Reviewerly/exHarmony huggingface dataset:
Detecting coherence with respect to general quantum measurements

Quantum coherence is crucial in quantum resource theory. Previous studies have mainly focused on standard coherence based on a complete orthogonal reference. Standard coherence has recently been extended to general positive-operator-valued measure (POVM)-based coherence, including block coherence as a special case. Therefore, it is necessary to construct block and POVM-based coherence witnesses to detect them. In this study, we present witnesses for block and POVM-based coherence and obtain the necessary and sufficient conditions for constructing these witnesses. We also discuss possible realizations of some block and POVM-based coherence witnesses in experiments and present examples of measuring block coherence witnesses based on real experimental data. Furthermore, we present an application of block coherence witnesses in a quantum-parameter estimation task with a degenerate Hamiltonian and estimate the unknown parameter by measuring the block coherence witnesses when the input state is block coherent. Finally, we prove that the quantum Fisher information of any block-incoherent state equals zero, which coincides with the result obtained from measuring block coherence witnesses.
Comparing Korean College Students Perceptions Toward Online and Offline Classes

The purpose of this study was to investigate how Korean college students perceived online and offline classes in terms of their social interactions (with their peers and instructor) and senses of classroom community during their online and offline classes. To accomplish this purpose, two research questions were constructed: 1) How did Korean college students perceive online classes in terms of their social interactions and senses of classroom community? 2) How did they perceive offline classes in terms of their social interactions and senses of classroom community? Thirty-three students responded to the Rovai’s (2002) 20-item Classroom Community Scale modified for the current study. An independent sample t-test as a main analysis method was employed. Findings of data analysis indicated that, first, students perceived two different learning environments in nearly the same way in terms of social interactions with their peers and the instructor. Second, students were more likely to rely on or trust others in online classes than in offline classes. It indicated that they might have been more used to online classes than offline classes because they had longer experience with online classes because of the pandemic. Pedagogical implications and suggestions for further research are presented.
TIG-DETR: Enhancing Texture Preservation and Information Interaction for Target Detection

FPN (Feature Pyramid Network) and transformer-based target detectors are commonly employed in target detection tasks. However, these approaches suffer from design flaws that restrict their performance. To overcome these limitations, we proposed TIG-DETR (Texturized Instance Guidance DETR), a novel target detection model. TIG-DETR comprises a backbone network, TE-FPN (Texture-Enhanced FPN), and an enhanced DETR detector. TE-FPN addresses the issue of texture information loss in FPN by utilizing a bottom-up architecture, Lightweight Feature-wise Attention, and Feature-wise Attention. These components effectively compensate for texture information loss, mitigate the confounding effect of cross-scale fusion, and enhance the final output features. Additionally, we introduced the Instance Based Advanced Guidance Module in the DETR-based detector to tackle the weak detection of larger objects caused by the limitations of window interactions in Shifted Window-based Self-Attention. By incorporating TE-FPN instead of FPN in Faster RCNN and employing ResNet-50 as the backbone network, we observed an improvement of 1.9 AP in average accuracy. By introducing the Instance-Based Advanced Guidance Module, the average accuracy of the DETR-based target detector has been improved by 0.4 AP. TIG-DETR achieves an impressive average accuracy of 44.1% with ResNet-50 as the backbone network.
Discriminator-Enhanced Knowledge-Distillation Networks

Query auto-completion (QAC) serves as a critical functionality in contemporary textual search systems by generating real-time query completion suggestions based on a user’s input prefix. Despite the prevalent use of language models (LMs) in QAC candidate generation, LM-based approaches frequently suffer from overcorrection issues during pair-wise loss training and efficiency deficiencies. To address these challenges, this paper presents a novel framework—discriminator-enhanced knowledge distillation (Dis-KD)—for the QAC task. This framework combines three core components: a large-scale pre-trained teacher model, a lightweight student model, and a discriminator for adversarial learning. Specifically, the discriminator aids in discerning generative-level differences between the teacher and the student models. An additional discriminator score loss is amalgamated with the traditional knowledge-distillation loss, resulting in enhanced performance of the student model. Contrary to the stepwise evaluation of each generated word, our approach assesses the entire generation sequence. This method alleviates the prevalent overcorrection issue in the generation process. Consequently, our proposed framework boasts improvements in model accuracy and a reduction in parameter size. Empirical results highlight the superiority of Dis-KD over established baseline methods, with the student model surpassing the teacher model in QAC tasks for sub-word languages.
Decoherence mitigation by embedding a logical qubit in a qudit

