Reviewerly/exHarmony huggingface dataset:
QEMCGAN: Quantized Evolutionary Gradient Aware Multiobjective Cyclic GAN for Medical Image Translation

Generative adversarial networks (GANs) have become popular in medical imaging because of their remarkable performance and ability to translate images across different domains. However, GANs face several issues in image-to-image translation, including training instability, lack of diversity, and mode collapse. These issues become even more complex when using cyclic GANs. Additionally, collecting paired images required for GANs may be costly, especially in the medical domain. Cyclic GANs are a favorable choice for addressing this issue, as they can convert cross-domain images. However, no pre-existing technique or algorithm is comprehensive enough to handle diverse datasets and applications. To address these issues, we propose a novel Quantized Evolutionary Gradient Aware Multiobjective Cyclic GAN (QEMCGAN) that employs evolutionary computation, multiobjective optimization, and an intelligent selection scheme. We use simulated annealing and Pareto ranking selection using three fitness criteria to address local optima stagnation. Additionally, we use model quantization because of its suitability for low-cost IoT-based applications. Extensive trials reveal that EMCGAN and QEMCGAN produces more visually realistic images than other approaches while preserving both background information and salient features. In addition, QEMCGAN performs on par with the baseline approach even when the model size is halved, making it more efficient.
Lower Bounds on Quantum Annealing Times

The adiabatic theorem provides sufficient conditions for the time needed to prepare a target ground state. While it is possible to prepare a target state much faster with more general quantum annealing protocols, rigorous results beyond the adiabatic regime are rare. Here, we provide such a result, deriving lower bounds on the time needed to successfully perform quantum annealing. The bounds are asymptotically saturated by three toy models where fast annealing schedules are known: the Roland and Cerf unstructured search model, the Hamming spike problem, and the ferromagnetic p-spin model. Our bounds demonstrate that these schedules have optimal scaling. Our results also show that rapid annealing requires coherent superpositions of energy eigenstates, singling out quantum coherence as a computational resource.
Machine learning enables the design of a bidirectional focusing diffractive lens

Machine learning can efficiently empower the inverse design of cascaded diffractive optical elements. In this work, we explore the inverse design of a bidirectional focusing diffractive lens in a cascaded configuration through the diffractive optical neural network (DONN) machine learning method. The bidirectional focusing diffractive lens consists of two on-axially cascaded multi-level diffractive lenses. Each lens consists of concentric rings with equal widths and varying heights. The height of each concentric ring is optimized as part of the design algorithm. The diffractive lens has a focal length f+ as light propagates in the forward (Z+) direction. As light propagates in the backward (Z-) direction, the focal length changes to f-. The designed lens is fabricated through a two-photon polymerization 3D printing technique. The proposed design is polarization insensitive and miniature and can be readily applied in future functional optical imaging systems.
Library: Scientific Knowledge Promotion and Other Points of Intersection

The article examines the potential of the library as a possible active participant in the interaction of science and society. The situation with the library nowadays can be described as the search for its new niche in the readers’ space. The purpose of the article is to analyze the possibilities of libraries for the development of communications between science and society. The first part of the article presents the history and analysis of the modern participation of libraries in the communication of science and society, the second part analyses the potential of libraries in this process. The authors come to the following conclusion: libraries can become one of the conditions for rational communication between science and society, which should act not only in the usual system of documentary communication, but also in the system of data communication, interpersonal communication. Libraries should expand their role in the society: to be an intermediary and serve not only “scientist to scientist” communication, but also “scientist to the society” communication. The study showed that various forms both one-time and repetitive mass events of a popular scientific nature have not yet become systemic. The practice of libraries’ participation in other facets of communication between science and society (increasing public understanding of science; involving the public in science) is not yet visible at all. The weak influence of libraries on the development of communications between science and society is also evidenced by the analysis of their activities presented on their websites. This practice is confirmed not only by the analysis of websites, but also by publications: their number is not large (31), and by the way, the authors tackle this topic only once. The paper suggests solving of the above-mentioned problems using huge potential of the libraries.
Coexistent Quantum Channel Characterization Using Spectrally Resolved Bayesian Quantum Process Tomography

