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In this article, we introduce the notion of regular fusible modules. Let $R$ be a ring with an identity and $M$ an $R$-module. An element $0\neq m\in M$ is said to be regular fusible if there exists $r\in R$, a non zero-divisor of $M$, such that $mr$ can be written as the sum of a torsion element and a torsion free element in $M$. $M$ is called regular fusible if every nonzero element of $M$ is regular fusible. We characterize regular fusible modules in terms of fusible modules. In addition, we show that a regular fusible module over a right duo ring is reduced and nonsingular. Moreover, we study the regular fusible property under Cartesian product, trivial extension ring, and module of a fractions. Also, we characterize division rings in terms of fusible modules.

This study investigates the integration of a high altitude platform station (HAPS), a non-terrestrial network (NTN) node, into the cell-switching paradigm for energy saving. By doing so, the sustainability and ubiquitous connectivity targets can be achieved. Besides, a delay-aware approach is also adopted, where the delay profiles of users are respected in such a way that we attempt to meet the latency requirements of users with a best-effort strategy. To this end, a novel, simple, and lightweight Q-learning algorithm is designed to address the cell-switching optimization problem. During the simulation campaigns, different interference scenarios and delay situations between base stations are examined in terms of energy consumption and quality-of-service (QoS), and the results confirm the efficacy of the proposed Q-learning algorithm.

We study the problem of feature selection in general machine learning (ML) context, which is one of the most critical subjects in the field. Although, there exist many feature selection methods, however, these methods face challenges such as scalability, managing high-dimensional data, dealing with correlated features, adapting to variable feature importance, and integrating domain knowledge. To this end, we introduce the "Adaptive Feature Selection with Binary Masking" (AFS-BM) which remedies these problems. AFS-BM achieves this by joint optimization for simultaneous feature selection and model training. In particular, we do the joint optimization and binary masking to continuously adapt the set of features and model parameters during the training process. This approach leads to significant improvements in model accuracy and a reduction in computational requirements. We provide an extensive set of experiments where we compare AFS-BM with the established feature selection methods using well-known datasets from real-life competitions. Our results show that AFS-BM makes significant improvement in terms of accuracy and requires significantly less computational complexity. This is due to AFS-BM's ability to dynamically adjust to the changing importance of features during the training process, which an important contribution to the field. We openly share our code for the replicability of our results and to facilitate further research.

In this study, we aim to introduce the concept of classical 1-absorbing prime submodules of a nonzero unital module $M$ over a commutative ring $A$ with unity. A proper submodule $P$ of $M$ is said to be a classical 1-absorbing prime submodule, if for each $m\in M$ and nonunits $a,b,c\in A,$ $abcm\in P$ implies that $abm\in P$ or $cm\in P$. We give many examples and properties of classical 1-absorbing prime submodules. Also, we investiage the classical 1-absorbing prime submodules of tensor product $F\otimes M$ of a (faithfully) flat $A$-module $F$ and any $A$-module $M$. Furthermore, we determine classical prime, classical 1-absorbing prime and classical 2-absorbing submodules of amalgamated duplication $M\bowtie I$ of an $A$-module $M$ along an ideal $I$. Also, we characterize local rings $(A,\mathfrak{m})$ with $\mathfrak{m}^{2}=0$ in terms of classical 1-absorbing prime submodules., Comment: 17 pages. arXiv admin note: text overlap with arXiv:2403.19659

In this paper, we study weakly classical 1-absorbing prime submodules of a nonzero unital module $M$ over a commutative ring $R$ having a nonzero identity. A proper submodule $N$ of $M$ is said to be a weakly classical 1-absorbing prime submodule, if for each $m\in M$ and nonunits $a,b,c\in R,$ $0\neq abcm\in N$ implies that $abm\in N$ or $cm\in N$. We give various examples and properties of weakly classical 1-absorbing prime submodules. Also, we investiage the weakly classical 1-absorbing prime submodules of tensor product $F\otimes M$ of a (faithfully) flat $R$-module $F$ and any $R$-module $M.$ Also, we prove that if every proper submodule of an $R$-module $M$ is weakly classical 1-absorbing prime, then $Jac(R)^{3}M=0$. In terms of this result, we characterize modules over local rings in which every proper submodule is weakly classical 1-absorbing prime., Comment: 16 pages

