Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Preprocessing Techniques for Brain Analysis Using Compressed and Uncompressed Magnetic Resonance Imaging

Digital Image Processing (DIP) contributes with many advantages to medical diagnostics. Images can be optimized by improving their quality, which allows specialists to better locate tissue damages, or other anomalies. The planning and execution of surgeries, design of prosthesis, monitoring and evaluation of progression of diseases can greatly benefit from DIP. In particular, Magnetic Resonance Imaging (MRI) is a commonly used technique for medical diagnostics. It is highly accepted for its high precision and resolution in anatomical explorations, allowing an accurate analysis of interest area and the behavior of surrounding tissues. The image sequences captured in different three-dimensional (3D) planes by MRI analysis, allow specialists to determine better diagnoses and treatment for patients. However, the data loss caused by commonly used image compression formats could affect results, if the images are used without preprocessing techniques. This paper will evaluate differences between compressed and uncompressed images, proposing a methodology to improve the quality of compressed images recovering information by the uses of bi-linear and bi-cubic interpolations. The obtained results will be measured with Signal-Noise Ratio (SNR) and variance differences for each case to validate the preprocessing techniques applied. According to the obtained results, data lost by compression algorithms could be recovered by the proposed interpolations techniques.
On Millimeter Wave 5G Backhaul Link Scheduling

Heterogeneous networks that consist of densely deployed base stations of different (macro and small-cell) tiers are envisioned to be among the key technologies for providing the high data rates required in 5G systems. Moreover, interconnecting small-cell base stations via millimeter-wave (mm-wave) backhaul links is a flexible and cost-effective alternative to fiber optic backhauling. Given a set of mm-wave backhaul links, this paper addresses the problem of scheduling all links in the minimum number of time slots such that the subset of links scheduled to each time slot can be activated simultaneously. In particular, a set of links can be activated simultaneously if all link signal-to-interference-plus-noise-ratio (SINR) targets are satisfied, and the number of RF chains of every base station is not exceeded. We give a succinct optimization-based formulation of the problem, capturing both SINR constraints and number of RF chains limitations. Using reduction from the set-cover problem, we devise a provably good polynomial-time algorithm for the problem. Our numerical results further indicate the significant superiority of the presented approach.
Estimation of Primary Quantization Steps in Double-Compressed JPEG Images Using a Statistical Model of Discrete Cosine Transform

Double compression of images occurs when one compresses twice, possibly with different quality factors, a digital image. Estimation of the first compression parameter of such a double compression is of a crucial interest for image forensics since it may help revealing, for instance, the software or the source camera. This paper proposes an accurate method for estimating the primary quantization steps in double-compressed JPEG images. This original methodology is based on an accurate statistical model of discrete cosine transform (DCT) coefficients that has been proposed in our previous works. We also present a thorough analysis of the double compression properties, taking into account carefully the effect of round-off noise. This analysis is used to derive an accurate range of possible value for quantization of primary DCT coefficients with respect to the secondary quantization step. Using both the statistical model of quantized DCT coefficients and the range of possible values of first quantization step, a model of the twice quantized DCT coefficients is established. Eventually, it is proposed to estimate the primary quantization value by finding, among a set of possible candidates, the one that best match the proposed statistical model in terms of minimal symmetrized Kullback–Leibler (KL) divergence. The numerical experiments on large databases of real images and comparisons with state-of-the-art approaches emphasize the relevance of the proposed method.
The socioeconomic impact of chronic back pain: is anyone benefiting?

Despite the well documented exorbitant costs of musculoskeletal pain, there is a need to take a broader view of its socioeconomic impact. Rather than simply lamenting skyrocketing expenditures, it may behove us to instead pose the question: Who is benefiting from the current state of affairs? In answering this question, I will attempt to illuminate the situation from several perspectives including the health-care system, and above all the individual and his/her family. All too often the health-care system or the individual patient seem to be blamed for the high costs, while the impact on these players themselves is not always highlighted.
Synchronous Time-pulse Control Method based on Equalization of Losses in IGBT of Bridge Series Resonant Inverter

By combining such approaches as synchronous time-pulse modulation, energy circulation and control impulses division within a single control method for a bridge series resonant inverter it becomes possible to equalize and decrease the losses in IGBT modules of inverter. A mathematical model of the inverter controlled by the method proposed is developed. The comparative analysis of a safe operating area for the IGBT modules of the inverter has confirmed that, by using the synchronous time-pulse control method with equalization of losses in modules, it is possible to increase an utilization factor of IGBT modules within the frequency range of 20-80 kHz approximately by 10%.
Temporal data mining: algorithms, theory and applications (TDM 2005)

