Miracle-dz/UniFAR-FTUs huggingface dataset:
A Tool for Mutation Analysis in Racket

Racket is a functional programming language that is used to teach CS1 at many high schools and colleges. Recent research results have shown that mutation analysis can be an effective substitute for manual grading of student test cases. In order to evaluate its efficacy in our college’s introductory programming courses, we created a prototype mutation analysis tool for Racket. We describe the design and features of the tool and perform a feasibility study using two assignments from an intro CS course where student test suite thoroughness was evaluated by hand by human graders. In our results, we find a moderate correlation between mutation score and hand-grading test suite quality score and conduct a qualitative analysis to identify situations where mutation score and hand-grading score do not correlate. We find that, compared to hand-grading, mutation analysis may require more stringent adherence to the interface specified in an assignment as well as more precisely specified assignments. On the other hand, inter-reviewer reliability is a known challenge of hand-grading, and we observe several instances where hand-graders may have assigned the wrong score. Given the relatively cheap cost to providing mutation analysis feedback to students (compared to hand-grading feedback), mutation analysis still provides the opportunity to provide faster, more frequent, feedback to learners, enabling them to improve their testing practices further. Future work will study the effectiveness of various mutation operators in Racket and perform larger-scale evaluations.
Placement of High Availability Geo-Distributed Data Centers in Emerging Economies

The data center markets in emerging economies are being built at a furious pace. When high availability is required, as it always is in the modern digital economy, the placement of geo-distributed data centers may be influenced by factors such as technician shortage and under-developed infrastructure, both of which are typical in emerging economies. Although the data center availability subject in general has been well studied, it remains unclear how rapid and unbalanced economic development in emerging economies may affect the availability of geo-distributed data centers and their cost of ownership. In this paper, we incorporate the unbalanced availability of infrastructure and technician into the data center placement. The problem is first formulated as a mixed integer nonlinear program (MINLP). To solve this potentially large scale problem, we transform it into a QCQP, capable of handling heterogeneous workloads. The resulting problem can then be efficiently solved by off-the-shelf optimization toolboxes. With real-life data in China, we show how unbalanced development of infrastructure and technician shortage may affect the placement of data centers, and analyze the tradeoff between cost and availability. Our results indicate that technician shortage and unbalanced network infrastructure will lead to increased cost and distinct data center placement strategies.
Automated Cardiovascular Disease Prediction Models: A Comparative Analysis

INTRODUCTION: Cardiovascular disease (CVD) is one of the primary causes of the increased mortality rate universally. Therefore, automated methods for early prediction of CVD are of utmost importance to prevent the disease. OBJECTIVES: In this study, we have pointed out the major advantages, drawbacks, and the scope of enhancing the prediction accuracy of the existing automated cardiovascular disease prediction methods. In addition to that, we have analyzed various combinations of attributes that can help in prediction at the earliest. METHODS: We have exploited various machine learning models to analyse their performances in predicting the CVD at the earliest. RESULTS: For a publicly available database, the Artificial Neural Network attained the highest accuracy of 88.5% and recall of 90%. CONCLUSION: We justified the notion that it will be beneficial to identify potential physiological and behavioural attributes to predict CVD accurately as early as possible.
Design and Modeling of a Label-free JLTFETBased Biosensor for Enhanced Sensitivity

This article discusses the operation of a Junctionless (JL) TFET as a biosensor for detecting various protein molecules. Due to JLTFET’s advantages over TFETs and JLFETs, a JLTFET-based biosensor is chosen. By carving cavities across the gates, the biomolecules are trapped. The performance of label -free biosensing with dielectric modulation has been investigated using the TCAD simulator. This paper also addresses the sensitivity to current and voltage. In addition, the effect of size, fill factor, and cavity location on the performance of biosensors is investigated in this work. This is simulationbased research of the dielectric modulation approach for detecting multiple proteins and amino acids. To obtain the expression for drain current, the analytical model for the proposed biosensor is solved. The sensitivity of biosensors based on JLTFET is on the order of 1013.
On Generalizations of Gowers Norms

