Reviewerly/exHarmony huggingface dataset:
Domain adaptation with contrastive and adversarial oriented transferable semantic augmentation

Recent studies indicate that explicit domain adaptation methods that directly align the source and target domains will distort the original target feature distributions and cause misalignment. Implicit domain adaptation using feature augmentation bridges the domain gap and mitigates the above problems. However, such methods have the following challenges: (1) insufficient intrinsic discrimination of augmented features may cause negative transfer; (2) most methods ignore learning the domain-invariant features between source and augmented samples. To address these issues, we propose a Contrastive and Adversarial oriented transferable semantic Augmentation Domain Adaptation method (CAADA). We embed the augmentation process into our contrastive loss implicitly, minimizing it to increase the similarity between each augmented sample and the source centroid, thereby improving the augmented features’ intrinsic discriminativeness. Moreover, the prediction distribution difference between the source and augmented samples is used as the loss, and adversarial learning is employed between the classifier and feature extractor to refine domain-invariant features. The proposed losses can be integrated into other domain adaptation methods as lightweight modules to enhance each other’s performance. Experiments indicate that our proposed model yields state-of-the-art results on benchmarks, particularly resulting in 0.7% and 0.8% improvements on Office-Home and ImageCLEF-DA, respectively.
Fast Preparation of <i>W</i> States by Designing the Evolution Operators with Rydberg Superatom

Abstract W states play an important role in quantum information processing and quantum computing. Here, it proposes a scheme for preparing W states by designing the evolution operators with Rydberg superatoms. It encodes quantum information on the effective energy level of the superatom, and construct shortcuts to adiabatic passages (STAP) by designing the evolution operators. Combined with Quantum Zeno dynamics and STAP, it can prepare three‐particle W states quickly and efficiently. In addition, the Rabi frequency in this scheme can be described as a linear superposition of Gaussian functions, which reduces the difficulty of practical experiment. The numerical simulation shows that the scheme is robust against decoherence, operational imperfection, and noise. At last, it generalizes this scheme to preparation of N ‐particle W states.
ULBPNet: Low-light image enhancement using U-shaped lightening back-projection

Images captured under low-light conditions often have various issues, such as low brightness and contrast, and also color distortion. The aim of low-light image enhancement is to improve the visual quality of such images to facilitate subsequent image processing and computer vision tasks. However, this task faces major challenges from the inherent limitations of the low-light environment. This paper proposes a novel low-light image enhancement network using a U-shaped enhanced lightening back-projection. The network architecture primarily comprises a series of lightening blocks and a color balance block. We construct the lightening block, which incorporates a U-shaped enhanced lightening back-projection module and a saturation-guided fusion module, by assuming that the saturation levels of the low-light and the normal images are similar. Additionally, a color balance block is included to address issues such as color fading or over-enhancement. The effectiveness of the proposed network is assessed via extensive experiments conducted on widely-used benchmarks. The simulation results confirm that the proposed low-light enhancement network outperforms other state-of-the-art approaches. https://github.com/JYP-1317/.
Postdigital Ethnicity

Ethnicity arises from particular kinds of encounter with cultural others, hence many ethnicities came into existence during the period of globalization of human society that started with the age of imperialism, and eventually led into the contemporary conditions of the hyper-connected postdigital age. In the globalized world in which we now live, therefore, it is reasonable to assume that there is little or no possibility of new ethnicities emerging in the future. A sense of cultural history is thus implicit within the notion of ethnicity. As the products of modernity become ubiquitous, ethnicity shifts towards symbolic cultural forms. The postdigital provides new ways to perform our ethnicities and tell our stories.
Self-testing of a single quantum system from theory to experiment

Abstract Self-testing allows one to characterise quantum systems under minimal assumptions. However, existing schemes rely on quantum nonlocality and cannot be applied to systems that are not entangled. Here, we introduce a robust method that achieves self-testing of individual systems by taking advantage of contextuality. The scheme is based on the simplest contextuality witness for the simplest contextual quantum system—the Klyachko-Can-Binicioğlu-Shumovsky inequality for the qutrit. We establish a lower bound on the fidelity of the state and the measurements as a function of the value of the witness under a pragmatic assumption on the measurements. We apply the method in an experiment on a single trapped 40 Ca + using randomly chosen measurements and perfect detection efficiency. Using the observed statistics, we obtain an experimental demonstration of self-testing of a single quantum system.
Reinforcement learning-based comprehensive learning grey wolf optimizer for feature selection