Structure prediction problem for proteins plays an important role in the protein process. This is a notoriously hard problem and been able to achieve good prediction performance with new methods will certainly have an impact in both the computational arena but also in the Bioinformatics field. This paper proposes a novel classification approach using a binary expansion method based on the density concept for homogenous clauses to predict protein folding structures. The successes of this approach are demonstrated on several protein data sets whose structure is partially known.
Subcutaneous Accelerometer-Based Monitoring of Respiration : A Pre-Clinical Exploration

Subcutaneous accelerometers offer an alternative method to transthoracic impedance measurements for chronically monitoring respiration and detecting sleep apnea in cardiac implantable electronic devices. Two 3D accelerometer prototypes were implanted subcutaneously in one anesthetized pig. In the first prototype the accelerometer was on the tip of a cardiac pacing lead, and in the second prototype it was embedded inside an inactive pacemaker ‘can’. Respiratory signals were recorded from both prototypes across various different combinations of tidal volume and respiration rate, as imposed by an external ventilator, as well as during a simulated apnea episode. Mean peak-to-trough amplitudes of the recorded signals achieved excellent correlations to the imposed nominal ventilation tidal volumes (R2>0.98 in the lead and R2>0.94 in the can). Similarly, mean peak-peak intervals achieved low root-mean-square-deviation errors with respect to the imposed ventilation intervals (<4% in the lead and <11% in the can). Such methods make use of accelerometers similar to the current activity sensors already present in various devices, or to previously-reported subcutaneous cardiac hemodynamic sensors. Unlike transthoracic impedance measurements, subcutaneous accelerometers are less invasive as they do not require intracardiac leads, they allow for a more direct assessment of respiration, and they do not suppose an added energetic cost to the system.
On Network Performance Indicators for Network Promoter Score Estimation

Estimation of user perceived quality of offered services, from massive number of Key Performance Indicator (KPI)’s that are measured in diverse components, has been a necessity for mobile network operators. The goal is first to have a good estimator for poor Quality of Experience (QoE), which can potentially be achieved with machine learning, and then pinpoint the features that are contributing to the poor performance. There is often a tradeoff between accuracy and interpretability of models. In this paper, we address this tradeoff by first developing a robust but complex teacher machine learning model to map the subjective Net Promoter Score (NPS) values computed from the user quality feedback to the underlying subset of KPI metrics. Next, we develop a rather interpretable student model supervised by the pre-trained teacher model. Eventually the compact student decision tree model learns to mimic the behavior of the teacher model with an at least 10 % improved accuracy in testset as compared to conventional way of directly training using the decision tree model. In the last step, we extract the rules and important influential features of the distilled student model.
Neural Network approach for rapid prediction of transcutaneous oxygen saturation

This study presented the use of Neural Network (NN) approach in the prediction of transcutaneous oxygen saturation level, StO2. This is to overcome the limitation of using conventional signal processing approaches that are computational exhaustive. The accuracy of the NN predictive model was tested on 35 sets of new noise-corrupted Monte Carlo simulation data. This study found mean absolute error of 2.91± 2.29 % in its predictions while the statistical test revealed a strong correlation between the considered features and the predictions (ρ = 0.000). This work concluded that the proposed technique could promote further advancement in the current technology specifically in the development of portable StO2 measurement system.
A Delayed Predator-prey Model for Worm Propagation in Computer Systems

In this paper, a delayed predator-prey model for worms inside a computer system is proposed. Local stability and Hopf bifurcation of the model is analyzed by regarding the time delay as the bifurcation parameter. Sufficient conditions for local stability and occurrence of the Hopf bifurcation are obtained. Numerical simulations are presented in order to support the obtained theoretical results.
Optimal Design of One-Sided Exponential EWMA Charts With Estimated Parameters Based on the Median Run Length

One-sided exponential exponentially weighted moving average (EWMA) charts are useful tools for monitoring high-quality processes. Average run length (<inline-formula> <tex-math notation="LaTeX">$ARL$ </tex-math></inline-formula>) is usually used as the only criterion to evaluate the performance of a control chart. However, the shapes of the run length (<inline-formula> <tex-math notation="LaTeX">$RL$ </tex-math></inline-formula>) distributions of the one-sided exponential EWMA charts are highly skewed, and furthermore, are affected by the number of Phase I samples used to estimate the process parameters. This results in some confusion in the interpretation of control charts based on <inline-formula> <tex-math notation="LaTeX">$ARL$ </tex-math></inline-formula> for practitioners. Fortunately, the median run length (<inline-formula> <tex-math notation="LaTeX">$MRL$ </tex-math></inline-formula>), which is more intuitional for users, could be employed as a good alternative measure. Based on these, the <inline-formula> <tex-math notation="LaTeX">$MRL$ </tex-math></inline-formula>-based one-sided exponential EWMA charts are investigated in this paper, by taking the effect of estimated parameters into account. The performances of control charts are investigated through a Markov chain approach. An optimal design procedure is developed for the one-sided exponential EWMA charts with estimated parameters based on <inline-formula> <tex-math notation="LaTeX">$MRL$ </tex-math></inline-formula>. Finally, an illustrative example is presented to show the implementation of the exponential EWMA chart when process parameters are estimated.
Modeling and Experimental Analysis of Dynamic Characteristics of Asynchronous Motor

