated. However, the heralded and herald photons are usually generated with wavelengths very close to the pump one, limiting the pump rejection efficiency and the application to the mid infrared. Moreover, the common sources of heralded photons based on spontaneous four wave mixing require spectral post filtering to achieve high purity, limiting the brightness and the integration of these sources. A solution to these problems can be provided by intermodal four wave mixing. In this work, we demonstrate the generation of photon pairs through intermodal four wave mixing in silicon waveguides, measuring the coincidences between the idler at 1.281 μm and the signal at 1.952 μm. We then discuss the application of intermodal four wave mixing to on-chip heralded single photon sources.
Hydrodynamic Limit and Viscosity Solutions for a Two‐Dimensional Growth Process in the Anisotropic KPZ Class

We study a two‐dimensional stochastic interface growth model that is believed to belong to the so‐called anisotropic KPZ (AKPZ) universality class [4,5]. It can be seen either as a two‐dimensional interacting particle process with drift that generalizes the one‐dimensional Hammersley process [1,24], or as an irreversible dynamics of lozenge tilings of the plane [5,29]. Our main result is a hydrodynamic limit: the interface height profile converges, after a hyperbolic scaling of space and time, to the solution of a nonlinear first‐order PDE of Hamilton‐Jacobi type with nonconvex Hamiltonian (nonconvexity of the Hamiltonian is a distinguishing feature of the AKPZ class). We prove the result in two situations: (1) for smooth initial profiles and times smaller than the time Tshock when singularities (shocks) appear or (2) for all times, including t > Tshock, if the initial profile is convex. In the latter case, the height profile converges to the viscosity solution of the PDE. As an important ingredient, we introduce a Harris‐type graphical construction for the process. © 2018 Wiley Periodicals, Inc.
An adaptive human action recognition system based on two-layer AP

Skeleton-based methods have been proposed to detect and recognize meaningful human motion. It is known that most of them must contain some parameters. To achieve better recognition performance, various evolutionary schemes have been applied to select the optimal parameters in each phase of these human recognition methods. Experimental evaluations of various parameters, in terms of action recognition performance, should be done for obtaining the optimal parameter. In this paper, we propose an adaptive skeleton-based human action recognition system which can automatically adjust the experimental parameters according to the input data. We first extract some spatiotemporal local features by obtaining position differences of joints, which models actions over time. Then a two-layer affinity propagation (AP) algorithm is employed to select crucial postures. Our experiment results demonstrates that the proposed method works well for different dataset.
Design of SARS-CoV-2 protease inhibitors with improved affinity and reduced sensitivity to mutations

Inhibitors of the SARS-CoV-2 main protease (Mpro) such as nirmatrelvir (NTV) and ensitrelvir (ETV) have proven effective in reducing the severity of COVID-19, but the presence of resistance-conferring mutations in sequenced viral genomes raises concerns about future drug resistance. Second-generation oral drugs that retain function on these mutants are thus urgently needed. We hypothesized that the covalent HCV protease inhibitor boceprevir (BPV) could serve as the basis for orally bioavailable drugs that inhibit SARS-CoV-2 Mpro more tightly than existing drugs. Performing structure-guided modifications of BPV, we developed a picomolar-affinity inhibitor, ML2006a4, with antiviral activity, oral pharmacokinetics, and therapeutic efficacy similar or superior to NTV. A crucial feature of ML2006a4 is a novel derivatization of the ketoamide reactive group that improves cell permeability and oral bioavailability. Finally, ML2006a4 is less sensitive to several mutations that cause resistance to NTV or ETV and occur in the natural SARS-CoV-2 population. Thus, anticipatory drug design can preemptively address potential resistance mechanisms.
Aerodynamics and aeroacoustic sources of a coaxial rotor