The coexistence of quantum and classical signals over the same optical fiber with minimal degradation of the transmitted quantum information is critical for operating large-scale quantum networks over the existing communications infrastructure. Here, we systematically characterize the quantum channel that results from simultaneously distributing approximate single-photon polarization-encoded qubits and classical light of varying intensities through fiber-optic channels of up to 15 km. Using spectrally resolved quantum process tomography with a Bayesian reconstruction method that we develop, we estimate the full quantum channel from experimental photon counting data, both with and without classical background. Furthermore, although we find the exact channel description to be a weak function of the pump polarization, we nevertheless show that the coexistent fiber-based quantum channel has high process fidelity with an ideal depolarizing channel when the noise is dominated by Raman scattering. These results provide a basis for the future development of quantum repeater designs and quantum error-correcting codes for real-world channels and inform models used in the analysis and simulation of quantum networks.
Canonical steering ellipsoids of pure symmetric multiqubit states with two distinct spinors and volume monogamy of steering

Quantum steering ellipsoid formalism provides a faithful representation of all two-qubit states and is useful for obtaining their correlation properties. The steering ellipsoids of two-qubit states that have undergone local operations on both the qubits, in order to bring the state to its canonical form, are the so-called canonical steering ellipsoids. The steering ellipsoids corresponding to the two-qubit subsystems of permutation-symmetric $N$-qubit states are considered here. We construct and analyze the geometric features of the canonical steering ellipsoids corresponding to pure permutation-symmetric $N$-qubit states with two distinct spinors. Depending on the degeneracy of the two spinors in the pure symmetric $N$-qubit state, several families arise which cannot be converted into one another through stochastic local operations and classical communication (SLOCC). The canonical steering ellipsoids of the two-qubit states drawn from the pure symmetric $N$-qubit states with two distinct spinors allow for a geometric visualization of the SLOCC-equivalent class of states. We show that the states belonging to the $W$ class correspond to oblate spheroids centered at $(0,0,1/(N\ensuremath{-}1))$ with fixed semiaxis lengths $1/\sqrt{N\ensuremath{-}1}$ and $1/(N\ensuremath{-}1)$. The states belonging to all other SLOCC-inequivalent families correspond to ellipsoids centered at the origin of the Bloch sphere. We also explore volume monogamy relations of states belonging to these families, mainly the $W$ class of states.
Human employees versus robotic employees: Customers and hotel managers’ perceived experience at unmanned smart hotels

Hotel managers and researchers are increasingly interested in artificial intelligence (AI) and robots, which are vital to the future of the hospitality industry. However, adopting robotic services is challenging due to the current state of the art and insufficient understanding of the relationship between customer satisfaction and robotic services. Hotels are building a competitive advantage through high technology permeating various service environments. This study examined customer perceptions and satisfaction with smart hotels during COVID-19 based on online reviews by building a list of smart hotel properties that identifies well-performing and poorly performing service quality. Results highlight the advantages of smart hotels in the tourism environment and provide helpful guidance for hoteliers to apply high technology in the service environment. This study examines and demonstrates that the utility of new technologies in a smart hotel at modern times to deal with the challenges of COVID-19, and assures precision and speed in service delivery to improve sustained competition and guest experience.
DataDancing: An Exploration of the Design Space For Visualisation View Management for 3D Surfaces and Spaces

Recent studies have explored how users of immersive visualisation systems arrange data representations in the space around them. Generally, these have focused on placement centred at eye-level in absolute room coordinates. However, work in HCI exploring full-body interaction has identified zones relative to the user’s body with different roles. We encapsulate the possibilities for visualisation view management into a design space (called “DataDancing”). From this design space we extrapolate a variety of view management prototypes, each demonstrating a different combination of interaction techniques and space use. The prototypes are enabled by a full-body tracking system including novel devices for torso and foot interaction. We explore four of these prototypes, encompassing standard wall and table-style interaction as well as novel foot interaction, in depth through a qualitative user study. Learning from the results, we improve the interaction techniques and propose two hybrid interfaces that demonstrate interaction possibilities of the design space.
Why a dynamic multicomponential model of decision making: some milestones and a preliminary tool

Why is it necessary today to propose a new tool to evaluate decision-making skills? The neuroscientific approach proposed in this paper constitutes a new frontier for grasping the multi-component nature of the decision-maker’s thought and action skills, using an innovative tool, through a modular methodology (5 domains that characterize the decision-making process: style, strategy, efficacy, awareness and metacognition), a multimethodological approach (through tasks, tests and self-report measures, in addition to the neurophysiological level) and a digitized format, thus restoring a 360-degree perspective on the potential of making decisions in real-life contexts. DassDec - Decisional Assessment for Decision-making- sets out to do all of this.
CBDC Application Analysis and Prospects