Multi-dielectric coated coverslip can be designed to reach large optical field enhancements when working under proper illumination conditions and in total internal reflection. In an objective-based total internal reflection fluorescence microscopy (TIRF-M), we propose to use the resulting large field enhancement supported in such coverslip to improve TIRF-M sensitivity by amplifying the collected fluorescence signal. We present here the optimization, realization and testing in TIRF-M of resonant coverslips designed to take into account the experimental constraints. The latter's are due to the inverted configuration of TIRF-M together with the use of a high numerical aperture objective. The challenge is therefore to find the best coating design compromising between the angular tolerance and the field enhancement. We will report here enhanced-TIRF-M imaging over model samples but the ultimate goal here is to be able to monitor and quantify dynamics of pathogens at the membrane of living cells., Comment: 2023 23rd International Conference on Transparent Optical Networks (ICTON), Jul 2023, Bucharest, Romania

The application of ionizing radiation for diagnostic imaging is common around the globe. However, the process of imaging, itself, remains to be a relatively hazardous operation. Therefore, it is preferable to use as low a dose of ionizing radiation as possible, particularly in computed tomography (CT) imaging systems, where multiple x-ray operations are performed for the reconstruction of slices of body tissues. A popular method for radiation dose reduction in CT imaging is known as the quarter-dose technique, which reduces the x-ray dose but can cause a loss of image sharpness. Since CT image reconstruction from directional x-rays is a nonlinear process, it is analytically difficult to correct the effect of dose reduction on image quality. Recent and popular deep-learning approaches provide an intriguing possibility of image enhancement for low-dose artifacts. Some recent works propose combinations of multiple deep-learning and classical methods for this purpose, which over-complicate the process. However, it is observed here that the straight utilization of the well-known U-NET provides very successful results for the correction of low-dose artifacts. Blind tests with actual radiologists reveal that the U-NET enhanced quarter-dose CT images not only provide an immense visual improvement over the low-dose versions, but also become diagnostically preferable images, even when compared to their full-dose CT versions.

We introduce a novel ensemble approach for feature selection based on hierarchical stacking for non-stationarity and/or a limited number of samples with a large number of features. Our approach exploits the co-dependency between features using a hierarchical structure. Initially, a machine learning model is trained using a subset of features, and then the output of the model is updated using other algorithms in a hierarchical manner with the remaining features to minimize the target loss. This hierarchical structure allows for flexible depth and feature selection. By exploiting feature co-dependency hierarchically, our proposed approach overcomes the limitations of traditional feature selection methods and feature importance scores. The effectiveness of the approach is demonstrated on synthetic and well-known real-life datasets, providing significant scalable and stable performance improvements compared to the traditional methods and the state-of-the-art approaches. We also provide the source code of our approach to facilitate further research and replicability of our results.

We introduce a new class of data-driven and distribution-free optimistic-robust bimodal inventory optimization (BIO) strategy to effectively allocate inventory across a retail chain to meet time-varying, uncertain omnichannel demand. While prior Robust optimization (RO) methods emphasize the downside, i.e., worst-case adversarial demand, BIO also considers the upside to remain resilient like RO while also reaping the rewards of improved average-case performance by overcoming the presence of endogenous outliers. This bimodal strategy is particularly valuable for balancing the tradeoff between lost sales at the store and the costs of cross-channel e-commerce fulfillment, which is at the core of our inventory optimization model. These factors are asymmetric due to the heterogenous behavior of the channels, with a bias towards the former in terms of lost-sales cost and a dependence on network effects for the latter. We provide structural insights about the BIO solution and how it can be tuned to achieve a preferred tradeoff between robustness and the average-case. Our experiments show that significant benefits can be achieved by rethinking traditional approaches to inventory management, which are siloed by channel and location. Using a real-world dataset from a large American omnichannel retail chain, a business value assessment during a peak period indicates over a 15% profitability gain for BIO over RO and other baselines while also preserving the (practical) worst case performance.

