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As the world enters the journey toward the 6th generation (6G) of wireless technology, the promises of ultra-high data rates, unprecedented low latency, and a massive surge in connected devices require crucial exploration of network energy saving (NES) solutions to minimize the carbon footprint and overall energy usage of future cellular networks. On the other hand, network-controlled repeaters (NCRs) have been introduced by 3rd generation partnership project (3GPP) as a cost-effective solution to improve network coverage. However, their impact on network power consumption and energy efficiency has not been thoroughly investigated. This paper studies NES schemes for next-generation 6G networks aided by NCRs and proposes optimal NES strategies aiming at maximizing the overall energy efficiency of the network. Repeaters are shown to allow for power savings at next-generation nodeB (gNB), and offer higher overall energy efficiency (EE) and spectral efficiency (SE), thus providing an energy-efficient and cost-efficient alternative to increase the performance of future 6G networks, Comment: 7 pages, conference

This paper studies performance of various types of relay nodes in a 5G wireless network: conventional amplify-forward repeaters, (semi-)smart/smart amplify-forward repeaters with different levels of side information, and half-duplex/full-duplex decode-forward relay nodes with and without spatial reuse. End-to-end effective signal to interference and noise ratios (SINRs) and achievable rates are derived for these different types of relay nodes. Performance and complexity tradeoffs are discussed with a simulation over a Manhattan topology setting. Over-the-air (OTA) test results corroborates the findings in this paper., Comment: Presented in IEEE ICC 2024 Industry workshop

The field of natural language processing (NLP) has seen remarkable advancements, thanks to the power of deep learning and foundation models. Language models, and specifically BERT, have been key players in this progress. In this study, we trained and introduced two new BERT models using Persian data. We put our models to the test, comparing them to seven existing models across 14 diverse Persian natural language understanding (NLU) tasks. The results speak for themselves: our larger model outperforms the competition, showing an average improvement of at least +2.8 points. This highlights the effectiveness and potential of our new BERT models for Persian NLU tasks.

To all appearances, electron impact mass spectrometer (EI-MS) is considered a "chirally blind" instrument. Yet, numerous non-identical R (right) and S (left) configurations of prochiral alcohols' mass spectra alcohols have appeared in the literature with almost no justification. Such observations are often attributed to impurities, experimental circumstances, inaccurate measurements, etc. In an experimental attempt to explain this phenomenon, here we have avoided the above mentioned pitfalls by conducting control experiments using different pure enantiomers under the same circumstances. Hence, we report the mass spectra of R- and S-enantiomers of 2-octanol (1R, 1S) and 1-octyn-3-ol (2R, 2S) collected by running 20 independent experiments for each R- and S-enantiomer. Statistical analyses confirmed that the peak intensities were significant to an acceptable level of confidence. The 1R and 1S enantiomers were separated reasonably in the PC space, implying that the chirally blind EI-MS is able to discriminate between R and S prochiral alcohols. Theoretically, self-complexation through H-bonding for S (or R) appears to give a new chiral center at the H-bonded oxygen atom, producing a new dimeric pair of diastereomers SRS and SSS (or RRR and RSR) before ionization, and SRS.+ and SSS.+ (or RRR.+ and RSR.+) after ionization. The results of our calculations have explicitly shown that these hydrogen bonds formed. Interestingly, the latter four ionized diastereomers appear with different structural and thermodynamic parameters at the M06-2X/6-311++g (d,p) level of theory.

In recent years, excitation of surface phonon polaritons (SPhPs) in van der Waals materials received wide attention from the nanophotonics community. Alpha-phase Molybdenum trioxide ($\alpha$-MoO3), a naturally occurring biaxial hyperbolic crystal, emerged as a promising polaritonic material due to its ability to support SPhPs for three orthogonal directions at different wavelength bands (range 10-20 $\mu$m). Here, we report on the fabrication and IR characterization of large-area (over 1 cm$^2$ size) $\alpha$-MoO3 polycrystalline films deposited on fused silica substrates by pulsed laser deposition. Single alpha-phase MoO3 films exhibiting a polarization-dependent reflection peak at 1006 cm$^{-1}$ with a resonance Q-factor as high as 53 were achieved. Reflection can be tuned via changing incident polarization with a dynamic range of $\Delta$R=0.3 at 45 deg. incidence angle. We also report a polarization-independent almost perfect absorption condition (R<0.01) at 972 cm$^{-1}$ which is preserved for a broad angle of incidence. The development of a low-cost polaritonic platform with high-Q resonances in the mid-infrared (mid-IR) range is crucial for a wide number of functionalities including sensors, filters, thermal emitters, and label-free biochemical sensing devices. In this framework our findings appear extremely promising for the further development of lithography-free, scalable films, for efficient and large-scale devices operating in the free space, using far-field detection setups., Comment: 17 pages, 12 figures

