Miracle-dz/UniFAR-FTUs huggingface dataset:
Channel pre- and post-equalization in uplink OFCDM systems with mobility

In this paper, we propose an up-link orthogonal frequency and code division multiplexing (OFCDM) mobile cellular system that uses channel pre-equalization at the mobile terminal (MT) and adaptive detection at the base station (BS). Assuming a time division duplex that may offer channel reciprocity between down- and up-links, a low-complexity channel pre-equalization avoids explicit channel estimation at the BS with a very good performance for low MT velocities. A data-aided adaptive detection at the BS starting directly in a tracking mode thanks to pre-equalization can compensate for the increasing mismatch between channel pre-equalization and actual channel response caused by large velocities all along the uplink frame. Simulation results using a 4G realistic scenario show that the proposed uplink OFCDM system offers a good performance with a high spectral efficiency, as it is able to transmit large data rates with mobility and no need of pilot symbols.
Sensory subtraction with cutaneous feedback to ensure safety in bimanual robot-assisted surgery

In this paper we present a novel approach to force feedback in robot-assisted surgery. Haptic stimuli, consisting of both kinesthetic and cutaneous components, are substituted with cutaneous feedback only. This new approach to sensory substitution in haptics is called sensory subtraction, since it can be thought as the subtraction between the complete haptic interaction and its kinesthetic component. In order to evaluate the feasibility of the proposed technique, we carried out a bimanual teleoperation experiment, wherein we compared the performance of our sensory subtraction approach with that of (1) complete haptic feedback, (2) visual feedback, and (3) auditory feedback. Results assess the proposed method as a viable solution to substitute haptic feedback in complex teleoperation scenarios. Moreover, as any other sensory substitution technique, this approach allows to overcome any stability issue affecting the haptic loop.
Breast lesions: evaluation with US strain imaging--clinical experience of multiple observers.

PURPOSE To prospectively determine the accuracy of using an ultrasonographic (US) strain imaging technique known as lesion size comparison to differentiate benign from malignant breast lesions. MATERIALS AND METHODS Institutional Review Board approval and patient informed consent were obtained for this HIPPA-compliant study. US strain imaging was performed prospectively for 89 breast lesions in 88 patients. Lesions were imaged by using freehand compression and a real-time strain imaging algorithm. Five observers obtained manual measurements of lesion height, width, and area from B-mode and strain images. By using these size measurements, individual observer and group performances were assessed by using the area under the receiver operating characteristic curve (A(z)). The performance of a single size parameter versus that of a combination of size parameters was evaluated by using univariate and multivariate logistic regression. RESULTS Group A(z) values showed that width ratio and area ratio yielded the best results for differentiating benign and malignant breast lesions, and they were not statistically different from one another (P = .499). For the group, the performance of area and width, which was superior to that of height and aspect ratio, was statistically significant for all cases (P < .011) except for those that compared area with aspect ratio (P = .118). By using a group threshold of 1.04 for width ratio and 1.13 for area ratio, the sensitivity and specificity of the technique were 96% and 21%, respectively, for width and 96% and 24%, respectively, for area. The best observer achieved a sensitivity of 96% and a specificity of 61% by using the area ratio. For all but one observer, combined size parameters did not improve observer performance (P > .258). Significant interobserver performance variability was observed (P < .001). CONCLUSION Results suggest that US strain imaging has the potential to aid diagnosis of breast lesions. However, manually tracing lesion boundaries for size ratio differentiation in a busy clinical setting did not match the diagnostic performance levels previously reported. Focusing on measurements of lesion width, along with additional observer training or automated processes, may yield a suitable method for routine clinical application.
A New Approach to Dimensionality Reduction: Theory and Algorithms

This paper applies Whitney's embedding theorem to the data reduction problem and introduces a new approach motivated in part by the (constructive) proof of the theorem. The notion of a good projection is introduced which involves picking projections of the high-dimensional system that are optimized such that they are easy to invert. The basic theory of the approach is outlined and algorithms for finding the projections are presented and applied to several test cases. A method for constructing the inverse projection is detailed and its properties, including a new measure of complexity, are discussed. Finally, well-known methods of data reduction are compared with our approach within the context of Whitney's theorem.
A Verification and Extension of Students' Motivations, Financial Orientation, and Identity Salience as Predictors of Likelihood to Withdraw from a University