With the increasing rise of digital currency, the central bank has begun to march into digital currency. This article reviews the research articles on CBDC to gain a deeper understanding of the research content and direction of CBDC. This article aims to review research on central bank digital currencies (CBDCs). Learn about the latest research directions on CBDC and evaluate the practical significance of CBDC and its contribution to the industry. According to the latest research, CBDC is defined as a means of payment equivalent to cash. The advantages and characteristics of using CBDC have also been proposed as the reasons why the central bank should issue them. The article also shows the relationship between CBDC and financial inclusion. Analysis of the current situation and prediction of the future development of CBDC, and some updated measures by regulators after the issuance of CBDC. The report has a certain contribution to what measures should be taken after the issuance of CBDC.
Circular local search for unconstrained optimisation problems

In this paper, a heuristics algorithm to solve unconstrained optimisation problems (UOPs) in two dimensions is proposed. This algorithm is called as: circular local search (CLS); where it is an efficient local search. The algorithm starts with an arbitrarily chosen point in the search domain. Secondly, a radius of CLS is defined around the current search point; where any point in this region is feasible. Finally, by an angle with a decay step length, CLS can move from current search point to obtain a new base point. The radius and the angle of CLS are modified during the search. CLS is tested on evaluated by many benchmark problems taken from the literature. According to the obtained numerical results, the proposed method show that its robustness and effectiveness.
Underwater image restoration based on adaptive parameter optimization of the physical model

Underwater images have the advantage of carrying high information density and are widely used for marine information acquisition. Due to the complex underwater environment, the captured images are often unsatisfactory and often suffer from color distortion, low contrast, and blurred details. Physical model-based methods are often used in relevant studies to obtain clear underwater images; however, water selectively absorbs light, making the use of a priori knowledge-based methods no longer applicable and thus rendering the restoration of underwater images ineffective. Therefore, this paper proposes an underwater image restoration method based on adaptive parameter optimization of the physical model. Firstly, an adaptive color constancy algorithm is designed to estimate the background light value of underwater image, which effectively guarantees the color and brightness of underwater image. Secondly, aiming at the problem of halo and edge blur in underwater images, a smoothness and uniformity transmittance estimation algorithm is proposed to make the estimated transmittance smooth and uniform, and eliminate the halo and blur of the image. Then, in order to further smooth the edge and texture details of the underwater image, a transmittance optimization algorithm for smoothing edge and texture details is proposed to make the obtained scene transmittance more natural. Finally, combined with the underwater image imaging model and histogram equalization algorithm, the image blurring is eliminated and more image details are retained. The qualitative and quantitative evaluation on the underwater image dataset (UIEBD) shows that the proposed method has obvious advantages in color restoration, contrast and comprehensive effect, and has achieved remarkable results in application testing. It shows that the proposed method can effectively restore underwater degraded images and provide a theoretical basis for the construction of underwater imaging models.
Finite-key security analysis of differential-phase-shift quantum key distribution

Differential-phase-shift (DPS) quantum key distribution (QKD) is one of the major QKD protocols that can be implemented with a simple setup using a laser source and a passive detection unit. Recently, an information-theoretic security proof of this protocol was established by Mizutani et al. [npj Quantum Inf. 5, 87 (2019)], assuming an infinitely large number of emitted pulses. To implement the DPS protocol in a real-life world, it is indispensable to analyze the security with the finite number of emitted pulses. The extension of the security proof to the finite-size regime requires the accommodation of the statistical fluctuations to determine the amount of privacy amplification. In doing so, Azuma's inequality is often employed, but unfortunately we show that in the case of the DPS protocol, this results in a substantially low key rate. This low key rate is due to a loose estimation of the sum of probabilities regarding three-photon emission whose probability of occurrence is very small. The main contribution of our work is to show that this obstacle can be overcome by exploiting the recently found novel concentration inequality, Kato's inequality. As a result, the key rate of the DPS protocol is drastically improved. For instance, assuming typical experimental parameters, a 3-Mbit secret key can be generated over 77 km for 8.3 hours, which shows the feasibility of DPS QKD under a realistic setup.
A privacy preserving CP-ABE-based access control on data sharing in VANETs