In addition to extending some facts from field coefficients to commutative ring coefficients for Leavitt path algebras with new shorter proofs, we also prove some results that are new even for field coefficients. In particular, we show that the ideal lattice of a Leavitt path algebra embeds into the ideal lattice of the path algebra of the same digraph, we construct a new basis for a Leavitt path algebra of polynomial growth and give a formula for the Gelfand-Kirillov dimension of a Leavitt path algebra in terms of its digraph., Comment: 24 pages. This is a slightly modified version of the first third of arXiv:2208.06357v1

Following the great success of various deep learning methods in image and object classification, the biomedical image processing society is also overwhelmed with their applications to various automatic diagnosis cases. Unfortunately, most of the deep learning-based classification attempts in the literature solely focus on the aim of extreme accuracy scores, without considering interpretability, or patient-wise separation of training and test data. For example, most lung nodule classification papers using deep learning randomly shuffle data and split it into training, validation, and test sets, causing certain images from the CT scan of a person to be in the training set, while other images of the exact same person to be in the validation or testing image sets. This can result in reporting misleading accuracy rates and the learning of irrelevant features, ultimately reducing the real-life usability of these models. When the deep neural networks trained on the traditional, unfair data shuffling method are challenged with new patient images, it is observed that the trained models perform poorly. In contrast, deep neural networks trained with strict patient-level separation maintain their accuracy rates even when new patient images are tested. Heat-map visualizations of the activations of the deep neural networks trained with strict patient-level separation indicate a higher degree of focus on the relevant nodules. We argue that the research question posed in the title has a positive answer only if the deep neural networks are trained with images of patients that are strictly isolated from the validation and testing patient sets.

This study considers a UAV-assisted multi-user massive multiple-input multiple-output (MU-mMIMO) systems, where a decode-and-forward (DF) relay in the form of an unmanned aerial vehicle (UAV) facilitates the transmission of multiple data streams from a base station (BS) to multiple Internet-of-Things (IoT) users. A joint optimization problem of hybrid beamforming (HBF), UAV relay positioning, and power allocation (PA) to multiple IoT users to maximize the total achievable rate (AR) is investigated. The study adopts a geometry-based millimeter-wave (mmWave) channel model for both links and proposes three different swarm intelligence (SI)-based algorithmic solutions to optimize: 1) UAV location with equal PA; 2) PA with fixed UAV location; and 3) joint PA with UAV deployment. The radio frequency (RF) stages are designed to reduce the number of RF chains based on the slow time-varying angular information, while the baseband (BB) stages are designed using the reduced-dimension effective channel matrices. Then, a novel deep learning (DL)-based low-complexity joint hybrid beamforming, UAV location and power allocation optimization scheme (J-HBF-DLLPA) is proposed via fully-connected deep neural network (DNN), consisting of an offline training phase, and an online prediction of UAV location and optimal power values for maximizing the AR. The illustrative results show that the proposed algorithmic solutions can attain higher capacity and reduce average delay for delay-constrained transmissions in a UAV-assisted MU-mMIMO IoT systems. Additionally, the proposed J-HBF-DLLPA can closely approach the optimal capacity while significantly reducing the runtime by 99%, which makes the DL-based solution a promising implementation for real-time online applications in UAV-assisted MU-mMIMO IoT systems.

This study presents structural and fundamental astrophysical parameters of poorly studied open cluster NGC 2509. We used the third photometric and astrometric data release of the Gaia (Gaia DR3) to perform analyses. By taking into account the Gaia DR3 astrometric data, we calculated the membership probabilities of the stars in the region of NGC 2509. As a result of the membership analysis, 244 stars with membership probabilities $P\geq50$% were determined as the physical members of the cluster. The colour excess, distance and age were obtained simultaneously by fitting solar metallicity PARSEC isochrones to $G\times G_{\rm BP}-G_{\rm RP}$ colour-magnitude diagram. We considered the most likely cluster member stars during the fitting procedure and calculated the colour excess, distance and age of the NGC 2509 as $E(G_{\rm BP}-G_{\rm RP})=0.100\pm0.015$ mag, $d=2518\pm667$ pc and $t=1.5\pm 0.1$ Gyr, respectively., Comment: 11 pages, 6 figures and 3 tables, accepted for publication in Sakarya University Journal of Science