Abstract Quantum information stored in a qubit is rapidly lost to the environment. The realization of robust qubits is one of the most important challenges in quantum computing. Herein, we propose to embed a logical qubit within the manifold of a qudit as a scheme to preserve quantum information over extended periods of time. Under identical conditions (e.g., decoherence channels), the submanifold of the logical qubit exhibits extended lifetimes compared to a pure two-level system (qubit). The retention of quantum information further improves with separation between the sublevels of the logical qubit. Lifetime enhancement can be understood in terms of entropy production of the encoding and nonencoding subspaces during evolution under a quantum map for a d -level system. The additional pathways for coherent evolution through intermediate sublevels within a d -level manifold provide an information-preserving mechanism: reversible alternative channels to the irreversible loss of information to the environment characteristic of open quantum systems.
An Optimal Network-Aware Scheduling Technique for Distributed Deep Learning in Distributed HPC Platforms

Deep learning is a growing technique used to solve complex artificial intelligence (AI) problems. Large-scale deep learning has become a significant issue as a result of the expansion of datasets and the complexity of deep learning models. For training large-scale models, the cloud can be used as a distributed HPC (high-performance computing) tool with benefits in cost and flexibility. However, one of the major performance barriers in distributed deep learning in a distributed HPC environment is the network. The performance is often limited by heavy traffic like many stochastic gradient descent transfers for distributed communication. There are many network studies in distributed deep learning to solve these problems, but most research only focuses on improving communication performance and applying new methods or algorithms like overlapping parameter synchronization to minimize communication delay rather than considering the actual network. In this paper, we are focusing on the actual network, especially in a distributed HPC environment. In such an environment, if cluster nodes are assigned to different zones/regions which means a set of an appropriate number of distributed HPC nodes when performing distributed deep learning tasks, performance degradation due to network delay may occur. The proposed network optimization algorithm ensures that distributed work is placed in the same zone as much as possible to reduce network delay. Furthermore, scoring using network monitoring tools like loss, delay, and throughput is applied to select the optimal node within the zone. Our proposal has been validated on the Kubernetes platform, an open source orchestrator for the automatic management and deployment of micro-services. The performance of distributed deep learning is improved through the proposed scheduler.
A universal quantum algorithm for weighted maximum cut and Ising problems

Abstract We propose a hybrid quantum-classical algorithm to compute approximate solutions of binary combinatorial problems. We employ a shallow-depth quantum circuit to implement a unitary and Hermitian operator that block-encodes the weighted maximum cut or the Ising Hamiltonian. Measuring the expectation of this operator on a variational quantum state yields the variational energy of the quantum system. The system is enforced to evolve toward the ground state of the problem Hamiltonian by optimizing a set of angles using normalized gradient descent. Experimentally, our algorithm outperforms the state-of-the-art quantum approximate optimization algorithm on random fully connected graphs and challenges D-Wave quantum annealers by producing good approximate solutions. Source code and data files are publicly available ( https://github.com/nkuetemeli/UQMaxCutAndIsing ).
Wavelet Threshold Ultrasound Echo Signal Denoising Algorithm Based on CEEMDAN

In this study, an algorithm for denoising ultrasound echo signals in industrial settings is proposed to address the problem of high noise and low signal-to-noise ratio. The algorithm combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), mutual information entropy (MIE), and wavelet threshold denoising to ensure effectiveness given the unique structure of ultrasound echo signals. Initially, CEEMDAN is used to decompose the signal into intrinsic mode function (IMFs) and residual signals. The MIE is then used to determine the correlation of neighboring IMF signals, which are then divided into a noise- and a signal-dominated part. Finally, using wavelet thresholding, noise is suppressed in the signal-dominant part, and the resulting denoised signal is reconstructed using the residual signal. The performance of the algorithm is verified through simulations and physical experiments, and the results show that it is superior to traditional signal denoising methods.
General Knowledge Representation and Sharing, with Illustrations in Risk/Emergency Management