Inspired by the definition of Gowers norms we study integrals of products of multivariate functions. The Lp norms, certain trace norms, and the Gowers norms are all defined by taking the proper root of one of these integrals. These integrals are important from a combinatorial point of view as inequalities between them are useful in understanding the relation between various subgraph densities. Lovasz asked the following questions: (1) Which integrals correspond to norm functions? (2) What are the common properties of the corresponding normed spaces? We address these two questions. We show that such a formula is a norm if and only if it satisfies a Holder type inequality. This condition turns out to be very useful: First we apply it to prove various necessary conditions on the structure of the integrals which correspond to norm functions. We also apply the condition to an important conjecture of Erdohs, Simonovits, and Sidorenko. Roughly speaking, the conjecture says that among all graphs with the same edge density, random graphs contain the least number of copies of every bipartite graph. This had been verified previously for trees, the 3-dimensional cube, and a few other families of bipartite graphs. The special case of the conjecture for paths, one of the simplest families of bipartite graphs, is equivalent to the Blakley-Roy theorem in linear algebra. Our results verify the conjecture for certain graphs including all hypercubes, one of the important classes of bipartite graphs, and thus generalize a result of Erdohs and Simonovits. In fact, for hypercubes we can prove statements that are surprisingly stronger than the assertion of the conjecture. To address the second question of Lovasz we study these normed spaces from a geometric point of view, and determine their moduli of smoothness and convexity. These two parameters are among the most important invariants in Banach space theory. Our result in particular determines the moduli of smoothness and convexity of Gowers norms. In some cases we are able to prove the Hanner inequality, one of the strongest inequalities related to the concept of smoothness and convexity. We also prove a complex interpolation theorem for these normed spaces, and use this and the Hanner inequality to obtain various optimum results in terms of the constants involved in the definition of moduli of smoothness and convexity.
An Application of Non-Local Means Filter to Denoise Brain Segmentations

Automated and reliable tissue classification is a challenging task as the intensity of the data typically does not allow a clear delimitation of the different tissue types due to of partial volume effects, image noise and intensity non-uniformities caused by magnetic field inhomogeneities. In this work, we propose the use of a of non-local (NL) means filter (Buades et al. 2005, Coupe et al. 2008) to denoise magnetic resonance (MR) images prior to brain tissue segmentation.
Narrowband Filters Designed from Hybrid One-Dimensional Periodic/Quasiperiodic Photonic Crystals with a Single Defect Layer

We report the transmission spectra and electric field amplitudes of electromagnetic modes propagating in hybrid periodic/quasiperiodic multilayer photonic structures in one dimension (1D). We consider the case of the combination of biperiodic Bragg mirror and triperiodic Bragg mirrors with quasiregular (FB, Fibonacci) layered components. The corresponding hybrid structure (HB) is formed by concatenating BM(N)-FB(M)-BM(N), where N (M) means the number of periods (sequence order) used for the Bragg mirrors (FB) structure. A single defect layer (D) is considered in the middle of two HBs (HB-D-HB). Optimizing the parameters (the order of sequence, number of Bragg mirror layers, thickness, and the refractive index of D) allows us to obtain narrowband filters. The manipulation of these parameters fixes the number of photonic band gaps as well as the position of transmission peaks. The existence of the selectively localized behavior of some optical modes in the structures is discussed.
Modeling mathematical and analysis of an alcohol drinking with n complications

In this article, we propose a new continuous mathematical approach to model and analyze the dynamics of the population of heavy drinkers and their health complications. The model has several compartments, including a new one that represents the number of heavy drinkers with different complications associated with excessive alcohol consumption. We study the stability of the model using mathematical theories such as the Routh-Hurwitz criteria for local stability, in order to study the equilibrium without and with excessive alcohol consumption, and the construction of Lyapunov functions makes it possible to study global stability. A sensitivity analysis is performed to determine which parameters have the most signiﬁcant impact on the number of reproductions R0. The results were validated using numerical simulations carried out under MATLAB. This model may be useful in guiding public health policies aimed at reducing the number of drinkers and the complications associated with alcohol consumption.
Melatonin Alleviates Retinal Ischemia–Reperfusion Injury by Inhibiting p53–Mediated Ferroptosis

