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In the face of increasing demand for zero-touch networks to automate network management and operations, two pivotal concepts have emerged: "Learn to Slice" (L2S) and "Slice to Learn" (S2L). L2S involves leveraging Artificial intelligence (AI) techniques to optimize network slicing for general services, while S2L centers on tailoring network slices to meet the specific needs of various AI services. The complexity of optimizing and automating S2L surpasses that of L2S due to intricate AI services' requirements, such as handling uncontrollable parameters, learning in adversarial conditions, and achieving long-term performance goals. This paper aims to automate and optimize S2L by integrating the two concepts of L2S and S2L by using an intelligent slicing agent to solve S2L. Indeed, we choose two candidate slicing agents, namely the Exploration and Exploitation (EXP3) and Deep Q-Network (DQN) from the Online Convex Optimization (OCO) and Deep Reinforcement Learning (DRL) frameworks, and compare them. Our evaluation involves a series of carefully designed experiments that offer valuable insights into the strengths and limitations of EXP3 and DQN in slicing for AI services, thereby contributing to the advancement of zero-touch network capabilities., Comment: 9 pages, 2 figures and 2 tables magazine paper

The forthcoming 6G networks will embrace a new realm of AI-driven services that requires innovative network slicing strategies, namely slicing for AI, which involves the creation of customized network slices to meet Quality of service (QoS) requirements of diverse AI services. This poses challenges due to time-varying dynamics of users' behavior and mobile networks. Thus, this paper proposes an online learning framework to optimize the allocation of computational and communication resources to AI services, while considering their unique key performance indicators (KPIs), such as accuracy, latency, and cost. We define a problem of optimizing the total accuracy while balancing conflicting KPIs, prove its NP-hardness, and propose an online learning framework for solving it in dynamic environments. We present a basic online solution and two variations employing a pre-learning elimination method for reducing the decision space to expedite the learning. Furthermore, we propose a biased decision space subset selection by incorporating prior knowledge to enhance the learning speed without compromising performance and present two alternatives of handling the selected subset. Our results depict the efficiency of the proposed solutions in converging to the optimal decisions, while reducing decision space and improving time complexity.

This paper solves the open problem of characterizing the performance of quantum illumination (QI) with discrete variable states. By devising a novel quantum measurement approach along with meticulous analysis, our investigation demonstrates that, in the limit as $M \rightarrow \infty$, the maximally entangled $M$ mode Bell state achieves optimal performance, matching the two-mode squeezed vacuum in a high-noise regime and exceeding it in low-noise. This result challenges the dominance of continuous variable states in photonic sensing applications and extends the novelty of QI to regimes where no quantum advantage was believed to exist. A closer analysis reveals that this advantage stems from retained entanglement in the transmitted Bell state, a paradigm-shifting discovery since interaction with the environment in optical systems is believed to break entanglement. The complete mathematical analysis of this work provides granular insights into the interaction between photonic systems and environmental noise, motivating further research into discrete variable quantum sensing., Comment: Supplementary material available upon request. Please contact the authors via email

Squeezed light sources, featuring significant degrees of squeezing, flexible time-frequency attributes, and a compact salable form factor, serve as crucial building blocks in an expanding range of applications, spanning from quantum computing to quantum sensing and communications. In this study, we introduce and demonstrate a novel approach to generating squeezed light that exclusively employs standard telecommunication fiber-optic components. The technique leverages the entanglement properties of spontaneous four-wave mixing (SFWM) to generate high squeezing with flexible time-frequency properties. Notably, a record squeezing of 7.5 \(\pm\)0.1 dB is measured within an all-fiber, all-guided-wave platform. The entanglement-assisted squeezing methodology empowers the attainment of squeezing for arbitrary time-frequency modes within the SFWM phase-matching bandwidth, extending beyond coherent laser modes, for the first time. In particular, we measured 5.1 dB and 1.1 dB squeezing on partially coherent and chaotic time-frequency modes that are defined by randomly modulated laser sources and filtered amplified spontaneous emission light, respectively.