Broadly, this research examines a student’s likelihood to withdraw from a university based on the relationship between motivations, financial orientation, and identity salience. Specifically, this study empirically examines the relationship between extrinsic motivation, intrinsic motivation, and apathetic motivation, perceived opportunity loss and lifestyle activities related to financial orientation, and satisfaction and reciprocity related to identity salience, as predictors of a student’s likelihood of withdrawing from a university. A questionnaire was designed by extending scale items related to the constructs of interest. First-year students at a private university located in the upper Midwest were sampled. A hypothized model was tested using partial least squares regression. Consistent with theory, four of the eight hypothized paths are statistically significant. Four research questions were analyzed using multigroup analysis. Differences in relationships are shown for gender, major, college GPA and ACT score. Conclusions provide more insight on understanding what drives undergraduate students’ likelihood to withdraw. Higher education administrators could create different programs, services, or strategies that accommodate to the needs of students’ at various levels of motivation, financial orientation, and identity salience. Limitations of this research include measuring a student’s likelihood to withdraw rather than a student actually withdrawing. Additionally, an R 2 value of 50% which means the model is missing variables. With this said, directions for future research should involve tracking who changes in motivations, financials, and identity salience as well as analyzing who actually withdrew versus the likelihood that they would withdraw. STUDENTS’ LIKELIHOOD OF WITHDRAWING iii
Robust MPC design for fault tolerance of constrained multisensor linear systems

In this paper, a robust fault-tolerant control strategy for constrained multisensor linear systems, subject to sensor faults and in the presence of bounded state and output disturbances is proposed. The scheme verifies that for each sensors-estimator combination, suitable residual variables lie inside pre-computed sets and selects the more appropriate combination based on a chosen criterion. An active fault tolerant output feedback controller yields an MPC-based control law and by means of the notion of “tube” of trajectories, we ensure robust closed-loop exponential stability and good performance in the fault-free case and under the occurrence of abrupt sensor faults.
Visualizing the behaviour of higher dimensional dynamical systems

In recent years scientific visualization has been driven by the need to visualize high-dimensional data sets within high-dimensional spaces. However most visualization methods are designed to show only some statistical features of the data set. The paper deals with the visualization of trajectories of high-dimensional dynamical systems which form a L/sub n//sup n/ data set of a smooth n-dimensional flow. Three methods that are based on the idea of parallel coordinates are presented and discussed. Visualizations done with these new methods are shown and an interactive visualization tool for the exploration of high-dimensional dynamical systems is proposed.
Mother's Expectations for Adolescent Children: A Comparison Between Families of Adolescents with Disabilities and Those with Non-labeled Adolescents

PARENTS ARRIVE AT their job of childrearing with a set of preconceived notions about children’s development (Goodnow, 1988; Hess, Kashiwagi, Azuma, Price, & Dickson, 1980; Stolz, 1967). Frequently, these notions are vague descriptions of qualities such as independence and respectfulness that parents hope their children will attain (Stolz, 1967). &dquo;Beliefs&dquo; parents have about their children may influence how they interact with their children and what they expect from their children (Goodnow, 1988). According to Goodnow (1988), findings from the sizeable body of research have &dquo;established that parents do hold a variety of views about children and parenting, and that these views are not always in agreement with formal psychology or with the characteristics of the children that parents are judging&dquo; (p. 287). Families who have a child diagnosed as severely mentally retarded have been found to play a central role in their child’s development and socialization. Parents’ influence is typically mentioned as being either an obstacle to children’s independence (Abramson, Ash, & Nash, 1979) or conversely as a crucial source of support (Konanc & Warren, 1984; Singer & Irvin, 1989). Recent characterizations have clarified that
Parallel Processing Framework on a P2P System Using Map and Reduce Primitives

This paper presents a parallel processing framework for structured Peer-To-Peer (P2P) networks. A parallel processing task is expressed using Map and Reduce primitives inspired by functional programming models. The Map and Reduce tasks are distributed to a subset of nodes within a P2P network for execution by using a self-organizing multicast tree. The distribution latency cost of multicast method is $O(log(N))$, where $N$ is a number of target nodes for task processing. Each node getting a task performs the Map task, and the task result is summarized and aggregated in a distributed fashion at each node of the multicast tree during the Reduce task. We have implemented this framework on the Brunet P2P system, and the system currently supports predefined Map and Reduce tasks or tasks inserted through Remote Procedure Call (RPC) invocations. A simulation result demonstrates the scalability and efficiency of our parallel processing framework. An experiment result on Planet Lab which performs a distributed K-Means clustering to gather statistics of connection latencies among P2P nodes shows the applicability of our system in applications such as monitoring overlay networks.
Automated Lung Cancer Detection Using Lightweight Neural Network