Retinal ischemia–reperfusion (RIR) injury caused by high intraocular pressure (IOP) is an important risk factor contributing to retinal ganglion cell (RGC) death, eventually causing blindness. A key progressive pathological process in the development of RIR is the death of RGCs. However, the detailed mechanisms underlying RGC death caused by RIR have not yet been clearly elucidated, and effective treatments are lacking. Ferroptosis is a recently defined form of programmed cell death that is closely related to organ injury. Melatonin (MT) is a promising neuroprotective agent, but its effects on RIR injury remain unclear. In this study, murine models of acute ocular hypertension and oxygen and glucose deprivation/reoxygenation (OGD/R) model were adopted to simulate retinal ischemia. MT alleviated retinal damage and RGC death in RIR mice, significantly attenuating RIR–induced ferroptosis. Furthermore, MT reduced the expression of p53, a master regulator of ferroptosis pathways, and the upregulation of p53 promoted ferroptosis and largely abolished the neuroprotective effects of MT. Mechanistically, the overexpression (OE) of p53 suppressed the expression of the solute carrier family 7 member 11 (Slc7a11), which was accompanied by increased 12–lipoxygenase (Alox12) expression, triggering retinal ferroptosis. Moreover, MT–ameliorated apoptosis, neuroinflammation and microglial activation were observed. In summary, MT conferred neuroprotection against RIR injury by inhibiting p53–mediated ferroptosis. These findings indicate that MT is a retina–specific ferroptosis inhibitor and a promising therapeutic agent for retinal neuroprotection.
Listening like a native: Unprofitable procedures need to be discarded

Two languages, historically related, both have lexical stress, with word stress distinctions signalled in each by the same suprasegmental cues. In each language, words can overlap segmentally but differ in placement of primary versus secondary stress (OCtopus, ocTOber). However, secondary stress occurs more often in the words of one language, Dutch, than in the other, English, and largely because of this, Dutch listeners find it helpful to use suprasegmental stress cues when recognising spoken words. English listeners, in contrast, do not; indeed, Dutch listeners can outdo English listeners in correctly identifying the source words of English word fragments (oc-). Here we show that Dutch-native listeners who reside in an English-speaking environment and have become dominant in English, though still maintaining their use of these stress cues in their L1, ignore the same cues in their L2 English, performing as poorly in the fragment identification task as the L1 English do.
Distributed Resilient State Estimation for Cyber-Physical Systems Against Bit Errors: A Zonotopic Set-Membership Approach

This paper addresses the distributed resilient state estimation issue for a class of large-scale cyber-physical systems (CPSs) against bit errors. Owing to the reliability of the binary data, a novel binary encoding-decoding scheme is developed to enable digital communication of the CPSs. We consider the situation where the transmitted binary bits may be flipped over noisy communication channels. The primary purpose of this paper is to design a distributed resilient estimation scheme under a bit-flipped detection mechanism to achieve a reliable estimation of the CPSs. First, by means of the Minkowski sum of sets, a novel distributed zonotopic resilient estimation scheme is proposed where the optimal estimator gain is obtained in the minimum $F$-radius sense. Furthermore, a two-stage bit-flipped detection scheme is developed by taking advantage of 1) the prediction output zonotope and 2) the intersection between the prediction zonotope and the estimation zonotope. Furthermore, in order to improve the algorithm efficiency, a reduction operator is introduced to reduce the order of the zonotope. Finally, the IEEE 30-bus systems are applied to verify theoretical results of the developed distributed resilient estimation algorithm.
Digital Signature Development using Truncated Polynomials