This study considers a novel full-duplex (FD) massive multiple-input multiple-output (mMIMO) system using hybrid beamforming (HBF) architecture, which allows for simultaneous uplink (UL) and downlink (DL) transmission over the same frequency band. Particularly, our objective is to mitigate the strong self-interference (SI) solely on the design of UL and DL RF beamforming stages jointly with sub-array selection (SAS) for transmit (Tx) and receive (Rx) sub-arrays at base station (BS). Based on the measured SI channel in an anechoic chamber, we propose a min-SI beamforming scheme with SAS, which applies perturbations to the beam directivity to enhance SI suppression in UL and DL beam directions. To solve this challenging nonconvex optimization problem, we propose a swarm intelligence-based algorithmic solution to find the optimal perturbations as well as the Tx and Rx sub-arrays to minimize SI subject to the directivity degradation constraints for the UL and DL beams. The results show that the proposed min-SI BF scheme can achieve SI suppression as high as 78 dB in FD mMIMO systems., Comment: This paper has been accepted for publication in IEEE Globecom 2023

This paper presents another improved version of Plantard arithmetic that could speed up Kyber implementations on two low-end 32-bit IoT platforms (ARM Cortex-M3 and RISC-V) without SIMD extensions. Specifically, we further enlarge the input range of the Plantard arithmetic without modifying its computation steps. After tailoring the Plantard arithmetic for Kyber's modulus, we show that the input range of the Plantard multiplication by a constant is at least 2.14 times larger than the original design in TCHES2022. Then, two optimization techniques for efficient Plantard arithmetic on Cortex-M3 and RISC-V are presented. We show that the Plantard arithmetic supersedes both Montgomery and Barrett arithmetic on low-end 32-bit platforms. With the enlarged input range and the efficient implementation of the Plantard arithmetic on these platforms, we propose various optimization strategies for NTT/INTT. We minimize or entirely eliminate the modular reduction of coefficients in NTT/INTT by taking advantage of the larger input range of the proposed Plantard arithmetic on low-end 32-bit platforms. Furthermore, we propose two memory optimization strategies that reduce 23.50% to 28.31% stack usage for the speed-version Kyber implementation when compared to its counterpart on Cortex-M4. The proposed optimizations make the speed-version implementation more feasible on low-end IoT devices. Thanks to the aforementioned optimizations, our NTT/INTT implementation shows considerable speedups compared to the state-of-the-art work. Overall, we demonstrate the applicability of the speed-version Kyber implementation on memory-constrained IoT platforms and set new speed records for Kyber on these platforms.

The assessment of the well-being of the peripheral auditory nerve system in individuals experiencing hearing impairment is conducted through auditory brainstem response (ABR) testing. Audiologists assess and document the results of the ABR test. They interpret the findings and assign labels to them using reference-based markers like peak latency, waveform morphology, amplitude, and other relevant factors. Inaccurate assessment of ABR tests may lead to incorrect judgments regarding the integrity of the auditory nerve system; therefore, proper Hearing Loss (HL) diagnosis and analysis are essential. To identify and assess ABR automation while decreasing the possibility of human error, machine learning methods, notably deep learning, may be an appropriate option. To address these issues, this study proposed deep-learning models using the transfer-learning (TL) approach to extract features from ABR testing and diagnose HL using support vector machines (SVM). Pre-trained convolutional neural network (CNN) architectures like AlexNet, DenseNet, GoogleNet, InceptionResNetV2, InceptionV3, MobileNetV2, NASNetMobile, ResNet18, ResNet50, ResNet101, ShuffleNet, and SqueezeNet are used to extract features from the collected ABR reported images dataset in the proposed model. It has been decided to use six measures accuracy, precision, recall, geometric mean (GM), standard deviation (SD), and area under the ROC curve to measure the effectiveness of the proposed model. According to experimental findings, the ShuffleNet and ResNet50 models' TL is effective for ABR to diagnose HL using an SVM classifier, with a high accuracy rate of 95% when using the 5-fold cross-validation method., Comment: 19