Many decision-making tasks, including the sustainability-oriented ones and those related to the management of risks or emergencies, must gather, integrate, and analyze an important amount of information of various kinds and origins. Hence, how should information be best organized and shared by agents – people or software – for all and only the pieces of information looked for by these agents to be maximize their retrieval, reuse, organization and analysis by these agents? To that end, various logic-based knowledge representation (KR) and sharing (KS) techniques, and hence KR bases, have been used. However, most KS researchers focus on what this article defines as “restricted KR and KS”, where information providers and consumers can or have to discuss for solving information ambiguities and other problems. The first part of this article highlights the usefulness of “general KR and KS” and, for supporting them, provides a panorama of complementary techniques, and hence, indirectly, best practices or kinds of tools to use for general KS purposes. These techniques collectively answer research questions about how to support Web users in the collaborative building of KR bases. The second part uses the risk/emergency management domain to illustrate the ways different types of information can be represented to support general KS.
Accurate extrinsic calibration of solid-state light detection and ranging and camera system by coarse-to-fine grid-aligning

A high-precision extrinsic calibration is the underlying premise of the accurate perception of light detection and ranging (LiDAR) and camera systems commonly used in the autonomous driving industry. We propose a coarse-to-fine strategy to get rigid-body transformation between solid-state LiDAR with non-repetitive scanning and a RGB camera system using a chessboard as the calibration target. This method exploits the reflectance intensity characteristics of the LiDAR point cloud, which exhibit the distinct distribution in white and black blocks of chessboard. In the coarse calibration step, a reflectance intensity Gaussian mixture model was used to extract the unicolor block point cloud from the chessboard point cloud. Therefore, the initial estimate of the extrinsic parameter was obtained by aligning the corners in the point cloud and calculating the centroid of the unicolor block point cloud and corners in the image. In the refinement step, we extracted points on the border of each block as LiDAR features and designed an iterative optimization algorithm to align the intensity of LiDAR features with grayscale features in the image. This method utilizes the intensity information and compensates for corner errors in the point cloud due to reflectance intensity binarization. The results of the comparative experiment revealed that the proposed method outperformed existing methods in terms of accuracy. Experiments based on simulations and real-world conditions revealed that the proposed algorithm demonstrated a high accuracy, robustness, and consistency.
Biome Generation Tool: A Mixed-Initiative Software for the Procedural Generation of Biomes

This article presents the Biome Generation Tool (BGT), a mixed-initiative procedural content generator plugin for the Unity3D game engine that uses an Evolutionary Algorithm (EA) for the creation of biomes in videogames. A double-blind evaluation was performed with 28 videogame developers split into two groups. The experimental group received a standard BGT, while the control group received a version optimizing for the negative of the fitness function. A survey with semantic differential questions focused on AI impact, productivity, and user satisfaction was applied. The experimental group’s appreciation of the AI suggestions, perceived productivity increase, and satisfaction were significantly higher than those of the control group. From the results, we conclude that the BGT succeeds in assisting the development of biomes, generating products of acceptable quality while reducing the designer’s workload. In the future, we expect to confirm these results with a separate group of users evaluating the biomes built using BGT.
Human Interaction Classification in Sliding Video Windows Using Skeleton Data Tracking and Feature Extraction

A "long short-term memory" (LSTM)-based human activity classifier is presented for skeleton data estimated in video frames. A strong feature engineering step precedes the deep neural network processing. The video was analyzed in short-time chunks created by a sliding window. A fixed number of video frames was selected for every chunk and human skeletons were estimated using dedicated software, such as OpenPose or HRNet. The skeleton data for a given window were collected, analyzed, and eventually corrected. A knowledge-aware feature extraction from the corrected skeletons was performed. A deep network model was trained and applied for two-person interaction classification. Three network architectures were developed-single-, double- and triple-channel LSTM networks-and were experimentally evaluated on the interaction subset of the "NTU RGB+D" data set. The most efficient model achieved an interaction classification accuracy of 96%. This performance was compared with the best reported solutions for this set, based on "adaptive graph convolutional networks" (AGCN) and "3D convolutional networks" (e.g., OpenConv3D). The sliding-window strategy was cross-validated on the "UT-Interaction" data set, containing long video clips with many changing interactions. We concluded that a two-step approach to skeleton-based human activity classification (a skeleton feature engineering step followed by a deep neural network model) represents a practical tradeoff between accuracy and computational complexity, due to an early correction of imperfect skeleton data and a knowledge-aware extraction of relational features from the skeletons.
High‐speed adaptive analog filter based on fully analog artificial neural network

Summary This paper presents an innovative concept of a high‐speed and higher order adaptive filter. A fully analog artificial neural network handles the adaption by using a filter bank for filtering and learning through analog feedback inspired by backpropagation. There is no clock control used in this concept, which makes real‐time adaption possible even for high frequencies. Its functionality is shown by several electrical circuit simulations involving component inaccuracies as well as a commonly used filter example.
A high geothermal setting in the Linyi geothermal field: Evidence from the lithospheric thermal structure