Procedures used for digital signature development rely on the algorithms provided for data confidentiality. The decryption algorithms used for data confidentiality are generally inverse of the encryption algorithms and hence can be used for authentication purposes. Existing Algorithms based on truncated polynomials offer many advantages compared to RSA. At the same time these truncated polynomial algorithms suffer from the drawback that the encryption and decryption algorithms are not inverses of each other as in the case of RSA, hence require separate computation for generation of digital signature. In this paper we propose an algorithm based on truncated polynomials that combine the advantages of both RSA and the algorithms based on truncated polynomials and at the same time overcoming the difficulties faced in each. This proposed algorithm does not require either large primes to be generated or extra computational effort for digital signature generation.
Face Tracking Algorithm Based on Particle Filter with Mean Shift Importance Sampling

The condensation tracking algorithm uses a prior transition probability as the proposal distribution, which does not make full use of the current observation. In order to overcome this shortcoming, a new face tracking algorithm based on particle filter with mean shift importance sampling is proposed. First, the coarse location of the face target is attained by the efficient mean shift tracker, and then the result is used to construct the proposal distribution for particle propagation. Because the particles obtained with this method can cluster around the true state region, particle efficiency is improved greatly. The experimental results show that the performance of the proposed algorithm is better than that of the standard condensation tracking algorithm.
Histology of the Primary Tumor Correlates with False Positivity of Integrated 18F-FDG-PET/CT Lymph Node Staging in Resectable Lung Cancer Patients

This study aimed to evaluate the diagnostic accuracy and false positivity rate of lymph node (LN) staging assessed by integrated 18F-fluorodeoxyglucose positron emission computed tomography (18F-FDG-PET/CT) in patients with operable lung cancer to the tumor histology. In total, 129 consecutive patients with non-small-cell lung cancer (NSCLC) undergoing anatomical lung resections were included. Preoperative LN staging was evaluated in the relationship to the histology of the resected specimens (group 1: lung adenocarcinoma/LUAD; group 2: squamous cell carcinoma/SQCA). Statistical analysis was performed by the Mann–Whitney U-test, the chi2 test, and binary logistic regression analysis. To establish an easy-to-use algorithm for the identification of LN false positivity, a decision tree including clinically meaningful parameters was generated. In total, 77 (59.7%) and 52 (40.3%) patients were included in the LUAD and SQCA groups, respectively. SQCA histology, non-G1 tumors, and tumor SUVmax > 12.65 were identified as independent predictors of LN false positivity in the preoperative staging. The corresponding ORs and their 95% CIs were 3.35 [1.10–10.22], p = 0.0339; 4.60 [1.06–19.94], p = 0.0412; and 2.76 [1.01–7.55], and p = 0.0483. The preoperative identification of false-positive LNs is an important aspect of the treatment regimen for patients with operable lung cancer; thus, these preliminary findings should be further evaluated in larger patient cohorts.
A Generalized Regression Neural Network Model for Accuracy Improvement of Global Precipitation Products: A Climate Zone-Based Local Optimization