Mathematical modeling of dynamic modes of asynchronous machines is an important tool both to the study of processes occurring in asynchronous motors, and to the design of asynchronous drives. Therefore, the problem of developing the mathematical models of asynchronous machines taking into account the influence of real factors on the parameters remains relevant. The mathematical model of asynchronous machine, based on differential equations in a two-phase orthogonal coordinate system associated with the stator, is present in the article. To take into account the influence of iron losses on mutual inductances, it is proposed using the functions of the magnetic lag angle. The model also takes into account the influence of current displacement in the rotor bars and the influence of saturation both along the path of the main magnetic flux and along the paths of the stator and rotor leakage fluxes. The description of the computer program that implements the model and allows simulating the transients of the asynchronous motor is present. The results of experimental studies of the asynchronous motor start under sinusoidal voltage using a computer stand, and comparison of the oscillograms obtained with simulation results on the proposed model are presented. The comparison between experimental and calculated oscillograms of the current and rotation speed of the investigated asynchronous motor allows making a conclusion about satisfactory accuracy of the offered model and possibility of its application to modeling and analysis of asynchronous electric drives.
Experimental In-Body to On-Body and In-Body to In-Body Path Loss Models of Planar Elliptical Ring Implanted Antenna in the Ultra-Wide Band

This paper presents in-body to on-body and in-body to in-body channel models of a planar wideband elliptical ring implanted antenna in the lower part of Ultra-WideBand (UWB, 3.1 to 5.1 GHz). The results are verified inside a practical UWB liquid phantom with the help of a wideband on-body monopole antenna. Moreover, the design principle of the in-body and on-body antennas, as well as their simulated and measured reflection coefficients are presented. The channel characterization inside the lossy medium gives a deep insight of wireless capsule endoscope technology (WCE) and helps to evaluate the radiation performance of the previously designed planar elliptical ring implanted antenna.
Measuring Facts of Media Richness

Current research regarding media richness has concluded that richness is an emergent property of the media and its context. The notion that media richness is an emergent property of the environment implies that it should be measured through perceptions as a psychological reaction to the media and its context. Recent studies have suffered from the lack of a standard set of tools by which to measure perceptions of the media. In an effort to provide a valid and reliable measurement tool for the perception of media, an instrument was developed and tested extensively in two different studies. Results in both a field study and laboratory study show strong evidence for the construct validity of our perceptual measure of media richness. The combined results of the two studies provide support of the usefulness of the media richness instrument developed and tested here. This instrument not only provides a means of measuring the perception of richness, but also allows researchers to be able to identify which characteristics of communication media are most important for defining richness in practice.
Between school factors and teacher factors: what inhibits Malaysian science teachers From using ICT?

Despite the Malaysian government’s efforts to increase the use of ICT in school, teachers’ uptake of the technology remains slow and dismal. In this study, teachers’ perceptions of the barriers that inhibited their use of ICT in the science classroom were explored. One hundred and fifty-one (N = 151) science teachers from selected secondary schools in Kuala Lumpur and Selangor took part in the survey. Teachers’ perceptions of the barriers were captured using a self-developed questionnaire consisting of 27 Likert-type items. The questionnaires were administered with the help of school principals. Principal components analysis (PCA) with Promax rotation extracted four underlying dimensions of barriers to ICT use, namely teachers’ self-handicapping thoughts, school support, attitude toward ICT use, and negative beliefs about ICT use. Three of these factors were teacher related. Self-handicapping thoughts emerged as the largest inhibitor, explaining about 38.2% of teachers’ lack of ICT utilization in the science classroom. The results corroborated previous findings that teacher factors tend to outweigh school factors in hampering teachers’ uptake of technology.
Practical Reflection and Metaprogramming for Dependent Types