The lithospheric thermal structure has a profound indicative significance for the potential evaluation and exploration of geothermal resources. The Linyi geothermal field, located in the southern section of the Yishu fault in Shandong Province, boasts abundant geothermal resources. However, its origin is still unclear. This study analyzed the characteristics of terrestrial heat flow using the temperature logging of geothermal wells and measurements of thermal conductivity and heat production. Combined with the geophysical information of the Xiangshui (Jiangsu Province)-Mandula (Nei Mongol) geoscience transect and the lithology revealed by the geothermal wells, this study built a conceptual model of the lithospheric thermal structure using the one-dimensional steady-state heat conduction equation. Based on this model, the heat flow is 64.9 mW/m 2 , indicating a high geothermal setting in the study area. Furthermore, the ratio of crustal to mantle heat flow is &lt; 1 (0.67), implying that surface heat flow originates predominantly from the mantle. The Yishu fault probably acts as a pathway for heat transfer from the mantle. The temperature of the Moho boundary in the study area was estimated to be 614.8 °C. The Curie depth was calculated to be 29.5 km (585 °C), which is consistent with the depth estimated using the aeromagnetic data. In sum, the Linyi geothermal field has a high geothermal setting, which contributes to the formation of geothermal resources.
Assessing the economic and technical efficacy of grid-connected photovoltaic systems through a combination of mathematical and deep learning models with experimental validation: A case study

The current study presents a techno-economic technique and modeling to evaluate the productivity of a 1.25 kW photovoltaic grid-connected (GCPV) system. The method used four key factor indicators: yield, and capacity factors, also payback period and cost of energy. MATLAB software was used to analyze secondly meteorological data and create a model for the GCPV. Also, the GCPV has been installed, tested, and data collected in Sohar, Oman. The data revealed that the daily average irradiation is 6.23 kWh/m2.day in the studied location. The numerical and experimental results of the assessment indicated that the investment in GCPV technology in this site is very encouraging, with a yield factor of 1710 and 1836 kWh/kWp and a capacity factor of 19.37% and 20.74%, respectively. Also, it is found that 8.25 years is the payback period. Additionally, the numerical and experimental results show 0.063 and 0.047 USD/kWh cost of energy, respectively. Also, four predictive models based on deep learning utilizing Recurrent Neural Network (RNN) and Time Lag Recurrent Network (TLRN) were developed to forecast GCPV current performance with irradiance, and temperature as input data. FRNN-2 and FRNN-3 showed the highest performance for prediction models due to their lower MSE (0.0018–0.0037), indicating better accuracy in comparison to experimental results. This research yields important technical and financial data for anyone looking to invest in GCPV technology in Oman.
Classification performance of machine learning methods in different data structures

With the popularity of machine learning methods in many areas, the use of computers for diagnosis and treatment in the health field has recently become more frequent. Dual classification studies for the diagnosis of the presence of the disease are quite common in the literature. In this study, classification performances of machine learning algorithms were compared in cases where the response variable is in ordinal structure and more than two categories, instead of binary classification. In the simulation study, data sets in different structures were derived and classification was made. The response variable in the study is an ordinal categorical variable. A comprehensive classification study was carried out using five different machine learning methods. The results show that the SVM method performs better classification than its competitors when the response variable is ordinal.
AI-Based Employment Situation Analysis of Innovative Talents in the New Media Era

As the number of undergraduates increases, competition for employment opportunities becomes more intense.To improve employment guidance and increase the employment rate and quality, it is essential to evaluate preliminary employment data, identify the primary variables that influence employment, and provide decision support for decision-makers.In the era of new media, the influx of massive information can make it difficult for undergraduates to distinguish between true and false information, making it necessary to analyze the employment situation of innovative talents using artificial intelligence.However, historical employment data is generally a dataset with high noise due to the diversity and complexity of actual employment situations.Differences in accuracy requirements among various institutions also make handling issues such as different accuracy requirements and noise adaptation abilities challenging.To address this issue, this study proposes applying a decision tree algorithm based on a multi-scale rough set model to analyze university employment data.By introducing the multiscale concept into rough set theory and drawing on variable precision rough sets, the analysis's accuracy and noise adaptation ability can be improved.Experimental results show that the proposed algorithm performs well in terms of classification accuracy and running time, and the employment situation of creative minds can be effectively analyzed.Moreover, the decision tree has a simple structure, concise rules, no inseparable datasets, and a fast running speed.