Due to growing lung cancer rates and COVID19 pandemic awareness, automated lung cancer diagnosis has garnered focus in recent years. In medical image processing, Convolutional Neural Networks (CNNs) have shown promise for accurate and efficient lung cancer identification. This research proposes the use of a lightweight CNN architecture to identify lung cancer. The lightweight CNN balances model complexity with performance, making it suited for deployment on mobile devices and edge computing systems. The proposed technique is tested using a heterogeneous lung cancer dataset with chest X-ray pictures. During training, our lightweight CNN achieved 99% dataset accuracy, superseding other models in comparison. The model's sensitivity and specificity, critical in lung cancer screening, reduces false negatives and positives. Precision and automated lung cancer detection may speed up early identification and care, improving patient outcomes. CNN's lightweight architecture allows real-time inference on resource-constrained devices, expanding lung cancer screening in rural and underprivileged areas. In conclusion, a lightweight convolutional neural network can identify lung cancer automatically. This approach's 99% validation accuracy on a difficult lung cancer dataset shows it’s potential to help doctors make accurate diagnoses leading to aids in timely lung cancer detection and improved health outcomes.
Parameter identification of the normal flow equation by using adaptive estimation

We address the problem of estimating the constant parameters involved in the normal flow equation, which is a Hamilton-Jacobi PDE widely used in many different research areas. The identification of such parameters allows one to estimate the flow function, which is the velocity vector field that governs the dynamics of the level sets associated with the solution of the equation. The estimates are obtained by using a Luenberger observer and a parameter estimator based on the adaptation law proposed by Pomet and Praly in 1992. Such a law makes it possible to explicitly take into account bounds on the parameters. Conditions for the stability of the parameter estimation error are established. Simulation results are presented that confirm the theoretical achievements.
A Bayesian-Based Improved Algorithm for Non-Rigid Structure Reconstruction

Non-rigid structure from Motion (NRSfM) is an important research task in computer vision, aiming to recover the motion information of three-dimensional non-rigid structures from a set of two-dimensional images. An algorithm based on the Riemannian manifold has shown promising results in non-rigid 3D reconstruction. However, this method employs fixed parameters for the reconstruction process, which makes it challenging to find the optimal parameter configuration for complex non-rigid objects. So, a novel NRSfM algorithm based on Bayesian parameter optimization is proposed in this paper. By leveraging the Bayesian optimization framework, this approach adaptively adjusts algorithm parameters via sampling the parameter space and evaluating depth errors, enhancing its suitability for diverse non-rigid structure recovery scenarios. A series of experiments and comparisons have been conducted in this study. Through experiments on non-rigid datasets, we have validated the superiority of the Bayesian parameter optimization method in NRSfM. Experimental results show that the proposed Bayesian parameter optimization significantly improves recovery accuracy compared to existing fixed parameter selection methods.
Convolutional Matching Technique for Urdu Text Recognition

Visual understanding of linguistic information is a crucial research topic because of its applications like Optical Character Recognition (OCR), opinion mining, and language translation. This paper focuses on the automatic Urdu text recognition system. Our work primarily consists of Urdu text recognition using a convolutional template matching technique. To this end, this paper investigates the character-wise explicit Urdu character detection and recognition method with Nastaleeq font. The proposed method comprises the grouping of homogeneous shaped classes that have their diacritic marks identified independently in a synthesis manner. Furthermore, we conduct experiments using an advanced Urdu News Ticker (UNT) dataset that provides precise character component-level labeling. The presented method attains 82.33% on the Urdu Printed Text Images (UPTI) dataset.
High Isolation Ka-band Multi-Port PIN Diode MMIC Switches

AlGaAs/GaAs PIN diode based Ka-band SP4T and SP2T switches are designed and tested, with measured insertion loss less than 2 dB and isolation better than 48 dB over 25-33 GHz band. The on-wafer measured results are in close match with simulations. These MMIC switches are EM simulated using ADS Momentum tool. Combination of series and shunt diode topology is used to get optimum insertion loss and isolation at Ka-band. These chips have on-chip bias networks and DC blocking capacitors and are well suited for realization of miniaturized RF switch matrix at Ka-band.
Six-month evaluation of Carbatrol (extended-release carbamazepine) in complex partial seizures

We evaluated the use of a new, controlled-release capsule form of carbamazepine, Carbatrol capsules, in an open-label, multicenter study of 124 patients with complex partial seizures. Ninety-one percent of the patients successfully completed the 6-month trial with good seizure control, with a significant improvement in quality of life. We conclude that switching patients with complex partial seizures from multiple daily-dose carbamazepine to twice-daily Carbatrol on a milligram-to-milligram basis is relatively safe.
Organized Sport Participation Is Associated with Higher Levels of Overall Health-Related Physical Activity in Children (CHAMPS Study-DK)