Computational statistics has traditionally utilized double-precision (64-bit) data structures and full-precision operations, resulting in higher-than-necessary accuracy for certain applications. Recently, there has been a growing interest in exploring low-precision options that could reduce computational complexity while still achieving the required level of accuracy. This trend has been amplified by new hardware such as NVIDIA's Tensor Cores in their V100, A100, and H100 GPUs, which are optimized for mixed-precision computations, Intel CPUs with Deep Learning (DL) boost, Google Tensor Processing Units (TPUs), Field Programmable Gate Arrays (FPGAs), ARM CPUs, and others. However, using lower precision may introduce numerical instabilities and accuracy issues. Nevertheless, some applications have shown robustness to low-precision computations, leading to new multi- and mixed-precision algorithms that balance accuracy and computational cost. To address this need, we introduce MPCR, a novel R package that supports three different precision types (16-, 32-, and 64-bit) and their combinations, along with its usage in commonly-used Frequentist/Bayesian statistical examples. The MPCR package is written in C++ and integrated into R through the \pkg{Rcpp} package, enabling highly optimized operations in various precisions.

In the realm of quantum information, entanglement stands as a cornerstone phenomenon. It underpins a vast array of quantum information processes, offering significant potential for advancements in quantum computing, communication, and sensing. This paper introduces a novel multi-photon entangled state, which generalizes the maximally entangled single-photon state and exhibits remarkable resilience to signal attenuation in photonic applications. We demonstrate the novelty of the proposed state through a simplified target detection model and illustrate its superior performance over traditional single-photon protocols, attributed to its higher entanglement level and enhanced noise suppression capabilities. Our findings suggest that the proposed multi-photon state holds significant promise for enhancing the efficiency and reliability of photonic applications subject to loss. This work lays the groundwork for future exploration into the practical applications of multi-photon entangled states in quantum technologies, potentially revolutionizing our approach to quantum sensing and beyond, Comment: QuIK'24 Workshop - IEEE ISIT conference

Human decision-making often relies on visual information from multiple perspectives or views. In contrast, machine learning-based object recognition utilizes information from a single image of the object. However, the information conveyed by a single image may not be sufficient for accurate decision-making, particularly in complex recognition problems. The utilization of multi-view 3D representations for object recognition has thus far demonstrated the most promising results for achieving state-of-the-art performance. This review paper comprehensively covers recent progress in multi-view 3D object recognition methods for 3D classification and retrieval tasks. Specifically, we focus on deep learning-based and transformer-based techniques, as they are widely utilized and have achieved state-of-the-art performance. We provide detailed information about existing deep learning-based and transformer-based multi-view 3D object recognition models, including the most commonly used 3D datasets, camera configurations and number of views, view selection strategies, pre-trained CNN architectures, fusion strategies, and recognition performance on 3D classification and 3D retrieval tasks. Additionally, we examine various computer vision applications that use multi-view classification. Finally, we highlight key findings and future directions for developing multi-view 3D object recognition methods to provide readers with a comprehensive understanding of the field.

A multi-parameter analysis was conducted to evaluate the impact of meteorological parameters, night sky brightness and seismic hazard on proposed sites for the new optical/infrared Egyptian astronomical telescope. The ERA5 reanalysis data set is used to get the following meteorological parameters: Total cloud coverage fraction, precipitable water vapor, relative humidity, wind speed & direction and Air temperature. To estimate the aerosol optical depth we used the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). Light pollution over the candidate sites was measured from Visible Infrared Imaging Radiometer Suite (VIIRS) Day Night Band (DNB). The seismic input in terms of maximum acceleration and response spectra were computed using a physics-based ground motion approach to assess the seismic hazards and consequently the designation of seismic resistant structure for the proposed sites to be able to assess the seismic hazards for the candidate sites. Of the seven nominated sites, two sites are found to have the best measurements and might be considered future sites for the new Egyptian Astronomical telescope. The first site is located in the south of the Sinai peninsula, while the second one is located in the Red Sea mountains region., Comment: The paper contains 17 pages and 14 figures