App reviews reflect various user requirements that can aid in planning maintenance tasks. Recently, proposed approaches for automatically classifying user reviews rely on machine learning algorithms. A previous study demonstrated that models trained on existing labeled datasets exhibit poor performance when predicting new ones. Therefore, a comprehensive labeled dataset is essential to train a more precise model. In this paper, we propose a novel approach that assists in augmenting labeled datasets by utilizing information extracted from an additional source, GitHub issues, that contains valuable information about user requirements. First, we identify issues concerning review intentions (bug reports, feature requests, and others) by examining the issue labels. Then, we analyze issue bodies and define 19 language patterns for extracting targeted information. Finally, we augment the manually labeled review dataset with a subset of processed issues through the Within-App, Within-Context, and Between-App Analysis methods. We conducted several experiments to evaluate the proposed approach. Our results demonstrate that using labeled issues for data augmentation can improve the F1-score to 6.3 in bug reports and 7.2 in feature requests. Furthermore, we identify an effective range of 0.3 to 0.7 for the auxiliary volume, which provides better performance improvements.

Free-form image inpainting is the task of reconstructing parts of an image specified by an arbitrary binary mask. In this task, it is typically desired to generalize model capabilities to unseen mask types, rather than learning certain mask distributions. Capitalizing on the advances in diffusion models, in this paper, we propose a Denoising Diffusion Probabilistic Model (DDPM) based model capable of filling missing pixels fast as it models the backward diffusion process using the generator of a generative adversarial network (GAN) network to reduce sampling cost in diffusion models. Experiments on general-purpose image inpainting datasets verify that our approach performs superior or on par with most contemporary works.

In recent years, the field of medicine has been increasingly adopting artificial intelligence (AI) technologies to provide faster and more accurate disease detection, prediction, and assessment. In this study, we propose an interpretable AI approach to diagnose patients with neurofibromatosis using blood tests and pathogenic variables. We evaluated the proposed method using a dataset from the AACR GENIE project and compared its performance with modern approaches. Our proposed approach outperformed existing models with 99.86% accuracy. We also conducted NF1 and interpretable AI tests to validate our approach. Our work provides an explainable approach model using logistic regression and explanatory stimulus as well as a black-box model. The explainable models help to explain the predictions of black-box models while the glass-box models provide information about the best-fit features. Overall, our study presents an interpretable AI approach for diagnosing patients with neurofibromatosis and demonstrates the potential of AI in the medical field., Comment: The authors would like to acknowledge the American Association for Cancer Research and its financial and material support in the development of the AACR Project GENIE registry, as well as members of the consortium for their commitment to data sharing. Interpretations are the responsibility of study authors

Four-dimensional weak-constraint variational data assimilation estimates a state given partial noisy observations and dynamical model by minimizing a cost function that takes into account both discrepancy between the state and observations and model error over time. It can be formulated as a Gauss-Newton iteration of an associated least-squares problem. In this paper, we introduce a parameter in front of the observation mismatch and show analytically that this parameter is crucial either for convergence to the true solution when observations are noise-free or for boundness of the error when observations are noisy with bounded observation noise. We also consider joint state-parameter estimation. We illustrated theoretical results with numerical experiments using the Lorenz 63 and Lorenz 96 models.

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder (NDD) that is caused by genetic, epigenetic, and environmental factors. Recent advances in genomic analysis have uncovered numerous candidate genes with common and/or rare mutations that increase susceptibility to ASD. In addition, there is increasing evidence that copy number variations (CNVs), single nucleotide polymorphisms (SNPs), and unusual de novo variants negatively affect neurodevelopment pathways in various ways. The overall rate of copy number variants found in patients with autism is 10%-20%, of which 3%-7% can be detected cytogenetically. Although the role of submicroscopic CNVs in ASD has been studied recently, their association with genomic loci and genes has not been properly studied. In this review, we focus on 47 ASD-associated CNV regions and their related genes. Here, we identify 1,632 protein-coding genes and long non-coding RNAs (lncRNAs) within these regions. Among them, 552 are significantly expressed in the brain. Using a list of ASD-associated genes from SFARI, we detect 17 regions containing at least one known ASD-associated protein-coding genes. Of the remaining 30 regions, we identify 24 regions containing at least one protein-coding genes with brain-enriched expression and nervous system phenotype in mouse mutant and one lncRNAs with both brain-enriched expression and upregulation in iPSC to neuron differentiation. Our analyses highlight the diversity of genetic lesions of CNV regions that contribute to ASD and provide new genetic evidence that lncRNA genes may contribute to etiology of ASD. In addition, the discovered CNVs will be a valuable resource for diagnostic facilities, therapeutic strategies, and research in terms of variation priority.