Turning heavy and high-dimensional raw data into knowledge for decision makers is a complex process. Feature selection (FS) can do this task well by removing irrelevant and/or redundant parts from the raw dataset, aiming at reducing dimensionality and improving accuracy. In this work, a reinforcement learning-based comprehensive learning grey wolf optimizer (RLCGWO) is designed to solve the FS problem, which is modeled as a combinatorial optimization problem. First, a comprehensive learning operator is proposed, containing the static and dynamic learning strategies. These two strategies provide GWO with the exploration capability in different ways. Second, a novel RL-based policy regulation technique is developed, which is based on the Q-learning framework. Individuals are considered as agents that obtain the state of the environment based on the state encoding technique. Meanwhile, agents select the most appropriate actions from the well-designed action set based on the information provided by the Q-table. Furthermore, agents update the stand-alone Q-table with rewards to provide themselves with more timely and accurate feedback. Third, a chaotic-based learning strategy is devised for leaders to improve the quality of the optimal solution. The comparison results of the proposed RLCGWO with six successful GWO variants and three typical algorithms on the benchmarks initially demonstrate its advantages in convergence speed and accuracy. The proposed RLCGWO is finally applied to the challenging FS problem. The comparison results with six popular algorithms on 15 UCI datasets and 3 real world high-dimensional datasets underscore its high adaptability and versatility. Taken together, the proposed RLCGWO is a promising technique for FS.
Immersive Media Technologies and Their Impact on Today’s and Future Media and Communication Landscape: Potentialities and Challenges Through the Lens of Riepl’s Law

As development of immersive media has shown, new technological innovations and an increased importance of emotion and empathy in immersive media products lead to new display formats and narrative possibilities. Consequently, also due to its unprecedented format, immersive media challenges existing media formats and their established functions for journalism. Using Riepl’s law (Das Nachrichtenwesen des Altertums. Mit besonderer Rücksicht auf die Römer, 1913) as a theoretical and analytical basis, this chapter discusses potential connecting factors between already existing media formats and the technological innovations brought on by immersive technologies. Additionally, the law is used to clarify competitive aspects of the advent of this new media format, which have not been researched extensively yet.
TRAMON: An automated traffic monitoring system for high density, mixed and lane-free traffic

This paper introduces a new visual dataset and framework to facilitate computer-vision-based traffic monitoring in high density, mixed and lane-free traffic (TRAMON). While there are advanced deep learning algorithms that can detect and track vehicles from traffic videos, none of the existing systems provides accurate traffic monitoring in mixed traffic. The mixed traffic flows in developing countries often includes the types of vehicles that are not widely known by the existing visual datasets. The computer vision algorithms also face difficulties in detecting and tracking a high density of vehicles that are not following lanes. This paper proposes a large-scale visual dataset of >282,000 labelled images of traffic vehicles, as well as a comprehensive framework and strategy to train common deep-learning-based computer vision algorithms to detect and track vehicles in high density, heterogeneous and lane-free traffic. A systematic evaluation of results shows that TRAMON, the proposed visual dataset and framework, performs well and better than the common visual dataset at all traffic densities.
Computational study of BisGMA: A component of dental resin material

BisGMA, a crucial component of dental resin widely employed in oral care, undergoes an in-depth computational investigation in this study. Employing advanced computational tools such as Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) and General Utility Lattice Program (GULP), we explore a wide range of physical parameters. These encompass the stress–strain variations, Young’s and linear moduli, pair distribution functions, RMSD trajectory analysis, enthalpy, entropy, elastic constants, self-energy, zero-point energy and optical and dielectric constants. This multifaceted analysis enhances our understanding on intricate correlation of this material, offering insights that are frequently challenging to obtain.
Mineral prospectivity modelling to delineate potential areas for gold deposits: a case study of Lupa Goldfield, South West Tanzania

This study used the weight of evidence model to delineate prospective areas for gold deposits in the Lupa Goldfield. The method establishes a spatial relationship between known mineral deposits and evidential maps to create a mineral prospectivity map. Four evidential maps derived from geochemical, geological, geophysical, and topographical Digital Elevation Model (DEM) datasets were analysed and integrated to create a mineral prospectivity map. Results have revealed five classes ranked from the highest, high, medium, low, and lowest favourability patterns with probability values of 0.051, 0.0426, 0.0225, 0.0172, and 0.0091, respectively. The highest favourable areas have the best gold potentials based on the presence of predictor patterns from all four evidential maps. The posterior probability map revealed good prediction, existing gold deposits, such as Saza Mine and Shanta Gold Mine, were mapped. Results have shown that the weight of evidence model was successful and can be applied in mineral exploration targeting.
Deep dynamic face capture and recognition based on internet of things