Embedded domain-specific languages are special-purpose programming languages that are implemented within existing generalpurpose programming languages. Dependent type systems allow strong invariants to be encoded in representations of domain-specific languages, but it can also make it difficult to program in these embedded languages. Interpreters and compilers must always take these invariants into account at each stage, and authors of embedded languages must work hard to relieve users of the burden of proving these properties. Idris is a dependently typed functional programming language whose semantics are given by elaboration to a core dependent type theory through a tactic language. This dissertation introduces elaborator reflection, in which the core operators of the elaborator are realized as a type of computations that are executed during the elaboration process of Idris itself, along with a rich API for reflection. Elaborator reflection allows domain-specific languages to be implemented using the same elaboration technology as Idris itself, and it gives them additional means of interacting with native Idris code. It also allows Idris to be used as its own metalanguage, making it into a programmable programming language and allowing code re-use across all three stages: elaboration, type checking, and execution. Beyond elaborator reflection, other forms of compile-time reflection have proven useful for embedded languages. This dissertation also describes error reflection, in which Idris code can rewrite DSL error messages before presenting domain-specific messages to users, as well as a means for integrating quasiquotation into a tactic-based elaborator so that high-level syntax can be used for low-level reflected terms.
Concentrating phenomenon for fractional nonlinear Schr\"{o}dinger-Poisson system with critical nonlinearity

In this paper, we study the following fractional Schrodinger-Poisson system \begin{equation*} \left\{ \begin{array}{ll} \varepsilon^{2s}(-\Delta)^su+V(x)u+\phi u=g(u) & \hbox{in $\mathbb{R}^3$,} \varepsilon^{2t}(-\Delta)^t\phi=u^2,\,\, u>0& \hbox{in $\mathbb{R}^3$,} \end{array} \right. \end{equation*} where $s,t\in(0,1)$, $\varepsilon>0$ is a small parameter. Under some suitable assumptions on potential function $V(x)$ and critical nonlinearity term $g(u)$, we construct a family of positive solutions $u_{\varepsilon}\in H^s(\mathbb{R}^3)$ which concentrates around the global minima of $V$ as $\varepsilon\rightarrow0$.
Dual Role of Cell-Cell Adhesion In Tumor Suppression and Proliferation

It is known that mechanical interactions couple a cell to its neighbors, enabling a feedback loop to regulate tissue growth. However, the interplay between cell-cell adhesion strength, local cell density and force fluctuations in regulating cell proliferation is poorly understood. Here, we show that variations in the tumor growth rates, which depend on the location of cells within tissue spheroids, are strongly influenced by cell-cell adhesion. As the strength of the cell-cell adhesion increases, intercellular pressure initially decreases, enabling dormant cells to more readily enter into a proliferative state. We identify an optimal cell-cell adhesion regime where pressure on a cell is a minimum, allowing for maximum proliferation. We use a theoretical model to validate this novel feedback mechanism coupling adhesion strength, local stress fluctuations and proliferation. Our results, predicting the existence of a non-monotonic proliferation behavior as a function of adhesion strength, are consistent with experimental results. Several experimental implications of the proposed role of cell-cell adhesion in proliferation are quantified, making our model predictions amenable to further experimental scrutiny. We show that the mechanism of contact inhibition of proliferation, based on a pressure-adhesion feedback loop, serves as a unifying mechanism to understand the role of cell-cell adhesion in proliferation.
Evaluating an automated mediator for joint narratives in a conflict situation

ABSTRACT Joint narratives are often used in the context of reconciliation interventions for people in social conflict situations, which arise, for example, due to ethnic or religious differences. The interventions aim to encourage a change in attitudes of the participants towards each other. Typically, a human mediator is fundamental for achieving a successful intervention. In this work, we present an automated approach to support remote interactions between pairs of participants as they contribute to a shared story in their own language. A key component is an automated cognitive tutor that guides the participants through a controlled escalation/de-escalation process during the development of a joint narrative. We performed a controlled study comparing a trained human mediator to the automated mediator. The results demonstrate that an automated mediator, although simple at this stage, effectively supports interactions and helps to achieve positive outcomes comparable to those attained by the trained human mediator.
Variable dust emission by WC type Wolf–Rayet stars observed in the NEOWISE-R survey

Photometry at 3.4 and 4.6 ${\mu m}$ of 128 Population I WC type Wolf–Rayet stars in the Galaxy and 12 in the Large Magellanic Cloud (LMC) observed in the WISE NEOWISE-R survey was searched for evidence of circumstellar dust emission and its variation. Infrared spectral energy distributions (SEDs) were assembled, making use of archival r, i, Z, and Y photometry to determine reddening and stellar wind levels for the WC stars found in recent IR surveys and lacking optical photometry. From their SEDs, 10 apparently non-variable stars were newly identified as dust makers, including three, WR 102-22, WR 110-10, and WR 124-10, having subtype earlier than WC8–9, the first such stars to show this phenomenon. The 11 stars found to show variable dust emission include six new episodic dust makers, WR 47c, WR 75-11, WR 91-1, WR 122-14, and WR 125-1 in the Galaxy and HD 38030 in the LMC. Of previously known dust makers, NEOWISE-R photometry of WR 19 captured its rise to maximum in 2018 confirming the 10.1-yr period, that of WR 125 the beginning of a new episode of dust formation suggesting a period near 28.3 yr. while that of HD 36402 covered almost a whole period and forced revision of it to 5.1 yr.