Introduction Many children fail to meet international guideline recommendations for health-related activity (≥60 minutes/day of moderate-to-vigorous physical activity [MVPA]), and intervention studies to date have reported negligible effects. Objective Explore the associations of organized leisure-time sport participation with overall physical activity levels and health-related physical activity guideline concordance. Methods This prospective cohort study was nested in the Childhood Health, Activity, and Motor Performance School Study Denmark. Study participants were a representative sample of 1124 primary school students. Organized leisure-time sport participation was reported via text messaging and physical activity was objectively measured over seven days with accelerometry. Associations between sport participation and physical activity level were explored with multilevel mixed-effects regression models and reported with beta coefficients (b) and adjusted odds ratios (aOR). Results Participants were 53% female, with mean(SD) age = 8.4(1.4) years. Boys were more active than girls (p<0.001), and physical activity levels and guideline concordance decreased with age (p<0.001). Soccer participation at any frequency was associated with greater overall MVPA (b[95% CI] = 0.66[0.20,1.13] to 2.44[1.44,3.44]). Depending on participation frequency, this equates to 5–20 minutes more MVPA on the average day and 3 to 15 fold increased odds of achieving recommended levels of health-related physical activity (aOR[95%CI] = 3.04[1.49,6.19] to 14.49[1.97,106.56]). Similar associations were identified among children playing handball at least twice per week. Relationships with other sports (gymnastics, basketball, volleyball) were inconsistent. Conclusions Many children, particularly girls and those in higher grade levels do not adhere to health-related physical activity recommendations. Organized leisure-time sport participation may be a viable strategy to increase overall health-related physical activity levels and international guideline concordance in children.
Trade-offs in empowerment through social action: voices from Detroit

Existing literature offers many examples of how social action organizations build empowerment in marginalized communities. The primary argument for this article is that multiple mechanisms of empowerment exist and should be understood as much in terms of their trade-offs as for what they achieve for activists, organizations, and communities. Rather than thinking of a single organization as empowering or disempowering, we argue that any activist organization can be analyzed for its combination of empowering mechanisms and corresponding trade-offs. In a case study of a faith-based, social action organization in Detroit, we find support for the connection between empowerment building and the mechanisms identified in existing research. We also present trade-offs among these mechanisms of empowerment, in part caused by the divisive regional context, and conclude with the argument that a better understanding of the multifaceted quality of empowerment could help organizers work in coalition to leverage their strengths toward shared goals.
Identification of a Mutation in the Bacillus subtilis S-Adenosylmethionine Synthetase Gene That Results in Derepression of S-Box Gene Expression

ABSTRACT Genes in the S-box family are regulated by binding of S-adenosylmethionine (SAM) to the 5′ region of the mRNA of the regulated gene. SAM binding was previously shown to promote a rearrangement of the RNA structure that results in premature termination of transcription in vitro and repression of expression of the downstream coding sequence. The S-box RNA element therefore acts as a SAM-binding riboswitch in vitro. In an effort to identify factors other than SAM that could be involved in the S-box regulatory mechanism in vivo, we searched for trans-acting mutations in Bacillus subtilis that act to disrupt repression of S-box gene expression during growth under conditions where SAM pools are elevated. We identified a single mutant that proved to have one nucleotide substitution in the metK gene, encoding SAM synthetase. This mutation, designated metK10, resulted in a 15-fold decrease in SAM synthetase activity and a 4-fold decrease in SAM concentration in vivo. The metK10 mutation specifically affected S-box gene expression, and the increase in expression under repressing conditions was dependent on the presence of a functional transcriptional antiterminator element. The observation that the mutation identified in this search affects SAM production supports the model that the S-box RNAs directly monitor SAM in vivo, without a requirement for additional factors.
Saliency based alphabet and numbers of American sign language recognition using linear feature extraction

Sign language is a way to communicate for deaf people, which hand shapes are used instead of sound patterns. In this paper, we present a method for recognizing alphabet and numbers in American sign language based on saliency detection of the image. After detecting saliency, the images were processed by Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), in order to reduce dimensions and maximize class internal similarity and minimize class external similarity. Finally resulting vectors will be trained and will be classified by the neural networks (NN). The use of this system is communication with deaf people in addition to connecting with computer; this is due to the use of standard letters in sign language. Also experiments were carried out on a new standard dataset in this field. The recognition rate of the system was 99.88% using 4-fold cross validation method in 4 training terms on average. The results of the proposed system represent high accuracy and proper performance of this method compared with the others.