Abstract Face recognition is widely used as identity authentication method in the Internet of Things (IoTs). However, compared with static camera, the background of IoT devices may change. Additionally, the facial expression or posture may change as well. Although face recognition has been researched for several decades, it is still a challenging task to design a robust face recognition system in IoT environment. This paper designs a special face recognition system by introducing deep transfer learning to adapt difference scenes in IoT environment. The face recognition system consists of two parts: terminal smart devices and cloud center. The terminal smart device consists of a camera to capture face picture, a wireless network part to communicate with cloud center, and a screen to show recognition result. The cloud center receives the face picture from terminal smart device and returns the face recognition result by using an offline deep transfer learning model. Compared with previous models, deep transfer learning model can better adapt different scenes. The experiments shows that the proposed face recognition system achieves more promising results than two source‐only models and two shallow transfer learning models.
Kinetic-based optimization enhanced by genetic dynamics

We propose and analyze a variant of the recently introduced kinetic-based optimization method that incorporates ideas like survival-of-the-fittest and mutation strategies well-known from genetic algorithms. Thus, we provide a first attempt to reach out from the class of consensus/kinetic-based algorithms towards genetic metaheuristics. Different generations of genetic algorithms are represented via two species identified with different labels, binary interactions are prescribed on the particle level and then we derive a mean-field approximation in order to analyze the method in terms of convergence. Numerical results underline the feasibility of the approach and show in particular that the genetic dynamics allows to improve the efficiency, of this class of global optimization methods in terms of computational cost.
Detection of TBM tunnel surrounding rock debris based on attention mechanism YOLOv5

There is background interference, random size, overlapping accumulation and other problems in the detection of surrounding rock debris pieces generated during tunnel boring machine excavation. The traditional manual detection method is highly dependent on the experience and subjective judgment of the detection personnel, and has shortcomings such as high energy consumption and hysteresis. This paper proposes a visual detection method of surrounding rock debris sheet based on YOLOv5-SE model. First, homomorphic filtering and limited contrast adaptive histogram equalization method are used for preprocessing to improve the light intensity and contrast of the debris sheet image; secondly, based on the YOLOv5s model, an attention mechanism is added to the feature channel to selectively emphasize the features of the debris; the SIOU function is used as the loss function, and the detection accuracy is further improved by considering the overlapping area, the center point distance and the true difference; the small target detection layer is added to improve the detection effect of small targets. The experimental results show that the recognition rate of the YOLOv5-SE model reaches 93.7%, which can effectively avoid missed detection and false detection, and the detection speed reaches 30 FPS, which can meet the real-time requirements.
LHC hadronic jet generation using convolutional variational autoencoders with normalizing flows

Abstract In high energy physics, one of the most important processes for collider data analysis is the comparison of collected and simulated data. Nowadays the state-of-the-art for data generation is in the form of Monte Carlo (MC) generators. However, because of the upcoming high-luminosity upgrade of the Large Hadron Collider (LHC), there will not be enough computational power or time to match the amount of needed simulated data using MC methods. An alternative approach under study is the usage of machine learning generative methods to fulfill that task. Since the most common final-state objects of high-energy proton collisions are hadronic jets, which are collections of particles collimated in a given region of space, this work aims to develop a convolutional variational autoencoder (ConVAE) for the generation of particle-based LHC hadronic jets. Given the ConVAE’s limitations, a normalizing flow (NF) network is coupled to it in a two-step training process, which shows improvements on the results for the generated jets. The ConVAE+NF network is capable of generating a jet in <?CDATA $18.30 \pm 0.04\,\,{\mu\text{s}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>18.30</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0.04</mml:mn> <mml:mrow> <mml:mi>μ</mml:mi> <mml:mtext>s</mml:mtext> </mml:mrow> </mml:math> , making it one of the fastest methods for this task up to now.
Interplays between classical and quantum entanglement-assisted communication scenarios

Abstract Prepare-and-measure scenarios ( pm ), in their many forms, can be seen as the basic building blocks of communication tasks. As such, they can be used to analyze a diversity of classical and quantum protocols—of which dense coding and random access codes are key examples—in a unified manner. In particular, the use of entanglement as a resource in pm scenarios have only recently started to be systematically investigated, and many crucial questions remain open. In this work, we explore such scenarios and provide answers to some seminal questions. More specifically, we show that, in scenarios where entanglement is a free resource, quantum messages are equivalent to classical ones with twice the capacity. We also prove that, in such scenarios, it is always advantageous for the parties to share entangled states of dimension greater than the transmitted message. Finally, we show that unsteerable states cannot provide advantages in classical communication tasks, thus proving that not all entangled states are useful resources in these scenarios.
同期モニタリングによる軍艦島建築群の即時損傷推定 その1：異常検知