Vehicles with coaxial, contra-rotating rotor systems (CACR) are being considered for a range of applications, including those requiring high speed and operations in urban environments. Community and environmental noise impact is likely to be a concern in these applications. Design parameters are identified that effect the fundamental aerodynamics and fluid dynamic features of a CACR in hover, vertical, and edgewise flight. Particular attention is paid to those features affecting thickness, loading, blade vortex interaction (BVI), and high speed impulsive (HSI) noise. Understanding the fluid dynamic features is a precursor to studying the aeroacoustics of a coaxial rotor. Rotor performance was computed initially using Navier-Stokes solver with prescribed blade section aerodynamic properties, the results validated against generic experimental test cases. The fluid dynamics of blade interactions was simplified and broken into a 2-D blade crossing problem, with crossing locations and velocity fields from the rotor results. Two trains of 8 airfoils passing were simulated to understand the effects due to shed vorticity. The airfoils are displaced vertically by a distance equivalent to the typical spacing between the upper and lower rotors of a coaxial system. A 2D potential flow code and 2D OVERFLOW compressible-flow Navier-Stokes solver were used to investigate the complex coaxial rotor system flow field. One challenge of analyzing the CACR is the difficulty in envisioning all the possible interactions and their possible locations as flight conditions and rotor designs change. A calculation tool has been developed to identify time and location of blade overlap. The tool was then integrated with a wake aerodynamics model to identify locations and instances of upper rotor tip vortex interaction with a lower rotor blade. This tool enables rapid identification of different types of BVI based on relative rotor orientation. Specific aerodynamic phenomena that occur for each noise source relevant to CACR are presented, along with computational tools to predict these occurrences.
Methanolic extracts of the edible halophyte Crithmum maritimum enhance oxidative stress resistance in Caenorhabditis elegans through hormetic mechanisms

Halophytes are promising sources of bioactive phenolic compounds for the food and pharmaceutical industries. However, their phenolic composition is influenced by environmental conditions, and the in vivo antioxidant activity of their phytochemicals is largely unknown. We evaluated the antioxidant capacity of phenolic-rich methanolic extracts from the edible halophyte Crithmum maritimum, grown in wild and greenhouse conditions. Additionally, their in vivo antioxidant capacity was analyzed for the first time using the model Caenorhabditis elegans. Wild plant extracts showed higher phenolic content and diversity, and in vitro antioxidant activity. Both extracts enhanced oxidative stress resistance and increased nematode survival rates, albeit to varying extents, and increased reactive oxygen species production in nematodes, without affecting their lifespan, suggesting a hormetic mechanism. Although no neuroprotective effects were observed in models of neurodegenerative diseases, these findings highlight the potential of C. maritimum as a valuable source of phenolics with antioxidant properties for the food industry.
The human genome’s vocabulary as proposed by the DNA language model GROVER

Large Language Models (LLMs) on natural language have achieved a level of performance that allows the generation of coherent and syntactically correct text. DNA sequence of genomes follows rules similar to natural language, but a distinguishing factor is the absence of a concept analogous to words. We established byte-pair tokenization on the human genome and trained a foundation language model called GROVER (“Genome Rules Obtained Via Extracted Representations”) to select the optimal vocabulary with a custom fine-tuning task of next-k-mer prediction. We thus defined a dictionary of words/tokens in the human genome that best carries the information content for DNA language models. Analyzing GROVER’s learned representations, we observed that token embeddings primarily encode information related to their frequency, sequence content, and length. Some tokens are almost exclusively localized in repeats, while the vast majority widely distributes over the genome. The model also learns context and lexical ambiguity. Average embeddings of genomic regions relate to functional genomics annotation and thus indicate that GROVER has learned these structures purely from the contextual relationships of tokens. That we can extract functional annotations from the genome, purely based on sequence representation to the trained model, highlights the extent of information content encoded by the sequence. This is supported by fine-tuning tasks on genome biology with questions of promoter identity and protein-DNA binding. GROVER learns sequence context, a sense for grammatical structures and language rules in the genome. This knowledge can be extracted and used to compose a grammar book for the code of life.