Vehicle ad hoc networks (VANETs) enable fast interconnection between vehicles and roadside infrastructure to ensure stable and continuous network services for vehicles. Data sharing, as the primary service of MANET, not only ensures vehicles can access the required information but also supports vehicles in sharing the required information with other vehicles to provide a better travel experience for drivers and passengers. However, without an effective access control scheme to protect data security, the adversaries can collect data transmitted by other vehicles during data sharing and threaten VANET's security by sending fake data. In order to address the current data-sharing scheme for vehicles that collects information from the environment with low efficiency, high maintenance cost and ineffective access control schemes, a secure and effective data-sharing scheme is proposed in this paper by improving the ciphertext policy attribute-based encryption algorithm (CP-ABE). Performance analysis and simulation experiments show that the scheme can achieve secure and efficient data sharing and enhance the driving experience. When the number of attributes is 20, the computational overhead of our scheme in the encryption phase is 3.18% of the compared scheme, while the computational overhead in the decryption phase is 44% of the compared scheme.
MOSAIC: Spatially-Multiplexed Edge AI Optimization over Multiple Concurrent Video Sensing Streams

Sustaining high fidelity and high throughput of perception tasks over vision sensor streams on edge devices remains a formidable challenge, especially given the continuing increase in image sizes (e.g., generated by 4K cameras) and complexity of DNN models. One promising approach involves criticality-aware processing, where the computation is directed selectively to "critical" portions of individual image frames. We introduce MOSAIC, a novel system for such criticality-aware concurrent processing of multiple vision sensing streams that provides a multiplicative increase in the achievable throughput with negligible loss in perception fidelity. MOSAIC determines critical regions from images received from multiple vision sensors and spatially bin-packs these regions using a novel multi-scale Mosaic Across Scales (MoS) tiling strategy into a single `canvas frame', sized such that the edge device can retain sufficiently high processing throughput. Experimental studies using benchmark datasets for two tasks, Automatic License Plate Recognition and Drone-based Pedestrian Detection, shows that MOSAIC, executing on a Jetson TX2 edge device, can provide dramatic gains in the throughput vs. fidelity tradeoff. For instance, for drone-based pedestrian detection, for a batch size of 4, MOSAIC can pack input frames from 6 cameras to achieve (a) 4.75X (475%) higher throughput (23 FPS per camera, cumulatively 138FPS) with ≤ 1% accuracy loss, compared to a First Come First Serve (FCFS) processing paradigm.
Probing Two Driven Double Quantum Dots Strongly Coupled to a Cavity

We experimentally and theoretically study a driven hybrid circuit quantum electrodynamics (cQED) system beyond the dispersive coupling regime. Treating the cavity as part of the driven system, we develop a theory applicable to such strongly coupled and to multiqubit systems. The fringes measured for a single driven double quantum dot (DQD)-cavity setting and the enlarged splittings of the hybrid Floquet states in the presence of a second DQD are well reproduced with our model. This opens a path to study Floquet states of multiqubit systems with arbitrarily strong coupling and reveals a new perspective for understanding strongly driven hybrid systems.
The Development Of E-Module Based on Science Writing Heuristic (SWH) To Improve the Effectiveness of Teaching and Learning Activities

The goal of this study is to create an e-module that utilizes the Science Writing Heuristic (SWH) and assess its feasibility and characteristics.This research follows the research and development (RnD) approach using the Thiagarajan 4D development model.Various methods were employed to collect data, including interviews, observation, questionnaires, and documentation.The e-module was developed in the form of digital Flipbooks, which present students with a step-by-step learning process based on the Science Writing Heuristic.The quality of the e-module was evaluated by expert validators and student feedback.The final product is an e-module based on the Science Writing Heuristic (SWH) in HTML five format.The e-module received high scores from material and media experts, with a score of 0.93 and 0.82 respectively.Student responses were also positive, indicating its potential as a valuable learning resource.Further testing is required to determine its effectiveness in larger classroom settings.
Using GAN-Based Encryption to Secure Digital Images with Reconstruction through Customized Super Resolution Network

Unlike traditional encryption methods, Generative Adversarial Network (GAN)-based methods possess a high level of security for digital images. Many existing simple encryption methods may be less secure than expected and have high storage costs. This paper proposes a GAN-based encryption method to secure digital images, solving these problems. First, a random sequence generator using a GAN with cross-coupled logistics and a Henon map is generated to encrypt an image. Next, the encrypted image is downsampled into one-fourth of the original size and sent to the receiver. Finally, image reconstruction uses a Customized Super Resolution Network (CSRNet) rather than decompressing the image at the receiver side. Our extensive experimental results demonstrate that the proposed method achieves NPCR, UACI, entropy, PSNR and SSIM up to 0.99604, 0.33460, 7.9993, 37.0462 dB and 0.94561, respectively. Further, our encryption method achieves up to 75% faster than the recent methods when evaluated on two standard datasets. Therefore, the proposed GAN-Based solution can possess a high level of security and save sufficient storage space for any practical application.