The rapid damage estimation of building group in Gunkanjima is performed through the synchronized monitoring of MEMS acceleration sensor network. The anomalous behavior is detected by tracking the observed acceleration time series of each building. Then, the global damage of each building is investigated by singular value decomposition of the whole frame. Finally, the local damage within each building is scrutinized by evaluating the spectral ratios between observed points of the partial frames. This paper deals with the detection of anomalous behaviors related to structural damage from real sensor network data during two successive typhoons.
Modelling of Direct Current (DC) Motor for Performance Improvement using Model Parameter Estimation

A key concern with the optimal use of direct current (DC) motor is model updates. In this article, DC motor models from probability density function (PDF) data have been obtained using the system identification method in MATLAB. At first, noise-free input and output data were used to obtain the parameters of the AutoRegressive with eXogenous input (ARX) model structure. Thereafter, the DC motor subjected to random noise had its input and output data used to obtain the ARX model parameters. However, the resulting model from the noisy data differed from the noise-free data model. To minimize this difference, 200 input/output data set for the noisy DC motor were generated and used to obtain the model parameter for each data set. The mean of the 200 data set was obtained, and the resulting model approached the noise-free model as much as possible. The root-mean-squared error of prediction stands at 3.9426E-17, 3.35E-2, and 3.15E-2 for noise-free, noisy, and mean of noisy DC motor model, respectively, showing accuracy of result for noise-free system. The article provides a way by which the model of the DC motor can be updated when it is not convenient or possible to measure the DC motor parameters when used for motion actuation. The parameters of the selected model structure are therefore estimated from the DC motor input/output data instead of the DC motor parameters itself.
Pauli transfer matrix direct reconstruction: channel characterization without full process tomography

Abstract We present a tomographic protocol for the characterization of multiqubit quantum channels. We discuss a specific class of input states, for which the set of Pauli measurements at the output of the channel directly relates to its Pauli transfer matrix components. We compare our results to those of standard quantum process tomography, showing an exponential reduction in the number of different experimental configurations required by a single matrix element extraction, while keeping the same number of shots. This paves the way for more efficient experimental implementations, whenever a selective knowledge of the Pauli transfer matrix is needed. We provide several examples and simulations.
A new paradigm for comprehensive design strategy for university campus renewal

Abstract As a place of knowledge inheritance and a symbol of human social civilization progress, university campuses play an essential role in human civilization's development and urban society's spread. The continuous expansion of universities brings a rapid urban space transformation, social phenomenon changes, and economical promotion to society. Modern campus construction and updating should strive to build an integrated system between schools &amp; cities and explore new models for their joint growth. At the same time, researchers should adopt multi-disciplinary cross-research methods, disrupt narrow disciplinary boundaries, and draw on theories and research references from various related disciplines comprehensively.
A deep learning-based approach to classroom concentration analysis

Classroom action recognition is a hot research in educational teaching environment combined with artificial intelligence technology, through which action recognition can quantify students' concentration in classroom. In this study, we propose an advanced 3D and 2D branching fusion action recognition model for recognizing students' actions in the classroom environment. The experimental results show that the classroom action recognition model has high accuracy and speed, and can accurately and quickly identify specific high-frequency classroom actions, which can then be effectively used for students' classroom concentration analysis. Accurate identification of students' classroom actions and quantification of their classroom attention can not only provide reference for teachers of relevant courses, but also provide data support for teaching quality assessment at school level, ultimately helping to improve the quality of classroom teaching.
A Fast, Robust Method for Quantitative Assessment of Collagen Fibril Architecture from Transmission Electron Micrographs

Collagen is the most abundant protein in mammals; it exhibits a hierarchical organization and provides structural support to a wide range of soft tissues, including blood vessels. The architecture of collagen fibrils dictates vascular stiffness and strength, and changes therein can contribute to disease progression. While transmission electron microscopy (TEM) is routinely used to examine collagen fibrils under normal and pathological conditions, computational tools that enable fast and minimally subjective quantitative assessment remain lacking. In the present study, we describe a novel semi-automated image processing and statistical modeling pipeline for segmenting individual collagen fibrils from TEM images and quantifying key metrics of interest, including fibril cross-sectional area and aspect ratio. For validation, we show first-of-their-kind illustrative results for adventitial collagen in the thoracic aorta from three different mouse models.
Co-creating maps and atlases <b>Rivals: How Scientists Learned to Cooperate</b> <i>Lorraine Daston</i> Columbia Global Reports, 2023. 160 pp.

Cooperation was critical to the creation of two major scientific resources.