The ability to obtain accurate precipitation data from various geographic locations is crucial for many applications. Various global products have been released in recent decades for estimating precipitation spatially and temporally. Nevertheless, it is extremely important to provide reliable and accurate products for estimating precipitation in a variety of environments. This is due to the complexity of topographic, climatic, and other factors. This study proposes a multi-product information combination for improving precipitation data accuracy based on a generalized regression neural network model using global and local optimization strategies. Firstly, the accuracy of ten global precipitation products from four different categories (satellite-based, gauge-corrected satellites, gauge-based, and reanalysis) was assessed using monthly precipitation data collected from 1896 gauge stations in Iran during 2003–2021. Secondly, to enhance the accuracy of the modeled precipitation products, the importance score of effective and auxiliary variables—such as elevation, the Enhanced Vegetation Index (EVI), the Land Surface Temperature (LST), the Soil Water Index (SWI), and interpolated precipitation maps—was assessed. Finally, a generalized regression neural network (GRNN) model with global and local optimization strategies was used to combine precipitation information from several products and auxiliary characteristics to produce precipitation data with high accuracy. Global precipitation products scored higher than interpolated precipitation products and surface characteristics. Furthermore, the importance score of the interpolated precipitation products was considerably higher than that of the surface characteristics. SWI, elevation, EVI, and LST scored 53%, 20%, 15%, and 12%, respectively, in terms of importance. The lowest RMSE values were associated with IMERGFinal, TRMM3B43, PERSIANN-CDR, ERA5, and GSMaP-Gauge. For precipitation estimation, these products had Kling–Gupta efficiency (KGE) values of 0.89, 0.86, 0.77, 0.78, and 0.60, respectively. The proposed GRNN-based precipitation product with a global (local) strategy showed RMSE and KGE values of 9.6 (8.5 mm/mo) and 0.92 (0.94), respectively, indicating higher accuracy. Generally, the accuracy of global precipitation products varies depending on climatic conditions. It was found that the proposed GRNN-derived precipitation product is more efficient under different climatic conditions than global precipitation products. Moreover, the local optimization strategy based on climatic classes outperformed the global optimization strategy.
Adaptability of High Dimensional Propensity Score Procedure in the Transition from ICD-9 to ICD-10 in the US Healthcare System

Background High-Dimensional Propensity Score procedure (HDPS) is a data-driven approach to assist control for confounding in pharmacoepidemiologic research. The transition to the International Classification of Disease (ICD-9/10) in the US health system may pose uncertainty in applying the HDPS procedure. Methods We assembled a base cohort of patients in MarketScan® Commercial Claims Database who had newly initiated celecoxib or traditional NSAIDs to compare gastrointestinal bleeding risk. We then created bootstrapped hypothetical cohorts from the base cohort with predefined patient selection patterns from the ICD eras. Three strategies for HDPS deployment were tested: 1) split the cohort by ICD era, deploy HDPS twice, and pool the relative risks (pooled RR), 2) consider codes from each ICD era as a separate data dimension and deploy HDPS in the entire cohort (data dimensions) and 3) map ICD codes from both eras to Clinical Classifications Software (CCS) concepts before deploying HDPS in the entire cohort (CCS mapping). We calculated percent bias and root-mean-squared error to compare the strategies. Results A similar bias reduction was observed in cohorts where patient selection pattern from each ICD era was comparable between the exposure groups. In the presence of considerable disparity in patient selection, we observed a bimodal distribution of propensity scores in the data dimensions strategy, indicating instrument-like covariates. Moreover, the CCS mapping strategy resulted in at least 30% less bias than pooled RR and data dimensions strategies (RMSE: 0.14, 0.19, 0.21, respectively) in this scenario. Conclusion Mapping ICD codes to a stable terminology like CCS serves as a helpful strategy to reduce residual bias when deploying HDPS in pharmacoepidemiologic studies spanning both ICD eras.
Encountering disability in music: Exploring perceptions on inclusive music education in higher music education

This research study evaluated an Artist-in-Residence-project (AIR-project) at the Conservatory of Amsterdam, focusing on inclusive music education. For this project, the Conservatory invited the organization Drake Music Scotland to work with its students. The project’s aim was to provide students with practical skills regarding music technology, to discuss the social model of disability, and to play in an inclusive music ensemble with a musician with a severe physical disability. The perceived learning experiences of students, the experienced value of the project, and recommendations for its improvement were explored through online questionnaires with the students and online semistructured interviews with the AIR-project leader, the musician with a disability, and the two workshop leaders of Drake Music Scotland. Findings suggest that alongside learning practical skills regarding music technology, students changed or broadened their perceptions about people with disabilities and inclusive music-making in positive ways. Furthermore, participants valued that the project created a space in which “taking enough time” and “belonging” could be experienced; these values are important in inclusive music environments as they can empower musicians with disabilities. The main recommendation for similar projects was that students wanted to gain hands-on experience in inclusive music education.